Journal articles on the topic 'Annual Workshop on Interactive Computing'

The first Young Researchers in Human–Robot Interaction Workshop, held on March 1, 2006 in Salt Lake City, Utah, provides insight into how to facilitate the establishment of the HRI community. Organized in conjunction with the first annual ACM/IEEE Human Robot Interaction Conference, the NSF-sponsored workshop assembled 15 graduate students from 5 different countries in computer science, psychology, engineering, and the arts to build the HRI community. This report highlights recommendations from discussion sessions, a synopsis of the plenary address, and representative examples of the participants’ presentations. Participants emphasized that HRI is a unique field, requiring knowledge in computing, psychology, and communications despite the differences in the courses, methods, and philosophies across disciplines. The following are needed for future growth in HRI: (i) stable, canonical robotics platforms for research purposes, (ii) a multidisciplinary community infrastructure to connect researchers, and (iii) a “Berlitz phrasebook” and collected reference materials for helping understand the “other” disciplines.

Abstract. 3D GeoInfo 2021, the 16th GeoInfo conference, is an annual ISPRS workshop offering a forum for leading international decision makers and prominent voices in the field of 3D Geoinformation across the academic, commercial, and public sectors. The 2021 workshop is organized in conjunction with the 7th International FIG workshop on 3D Cadastres. The 2021 event was held virtually. Topics included: 3D data creation and acquisition 3D data processing and analysis 3D data management - data quality, metadata, provenance and trust Data integration, information fusion, multi-modal data analysis 3D visualization, including gamification, virtual reality, augmented reality 3D and Artificial Intelligence/Machine Learning 3D and Big Data, parallel computing, cloud computing 3D city modeling, underground infrastructure modeling, topography, and bathymetry modeling Building Information Modeling, Digital Twins, Smart Cities, Smart Infrastructure Usability and Human-Computer interaction in 3D GIS 3D GIS, spatial analysis and other applications (such as 3D cadastral systems, land administration, utilities, asset management, infrastructure, navigation, urban planning, geology, archaeology, marine systems, simulations, autonomous vehicles, facilities management, energy modeling, disaster and risk management, pandemic monitoring) The 3D GeoInfo 2021 tracks received 73 manuscripts including 30 full papers and 43 extended abstracts. The manuscripts were reviewed with a double-blind review process by members of the organizing and scientific committee and external reviewers. Ultimately 24 papers were accepted for the ISPRS Annals and 24 papers for the ISPRS Archives. We thank all of the authors and reviewers for their contributions.We look forward to the 3D GeoInfo 2021 virtual oral presentations and the opportunity to exchange ideas within our workshop and with the presenters and visitors of the 7th International FIG workshop on 3D Cadastres.

Abstract. 3D GeoInfo 2021, the 16th GeoInfo conference, is an annual ISPRS workshop offering a forum for leading international decision makers and prominent voices in the field of 3D Geoinformation across the academic, commercial, and public sectors. The 2021 workshop is organized in conjunction with the 7th International FIG workshop on 3D Cadastres. The 2021 event was held virtually. Topics included: 3D data creation and acquisition 3D data processing and analysis 3D data management - data quality, metadata, provenance and trust Data integration, information fusion, multi-modal data analysis 3D visualization, including gamification, virtual reality, augmented reality 3D and Artificial Intelligence/Machine Learning 3D and Big Data, parallel computing, cloud computing 3D city modeling, underground infrastructure modeling, topography, and bathymetry modeling Building Information Modeling, Digital Twins, Smart Cities, Smart Infrastructure Usability and Human-Computer interaction in 3D GIS 3D GIS, spatial analysis and other applications (such as 3D cadastral systems, land administration, utilities, asset management, infrastructure, navigation, urban planning, geology, archaeology, marine systems, simulations, autonomous vehicles, facilities management, energy modeling, disaster and risk management, pandemic monitoring) The 3D GeoInfo 2021 tracks received 73 manuscripts including 30 full papers and 43 extended abstracts. The manuscripts were reviewed with a double-blind review process by members of the organizing and scientific committee and external reviewers. Ultimately 24 papers were accepted for the ISPRS Annals and 24 papers for the ISPRS Archives. We thank all of the authors and reviewers for their contributions.We look forward to the 3D GeoInfo 2021 virtual oral presentations and the opportunity to exchange ideas within our workshop and with the presenters and visitors of the 7th International FIG workshop on 3D Cadastres.

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference.

Spatiotemporal (ST) analytics applied to major data sources such as the World Bank and World Health Organization has shown tremendous value in shedding light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. WSTAMP engages this opportunity by situating analysts, data, and analytics together within a visually rich and computationally rigorous online analysis environment. Since introducing WSTAMP at the First International Workshop on Spatiotemporal Computing, several transformative advances have occurred. Collaboration with human computer interaction experts led to a complete interface redesign that deeply immerses the analyst within a ST context, significantly increases visual and textual content, provides navigational crosswalks for attribute discovery, substantially reduce mouse and keyboard actions, and supports user data uploads. Secondly, the database has been expanded to include over 16,000 attributes, 50 years of time, and 200+ nation states and redesigned to support non-annual, non-national, city, and interaction data. Finally, two new analytics are implemented for analyzing large portfolios of multi-attribute data and measuring the behavioral stability of regions along different dimensions. These advances required substantial new approaches in design, algorithmic innovations, and increased computational efficiency. We report on these advances and inform how others may freely access the tool.

DAMSS-2022 is the 13th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same place and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference is back on track. 2021 conference was successful, and the main objective of lively scientific communication was again achieved. The conference focuses on live interaction among participants, but there is also some scope for a limited number of virtual presentations. For better efficiency of communication among participants, most of the presentations are poster presentations. This format is really effective. The history of the conference goes back to 2009, when 16 papers were delivered. It started as a workshop and has now grown into a well-known conference. The idea of such workshop was conceived at the Institute of Mathematics and Informatics, which is now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea has been welcomed by the Lithuanian scientific community and abroad. The number of this year’s presentations is 81. The number of registered participants is 121 from 10 countries. This is significantly more than in 2021. The conference brings together researchers from six Lithuanian universities. This makes the conference the main annual meeting point for Lithuanian computer scientists. The main goal of the conference is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference allows the fast interchanging of new ideas among the scientific community. Seven IT companies supported the conference this year. This means that the topics of the conference are actual for business, too. Topics of the conference cover Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules Software Engineering, Data Science, Deep Learning, Digital Technologies, High-Performance Computing, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization in Data Science, Signal Processing, Visualization Methods for Multidimensional Data. This book gives an overview of all presentations of DAMSS-2022 conference.

In the final edition of this two-part series, the following article aims to capture a snapshot of a few of the opportunities that PsyPAG Annual Conference 2018 will offer – focusing on workshops. In organising the conference, there was unanimous agreement amongst the organising committee that all workshops should be interactive and engaging, in the hope that, alongside experience of presenting at the conference, delegates will also take home some practical skills and important messages. As of reading – details of all workshops will be available on the conference website –www.psypag2018.com. We look forward to seeing you next month!

We present the format for a workshop on introductory computer programming, which was held at the 2015 American Society for Horticultural Science (ASHS) Annual Conference in New Orleans, LA. The main workshop objective was to familiarize attendees with basic computer programming, including data structures, data management, and data analysis. The workshop used the general programming language R, though the concepts and principles presented are transferable across programming languages. Given the increased importance of statistical analysis in the agricultural sciences, the workshop was well attended. Participants appreciated the opportunity to improve their computational literacy and supported follow-up workshops like this at future ASHS events. We have released the presentation and the companion R script online.

This issue of the Proceedings of the ACM on Human-Computer Interaction features contributions in the intersection of human-computer interaction and software engineering, with further disciplines blending into a rich set of scientific works. 2021 is the first time the annual conference on Engineering Interactive Computing Systems (EICS) is hosted in the Netherlands and in the context of an Industrial Design department. We take this opportunity to focus on the relations and influence of the design discipline on the work of the EICS community. This resulted in a new set of topics for EICS, which were already partly reflected in the many submissions we received in three extensive review rounds throughout 2020 and the beginning of 2021. In this editorial we offer a perspective on what EICS is not yet, looking at the inclusion of and interplay with design as a related discipline.

The workshop program at the Eleventh Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment was held November 14–15, 2015 at the University of California, Santa Cruz, USA. The program included 4 workshops (one of which was a joint workshop): Artificial Intelligence in Adversarial Real-Time Games, Experimental AI in Games, Intelligent Narrative Technologies and Social Believability in Games, and Player Modeling. This article contains the reports of three of the four workshops.

Computing education in schools faces several problems, such as a lack of computing teachers and time in an already overloaded curriculum. A solution can be a multidisciplinary approach, integrating the teaching of computing within other subjects, creating the need to motivate teachers from other disciplines to teach computing in middle school. Therefore, the motivation and training of in-service teachers becomes crucial, as they need to have computing content and technological knowledge as well as pedagogical content knowledge. Yet, so far there exist very few training programs. Thus, as part of a comprehensive outreach program, we present a study on a one-day taster workshop for middle school teachers on physical computing education. Participants learn computer programming practice and computational thinking by programming an interactive robot. The workshop also approaches pedagogical aspects for teaching computing and technical issues regarding the installation and preparation of the required hardware/software. Preliminary results of its application with public school teachers in Florianopolis/Brazil are positive, motivating the majority of participants to introduce computing into their classes. However, our results also highlight that in order to enable teacher to apply the workshops effectively, longer training courses and ongoing support is required.

Purpose The purpose of this paper is to describe the development, implementation and impact evaluation of an interactive theater-based workshop by the ADVANCE program at the University of New Hampshire (UNH). The workshop is part of a larger institutional transformation program funded by the National Science Foundation. Design/methodology/approach This institutional transformation program relied upon a systems approach to diagnose potential causes for the underrepresentation of women faculty in certain disciplines. This revealed that increasing awareness of, and reducing, implicit gender bias among members of faculty search committees could, in time, contribute to increasing the representation of women faculty at UNH. A committee charged with developing a faculty workshop to achieve this change identified interactive theater as an effective faculty training approach. The committee oversaw the development of customized scripts, and the hiring of professional actors and a facilitator to implement the workshop. Findings The workshop’s effectiveness in fulfilling its goals was assessed using faculty hiring and composition data, program evaluations, participant interviews and questions in an annual faculty climate survey. Findings indicate that the representation of women faculty increased significantly at UNH since the implementation of the interactive theater workshop. Analysis of the multiple sources of data provides corroborating evidence that a significant portion of the increase is directly attributable to the workshop. Originality/value This paper demonstrates the effectiveness of interactive theater-based workshops in an academic environment and of the systems approach in diagnosing and solving organizational problems.

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) was held October 14–18, 2013, at Northeastern University in Boston, Massachusetts. Workshops were held on the two days prior to the start of the main conference, giving attendees a chance to hold in-depth discussions on topics that complement the themes of the main conference program. This year the workshops included the First Workshop on AI and Game Aesthetics (1 day), The Second Workshop on AI in the Game Design Process (1 day), The Second International Workshop on Musical Metacreation (2 day), The Sixth Workshop on Intelligent Narrative Technologies (2 day).

Experience using computational perception within the context of art and performance is reported. Four different types of pedagogical projects are presented: a new non-majors introductory computing course, an upper-level course covering computer vision and graphics in an integrated manner, an interactive dance piece, and a peer-led tele-workshop outreach series.

The first International Workshop on Chance Discovery (CD) was held in the hot-springs resort of Matsue, Shimani Prefecture, Japan, on 22 May 2001, as part of the Fifteenth Annual Conference of the Japanese Society for Artificial Intelligence (JSAI-2001). Thirteen presentations were made to the workshop (Ohsawa, 2001), with 25 people attending. The majority of presenters and attendees were from Japan. An edited selection of the papers presented will be included in a volume of papers from the various International Workshops of JSAI-2001, to be published in the Springer series, Advanced Information Processing. A forthcoming special issue of the journal New Generation Computing will also address this topic.

Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs with useful zooming in and zooming out capabilities. We will use the Shiny R package, which is a web application framework for R. A Shiny application has great potential to deliver interactive data analysis and visualization in a web browser. Yet there is limited application of this type of tool in agricultural science. We will learn the capabilities of R Shiny and its use with example applications in animal science and how to aid scientific discoveries and decision-making processes using interactive data exploration tools. After taking this workshop, the participants will be able to understand the concept of R Shiny and develop a web-based interactive visualization tool. The interactive and integrative data visualization features embedded in Shiny applications offer a new resource for users to readily extract extensive information from complex data.

Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs with useful zooming in and zooming out capabilities. We will use the Shiny R package, which is a web application framework for R. A Shiny application has great potential to deliver interactive data analysis and visualization in a web browser. Yet there is limited application of this type of tool in agricultural science. We will learn the capabilities of R Shiny and its use with example applications in animal science and how to aid scientific discoveries and decision-making processes using interactive data exploration tools. After taking this workshop, the participants will be able to understand the concept of R Shiny and develop a web-based interactive visualization tool. The interactive and integrative data visualization features embedded in Shiny applications offer a new resource for users to readily extract extensive information from complex data.

This volume collects together extended and improved versions of papers presented at the twentysecond Workshop on Logic, Language, Information and, Computation (WoLLIC 2015) held at the School of Informatics and Computing, Indiana University, Bloomington, Indiana. WoLLIC is an annual international forum on inter-disciplinary research involving formal logic, computing and programming theory, and natural language and reasoning. Each meeting includes invited talks and tutorials as well as contributed papers. Contributions were invited on all appropriate subjects of Logic, Language, Information, and Computation, with particular interest in cross-disciplinary topics.

Abstract The overall goal of the annual Transdisciplinary Research in Energetics and Cancer (TREC) Training Workshop is to provide transdisciplinary training for scientists in energetics and cancer and clinical care. The 2022 Workshop included 27 early-to-mid career investigators (trainees) pursuing diverse TREC research areas in basic, clinical, and population sciences. The 2022 trainees participated in a gallery walk, an interactive qualitative program evaluation method, to summarize key takeaways related to program objectives. Writing groups were formed and collaborated on this summary of the 5 key takeaways from the TREC Workshop. The 2022 TREC Workshop provided a targeted and unique networking opportunity that facilitated meaningful collaborative work addressing research and clinical needs in energetics and cancer. This report summarizes the 2022 TREC Workshop’s key takeaways and future directions for innovative transdisciplinary energetics and cancer research.

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities — Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.

The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.

The 20th annual ACM Conference on International Computing Education Research (ICER) will be hosted by RMIT University in the vibrant city of Melbourne, Australia. The conference will take place Tuesday 13th - Thursday 15th August 2024, following the Doctoral Consortium and the Work in Progress Workshop which will be held on Monday 12th August. You can participate physically by traveling to Melbourne or virtually where there will be an option for online discussions during the conference occurring in the local time zone.

EDITOR'S SUMMARYAt the fifth SIG/MET workshop, held during the 2015 ASIS&T Annual Meeting, the group shared papers, posters and discussions exploring developments in information measurement. The opening session on bibliometric case studies examined interdisciplinarity among consumers of academic research, increased funding for coauthors of previously funded authors and a classification of acknowledgement types. A session on information retrieval in relation to bibliometrics included studies on overcoming the limits of computational linguistics in very large corpora, an interactive context explorer of bibliographic data called Ariadne and comparative approaches to visualizing the structure of a very large dataset. The alternative metrics session covered application of altmetrics for analyzing public policy documents, a novel usage indicator promoting article discovery, the basis for connections among faculty members using Twitter and the heavy use of Twitter among academics. The daylong workshop included awards for best papers, best student papers and two featured presentations on the application and use of metrics.

Objective: To obtain feedback about the potential usefulness of a continuing professional development (CPD) portfolio for enhancing a faculty or practitioner’s scholarship of teaching and learning (SoTL). Method: A CPD portfolio approach to the SoTL was distributed in advance to registrants of the 2011 Annual AACP Teacher’s Seminar. In an interactive workshop, faculty facilitators described a model for a CPD process applied to the development of an individual’s SoTL. During the workshop, participants were asked to complete the initial sections of the portfolio to develop a personal plan for success in the SoTL. Post workshop, an evaluation form was distributed to the participants to obtain feedback about the CPD approach. Completed evaluation forms were collected, collated, and summarized. Results: A total of 53 (14.1%) workshop participants completed the evaluation form of the 375 attendees. In all, 25 assistant professors, 14 associate professors, 4 full professors, 10 residents/students, 22 clinical, and 2 research faculty submitted evaluations. The proposed uses for the portfolio model selected most often by the responders were for personal development, faculty evaluation, increasing the SoTL, new faculty development, preceptor development, and residency training. Implications: A structured CPD portfolio model might be useful for the professional development of the SoTL.

DAMSS-2019 is the 11th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics that now is the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 77. The number of registered participants is 127 from 9 countries. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 9 companies and institutions supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover Artificial Intelligence, Big data, Bioinformatics, Blockchain technologies, Business Rules Software Engineering, Data Science, Deep Learning, Digital Technologies, High-Performance Computing, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization in Data Science, Signal Processing, Visualization Methods for Multidimensional Data. A special session and discussions are organized about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2019.

This paper makes several considerations for organizing short project-based programming camps to support programming knowledge and motivation. The paper addresses how current research has not explored how summer programming camps helped students not only increase interest to pursue computing career, but also increase their programming knowledge. Informal CS education through summer programming camps provides K-12 students the opportunity to learn how to code through fun and interactive activities outside of their typical classroom experiences. For rural communities with limited computing education resources, summer programming camps may be one of few opportunities to learn about computing. A one-week easy-to-implement programming camp curriculum may help students not only have fun with code but also learn fundamental programming and computational thinking skills through game development. Our annual week-long programming camp at a rural community utilized a project-based learning approach through game development in Python. Findings showed that students were able to achieve basic abstraction and algorithmic thinking but not code analysis and debugging skills. On their motivation to pursue computing careers, results did not show any difference before and after the camp due to their prior existing interest in attending the camp.

Researchers in the life sciences are increasingly faced with the task of obtaining compute resources and training to analyze large, high-throughput technology generated datasets. As demand for compute resources has grown, high performance computing (HPC) systems have been implemented by research organizations and international consortiums to support academic researchers. However, life science researchers lack effective time-of-need training resources for utilization of these systems. Current training options have drawbacks that inhibit the effective training of researchers without experience in computational analysis. We identified the need for flexible, centrally-organized, easily accessible, interactive, and compute resource specific training for academic HPC use. In our delivery of a modular workshop series, we provided foundational training to a group of researchers in a coordinated manner, allowing them to further pursue additional training and analysis on compute resources available to them. Efficacy measures indicate that the material was effectively delivered to a broad audience in a short time period, including both virtual and on-site students. The practical approach to catalyze academic HPC use is amenable to diverse systems worldwide.

Following the 2018 SEG Annual Meeting, the Gravity and Magnetics Committee held a postconvention workshop titled “Integrated imaging.” The half-day workshop attracted nearly 50 participants from various backgrounds. Three primary objectives of the workshop were to explore the nonseismic toolbox, highlight real examples of integrated projects that benefitted (or did not benefit) from nonseismic data, and provide geoscientists from all backgrounds a learning opportunity to see how they might optimize the value of their imaging projects via integration with relatively low-cost nonseismic methods. The workshop had a highly interactive format that differed from traditional presentation-based settings. After eight brief case studies were presented, three concurrent guided discussions ensued. Participants were divided into three groups, and each group focused on one discussion topic at a time. The groups rotated, allowing everyone to discuss all three topics. The first discussion was centered on two general questions: what is integrated imaging, and what tools are available for it? The second discussion provided an opportunity to examine the relationships between different physical properties that must be managed during integrated multiphysics analysis. The third discussion focused on the costs and benefits of a multiparameter data acquisition. According to feedback from participants, these discussions were the most valuable part of the workshop. The participants agreed that an integrated approach in geophysical data analysis is a powerful but currently undervalued tool. Also noted were the value of integration with nonseismic methods illustrated in the case studies and the need for the integrated approach in data analysis to be taught in schools in addition to the classic overview of individual geophysical methods.

Starting in 1999, the University of Arkansas implemented an annual workshop to prepare employees of the landscape and retail sectors for the spring rush. Since the sales and service sectors account for 91% of the annual gross sales for the Arkansas Green Industry it was felt a specialized workshop was justified. The program format consists of three, one-hour sections devoted to the most common disease, insect, and weed problems that these professionals face. The program is presented in the evening so more employees can attend. The format has changed over the years from the typical road-trip, to a compressed video conference format, and finally back to a live performance in the two major population markets in the state. A detailed handout is provided so participants can easily follow the program. These same notes serve as a handy reference when these employees return to their jobs. To further expand the audience, an interactive CD is now available that summarizes the three topic areas. Because the CD program is hyperlinked, viewers can either proceed through the program in a linear fashion or easily search for answers on specific topics of interest. For disease and insect topics, life cycle and host information is provided. Control measures are separated into chemical and biological options. A recent addition to the CD is the inclusion of actual photographs of consumer products to illustrate examples of suitable active ingredient options for the control of specific pests or diseases.

The Summer Workshop on Interval Methods (SWIM) is an annual meeting initiated in 2008 by the French MEA working group on Set Computation and Interval Techniques of the French research group on Automatic Control. A special focus of the MEA group is on promoting interval analysis techniques and applications to a broader community of researchers, facilitated by such multidisciplinary workshops. Since 2008, SWIM has become a keystone event for researchers dealing with various aspects of interval and set-based methods. In 2018, the 11th edition in this workshop series was held at the University of Rostock, Germany, with a focus on research topics in the fields of engineering, computer science, and mathematics. A total of 31 talks were given during this workshop, covering the following areas: verified solution of initial value problems for ordinary differential equations, differential-algebraic system models, and partial differential equations, scientific computing with guaranteed error bounds, design of robust and fault-tolerant control systems, modeling and quantification of errors in engineering tasks, implementation of software libraries, and usage of the aforementioned approaches for system models in control engineering, data analysis, signal and image processing. After a peer-review process, 15 high-quality articles were selected for publication in this special issue. They are roughly divided into two thematic groups: Uncertainty Modeling, Software, Verified Computing and Optimization as well as Interval Methods in Control and Robotics. The first part, Uncertainty Modeling, Software, Verified Computing and Optimization, contains methodological aspects concerning reliable modeling of dynamic systems as well as visualization and quantification of uncertainty in the fields of measurement and simulation. Moreover, existence proofs for solutions of partial differential equations and their reliable optimal control synthesis are considered. A paper making use of quantifier elimination for robust linear output feedback control by means of eigenvalue placement concludes this section. The second part of this special issue, Interval Methods in Control and Robotics, is focused on the design as well as numerical and experimental validation of robust state observation and control procedures along with reliable parameter and state estimation approaches in the fields of control for thermal systems, robotics, localization of drones and global positioning systems.

Background: Lack of knowledge of quality improvement (QI) methodology and change management principles can explain many of the difficulties encountered when trying to develop effective QI initiatives in health care. Methods: An interactive QI workshop at the 14th Annual Meeting of the Pediatric Cardiac Intensive Care Society provided an overview of the role of QI in health care, basic QI frameworks and tools, and leadership and organizational culture pitfalls. The top five QI projects submitted to the meeting were later presented to an expert QI panel in a separate session to illustrate examples of QI principles. Results: Workshop presenters introduced two major QI methodologies used to design QI projects. Important first steps include identifying a problem, forming a multidisciplinary team, and developing an aim statement. Key driver diagrams were highlighted as an important tool to develop a project’s framework. Several diagnostic tools used to understand the problem were discussed, including the “5 Why’s,” cause-and-effect charts, and process flowcharts. The importance of outcome, process, and balancing measures was emphasized. Identification of interventions, the value of plan-do-study-act cycles to fuel continuous QI, and use of statistical process control, including run charts or control charts, were reviewed. The importance of stakeholder engagement, transparency, and sustainability was discussed. Later, the top five QI projects presented highlighted multiple “QI done well” practices discussed during the preconference QI workshop. Conclusions: Understanding QI methodology and appropriately applying basic QI tools are pivotal steps to realizing meaningful and sustained improvement.

This article serves as both an état présent of emerging scholarship in the interdisiplinary field of Memory Studies and a conference report following the first MSA Forward interactive workshop which preceded the second annual conference of the Memory Studies Association (MSA) in December 2017. MSA Forward is the postgraduate arm of the Memory Studies Association and offers a platform for exchanging ideas amongst a cohort of emerging scholars engaging with recent developments in Memory Studies and interacting with key academics in the field. The idea of engagement, with its political undertone, draws attention to the political valence and ethical sensitivity of emerging research as evidenced in this article, which contends that if Memory Studies is to be moving forwards as well as looking back, then it is important for emerging scholars as well as established academics to be at the forefront of the field.

Abstract Despite the demonstrated value of patient-centered care, health systems have been slow to integrate the patient’s voice into care delivery through patient-reported outcomes (PROs) with electronic tools. This is due in part to the complex interplay of technology, workflow, and human factors that shape the success of electronic PROs (ePROs) use. The 2018 American Medical Informatics Association Annual Symposium served as the setting for a half-day interactive workshop with diverse stakeholders to discuss proposed best practices for the planning, design, deployment, and evaluation of ePROs. We provide this collective commentary that synthesizes participant feedback regarding critical challenges that prohibit the scale and spread of ePROs across healthcare delivery systems, including governance and leadership, workflow and human factors, informatics, and data science. In order to realize the promise of ePROs at scale, adaptable approaches are critical to balance the needs of individual users with health systems at large.

Abstract Production efficiency in beef grazing systems can be improved by several relatively simple changes in forage management practices. During a 4-year project period funded by the NRCS Conservation Innovation Grant Program (68-3A75-14–251) and in collaboration with the University of Georgia, the Amazing Grazing Program in North Carolina utilized several innovative forage management approaches on 6 private farms and 3 public research stations to educate beef producers. On-farm grid sampling illustrated spatial distribution of soil nutrients as influenced by long-term historical management, particularly of winter hay feeding stations. Side-by-side testing of simple and complex annual forage mixes on three private farms demonstrated the opportunities to enhance forage nutritive value, capture the value of diverse botanical composition, and to defer grazing on perennial pastures. Smallplot testing of yield response to fall-stockpiled tall fescue nitrogen fertilizer rate allowed producers to reap the economic benefits of improved soil health and to help improve the environment surrounding their farm. At each private farm, summer, and winter forage management workshops explored innovative approaches to beef farm management, including the nature and importance of dung beetles, value of simple compared with complex annual forages, how to approach winter grazing, benefits of hay unrolling, need for mineral supplementation, utilizing temporary fencing, and frost-seeding clovers. As the project progressed, interest and attendance at workshops grew and cooperating farmer engagement increased. We found that many small-scale beef farmers were interested in simple practices such as hay unrolling and frost seeding when demonstrated by their peers. Using a variety of educational approaches, workshop audiences responded positively to hands-on activities and time for open discussion. Attendees were interested, attentive, and motivated to adopt new practices based on these practical demonstrations on peer farms.

This paper discusses elementary, and secondary (K-12) teachers’ perceptions of cross-reality (XR) tools for data visualization and use of sensor data from the built environment in classroom curricula. Our objective was to explore the use of sensor-informed XR in the built environment and civil engineering (BECE) field to support K-12 science, technology, engineering, and mathematics (STEM) experiential learning and foster BECE-related career awareness. We conducted surveys and informal questionnaires with 33 primary and secondary teachers attending an annual two-day university-based teacher professional development workshop as part of a statewide STEM afterschool program serving students in rural communities. We assessed teachers’ familiarity with, knowledge about, and appraisal of using cross-reality platforms and sensor data in classrooms and after school curricula. Findings show that, while all teachers reported relatively high interest in learning about sensor applications and innovative interactive techniques, middle school teachers in particular were most likely to see value in using these applications for teaching and learning. Implications for teacher professional development are discussed.

Background: New York City is a gateway for emerging pathogens and global threats. In 2013, faculty from Montefiore Medical Center and Memorial Sloan Kettering developed a free half-day workshop for postgraduate trainees in antimicrobial stewardship (AS), infection prevention (IP), hospital epidemiology, and public health. This annual workshop, sponsored by the Infectious Diseases Society of New York (IDSNY), incorporates case studies and expert panel discussions on timely topics such as Ebola, Candida auris, Clostridiodes difficile, measles, nosocomial influenza, drug shortages, and AS/IP “big data.” Methods: From 2013 through 2017, the workshop involved 10–15 interactive AS/IP cases with audience response questions and panel discussions. In 2018–2019, based on feedback, the format was revised to emphasize breakout sessions in which participants actively practiced AS/IP tools, (eg, medication utilization evaluations, epidemiologic curves, and performance improvement devices). Examples of 2018–2019 cases are shown in Figure 1. A pre- and postseminar paper survey was conducted yearly to understand baseline training in AS/IP, desire for future AS/IP careers, and self-reported effectiveness of the workshop. Results: Initially, the primary audience was NYC ID fellows. From 2018 onward, we opened enrollment to pharmacy residents. Approximately 45 NYC ID fellows were eligible for the course each year. Results from 2013 to 2016 surveys were reported previously (Fig. 2). There were 32 attendees in 2018, 42 in 2019. The survey response rate was 88% in 2018 and 95% in 2019, with 68 (92%) total participants. Most participants had received previous training in IP (82%) and AS (94%) (Fig. 3). Most participants reported that the program was a good supplement to their ID training (98%) and that case studies were an effective means of learning IP (100%) and AS (98%). Furthermore, 92% stated they would like additional AS/IP training, and many since 2013 have requested a full-day course. Self-reported interest in future involvement in AS/IP increased after the workshop: IP, 68%–83% (P =.04) and AS, 88%–91% (P = .61). Conclusions: Most trainees reported satisfaction with the workshop and case-study learning method; interest in future AS/IP careers increased after the seminar. We intend to explore Funding: to expand to a full-day program for all NYC postgraduate trainees and AS/IP junior faculty. As such, we hope to obtain the endorsement of professional societies such as SHEA. This workshop could address a crucial educational gap in AS/IP postgraduate training and help sustain our future workforce.Funding: NoneDisclosures: None

In October 2002 I was lucky enough to spend three stimulating days at the New York University Law School Library participating in the annual Legal Information Transfer Network workshop. The Legal Information Transfer Network (ITN) is funded by a generous grant from The Starr Foundation (established in 1955 by insurance entrepreneur Cornelius Van der Starr) and is headed by the dynamic Director of the NYU Law School Library, Professor Kathie Price. ITN aims to establish a global network of prestigious law libraries which ultimately can offer a 24/7 virtual reference service, both to its own partner libraries in the developed world and to academic legal communities in less developed countries. Previous annual workshops in such cities as Lausanne in Switzerland have given senior librarians from ITN partner libraries the opportunity to meet and make progress on issues such as providing a global virtual reference desk, sharing database access across the libraries, developing interactive legal research guides, and creating imaginative training programmes for local law librarians in China and Southern Africa (http://www.law.nyu.edu/library/itn). Between workshops the exchange of ideas is continued by email discussion. Currently the list of law library partners includes New York University, Washington University in Seattle, Toronto University in Canada, IALS Library in the UK, the Catholic University of Leuven in Belgium, Tilburg University in the Netherlands, Konstanz University in Germany, Cape Town University in South Africa, Melbourne University in Australia, Yerevan State University in Armenia, and Tsinghua University in China.

The Summer Workshop on Interval Methods (SWIM) is an annual meeting initiated in 2008 by the French MEA working group on Set Computation and Interval Techniques of the French research group on Automatic Control. A special focus of the MEA group is on promoting interval analysis techniques and applications to a broader community of researchers, facilitated by such multidisciplinary workshops. Since 2008, SWIM has become a keystone event for researchers dealing with various aspects of interval and set-based methods. In 2019, the 12th edition in this workshop series was held at ENSTA Paris, France, with a total of 25 talks. Traditionally, workshops in the series of SWIM provide a platform for both theoretical and applied researchers who work on the development, implementation, and application of interval methods, verified numerics, and other related (set-membership) techniques.For this edition, given talks were in the fields of the verified solution of initial value problems for ordinary differential equations, differential-algebraic system models, and partial differential equations, scientific computing with guaranteed error bounds, the design of robust and fault-tolerant control systems, the implementation of corresponding software libraries, and the usage of the mentioned approaches for a large variety of system models in areas such as control engineering, data analysis, signal and image processing. Seven papers were selected for submission to this Acta Cybernetica special issue. After a two turn peer-review process, six high-quality articles were selected for publication in this special issue. Three papers propose a contribution regarding differential equations, two papers focus on robust control, and one paper considers fault detection.

Make the IRG pre-conference workshop part of your annual HTAi (Health Technology Assessment International) meeting plans! Join us in Barcelona on Sunday, June 17th for a full day of training and lively discussions. Our workshop includes a mixture of presentations and interactive sessions highlighting important and emerging developments in HTA information management. Julie Glanville, IS Manager and Associate Director at the Centre for Reviews and Dissemination in York, will outline recent research into search filters to identify diagnostic tests and assessments of which databases and other resources should be searched for diagnostic test studies. Greg Bak, an information specialist from the Canadian Agency for Drugs and Technologies in Health, will discuss a CADTH-adapted critical appraisal instrument that can be used to rank and assess the quality of methodological search filters. His presentation will provide participants with the skills needed to make informed decisions about when and which methodological search filters to use. Sari Ormstad, a research librarian at the Norwegian Knowledge Centre for the Health Services, Oslo, will present on a new database that offers local decision-makers and health personnel an overview of all current international assessments of new health technologies and summarises those that are most relevant for them. Miquel Mayer, Director of Web Mèdica Acreditada, an international accreditation program of medical websites, will discuss the quality and use of health-related websites, different strategies to select and improve their quality, and finally how metadata and semantic web technologies could help users find the best health information. Elaine Alligood, an information specialist from Veterans Affairs Technology Assessment Program, will update participants on current search engines and how they can be used in HTA searches to complement traditional database searching. Andrew Booth, Director of Information Resources at the School of Health & Related Research (ScHARR), and Amanda Burls, Senior Clinical Lecturer and Director of West Midlands Health Technology Assessment Collaboration, will deliver a half-day bilingual (English and Spanish) critical appraisal workshop in which they provide a "tapas" of critical appraisal skills and techniques. The workshop will demonstrate that skills in critical appraisal can be modeled and facilitated by library, information and other health staff working locally. Full details regarding conference registration are available at: http://www.htai.org/barcelona-2007/. For more information, contact Becky Skidmore, IRG Chair: bskidmore@sogc.com or Malene Fabricius Jensen, IRG Chair-Elect: mfj@sst.dk.

Purpose – This paper aims to test the need in the Australian higher education (HE) sector for a national network for the peer review of assessment in response to the proposed HE standards framework and propose a sector-wide framework for calibrating and assuring achievement standards, both within and across disciplines, through the establishment of a peer review of assessment network (PRAN). Design/methodology/approach – This study used a “proof of concept”approach to test the need for a national network, using consultations (n = 67) which included teleconference meetings [39], face-to-face meetings [2], Skype [1], presentations [19], state-based workshops [6] and a national forum. Quantitative data from evaluation surveys from state-based workshops and national forum were computer-analysed to generate descriptive statistics. Qualitative data arising from open-ended questionnaire responses were analysed through progressive categorisation and data coding designed to identify and refine data themes. Findings – In all, 63 per cent of participants to the state-based workshops were satisfied with the workshop content. A further 29 per cent reported a high level of satisfaction. The interactive group discussions fostered a collaborative approach and facilitated engagement with the workshop content. A total of 58 per cent of participants to the national forum were satisfied with the forum, with a further 40 per cent reporting a high level of satisfaction. Participants indicated that presentation content was informative and covered a diverse range of topics and viewpoints highly relevant to the current clime across the HE sector. Practical implications – Many participants strongly supported the establishment of a national PRAN, with overwhelming support (88 per cent) for the forum to be made an annual event. Originality/value – This study contributes to existing literature and provides further evidence for the value of networks in the peer review of assessment to support academics in professional learning and calibration exercises.

1. Bilal, A. Jourabloo, M. Ye, X. Liu, and L. Ren. Do Convolutional Neural Networks Learn Class Hierarchy? IEEE Transactions on Visualization and Computer Graphics, 24(1):152–162, Jan. 2018. 2. M. Carney, B. Webster, I. Alvarado, K. Phillips, N. Howell, J. Griffith, J. Jongejan, A. Pitaru, and A. Chen. Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20. ACM, Honolulu, HI, USA, 2020. 3. A. Karpathy. CS231n Convolutional Neural Networks for Visual Recognition, 2016 4. M. Kahng, N. Thorat, D. H. Chau, F. B. Viegas, and M. Wattenberg. GANLab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation. IEEE Transactions on Visualization and Computer Graphics, 25(1):310–320, Jan. 2019. 5. J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson. Understanding Neural Networks Through Deep Visualization. In ICML Deep Learning Workshop, 2015 6. M. Kahng, P. Y. Andrews, A. Kalro, and D. H. Chau. ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models. IEEE Transactions on Visualization and Computer Graphics, 24(1):88–97, Jan. 2018. 7. https://cs231n.github.io/convolutional-networks/ 8. https://www.analyticsvidhya.com/blog/2020/02/learn-imageclassification-cnn-convolutional-neural-networks-3-datasets/ 9. https://towardsdatascience.com/understanding-cnn-convolutionalneural- network-69fd626ee7d4 10. https://medium.com/@birdortyedi_23820/deep-learning-lab-episode-2- cifar- 10-631aea84f11e 11. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen. Recent advances in convolutional neural networks. Pattern Recognition, 77:354–377, May 2018. 12. Hamid, Y., Shah, F.A. and Sugumaram, M. (2014), ―Wavelet neural network model for network intrusion detection system‖, International Journal of Information Technology, Vol. 11 No. 2, pp. 251-263 13. G Sreeram , S Pradeep, K SrinivasRao , B.Deevan Raju , Parveen Nikhat , ― Moving ridge neuronal espionage network simulation for reticulum invasion sensing‖. International Journal of Pervasive Computing and Communications.https://doi.org/10.1108/IJPCC-05- 2020-0036 14. E. Stevens, L. Antiga, and T. Viehmann. Deep Learning with PyTorch. O’Reilly Media, 2019. 15. J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson. Understanding Neural Networks Through Deep Visualization. In ICML Deep Learning Workshop, 2015. 16. Aman Dureja, Payal Pahwa, ―Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks‖, Recent Advances in Computer Science , Vol 2, Issue 3, 2019 ,PP-156-161 17. https://missinglink.ai/guides/neural-network-concepts/7-types-neuralnetwork-activation-functions-right/

Abstract. Digital technology provides methods to record and preserve cultural heritage, support conservation and restoration efforts, and share our collective past with a worldwide audience. Between 2011 and 2017, the 3D Survey Group from Politecnico di Milano operated an annual workshop in the medieval village of Ghesc in which photogrammetry and laser-scanner surveys were carried out. The point cloud data acquired in these activities has become “time slices” documenting different stages of the preservation interventions in Ghesc and the evolution of advanced survey techniques. The main objective of this research is to streamline the workflow of delivering immersive and interactive experiences for complex heritage by directly utilising the 3D survey point cloud data, whether derived from a photogrammetric survey, static laser scanner, or mobile mapping.A point cloud-based multiplatform application is designed and delivered with versatile functions. It runs on PC and VR devices to provide virtual access to the village and narrate its revitalisation story. Additionally, it operates on mobile devices with an AR feature that brings vibrancy to the on-site experience. This application integrates high-fidelity point cloud models, detailed information on vernacular architecture in the Ossola Valley, and information on the preservation project with gamified learning experiences. The unconventional approach of using points as rendering primitives in virtual applications offers a practical solution for visualising complex heritage, enabling an efficient transition from the data collection stage to the data sharing stage without the need for 3D reconstruction and intricate BIM modelling.

Artificial Intelligence (AI) combines computer science and robust datasets to mimic natural intelligence demonstrated by human beings to aid in problem-solving and decision-making involving consciousness up to a certain extent. From Apple’s virtual personal assistant, Siri, to Tesla’s self-driving cars, research and development in the field of AI is progressing rapidly along with privacy concerns surrounding the usage and storage of user data on external servers which has further fueled the need of modern ultra-efficient AI networks and algorithms. The scope of the work presented within this paper focuses on introducing a modern image classifier which is a light-weight and ultra-efficient CNN intended to be deployed on local embedded systems, also known as edge devices, for general-purpose usage. This work is an extension of the award-winning paper entitled ‘CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems’ published for the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). The proposed neural network dubbed CondenseNeXtV2 utilizes a new self-querying augmentation policy technique on the target dataset along with adaption to the latest version of PyTorch framework and activation functions resulting in improved efficiency in image classification computation and accuracy. Finally, we deploy the trained weights of CondenseNeXtV2 on NXP BlueBox which is an edge device designed to serve as a development platform for self-driving cars, and conclusions will be extrapolated accordingly.

Special Interest Group in Evaluation (SIG Eval) of Japan Society for Fuzzy Theory and intelligent informatics was founded by Professor Hisao Shiizuka, Kogakuin University, in 1993 to facilitate the exchange of research information within Japan on evaluation problems. Since 1996, SIG Eval has held an annual workshop, the Workshop on Evaluation of Heart and Mind. In addition to the workshop, SIG Eval has edited this special issue on “Heart and Mind” Evaluation. Contributors include those who often speak at the workshop. The first article, “Feasibility Study on Marketing Research Using Eye Movement: An Investigation of Image Presentation using an Eye Camera and Data Processing,” by Shin'ya Nagasawa, Sora Yim, and Hitoshi Hongo, asserts that, in physiological experiments using an eye camera, the user's interest influences purchasing behavior. The second article, “Statistical Image Analysis of Psychological Projective Drawings,” by Kazuhisa Takemura, Iyuki Takasaki, and Yumi Iwamitsu, discusses the use of statistical image analysis to overcome the difficulty in assessing the reliability of projective drawing techniques. The third article, “Fuzzy Least Squares Regression Analysis for Social Judgment Study,” by Kazuhisa Takemura, proposes fuzzy regression analysis in which a dependent variable, independent variables, and regression parameters are represented by triangular fuzzy numbers. The fourth to sixth articles discuss fuzzy measures, or capacities, which are quite popular for their application in subjective evaluation. The fourth article, “Identification of Fuzzy Measures with Distorted Probability Measures,” by Aoi Honda and Yoshiaki Okazaki, classifies fuzzy measures by introducing the concept of order type, and proposes the method of identifying fuzzy measure μ as a distorted probability of the same, or similar, order type as μ The fifth article, “Semiatoms in Choquet Integral Models of Multiattribute Decision Making,” by Toshiaki Murofushi, characterizes the concept of the semiatom in fuzzy measure theory in the multiattribute preference relation represented by a Choquet integral. The last article, “Some Characterizations of k-Monotonicity through the Bipolar Möbius Transform in Bi-Capacities,” by Katsushige Fujimoto and Toshiaki Murofushi, proposes the bipolar Möbius transform as an extension of the conventional Möbius transform of capacities to that of bi-capacities; the concept of bi-capacity was proposed by Grabisch and Labreuche (2002) for modeling decision making on a bipolar scale. We thank the reviewers and contributers for their time and effort in making this special issue possible, and we wish to thank the JACIII editorial board, especially Professors Kaoru Hirota and Toshio Fukuda, the Editors-in-Chief, and Kenta Uchino, Managing Editor, for their support and advice in putting this special issue together. I have assumed the role of General Chair of the Joint Conference of the Third International Conference on Soft Computing and Intelligent Systems and the Seventh International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2006), to be held at Tokyo Institute of Technology, Japan, on September 20--24, 2006. As is customary, selected papers will be published in special issues of this journal. We invite you to submit your research papers and to participate in SCIS & ISIS 2006. For further information, please visit <u>http://scis2006.cs.dm.u-tokai.ac.jp/</u>.

Abstract Introduction. The NCI-funded Cancer Genomics Cloud (CGC) and the NCI Predicative Oncology Model and Data Clearinghouse (MoDaC) advance NCI computing infrastructure and tools that aim to reduce the burden of cancer on patients. The CGC provides a collaborative cloud base computation infrastructure that collocates computation, bioinformatics workflows, and 3+ PB data to researchers. MoDaC provides a publicly available resource generated from the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) Program and the Accelerating Therapeutics for Opportunities in Medicine (ATOM Consortium). The NCI-sponsored MoDaC program aims to add machine learning toolsets to identifying novel treatments for Cancer Patients. We present our progress collaborating with MoDaC to make machine learning models available on the Cancer Genomics Cloud. Methods. Our Bioinformatics teams migrated MoDaC tools on the CGC, defined standards for releasing models, and collected recommendations for decreasing the time required to make MoDaC tools available on the Cancer Genomics Cloud. We mirrored the ATOM Modeling Pipeline (AMPL), a drug discovery platform, on the CGC by making the GIT repository cloud accessible through Jupyter Notebook access, supporting interactive analysis. We translated AMPL and JDACS4C models into CWL. Converting these ML models into CWL supports reproducible execution, scalable deployment, and computational portability. Results. The AMPL drug discovery platform on the CGC includes data ingestion & curation, featurization, model training & tuning, prediction generation, visualization & analysis functionality as Jupyter notebooks and CWL workflows. The release consists of chemo-informatics tools for integrating cancer treatment features in deep-learning graph models. JDACS4C ML Models ported to the CGC include classifiers (tumor and normal-tumor pairs), autoencoders (Gene Expression), drug response predictors (single and combination), and Multitask Convolutional Neural Networks (extract information from cancer pathology reports). Conclusion. We optimized the MoDaC Drug Discovery and Machine Learning tools into cloud-native resources on the CGC, supporting interactive and GUI-driven analysis. The release supports technical and newer users to machine learning, allowing access to a broader user base than those who traditionally have access to ML toolsets. These MoDaC toolsets will support pre-clinical study evaluation, treatment identification, and experimental design. Moreover, existing MoDaC-AMPL tutorials on the CGC support distributed ML-Drug Discovery training. Lastly, collaborating with MoDaC teams identified standardization approaches that can reduce the time and effort to make these tools widely available across the NIH-NCI computational infrastructure. Citation Format: Soner Koc, Vojislav Varjacic, Miona Rankovic, Marijeta Slavkovic-Ilic, Aleksandar Danicic, Sean Black, Naomi Ohashi, Titli Sarkar, Zelia Worman, Jack DiGiovanna, Brandi Davis-Dusenbery, Dennis A. Dean. Collaborating to ensure data-driven drug discovery on the Cancer Genomics Cloud: Realizing the possibilities for MoDaC and ATOM. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5356.

The purpose of the study is to substantiate the benchmarks of digitization of the institutional environment of state financial control in Ukraine based on foreign experience. The methods of qualitative, quantitative, and correlation analysis, graphical modelling, causality tests, comparisons and generalizations were used. Results. The essence of the institutional environment of the state financial control is determined and its contradictions are revealed. It is established that in order to overcome the contradictions of the institutional environment of state financial control, it is necessary to resolve the issue of intensifying its digitalization since the use of financial technologies affects the state of governance. Directions of the digitization of the institutional environment of the state financial control were examined through the prism of relations “control-state”, “control-enterprise”, “controlsociety”, and “control-European Community”, with the justification of proposals that should be implemented in the context of isolated components. Practical importance. The digitalization of the institutional environment of state financial control should be based on electronic democracy, parameterization of annual reports of controlling bodies, blockchain interoperability, automation of control measures, use of artificial intelligence and cloud computing technologies, services based on open data, interactive methods of information sharing (social networks, television programs, and video blogs), and electronic tax fraud identification services in international trade. Value/ originality. The level of digitalization of the external institutional environment of the state financial control is higher than the level of digitalization of the internal environment. This creates the basis for the development of the theory of state financial control, as well as the practice of its implementation, which requires appropriate innovative and technological transformations.

To date, discrete event stochastic simulations of large scale biological reaction systems are extremely compute-intensive and time-consuming. Besides, it has been widely accepted that spatial factor plays a critical role in the dynamics of most biological reaction systems. The NSM (the Next Sub-Volume Method), a spatial variation of the Gillespie’s stochastic simulation algorithm (SSA), has been proposed for spatially stochastic simulation of those systems. While being able to explore high degree of parallelism in systems, NSM is inherently sequential, which still suffers from the problem of low simulation speed. Fine-grained parallel execution is an elegant way to speed up sequential simulations. Thus, based on the discrete event simulation framework JAMES II, we design and implement a PDES (Parallel Discrete Event Simulation) TW (time warp) simulator to enable the fine-grained parallel execution of spatial stochastic simulations of biological reaction systems using the ANSM (the Abstract NSM), a parallel variation of the NSM. The simulation results of classical Lotka-Volterra biological reaction system show that our time warp simulator obtains remarkable parallel speed-up against sequential execution of the NSM.I.IntroductionThe goal of Systems biology is to obtain system-level investigations of the structure and behavior of biological reaction systems by integrating biology with system theory, mathematics and computer science [1][3], since the isolated knowledge of parts can not explain the dynamics of a whole system. As the complement of “wet-lab” experiments, stochastic simulation, being called the “dry-computational” experiment, plays a more and more important role in computing systems biology [2]. Among many methods explored in systems biology, discrete event stochastic simulation is of greatly importance [4][5][6], since a great number of researches have present that stochasticity or “noise” have a crucial effect on the dynamics of small population biological reaction systems [4][7]. Furthermore, recent research shows that the stochasticity is not only important in biological reaction systems with small population but also in some moderate/large population systems [7].To date, Gillespie’s SSA [8] is widely considered to be the most accurate way to capture the dynamics of biological reaction systems instead of traditional mathematical method [5][9]. However, SSA-based stochastic simulation is confronted with two main challenges: Firstly, this type of simulation is extremely time-consuming, since when the types of species and the number of reactions in the biological system are large, SSA requires a huge amount of steps to sample these reactions; Secondly, the assumption that the systems are spatially homogeneous or well-stirred is hardly met in most real biological systems and spatial factors play a key role in the behaviors of most real biological systems [19][20][21][22][23][24]. The next sub-volume method (NSM) [18], presents us an elegant way to access the special problem via domain partition. To our disappointment, sequential stochastic simulation with the NSM is still very time-consuming, and additionally introduced diffusion among neighbor sub-volumes makes things worse. Whereas, the NSM explores a very high degree of parallelism among sub-volumes, and parallelization has been widely accepted as the most meaningful way to tackle the performance bottleneck of sequential simulations [26][27]. Thus, adapting parallel discrete event simulation (PDES) techniques to discrete event stochastic simulation would be particularly promising. Although there are a few attempts have been conducted [29][30][31], research in this filed is still in its infancy and many issues are in need of further discussion. The next section of the paper presents the background and related work in this domain. In section III, we give the details of design and implementation of model interfaces of LP paradigm and the time warp simulator based on the discrete event simulation framework JAMES II; the benchmark model and experiment results are shown in Section IV; in the last section, we conclude the paper with some future work.II. Background and Related WorkA. Parallel Discrete Event Simulation (PDES)The notion Logical Process (LP) is introduced to PDES as the abstract of the physical process [26], where a system consisting of many physical processes is usually modeled by a set of LP. LP is regarded as the smallest unit that can be executed in PDES and each LP holds a sub-partition of the whole system’s state variables as its private ones. When a LP processes an event, it can only modify the state variables of its own. If one LP needs to modify one of its neighbors’ state variables, it has to schedule an event to the target neighbor. That is to say event message exchanging is the only way that LPs interact with each other. Because of the data dependences or interactions among LPs, synchronization protocols have to be introduced to PDES to guarantee the so-called local causality constraint (LCC) [26]. By now, there are a larger number of synchronization algorithms have been proposed, e.g. the null-message [26], the time warp (TW) [32], breath time warp (BTW) [33] and etc. According to whether can events of LPs be processed optimistically, they are generally divided into two types: conservative algorithms and optimistic algorithms. However, Dematté and Mazza have theoretically pointed out the disadvantages of pure conservative parallel simulation for biochemical reaction systems [31]. B. NSM and ANSM The NSM is a spatial variation of Gillespie’ SSA, which integrates the direct method (DM) [8] with the next reaction method (NRM) [25]. The NSM presents us a pretty good way to tackle the aspect of space in biological systems by partitioning a spatially inhomogeneous system into many much more smaller “homogeneous” ones, which can be simulated by SSA separately. However, the NSM is inherently combined with the sequential semantics, and all sub-volumes share one common data structure for events or messages. Thus, directly parallelization of the NSM may be confronted with the so-called boundary problem and high costs of synchronously accessing the common data structure [29]. In order to obtain higher efficiency of parallel simulation, parallelization of NSM has to firstly free the NSM from the sequential semantics and secondly partition the shared data structure into many “parallel” ones. One of these is the abstract next sub-volume method (ANSM) [30]. In the ANSM, each sub-volume is modeled by a logical process (LP) based on the LP paradigm of PDES, where each LP held its own event queue and state variables (see Fig. 1). In addition, the so-called retraction mechanism was introduced in the ANSM too (see algorithm 1). Besides, based on the ANSM, Wang etc. [30] have experimentally tested the performance of several PDES algorithms in the platform called YH-SUPE [27]. However, their platform is designed for general simulation applications, thus it would sacrifice some performance for being not able to take into account the characteristics of biological reaction systems. Using the similar ideas of the ANSM, Dematté and Mazza have designed and realized an optimistic simulator. However, they processed events in time-stepped manner, which would lose a specific degree of precisions compared with the discrete event manner, and it is very hard to transfer a time-stepped simulation to a discrete event one. In addition, Jeschke etc.[29] have designed and implemented a dynamic time-window simulator to execution the NSM in parallel on the grid computing environment, however, they paid main attention on the analysis of communication costs and determining a better size of the time-window.Fig. 1: the variations from SSA to NSM and from NSM to ANSMC. JAMES II JAMES II is an open source discrete event simulation experiment framework developed by the University of Rostock in Germany. It focuses on high flexibility and scalability [11][13]. Based on the plug-in scheme [12], each function of JAMES II is defined as a specific plug-in type, and all plug-in types and plug-ins are declared in XML-files [13]. Combined with the factory method pattern JAMES II innovatively split up the model and simulator, which makes JAMES II is very flexible to add and reuse both of models and simulators. In addition, JAMES II supports various types of modelling formalisms, e.g. cellular automata, discrete event system specification (DEVS), SpacePi, StochasticPi and etc.[14]. Besides, a well-defined simulator selection mechanism is designed and developed in JAMES II, which can not only automatically choose the proper simulators according to the modeling formalism but also pick out a specific simulator from a serious of simulators supporting the same modeling formalism according to the user settings [15].III. The Model Interface and SimulatorAs we have mentioned in section II (part C), model and simulator are split up into two separate parts. Thus, in this section, we introduce the designation and implementation of model interface of LP paradigm and more importantly the time warp simulator.A. The Mod Interface of LP ParadigmJAMES II provides abstract model interfaces for different modeling formalism, based on which Wang etc. have designed and implemented model interface of LP paradigm[16]. However, this interface is not scalable well for parallel and distributed simulation of larger scale systems. In our implementation, we accommodate the interface to the situation of parallel and distributed situations. Firstly, the neighbor LP’s reference is replaced by its name in LP’s neighbor queue, because it is improper even dangerous that a local LP hold the references of other LPs in remote memory space. In addition, (pseudo-)random number plays a crucial role to obtain valid and meaningful results in stochastic simulations. However, it is still a very challenge work to find a good random number generator (RNG) [34]. Thus, in order to focus on our problems, we introduce one of the uniform RNGs of JAMES II to this model interface, where each LP holds a private RNG so that random number streams of different LPs can be independent stochastically. B. The Time Warp SimulatorBased on the simulator interface provided by JAMES II, we design and implement the time warp simulator, which contains the (master-)simulator, (LP-)simulator. The simulator works strictly as master/worker(s) paradigm for fine-grained parallel and distributed stochastic simulations. Communication costs are crucial to the performance of a fine-grained parallel and distributed simulation. Based on the Java remote method invocation (RMI) mechanism, P2P (peer-to-peer) communication is implemented among all (master-and LP-)simulators, where a simulator holds all the proxies of targeted ones that work on remote workers. One of the advantages of this communication approach is that PDES codes can be transferred to various hardwire environment, such as Clusters, Grids and distributed computing environment, with only a little modification; The other is that RMI mechanism is easy to realized and independent to any other non-Java libraries. Since the straggler event problem, states have to be saved to rollback events that are pre-processed optimistically. Each time being modified, the state is cloned to a queue by Java clone mechanism. Problem of this copy state saving approach is that it would cause loads of memory space. However, the problem can be made up by a condign GVT calculating mechanism. GVT reduction scheme also has a significant impact on the performance of parallel simulators, since it marks the highest time boundary of events that can be committed so that memories of fossils (processed events and states) less than GVT can be reallocated. GVT calculating is a very knotty for the notorious simultaneous reporting problem and transient messages problem. According to our problem, another GVT algorithm, called Twice Notification (TN-GVT) (see algorithm 2), is contributed to this already rich repository instead of implementing one of GVT algorithms in reference [26] and [28].This algorithm looks like the synchronous algorithm described in reference [26] (pp. 114), however, they are essentially different from each other. This algorithm has never stopped the simulators from processing events when GVT reduction, while algorithm in reference [26] blocks all simulators for GVT calculating. As for the transient message problem, it can be neglect in our implementation, because RMI based remote communication approach is synchronized, that means a simulator will not go on its processing until the remote the massage get to its destination. And because of this, the high-costs message acknowledgement, prevalent over many classical asynchronous GVT algorithms, is not needed anymore too, which should be constructive to the whole performance of the time warp simulator.IV. Benchmark Model and Experiment ResultsA. The Lotka-Volterra Predator-prey SystemIn our experiment, the spatial version of Lotka-Volterra predator-prey system is introduced as the benchmark model (see Fig. 2). We choose the system for two considerations: 1) this system is a classical experimental model that has been used in many related researches [8][30][31], so it is credible and the simulation results are comparable; 2) it is simple but helpful enough to test the issues we are interested in. The space of predator-prey System is partitioned into a2D NXNgrid, whereNdenotes the edge size of the grid. Initially the population of the Grass, Preys and Predators are set to 1000 in each single sub-volume (LP). In Fig. 2,r1,r2,r3stand for the reaction constants of the reaction 1, 2 and 3 respectively. We usedGrass,dPreyanddPredatorto stand for the diffusion rate of Grass, Prey and Predator separately. Being similar to reference [8], we also take the assumption that the population of the grass remains stable, and thusdGrassis set to zero.R1:Grass + Prey ->2Prey(1)R2:Predator +Prey -> 2Predator(2)R3:Predator -> NULL(3)r1=0.01; r2=0.01; r3=10(4)dGrass=0.0;dPrey=2.5;dPredato=5.0(5)Fig. 2: predator-prey systemB. Experiment ResultsThe simulation runs have been executed on a Linux Cluster with 40 computing nodes. Each computing node is equipped with two 64bit 2.53 GHz Intel Xeon QuadCore Processors with 24GB RAM, and nodes are interconnected with Gigabit Ethernet connection. The operating system is Kylin Server 3.5, with kernel 2.6.18. Experiments have been conducted on the benchmark model of different size of mode to investigate the execution time and speedup of the time warp simulator. As shown in Fig. 3, the execution time of simulation on single processor with 8 cores is compared. The result shows that it will take more wall clock time to simulate much larger scale systems for the same simulation time. This testifies the fact that larger scale systems will leads to more events in the same time interval. More importantly, the blue line shows that the sequential simulation performance declines very fast when the mode scale becomes large. The bottleneck of sequential simulator is due to the costs of accessing a long event queue to choose the next events. Besides, from the comparison between group 1 and group 2 in this experiment, we could also conclude that high diffusion rate increased the simulation time greatly both in sequential and parallel simulations. This is because LP paradigm has to split diffusion into two processes (diffusion (in) and diffusion (out) event) for two interactive LPs involved in diffusion and high diffusion rate will lead to high proportional of diffusion to reaction. In the second step shown in Fig. 4, the relationship between the speedups from time warp of two different model sizes and the number of work cores involved are demonstrated. The speedup is calculated against the sequential execution of the spatial reaction-diffusion systems model with the same model size and parameters using NSM.Fig. 4 shows the comparison of speedup of time warp on a64X64grid and a100X100grid. In the case of a64X64grid, under the condition that only one node is used, the lowest speedup (a little bigger than 1) is achieved when two cores involved, and the highest speedup (about 6) is achieved when 8 cores involved. The influence of the number of cores used in parallel simulation is investigated. In most cases, large number of cores could bring in considerable improvements in the performance of parallel simulation. Also, compared with the two results in Fig. 4, the simulation of larger model achieves better speedup. Combined with time tests (Fig. 3), we find that sequential simulator’s performance declines sharply when the model scale becomes very large, which makes the time warp simulator get better speed-up correspondingly.Fig. 3: Execution time (wall clock time) of Seq. and time warp with respect to different model sizes (N=32, 64, 100, and 128) and model parameters based on single computing node with 8 cores. Results of the test are grouped by the diffusion rates (Group 1: Sequential 1 and Time Warp 1. dPrey=2.5, dPredator=5.0; Group 2: dPrey=0.25, dPredator=0.5, Sequential 2 and Time Warp 2).Fig. 4: Speedup of time warp with respect to the number of work cores and the model size (N=64 and 100). Work cores are chose from one computing node. Diffusion rates are dPrey=2.5, dPredator=5.0 and dGrass=0.0.V. Conclusion and Future WorkIn this paper, a time warp simulator based on the discrete event simulation framework JAMES II is designed and implemented for fine-grained parallel and distributed discrete event spatial stochastic simulation of biological reaction systems. Several challenges have been overcome, such as state saving, roll back and especially GVT reduction in parallel execution of simulations. The Lotka-Volterra Predator-Prey system is chosen as the benchmark model to test the performance of our time warp simulator and the best experiment results show that it can obtain about 6 times of speed-up against the sequential simulation. The domain this paper concerns with is in the infancy, many interesting issues are worthy of further investigated, e.g. there are many excellent PDES optimistic synchronization algorithms (e.g. the BTW) as well. Next step, we would like to fill some of them into JAMES II. 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731 Background: According to the World Health Organization International Agency for Research on Cancer, gastrointestinal (GI) cancers account for 1 in 4 cancer cases and 1 in 3 cancer-related deaths worldwide. An estimated 4.8 million new cases of GI cancers and 3.4 million related deaths occur annually. Understanding the need for increased GI cancer awareness, patient education and support, the GI Cancers Alliance developed a series of engagement activities for the GI cancer community as a needs assessment to identify critical gaps in support services, fragmented education, and disease awareness. Methods: Our 12-month patient-reported outcomes (PRO) research included an online anonymous survey, individual and small group interviews, focus groups, advisory boards, and an interactive workshop at the 2023 American Society of Clinical Oncology annual meeting. 1,122 patients participated during this 12-month PRO research period. Participants reported being diagnosed with one of the following GI cancer primary tumor types: Anus (3%), Appendix (2%), Bile Duct (6%), Colon (19%), Esophagus (7%), Gallbladder (5%), Gastric (14%), GI NET / GI Carcinoid Tumor (3%), Liver (7%), Pancreas (6%), Small Intestine (4%), Rectum (23%), Unknown primary location (1%). Results: Overarching areas of unmet need include: health disparities throughout the care continuum for underserved populations (21%); lack of adequate precision oncology patient education, including biomarker education and testing (19%); the rise of early-age onset (EAO) in GI cancers and the unique needs of the young adult (YA) population (18%); nutritional wellness education (17%); insufficient support for family caregivers and caregiver respite services (16%) scarcity of patient-centered care and adequate patient-clinician communication (11%). Conclusions: Our PRO research underscores the unmet needs and gaps in support services of the GI cancer community. Our global call to action includes partnering with our 100+ member organizations to amplify the patient voice and patient-lived experience, and cross-collaboration with our community of advocates, patients, caregivers, and clinicians. Our shared call to action and continued collaboration provides a greater impact for our GI cancer community to help meet patient needs and eliminate critical gaps in services.

Abstract. Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, decision making relating to education and health and urban planning, as well as in relation to initiatives such as startups and fostering economic growth and employment within the city. As more data becomes available, the challenges of storing, managing and integrating such data are also multiplied.The first Urban Data Management Symposium (UDMS) was held in 1971 in Bonn, Germany, made the choice of hosting the 6th international conference on Smart Data and Smart Cities (SDSC) in Stuttgart a very natural one. SDSC was established in 2016 as the successor of the UDMS, and this year we celebrate the 40th anniversary of the series of symposia and conferences. The SDSC 2021 will be part of the scientific week on intelligent cities at HFT Stuttgart. Together four events were held during the week of 14th – 17th September 2021, and alongside SDSC participants were invited to attend the “Energy, water and food for the cities of the future” conference, the “LIS-City – liveable, intelligent, and sustainable City” workshop, and the mobility day Stuttgart. Participant interaction – and the ability to attend sessions across the four events – was particularly encouraged. SDSC 2021 itself was organised by the Urban Data Management Society (UDMS www.udms.net), ISPRS and HFT Stuttgart (the University of Applied Science Stuttgart), and Professor Volker Coors Chaired the SDSC committee.As in previous years, three key conference themes were proposed to represent the Smart Cities: Smart Data (sensor network databases, on-the-fly data mining, geographic and urban knowledge modeling and engineering, green computing, urban data analytics and big data, big databases and data management), Smart People (volunteered information, systems for public participation) and Smart Cities (systems of territorial intelligence, systems for city intelligence management, 3D modeling of cities, internet of things, social networks, monitoring systems, mobility and transportation, smart-city-wide telecommunications infrastructure, urban knowledge engineering, urban dashboard design and implementation, new style of urban decision-making systems, geovisualization devoted to urban problems, disaster management systems).This volume consists of 14 papers, which were selected from 41 submissions on the basis of double blind review, with each paper being reviewed by a minimum of three reviewers. These papers present novel research concerning the use of spatial information and communication technologies in Smart Cities, addressing different aspects of Smart Data and Smart Citizens. The selected papers tackle different aspects of Smart Cities: 3D; Citizen Engagement; transport, sustainable mobility; dashboards and web GIS; citizen engagement and participation; sensors; urban decision making.The editors are grateful to the members of the Scientific Committee for their time and valuable comments, which contributed to the high quality of the papers. Reviews were contributed by: Alias Abdul-Rahman, Giorgio Agugiaro, Ken Arroyo Ohori, John Barton, Martina Baucic, Filip Biljecki, Lars Bodum, Pawel Boguslawski, Azedine Boulmakoul, Matteo Caglioni, Caesar Cardenas, Eliseo Clementini, Volker Coors, Youness Dehbi, Abdoulaye Abou Diakité, Adil El Bouziri, Claire Ellul, Tarun Ghawana, Gesquiere Gilles, Didier Grimaldi, Ori Gudes, Stephen Hirtle, Martin Kada, Lamia Karim, Robert Laurini, Christina Mickrenska-Cherneva, Christopher Petit, Alenka Poplin, Ivana Racetin, Dimos Pantazis, Preston Rodrigues, Camilo Leon Sanchez, Genoveva Vargas Solar, Nils Walravens, Parag Wate, Besri Zineb, Sisi Zlatanova. We are also grateful to the work of the local organising committee at HFT Stuttgart, without whom this conference would not have been possible.

<p><strong>Abstract.</strong> Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, decision making relating to education and health and urban planning, as well as in relation to initiatives such as startups and fostering economic growth and employment within the city. As more data becomes available, the challenges of storing, managing and integrating such data are also multiplied.</p><p> This increasing interest in Smart Cities world-wide, along with a growing understanding of the importance of integrating “Smart” data with other data and wider applications for the benefit of citizens, made the choice of hosting the third Smart Data, Smart Cities conference in Delft – in conjunction with three other conferences – a very natural one. Together the four conferences were held during the week of 1st–5th October 2018, and alongside SDSC participants were invited to attend the ISPRS Technical Commission IV Symposium, the 13th 3D GeoInfo Conference and the 6th International FIG Workshop on 3D Cadastres. Participant interaction – and the ability to attend sessions across the four events – was particularly encouraged. SDSC 2018 itself was organised by the Urban Data Management Society (UDMS www.udms.net), ISPRS and TU Delft (the Delft University of Technology), and Professor Volker Coors Chaired the SDSC committee.</p><p> As in previous years, three key conference themes were proposed to represent the Smart Cities: <b>Smart Data</b> (sensor network databases, on-the-fly data mining, geographic and urban knowledge modeling and engineering, green computing, urban data analytics and big data, big databases and data management), <b>Smart People</b> (volunteered information, systems for public participation) and <b>Smart Cities</b> (systems of territorial intelligence, systems for city intelligence management,3D modeling of cities, internet of things, social networks, monitoring systems, mobility and transportation, smart-city-wide telecommunications infrastructure, urban knowledge engineering, urban dashboard design and implementation, new style of urban decision-making systems, geovisualization devoted to urban problems, disaster management systems).</p><p> This volume consists of 18 papers, which were selected from 34 submissions on the basis of double blind review, with each paper being reviewed by a minimum of three reviewers. These papers present novel research concerning the use of spatial information and communication technologies in Smart Cities, addressing different aspects of Smart Data and Smart Citizens. The selected papers tackle different aspects of Smart Cities: 3D; Citizen Engagement; transport, sustainable mobility; dashboards and web GIS; citizen engagement and participation; sensors; urban decision making.</p><p> The editors are grateful to the members of the Scientific Committee for their time and valuable comments, which contributed to the high quality of the papers. Reviews were contributed by: Giorgio Agugiaro, Maria Antoniabrovelli, Ken Arroyoohori, Martina Baucic, Michela Bertolotto, Pawel Boguslawski, Azedine Boulmakoul, Caesar Cardenas, Ofelia Cervantes, Volker Coors, Isabel Cruz, Vincenzo Delfatto, Claire Ellul, Tarun Ghawana, Gesquiere Gilles, Gerhard Groeger, Eberhard Gulch, Jan-Henrik Haunert, Stephen Hirtle, Umit Isikdag, Martin Kada, Snjezana Knezic, Robert Laurini, Liu Liu, Ed Manley, Viviana Mascardi, Marco Minghini, Raul Monroy, Regina Motz, Beniamino Murgante, Marco Painho, Dev Paudyal, Alenka Poplin, Ivana Racetin, Ismail Rakip Karas, Preston Rodrigues, David Sol, Wei Tu, Wei Tu, Genoveva Vargas, Kavita Vemuri, Edward Verbree, Mingshu Wang, Maribel Yasminasantos, Sisi Zlatanova. We are also grateful to the work of the local organising committee at TU Delft, without whom this conference would not have been possible. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018 3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands</p>