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Li, Feifeng, and Gang Fang. "Process-Aware Accounting Information System Based on Business Process Management." Wireless Communications and Mobile Computing 2022 (May 9, 2022): 1–15. http://dx.doi.org/10.1155/2022/7266164.
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In order to improve based on financial shared services, enterprises can better promote the establishment, deepening, and optimal development of their own management accounting informatization. This paper introduces BPM system, analyzes the process perception accounting information system of business process management, and verifies that process perception can promote the research of accounting information system with the help of case analysis. Based on the problems, optimization scheme, and optimization effect of JKJT accounting informatization in a city, this paper makes a practical analysis and case analysis method. It is proposed that the four optimization effects of JKJT accounting informatization can be obtained by improving the authenticity and accuracy of subsystem business data, standardizing the division of personnel post rights and responsibilities, strictly implementing the system, changing the cost accounting method, and strengthening the training of management innovative accounting informatization talents and other optimization measures. Its main financial indicators show that the operating cost of JKJT in City A in 2019 was RMB 90.0309 million, a decrease of RMB 8.1781 million over 2020, a year-on-year decrease of 8.33%, mainly due to the establishment of financial sharing mode in 2019 and the strengthening of cost management and control of funds of branches by the finance department. At the same time, the acceptance measurement of the local project in City A is 57.0251 million yuan, because the project is operated locally, the financial service advantage is obvious, and the cost is relatively low. At the same time, by summarizing the application process of information management system, the in-depth analysis of information management system is realized. Information management system has strong efficiency in economic promotion. We should actively promote the application of the system to promote the improvement of economic construction level.
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Russo, Alessandro, Massimo Mecella, and Massimiliano Leoni. "ROME4EU - A service-oriented process-aware information system for mobile devices." Software: Practice and Experience 42, no. 10 (October 27, 2011): 1275–314. http://dx.doi.org/10.1002/spe.1138.
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Antoniadis, Rallis, Nikitas Assimakopoulos, and Panagiotis Papaioannou. "Organisational resilience through the adaptation of process-aware information systems - a System Dynamics approach." International Journal of Applied Systemic Studies 9, no. 4 (2022): 401. http://dx.doi.org/10.1504/ijass.2022.10051553.
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Papaioannou, Panagiotis, Rallis Antoniadis, and Nikitas Assimakopoulos. "Organisational resilience through the adaptation of process-aware information systems - a System Dynamics approach." International Journal of Applied Systemic Studies 9, no. 4 (2022): 401. http://dx.doi.org/10.1504/ijass.2022.126765.
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Bahramian, Z., R. Ali Abbaspour, and C. Claramunt. "A CONTEXT-AWARE TOURISM RECOMMENDER SYSTEM BASED ON A SPREADING ACTIVATION METHOD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 333–39. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-333-2017.
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Users planning a trip to a given destination often search for the most appropriate points of interest location, this being a non-straightforward task as the range of information available is very large and not very well structured. The research presented by this paper introduces a context-aware tourism recommender system that overcomes the information overload problem by providing personalized recommendations based on the user’s preferences. It also incorporates contextual information to improve the recommendation process. As previous context-aware tourism recommender systems suffer from a lack of formal definition to represent contextual information and user’s preferences, the proposed system is enhanced using an ontology approach. We also apply a spreading activation technique to contextualize user preferences and learn the user profile dynamically according to the user’s feedback. The proposed method assigns more effect in the spreading process for nodes which their preference values are assigned directly by the user. The results show the overall performance of the proposed context-aware tourism recommender systems by an experimental application to the city of Tehran.
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Zheng, Yong. "Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison." Information 13, no. 1 (January 17, 2022): 42. http://dx.doi.org/10.3390/info13010042.
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Recommender systems can assist with decision-making by delivering a list of item recommendations tailored to user preferences. Context-aware recommender systems additionally consider context information and adapt the recommendations to different situations. A process of context matching, therefore, enables the system to utilize rating profiles in the matched contexts to produce context-aware recommendations. However, it suffers from the sparsity problem since users may not rate items in various context situations. One of the major solutions to alleviate the sparsity issue is measuring the similarity of contexts and utilizing rating profiles with similar contexts to build the recommendation model. In this paper, we summarize the context-aware collaborative filtering methods using context similarity, and deliver an empirical comparison based on multiple context-aware data sets.
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Catarci, Tiziana, Francesco Leotta, Andrea Marrella, Massimo Mecella, and Mahmoud Sharf. "Process-Aware Enactment of Clinical Guidelines through Multimodal Interfaces." Computers 8, no. 3 (September 11, 2019): 67. http://dx.doi.org/10.3390/computers8030067.
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Healthcare is one of the largest business segments in the world and is a critical area for future growth. In order to ensure efficient access to medical and patient-related information, hospitals have invested heavily in improving clinical mobile technologies and spreading their use among doctors towards a more efficient and personalized delivery of care procedures. However, there are also indications that their use may have a negative impact on patient-centeredness and often places many cognitive and physical demands on doctors, making them prone to make medical errors. To tackle this issue, in this paper, we present the main outcomes of the project TESTMED, which aimed at realizing a clinical system that provides operational support to doctors using mobile technologies for delivering care to patients, in a bid to minimize medical errors. The system exploits concepts from Business Process Management (BPM) on how to manage a specific class of care procedures, called clinical guidelines, and how to support their execution and mobile orchestration among doctors. To allow a non-invasive interaction of doctors with the system, we leverage the use of touch and vocal user interfaces. A robust user evaluation performed in a real clinical case study shows the usability and effectiveness of the system.
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Wang, Chen-Shu, Shiang-Lin Lin, and Heng-Li Yang. "Impersonate human decision making process: an interactive context-aware recommender system." Journal of Intelligent Information Systems 47, no. 2 (April 6, 2016): 195–207. http://dx.doi.org/10.1007/s10844-016-0401-z.
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Thabet, Rafika, Dominik Bork, Amine Boufaied, Elyes Lamine, Ouajdi Korbaa, and Hervé Pingaud. "Risk-aware business process management using multi-view modeling: method and tool." Requirements Engineering 26, no. 3 (March 23, 2021): 371–97. http://dx.doi.org/10.1007/s00766-021-00348-2.
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AbstractRisk-aware Business Process Management (R-BPM) has been addressed in research since more than a decade. However, the integration of the two independent research streams is still ongoing with a lack of research focusing on the conceptual modeling perspective. Such an integration results in an increased meta-model complexity and a higher entry barrier for modelers in creating conceptual models and for addressees of the models in comprehending them. Multi-view modeling can reduce this complexity by providing multiple interdependent viewpoints that, all together, represent a complex system. Each viewpoint only covers those concepts that are necessary to separate the different concerns of stakeholders. However, adopting multi-view modeling discloses a number of challenges particularly related to managing consistency which is threatened by semantic and syntactic overlaps between the viewpoints. Moreover, usability and efficiency of multi-view modeling have never been systematically evaluated. This paper reports on the conceptualization, implementation, and empirical evaluation of e-BPRIM, a multi-view modeling extension of the Business Process-Risk Management-Integrated Method (BPRIM). The findings of our research contribute to theory by showing, that multi-view modeling outperforms diagram-oriented modeling by means of usability and efficiency of modeling, and quality of models. Moreover, the developed modeling tool is openly available, allowing its adoption and use in R-BPM practice. Eventually, the detailed presentation of the conceptualization serves as a blueprint for other researchers aiming to harness multi-view modeling.
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Zhu, Jiayi, Keruo Jiang, Lifei Xiao, Peifu Mou, Lang Geng, and Yicheng Liu. "Research on Full Link Intelligent Control Platform for Substation Maintenance through Edge-Aware." Journal of Physics: Conference Series 2083, no. 2 (November 1, 2021): 022018. http://dx.doi.org/10.1088/1742-6596/2083/2/022018.
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Abstract In view of the imperfect process control, safety control and data control in substation maintenance operation, this paper proposes a full link intelligent control platform for substation maintenance operation based on edge-aware. The platform is composed of on-site edge-aware system and big data decision service system. The on-site edge-aware system uses various intelligent tools to collect data, carries out real-time safety behavior early warning, maintenance quality control and maintenance process supervision through edge computing technology, and uploads the analyzed and processed information to the big data decision service system. The big data decision service system provides support for online decision-making of substation equipment maintenance according to the panoramic multi-source information of the maintenance site provided by the site edge perception system. The full link intelligent control platform for substation maintenance operation has built a control system for real-time control of on-site maintenance operation process, on-site maintenance information and on-site maintenance safety information, which improves the management efficiency of substation maintenance on-site operation and enhances the safety guarantee of maintenance operation.
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Cho, Minsu, Gyunam Park, Minseok Song, Jinyoun Lee, and Euiseok Kum. "Quality-Aware Resource Model Discovery." Applied Sciences 11, no. 12 (June 21, 2021): 5730. http://dx.doi.org/10.3390/app11125730.
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Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems.
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Cherfia, Taha Abdelmoutaleb, and Faïza Belala. "Bigraphical Reactive Systems Based Approaches for Modeling Context-Aware Systems." International Journal of Adaptive, Resilient and Autonomic Systems 5, no. 4 (October 2014): 1–19. http://dx.doi.org/10.4018/ijaras.2014100101.
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In the past few years, context-aware computing has become one of the most promising topics of ubiquitous (pervasive) computing where computers are integrated and vanish in the background of users everyday activities. A context-aware system is a ubiquitous system, which is able to adapt its behavior automatically according to the gathered context information. However, due to the increasing complexity and diversity of such systems, the modeling process has become a major challenge for the ubiquitous computing community. In order to address this critical issue, different bigraphical reactive systems based approaches have been proposed to ease the modeling of some aspects of context-aware systems. Therefore, this paper presents a study attempting to show how bigraphs work under these approaches, and to illustrate the efficiency of our proposed approach in terms of addressing various aspects of context-aware systems.
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Gillmann, Christina, Pablo Arbelaez, Jose Hernandez, Hans Hagen, and Thomas Wischgoll. "An Uncertainty-Aware Visual System for Image Pre-Processing." Journal of Imaging 4, no. 9 (September 10, 2018): 109. http://dx.doi.org/10.3390/jimaging4090109.
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Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input image’s uncertainty and propagates it trough the entire pipeline. To accomplish this, we utilize rules for transformation and propagation of uncertainty to incorporate this additional information with a variety of operations. Resulting from this, we are able to adapt prominent image pre-processing algorithms such that they consider the input images uncertainty. Furthermore, we allow the composition of arbitrary image pre-processing pipelines and visually encode the accumulated uncertainty throughout this pipeline. The effectiveness of the demonstrated approach is shown by creating image pre-processing pipelines for a variety of real world datasets.
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Gao, Ruoyuan. "Toward a fairer information retrieval system." ACM SIGIR Forum 55, no. 1 (June 2021): 1–2. http://dx.doi.org/10.1145/3476415.3476429.
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With the increasing popularity and social influence of information retrieval (IR) systems, various studies have raised concerns on the presence of bias in IR and the social responsibilities of IR systems. Techniques for addressing these issues can be classified into pre-processing , in-processing and post-processing. Pre-processing reduces bias in the data that is fed into machine learning models. In-processing encodes fairness constraints as a part of the objective function or learning process. Post-processing operates as a top layer over the trained model to reduce the presentation bias exposed to users. This dissertation explored ways to bring the pre-processing and post-processing approaches, together with the fairness-aware evaluation metrics, into a unified framework as an attempt to break the vicious cycle of bias and improve fairness in IR. We first investigated the existing bias presented in search engine results. Specifically, we focused on the top-k fairness ranking in terms of statistical parity fairness and disparate impact fairness definitions. With Google search and a general purposed text cluster as a lens, we explored several topical diversity fairness ranking strategies to understand the relationship between relevance and fairness in search results. Our experimental results showed that different fairness ranking strategies resulted in distinct utility scores and performed differently with distinct datasets. Second, to further investigate the relationship of data and fairness algorithms, we developed a statistical framework that was able to facilitate various analysis and decision making. Our framework could effectively and efficiently estimate the domain of data and solution space. We derived theoretical expressions to identify the fairness and relevance bounds for data of different distributions, and applied them to both synthetic datasets and real world datasets. We presented a series of use cases to demonstrate how our framework was applied to associate data and provide insights to fairness optimization problems. Third, we proposed an evaluation metric FAIR for the ranking results that encoded fairness, diversity, novelty and relevance. This metric offered a new perspective of evaluating fairness-aware ranking results. Based on this metric, we developed an effective ranking algorithm that jointly optimized for fairness and utility. Our experiments showed that our new metric was able to highlight results that achieved good user utility and fair information exposure at the same time. We showed how FAIR metric related to existing metrics through correlation analysis and case studies, and demonstrated the effectiveness of our FAIR-based algorithm.
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Bergh, Jan Van den, Sven Coppers, Shirley Elprama, Jelle Nelis, Stijn Verstichel, An Jacobs, Karin Coninx, Femke Ongenae, Filip De Turck, and Femke De Backere. "Social-aware Event Handling within the FallRisk Project." Methods of Information in Medicine 56, no. 01 (2017): 63–73. http://dx.doi.org/10.3414/me15-02-0010.
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SummaryObjectives: With the uprise of the Internet of Things, wearables and smartphones are moving to the foreground. Ambient Assisted Living solutions are, for example, created to facilitate ageing in place. One example of such systems are fall detection systems. Currently, there exists a wide variety of fall detection systems using different methodologies and technologies. However, these systems often do not take into account the fall handling process, which starts after a fall is identified or this process only consists of sending a notification. The FallRisk system delivers an accurate analysis of incidents occurring in the home of the older adults using several sensors and smart devices. Moreover, the input from these devices can be used to create a social-aware event handling process, which leads to assisting the older adult as soon as possible and in the best possible way.Methods: The FallRisk system consists of several components, located in different places. When an incident is identified by the FallRisk system, the event handling process will be followed to assess the fall incident and select the most appropriate caregiver, based on the input of the smartphones of the caregivers. In this process, availability and location are automatically taken into account.Results: The event handling process was evaluated during a decision tree workshop to verify if the current day practices reflect the requirements of all the stakeholders. Other knowledge, which is uncovered during this workshop can be taken into account to further improve the process.Conclusions: The FallRisk offers a way to detect fall incidents in an accurate way and uses context information to assign the incident to the most appropriate caregiver. This way, the consequences of the fall are minimized and help is at location as fast as possible. It could be concluded that the current guidelines on fall handling reflect the needs of the stakeholders. However, current technology evolutions, such as the uptake of wearables and smartphones, enables the improvement of these guidelines, such as the automatic ordering of the caregivers based on their location and availability.
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Zakiyudin, Mohd Zulakhmar, Mohamad Syazli Fathi, Shuib Rambat, Siti Uzairiah Mohd Tobi, Narimah Kasim, and Aryani Ahmad Latiffi. "The Potential of Context-Aware Computing for Building Maintenance Management Systems." Applied Mechanics and Materials 405-408 (September 2013): 3505–8. http://dx.doi.org/10.4028/www.scientific.net/amm.405-408.3505.
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The increasing awareness of the importance of a proper building maintenance management system becomes the main contributing factor for the development of facilities management worldwide. Various building maintenance management system tools become necessary to remain competitive. The widespread use of mobile devices envisions a growing market for new applications in building maintenance. It is well-understood that the use of mobile technologies is critical in building maintenance as the right information needs to be delivered to the right person, at the right time, and at the right place. However, existing building maintenance applications only support the delivery of static modes of information (such as building data and checklists); the information is pre-programmed and is thus not able to take into account the users varying contexts. Building maintenance applications could be made easy and responsive to users wishes if they were context-aware. This paper discusses the key issues in building maintenance management systems and their potential for the use of context-aware computing. Findings show that many building maintenance management systems are complicated and only slowly being adopted in Malaysia. In conclusion, it was found that context-aware building maintenance systems have the potential to simplify the process and increase the usage of computerized maintenance management systems.
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Jeong, Soo-Yeon, and Young-Kuk Kim. "Deep Learning-Based Context-Aware Recommender System Considering Contextual Features." Applied Sciences 12, no. 1 (December 21, 2021): 45. http://dx.doi.org/10.3390/app12010045.
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A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features. Based on existing deep learning models, we combine a neural network and autoencoder to extract characteristics and predict scores in the process of restoring input data. The newly proposed model is able to easily reflect various type of contextual information and predicts user preferences by considering the feature of user, item and context. The experimental results confirm that the proposed method is mostly superior to the existing method in all datasets. Also, for the dataset with data sparsity problem, it was confirmed that the performance of the proposed method is higher than that of existing methods. The proposed method has higher precision by 0.01–0.05 than other recommender systems in a dataset with many context dimensions. And it showed good performance with a high precision of 0.03 to 0.09 in a small dimensional dataset.
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Shi, Gang, and Yuechen Yang. "Analysis of an Automatic Early Warning System Based on Fog Architecture." Journal of Physics: Conference Series 2095, no. 1 (November 1, 2021): 012046. http://dx.doi.org/10.1088/1742-6596/2095/1/012046.
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Abstract In the process of analysing and processing terminal sensor information, a large number of terminal sensors are needed to collect front-end information. These front-end data collection, analysis and processing require high real-time, and need the support of location aware mobile computing services. Traditional cloud computing architecture is not the best choice for service scenarios with high real-time requirements. The fog computing architecture is to extend cloud computing services to the edge of the sensor network, coupled with appropriate fitness algorithms, can effectively improve the information analysis and early warning response speed of the geological disaster information early warning system.
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Surya, Mahenda Metta, Albert Wongso, and Richard Richard. "Analisis dan Perancangan Sistem Informasi Direktorat Research & Technology Transfer Binus University." ComTech: Computer, Mathematics and Engineering Applications 5, no. 2 (December 1, 2014): 1168. http://dx.doi.org/10.21512/comtech.v5i2.2434.
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Rapid growth of information technology development as well as increasing level of competition make every company need to establish an information system to support its business process. Research & Technology Transfer Directorate of Binus University is aware of this matter and makes a goal to improve the existing business process and develop a web-based information system that is able to support the existing business process to be more effective and efficient. This study aims to conduct an analysis and a design of information system for Research & Technology Transfer Directorate Binus University that can enhance the existing business process. Research used two methods, firstly data gathering done by conducting field studies and literature reviews, secondly the analysis and design of the system with object-oriented method. The result achieved from this research is a web-based information system that can support Research & Technology Transfer Directorate business process. The conclusion of this research is a new integrated web-based information system that can support and enhance current business process by connecting all parts of the system with the result to make all process more effective and efficient.
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Grzywaczewski, Adam, Rahat Iqbal, Anne James, and John Halloran. "An Investigation of User Behaviour Consistency for Context-Aware Information Retrieval Systems." International Journal of Advanced Pervasive and Ubiquitous Computing 1, no. 4 (October 2009): 69–90. http://dx.doi.org/10.4018/japuc.2009100105.
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Users interact with the Internet in dynamic environments that require the IR system to be context aware. Modern IR systems take advantage of user location, browsing history or previous interaction patterns, but a significant number of contextual factors that impact the user information retrieval process are not yet available. Parameters like the emotional state of the user and user domain expertise affect the user experience significantly but are not understood by IR systems. This article presents results of a user study that simplifies the way context in IR and its role in the systems’ efficiency is perceived. The study supports the hypothesis that the number of user interaction contexts and the problems that a particular user is trying to solve is related to lifestyle. Therefore, the IR system’s perception of the interaction context can be reduced to a finite set of frequent user interactions.
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Ghoniem, Algarni, and Shaalan. "Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information." Information 10, no. 7 (July 11, 2019): 239. http://dx.doi.org/10.3390/info10070239.
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In multi-modal emotion aware frameworks, it is essential to estimate the emotional features then fuse them to different degrees. This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. To overcome issues of feature extraction and multi-modal fusion, hybrid fuzzy-evolutionary computation methodologies are employed to demonstrate ultra-strong capability of learning features and dimensionality reduction. This paper proposes a novel multi-modal emotion aware system by fusing speech with EEG modalities. Firstly, a mixing feature set of speaker-dependent and independent characteristics is estimated from speech signal. Further, EEG is utilized as inner channel complementing speech for more authoritative recognition, by extracting multiple features belonging to time, frequency, and time–frequency. For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. To fuse speech with EEG information, a separate classifier is used for each modality, then output is computed by integrating their posterior probabilities. Results show the superiority of the proposed model, where the overall performance in terms of accuracy average rates is 98.06%, and 97.28%, and 98.53% for EEG, speech, and multi-modal recognition, respectively. The proposed model is also applied to two public databases for speech and EEG, namely: SAVEE and MAHNOB, which achieve accuracies of 98.21% and 98.26%, respectively.
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Fernández-Isabel, Alberto, and Rubén Fuentes-Fernández. "Social-Aware Driver Assistance Systems for City Traffic in Shared Spaces." Sensors 19, no. 2 (January 9, 2019): 221. http://dx.doi.org/10.3390/s19020221.
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Shared spaces are gaining presence in cities, where a variety of players and mobility types (pedestrians, bicycles, motorcycles, and cars) move without specifically delimited areas. This makes the traffic they comprise challenging for automated systems. The information traditionally considered (e.g., streets, and obstacle positions and speeds) is not enough to build suitable models of the environment. The required explanatory and anticipation capabilities need additional information to improve them. Social aspects (e.g., goal of the displacement, companion, or available time) should be considered, as they have a strong influence on how people move and interact with the environment. This paper presents the Social-Aware Driver Assistance System (SADAS) approach to integrate this information into traffic systems. It relies on a domain-specific modelling language for social contexts and their changes. Specifications compliant with it describe social and system information, their links, and how to process them. Traffic social properties are the formalization within the language of relevant knowledge extracted from literature to interpret information. A multi-agent system architecture manages these specifications and additional processing resources. A SADAS can be connected to other parts of traffic systems by means of subscription-notification mechanisms. The case study to illustrate the approach applies social knowledge to predict people’s movements. It considers a distributed system for obstacle detection and tracking, and the intelligent management of traffic signals.
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Yang, Jiageng, Xinguo Zhang, Hui Lu, Muhammad Shafiq, and Zhihong Tian. "StFuzzer: Contribution-Aware Coverage-Guided Fuzzing for Smart Devices." Security and Communication Networks 2021 (September 18, 2021): 1–15. http://dx.doi.org/10.1155/2021/1987844.
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The root cause of the insecurity for smart devices is the potential vulnerabilities in smart devices. There are many approaches to find the potential bugs in smart devices. Fuzzing is the most effective vulnerability finding technique, especially the coverage-guided fuzzing. The coverage-guided fuzzing identifies the high-quality seeds according to the corresponding code coverage triggered by these seeds. Existing coverage-guided fuzzers consider that the higher the code coverage of seeds, the greater the probability of triggering potential bugs. However, in real-world applications running on smart devices or the operation system of the smart device, the logic of these programs is very complex. Basic blocks of these programs play a different role in the process of application exploration. This observation is ignored by existing seed selection strategies, which reduces the efficiency of bug discovery on smart devices. In this paper, we propose a contribution-aware coverage-guided fuzzing, which estimates the contributions of basic blocks for the process of smart device exploration. According to the control flow of the target on any smart device and the runtime information during the fuzzing process, we propose the static contribution of a basic block and the dynamic contribution built on the execution frequency of each block. The contribution-aware optimization approach does not require any prior knowledge of the target device, which ensures our optimization adapting gray-box fuzzing and white-box fuzzing. We designed and implemented a contribution-aware coverage-guided fuzzer for smart devices, called StFuzzer. We evaluated StFuzzer on four real-world applications that are often applied on smart devices to demonstrate the efficiency of our contribution-aware optimization. The result of our trials shows that the contribution-aware approach significantly improves the capability of bug discovery and obtains better execution speed than state-of-the-art fuzzers.
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Wang, Lehan, Jingzhou Sun, Yuxuan Sun, Sheng Zhou, and Zhisheng Niu. "A UoI-Optimal Policy for Timely Status Updates with Resource Constraint." Entropy 23, no. 8 (August 20, 2021): 1084. http://dx.doi.org/10.3390/e23081084.
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Timely status updates are critical in remote control systems such as autonomous driving and the industrial Internet of Things, where timeliness requirements are usually context dependent. Accordingly, the Urgency of Information (UoI) has been proposed beyond the well-known Age of Information (AoI) by further including context-aware weights which indicate whether the monitored process is in an emergency. However, the optimal updating and scheduling strategies in terms of UoI remain open. In this paper, we propose a UoI-optimal updating policy for timely status information with resource constraint. We first formulate the problem in a constrained Markov decision process and prove that the UoI-optimal policy has a threshold structure. When the context-aware weights are known, we propose a numerical method based on linear programming. When the weights are unknown, we further design a reinforcement learning (RL)-based scheduling policy. The simulation reveals that the threshold of the UoI-optimal policy increases as the resource constraint tightens. In addition, the UoI-optimal policy outperforms the AoI-optimal policy in terms of average squared estimation error, and the proposed RL-based updating policy achieves a near-optimal performance without the advanced knowledge of the system model.
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Wei, Zhi Qiang, Xiao Wen Wang, Dong Ning Jia, and Hao Liu. "An Information Collection and Transmission Strategy of Vehicle State-Aware System Based on OBD Technology and Android Mobile Terminals." Applied Mechanics and Materials 719-720 (January 2015): 573–79. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.573.
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To provide the Traffic Regulatory Authority with detailed driving status of vehicles when an accident occurs, this paper proposes an information collection and transmission strategy based on OBD technology and Android mobile terminals. This strategy records the representative detailed operation parameters and location information of traveling vehicles. It removes the drawbacks of server load existing in the conventional vehicle state-aware systems, due to collect and upload the data in real-time. At last, we achieve a highly efficient transmission process by compressing the uploaded data.
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Song, Chengqun, Jun Cheng, and Wei Feng. "A Crowdsensing-Based Real-Time System for Finger Interactions in Intelligent Transport System." Wireless Communications and Mobile Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/7385052.
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Crowdsensing leverages human intelligence/experience from the general public and social interactions to create participatory sensor networks, where context-aware and semantically complex information is gathered, processed, and shared to collaboratively solve specific problems. This paper proposes a real-time projector-camera finger system based on the crowdsensing, in which user can interact with a computer by bare hand touching on arbitrary surfaces. The interaction process of the system can be completely carried out automatically, and it can be used as an intelligent device in intelligent transport system where the driver can watch and interact with the display information while driving, without causing visual distractions. A single camera is used in the system to recover 3D information of fingertip for hand touch detection. A linear-scanning method is used in the system to determine the touch for increasing the users’ collaboration and operationality. Experiments are performed to show the feasibility of the proposed system. The system is robust to different lighting conditions. The average percentage of correct hand touch detection of the system is 92.0% and the average time of processing one video frame is 30 milliseconds.
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Nan, Xiang, Kayo kanato, and Xiaolan Wang. "Design and Implementation of a Personalized Tourism Recommendation System Based on the Data Mining and Collaborative Filtering Algorithm." Computational Intelligence and Neuroscience 2022 (August 31, 2022): 1–14. http://dx.doi.org/10.1155/2022/1424097.
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A personalized tourism recommendation system provides convenient and economically affordable travel information for individuals/groups. This recommendation system banks on accumulated and analyzed data for providing context-aware travel solutions. For improving the recommendation efficiency and data analysis of such systems, this article introduces a mining and filtering harmonized collaborative process, named as the collaborative mining and filtering process (CMFP), for reducing the data processing overheads and improving the recommendation ratio. In this process, the accumulated data from the global and personal travel, expenditure, and other information are collaboratively analyzed. This analysis is powered by knowledge-based transfer learning for reducing the retardation in the large data processing. Based on the context-based data analysis, the filtering and mining are jointly performed for providing recommendations. In the filtering process, the maximum processed contextual data are extracted for updating the current knowledge base. From this base, the recommendation for adaptable travel is recommended for the user. This process’s performance is analyzed using the metrics accuracy, data handling rate, mining time, and overhead.
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Kord Toudeshki, Leila, Mir Ali Seyyedi, and Afshin Salajegheh. "A Context-Aware Architecture for Realizing Business Process Adaptation Strategies Using Fuzzy Planning." International Journal of Software Engineering and Knowledge Engineering 32, no. 01 (January 2022): 37–70. http://dx.doi.org/10.1142/s0218194022500024.
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Business competency emerges in flexibility and reliability of services that an enterprise provides. To reach that, executing business processes on a context-aware business process management suite which is equipped with monitoring, modeling and adaptation mechanisms and smart enough to react properly using adaptation strategies at runtime, are a major requisite. In this paper, a context-aware architecture is described to bring adaptation to common business process execution software. The architecture comes with the how-to-apply methodology and is established based on process standards like business process modeling notation (BPMN), business process execution language (BPEL), etc. It follows MAPE-K adaptation cycle in which the knowledge, specifically contextual information and their related semantic rules — as the input of adaptation unit — is modeled in our innovative context ontology, which is also extensible for domain-specific purposes. Furthermore, to support separation of concerns, we took apart event-driven adaptation requirements from process instances; these requirements are triggered based on ontology reasoning. Also, the architecture supports fuzzy-based planning and extensible adaptation realization mechanisms to face new or changing situations adequately. We characterized our work in comparison with related studies based on five key adaptation metrics and also evaluated it using an online learning management system case study.
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Chen, Junqi, Yong Wang, Miao Ye, Qinghao Zhang, and Wenlong Ke. "A Load-Aware Multistripe Concurrent Update Scheme in Erasure-Coded Storage System." Wireless Communications and Mobile Computing 2022 (May 19, 2022): 1–15. http://dx.doi.org/10.1155/2022/5392474.
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Erasure coding has been widely deployed in today’s data centers for it can significantly reduce extra storage costs while providing high storage reliability. However, erasure coding introduced more network traffic and computational overhead in the data update process. How to improve the efficiency and mitigate the system imbalance during the update process in erasure coding is still a challenging problem. Recently, most of the existing update schemes of erasure codes only focused on the single stripe update scenario and ignored the heterogeneity of the node and network status which cannot sufficiently deal with the problems of low update efficiency and load imbalance caused by the multistripe concurrent update. To solve this problem, this paper proposes a Load-Aware Multistripe concurrent Update (LAMU) scheme in erasure-coded storage systems. Notably, LAMU introduces the Software-Defined Network (SDN) mechanism to measure the node loads and network status in real time. It selects nonduplicated nodes with better performance such as CPU utilization, remaining memory, and I/O load as the computing nodes for multiple update stripes. Then, a multiattribute decision-making method is used to schedule the network traffic generated in the update process. This mechanism can improve the transmission efficiency of update traffic and make LAMU adapt to the multistripe concurrent update scenarios in heterogeneous network environments. Finally, we designed a prototype system of multistripe concurrent updates. The extensive experimental results show that LAMU could improve the update efficiency and provide better system load-balancing performance.
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Li, Mingxiao, and Marie-Francine Moens. "Dynamic Key-Value Memory Enhanced Multi-Step Graph Reasoning for Knowledge-Based Visual Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10983–92. http://dx.doi.org/10.1609/aaai.v36i10.21346.
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Knowledge-based visual question answering (VQA) is a vision-language task that requires an agent to correctly answer image-related questions using knowledge that is not presented in the given image. It is not only a more challenging task than regular VQA but also a vital step towards building a general VQA system. Most existing knowledge-based VQA systems process knowledge and image information similarly and ignore the fact that the knowledge base (KB) contains complete information about a triplet, while the extracted image information might be incomplete as the relations between two objects are missing or wrongly detected. In this paper, we propose a novel model named dynamic knowledge memory enhanced multi-step graph reasoning (DMMGR), which performs explicit and implicit reasoning over a key-value knowledge memory module and a spatial-aware image graph, respectively. Specifically, the memory module learns a dynamic knowledge representation and generates a knowledge-aware question representation at each reasoning step. Then, this representation is used to guide a graph attention operator over the spatial-aware image graph. Our model achieves new state-of-the-art accuracy on the KRVQR and FVQA datasets. We also conduct ablation experiments to prove the effectiveness of each component of the proposed model.
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Mannweiler, C., A. Klein, J. Schneider, and H. D. Schotten. "Context-awareness for heterogeneous access management." Advances in Radio Science 8 (November 17, 2010): 257–62. http://dx.doi.org/10.5194/ars-8-257-2010.
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Abstract. The increasing availability of both static and dynamic context information has steadily been driving the development of context-aware communication systems. Adapting system behavior according to current context of the network, the user, and the terminal can yield significant end-to-end performance improvements. In this paper, we present a concept for how to use context information, in particular location information and movement prediction, for Heterogeneous Access Management (HAM). In a first step, we outline the functional architecture of a distributed and extensible context management system (CMS) that defines the roles, tasks, and interfaces of all modules within such a system for large-scale context acquisition and dissemination. In a second step, we depict how the available context information can be exploited for optimizing terminal handover decisions to be made in a multi-RAT (radio access technology) environment. In addition, the utilized method for predicting terminal location as well as the objective functions used for evaluating and comparing system performance are described. Finally, we present preliminary simulation results demonstrating that HAM systems that include current and future terminal context information in the handover decision process clearly outperform conventional systems.
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Liu, Qihua, and Xiaohong Gan. "Combining User Contexts and User Opinions for Restaurant Recommendation in Mobile Environment." Journal of Electronic Commerce in Organizations 14, no. 1 (January 2016): 45–63. http://dx.doi.org/10.4018/jeco.2016010105.
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In a mobile setting, user preferences vary in different contexts. Advances in mobile technologies have made the collection of user context information feasible, and as a result, the context-aware mobile recommender system field has been formed. Although there exist several different approaches to incorporating context into the recommendation process, context-aware recommendations are still difficult to compute. It is unclear which contextual factors are important and to which degree they influence user-item selection decisions. In this paper, the authors design a novel mobile restaurant recommender system combining user contexts and user opinions to provide restaurants to mobile users. This system extracts restaurant features from online user reviews and calculates the polarity of them based on sentiment analysis. It takes a new approach for assessing and modeling the relationship between contextual factors and restaurant features. On this basis, a hybrid recommender method is constructed, which integrates the contextual matching algorithm based on analytic hierarchy process and the collaborative filtering algorithm based on context similarity. From the user study, this system suggests restaurants that make the user more satisfied than another comparative system.
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Simović, Aleksandar. "A Big Data smart library recommender system for an educational institution." Library Hi Tech 36, no. 3 (September 17, 2018): 498–523. http://dx.doi.org/10.1108/lht-06-2017-0131.
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Purpose With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations. Design/methodology/approach This paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions. Findings As an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction. Social implications The need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization. Originality/value A unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data.
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Anusha, D. J., R. Anandan, and P. Venkata Krishna. "Modified Context Aware Middleware Architecture for Precision Agriculture." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 7 (July 31, 2022): 112–20. http://dx.doi.org/10.17762/ijritcc.v10i7.5635.
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The opportunities for researchers are enhanced with the progression in the technology of communication and computing. The ease in life of many people like farmers, educators, administers, managers, etc., is increased with more inventions of the researchers using this new technology. The progressive technology for data management is providing more amount of information. However, is the user able to access the needed information when required? This question rises to the more questions like, how to identify whether the information is as per the requirement? Whether a user is authorized or not? The answer for all the questions is to make the support aware of the context. Therefore, the present technology needs to be modified to make the system aware about the context. The process of demonstrating the services based on the context using Wireless Sensor Networks (WSN) with the help of illustration on mango crop is emphasized in this paper. There are many serious problems like unsuitable fertilizer use, wrong selection of crops in wrong seasons, water waste, poor publicizing in the case of farming. These problems are addressed using the ubiquitous context aware middleware architecture for precision agriculture in mango crop.
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PSV, Sathyanarayanan. "A Cloud Based Irrigation System for Agriculture." JOIV : International Journal on Informatics Visualization 2, no. 1 (January 5, 2018): 28. http://dx.doi.org/10.30630/joiv.2.1.91.
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By 2050, it's normal that the total populace will contact 9.2 billion individuals, 34 percent higher than today. A lot of this development will occur in creating nations like India, which has the biggest zone on the planet with arable land for agriculture. To stay aware of rising populaces and wage development, worldwide sustenance generation must increment by 70 percent so as to have the capacity to encourage the world. With help of IoT, control focuses gather and process information continuously to enable ranchers to settle on the best choices as to planting, treating and collecting crops. Sensors set all through the fields are utilized to gauge temperature and stickiness of the dirt and encompassing air. We Propose a Cloud based Irrigation System for better agriculture farming.
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Shershakov, Sergey A. "“VTMine for Visio”: Graphical Tool for Modeling in Process Mining." Modeling and Analysis of Information Systems 27, no. 2 (June 24, 2020): 194–217. http://dx.doi.org/10.18255/1818-1015-2020-2-194-217.
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Process-Aware Information Systems (PAIS) is a special class of the IS intended for the support the tasks of initialization, end-to-end management and completion of business processes. During the operation such systems accumulate a large number of data that are recorded in the form of the event logs. Event logs are a valuable source of knowledge about the actual behavior of a system. For example, there can be found information about the discrepancy between the real and the prescribed behavior of the system; to identify bottlenecks and performance issues; to detect anti-patterns of building a business system. These problems are studied by the discipline called “Process Mining”.The practical application of the process mining methods and practices is carried out using the specialized software for data analysts. The subject area of the process analysis involves the work of an analyst with a large number of graphical models. Such work will be more efficient with a convenient graphical modeling tool. The paper discusses the principles of building a graphical tool “VTMine for Visio” for the process modeling, based on the widespread application for business intelligence Microsoft Visio. There are presented features of the architecture design of the software extension for application in the process mining domain and integration with the existing libraries and tools for working with data. The application of the developed tool for solving various types of tasks for modeling and analysis of processes is demonstrated on a set of experimental schemes.
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Pakhota, N. V. "Information Wars in Modern International Relations." Business Inform 1, no. 528 (2022): 53–58. http://dx.doi.org/10.32983/2222-4459-2022-1-53-58.
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The article examines the concept of hybrid warfare as a major factor in shaping the system of modern international relations. Traditionally, this process took place in the military sphere: in the past, the most famous systems of international relations were created as a result of large-scale wars. Today, the main tool for achieving supremacy on the world stage are hybrid wars, the main danger of which is the lack of laws, principles and rules for hostilities. Hybrid wars are dangerous both for a particular region (leading to the loss of cultural and civilizational identity) and for the system of international relations in general (serving as a source of global instability and becoming more frequent). Such a war theoretically allows the attackers to maintain the conflict for a long hour: their financial costs and human losses will be incomparably less than in a normal war. The main features, examples and analytical model of hybrid warfare are considered. The difference between the concepts of hybrid warfare and hybrid threats as an element of the aggressor’s preparation for information warfare is considered. In the process of evolution, human society has become increasingly aware of the value, significance and effectiveness of information, not only as a tool for learning about the world and the basis for building communication, but also as an incredibly effective and powerful «weapon» that sometimes surpasses all known weapons. Current trends in global communication show that the flow of information is so diverse and contradictory that information is beginning to be both creative and destructive. Therefore, choosing relevant and important information is an important task for every Ukrainian. It is important to study and master media literacy, i. e., the the ability to find, navigate and critically evaluate information found in digital environments.
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Haris Ihsanil Huda and Maya Utami Dewi. "PENDAFTARAN SISWA BARU ONLINE DI SMK NEGERI 4 KENDAL." Elkom : Jurnal Elektronika dan Komputer 11, no. 2 (December 14, 2018): 56–66. http://dx.doi.org/10.51903/elkom.v11i2.22.
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The development of information systems is increasingly rapidly nowadays requires a lot of educational institutions to develop information systems in educational environments that do not fall behind with other educational institutions. It's possible the parents and students will move to the educational institutions that have information systems that fast, accurate and reliable way to get the information required with skills no doubt in supporting the learning system. SMK Negeri 4 Kendal is located at Jl. Soekarno-Hatta Kendal, very aware of the importance of information systems. The information that fast, accurate and available at all times would be very helpful in the implementation of the learning process is also in the process of admission of new students. A newly established education institution four years ago was very concerned about the availability of information systems needed by students and parents so that information systems and the fabric of cooperation to do with ease. The purpose of this research is to increase the number of new admissions and quality of service in the admission of new students and in conducting the data collection and reporting so that information needed by prospective students as well as guardians and leaders mmurid can be obtained at any time. In this research, the application used is PHP programming MySQL database.
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Maingak, Akmal Zaifullah, Candiwan Candiwan, and Listyo Dwi Harsono. "Information Security Assessment Using ISO/IEC 27001:2013 Standard on Government Institution." TRIKONOMIKA 17, no. 1 (September 17, 2018): 28. http://dx.doi.org/10.23969/trikonomika.v17i1.1138.
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The purpose of this research is to determine the existing gap to achieve ISO/IEC 27001:2013 certification and determine the maturity level of the information system owned by X Government Institution. The information system of X Government Institution would be assessed based on 14 clauses contained in ISO/IEC 27001: 2013. The method used is qualitative method, data collection and data validation with triangulation technique (interview, observation, and documentation). Data analysis used gap analysis and to measure the maturity level of this research used CMMI (Capability Maturity Model for Integration). The result of the research showed that information security which had been applied by X Government Institution was at level 1 (Initial) which meant there was evidence that the institution was aware of problems that needed to be overcome, unstandardized process, and tended to handle the problem individually or by case.
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DEHNERT, JULIANE, and WIL M. P. VAN DER AALST. "BRIDGING THE GAP BETWEEN BUSINESS MODELS AND WORKFLOW SPECIFICATIONS." International Journal of Cooperative Information Systems 13, no. 03 (September 2004): 289–332. http://dx.doi.org/10.1142/s0218843004000973.
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This paper presents a methodology to bridge the gap between business process modeling and workflow specification. While the first is concerned with intuitive descriptions that are mainly used for communication, the second is concerned with configuring a process-aware information system, thus requiring a more rigorous language less suitable for communication. Unlike existing approaches the gap is not bridged by providing formal semantics for an informal language. Instead it is assumed that the desired behavior is just a subset of the full behavior obtained using a liberal interpretation of the informal business process modeling language. Using a new correctness criterion (relaxed soundness), it is verified whether a selection of suitable behavior is possible. The methodology consists of five steps and is illustrated using event-driven process chains as a business process modeling language and Petri nets as the workflow specification language.
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Yang, Lei, Zhi Zhang, Xiao Fang, Shiyu Cao, Zhegong Shangguan, and Shiyin Li. "Location Information-Assisted Robust Beamforming Design for Ultra-Wideband Communication Systems." Symmetry 14, no. 6 (June 7, 2022): 1171. http://dx.doi.org/10.3390/sym14061171.
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The future of mobile communication systems is evolving rapidly toward being more intelligent, while also having the ability to interconnect with everything and be aware of the current wireless environment. For complex scenarios, the asymmetry features of channel state information (CSI) will seriously restrict the performance of the communication system. With its accurate positioning technology and high-speed communication rate, ultra-wideband (UWB) has a promising future as a solution to integrate communication and positioning functions. The traditional CSI channel estimation usually requires channel training, which will greatly increase the overhead of the system. This paper proposes a location information-assisted beamforming to replace the traditional channel training process. First, we use the user location parameter information to reconstruct the channel model and derive the CSI error distribution based on the location distribution. Second, considering the uncertainty of the user positioning error, we model the robust beamforming optimization problem that minimizes the total transmit power. To solve this non-convex problem effectively, we design a new beamforming algorithm by using semidefinite relaxation (SDR) and Bernstein-type inequalities. Finally, simulation results verify the robustness of the proposed robust beamforming compared to the worst-case robust beamforming.
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Faisal, Tarig, Moath Awawdeh, and Anees Bashir. "Design and development of intelligent waste bin system with advertisement solution." Bulletin of Electrical Engineering and Informatics 10, no. 2 (April 1, 2021): 940–49. http://dx.doi.org/10.11591/eei.v10i2.2753.
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In cities where a large geographical area of the city is densely populated, the process of waste collection is cumbersome, tiresome and expensive. Often, the burden of manually tracking and collecting of waste causes waste management companies enormous wasted effort and get them involved in tasks that are not necessary. No doubt, a digital interaction between waste management companies and targeted waste collection areas could ensure the process becomes fast, efficient and traceable as they become aware of the states of the wastes, aptly. It will considerably reduce any discrepancies that may occur due to the lack of information available during a particular time. Accordingly, this paper proposes a novel approach towards waste management combined with the internet of things to reduce the problems that would occur due to the accumulation of wastes and hence improvise waste collection/management process. Additionally, an innovative feature which generates revenue and creates business opportunities for waste management companies is introduced via advertisement solution based on network-attached storage technology.
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Nia, Raheleh Ghouchan Nezhad Noor, and Mehrdad Jalali. "RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm." Computational Intelligence and Neuroscience 2022 (April 7, 2022): 1–15. http://dx.doi.org/10.1155/2022/8714870.
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Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems.
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Jost, Thomas Ernst, Christian Stary, and Richard Heininger. "Geo-Spatial Context Provision for Digital Twin Generation." Applied Sciences 12, no. 21 (October 30, 2022): 10988. http://dx.doi.org/10.3390/app122110988.
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Light detection and ranging technology allows for the creation of detailed 3D point clouds of physical objects and environments. Therefore, it has the potential to provide valuable information for the operation of various kinds of cyber-physical systems that need to be aware of, and interact with, their surroundings, such as autonomous vehicles and robots. Point clouds can also become the basis for the creation of digital representations of different assets and a system’s operational environment. This article outlines a system architecture that integrates the geo-spatial context information provided by LiDAR scans with behavioral models of the components of a cyber-physical system to create a digital twin. The clear distinction between behavior and data sets the proposed digital twin architecture apart from existing approaches (that primarily focus on the data aspect), and promotes contextual digital twin generation through executable process models. A vaccine logistics automation use case is detailed to illustrate how information regarding the environment can be used for the operation of an autonomous robot carrying out transport preparation tasks. Besides supporting operation, we propose to combine context data retrieved from the system at different points in the logistics process with information regarding instances of executable behavior models as part of the digital twin architecture. The twin can subsequently be used to facilitate system and process monitoring through relevant stakeholders and structure context data in a user-centric fashion.
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Du, Jin. "Real-Time Information Exchange Strategy for Large Data Volumes Based on IoT." Computational Intelligence and Neuroscience 2022 (April 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/2882643.
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In this paper, we study and analyse the real-time information exchange strategy of big data in the Internet of Things (IoT) and propose a primitive sensory data storage method (TSBPS) based on spatial-temporal chunking preprocessing, which substantially improves the speed of near real-time storage and writing of microsensory data through spatial-temporal prechunking, data compression, cache batch writing, and other techniques. The model is based on the idea of partitioning, which divides the storage and query of perceptual data into the microperceptual data layer and the perceptual data layer. The microaware data layer mainly studies the storage optimization and query optimization of raw sensory data and cleaned valid data; the aware data is the aggregation and statistics of microaware data, and the aware data layer mainly studies the storage optimization and query optimization of aware data. By arranging multiple wireless sensors at key monitoring points to collect corresponding data, building the core data service backend of the system, defining multifunctional servers, and constructing an optimal database model, we effectively solve the parameter collection and classification aggregation processing of different devices. To address the requirement of reliable and secure transmission in the process, we design a highly concurrent and high-performance TCP-based socket two-layer transmission framework and introduce the asymmetric encryption method (RSA) and data integrity verification method to design a transmission protocol that is both reliable and secure. The integration of big data and IoT is bound to bring the intelligence of human society to a new level with unlimited development prospects.
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Wan, Ming, Yan Song, Yuan Jing, and Junlu Wang. "Function-Aware Anomaly Detection Based on Wavelet Neural Network for Industrial Control Communication." Security and Communication Networks 2018 (September 12, 2018): 1–11. http://dx.doi.org/10.1155/2018/5103270.
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Function control, which is an essential link in industrial automation, is undergoing a growing integration with ICTs (Information Communication Technologies) because of the flexible manufacturing and convenient interoperability in CPSs (Cyber-Physical Systems). However, it has also brought the increasing dangers of cyberattacks caused by malicious or intentional industrial process control exploitations. In order to effectively detect these cyber intrusions and anomalies, this paper proposes a function-aware anomaly detection approach based on WNN (Wavelet Neural Network), which perceives the abnormal function control changes in industrial control communication. By appropriately extracting the time-related function control characteristics from industrial communication packets, this approach builds an optimized wavelet neural network to model the normal function control behaviors and calculates the detection threshold to differentiate the aberrant industrial process control activities. Additionally, a real-world control system, whose communication protocol is Modbus/TCP, is simulated to furnish the analyzed function control data. According to the experimental results, we fully demonstrate this approach has the fine detection accuracy and adequate real-time capability.
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MUCCINI, HENRY, and FABIO MANCINELLI. "A SOFTWARE ENGINEERING APPROACH FOR COORDINATION POLICIES ELICITATION." International Journal of Cooperative Information Systems 13, no. 01 (March 2004): 9–36. http://dx.doi.org/10.1142/s0218843004000870.
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In a software system, software processes can be concurrently run by system actors (e.g. agents, components), by making use of shared or dedicated software resources. Coordination is a research area oriented to understand, explicitly model and analyze how activities can depend on each other, in a concurrent system. Many coordination models and languages have been presented in the last few years in order to make coordination a first class entity in the development of a software system. However, what usually happens, is that coordination is described late in the software development and it is not integrated in a typical software process. What we claim, with our research, is that if coordination becomes explicit and formalized as soon as possible in the software life cycle, then it is possible to create coordination-aware software systems, which are easily maintained and developed. What we present, in the following, is a software engineering view of coordination. Diagrams produced during a UML-based software modeling, are analyzed in order to help a software engineer in the difficult task to elicit coordination requirements from informal ones. We show how UML models can drive an elicitation process in order to identify coordination requirements, and can graphically describe which elements need coordination.
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Sharma, Richa, Hema Banati, and Punam Bedi. "Building Socially-Aware E-Learning Systems Through Knowledge Management." International Journal of Knowledge Management 8, no. 3 (July 2012): 1–26. http://dx.doi.org/10.4018/jkm.2012070101.
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Conformance to social context while designing an e-learning course is crucial in enhancing acceptability of the course. Building socially aware e-learning courses requires elicitation of social opinion from various stakeholders associated with the system. Stakeholders are disparate in their perception towards the intricacies of the system, leading to generation of numerous assorted ideas. Knowledge Management (KM) assimilates these ideas to bring congruency into the system. This paper proposes i) a model KMeLS (Knowledge Management in e-Learning Systems) built upon the SECI (Socialization, Externalization, Combination and Internalization) framework, and ii) an algorithm PARSeL (Prioritizing Alternatives using Recommendations of Stakeholders in e-Learning) to incorporate KM into designing an e-learning course. PARSeL prioritizes the content using stakeholder recommendations using Analytic Hierarchy Process (AHP) and fuzzy modeling. A case study is also presented with a goal of prioritizing a set of programming languages for an online computing course. The proposed methodology can be promising in recommending appropriate content for the e-learners and can be implemented to benefit e-learning organizations in a wider spectrum.
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Zhang, Puning, and Jie Ma. "Channel Characteristic Aware Privacy Protection Mechanism in WBAN." Sensors 18, no. 8 (July 24, 2018): 2403. http://dx.doi.org/10.3390/s18082403.
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Advances of information and communication technologies in medical areas have led to the emergence of wireless body area network (WBAN). The high accessibility of media in WBAN can easily lead to the malicious tapping or tampering attacks, which may steal privacy data or inject wrong data. However, existing privacy protection mechanisms in WBAN depend on the third-party key management system and have a complex key exchange process. To enhance user privacy at a low cost and with high flexibility, a channel characteristic aware privacy protection mechanism is proposed for WBAN. In the proposed mechanism, the similarity of RSS is measured to authenticate nodes. The key extraction technique can reduce the cost of the key distribution process. Due to the half duplex communication mode of sensors, the biased random sequences are extracted from the RSS of sensor nodes and coordinator. To reduce the inconsistency, we propose the n-dimension quantification and fuzzy extraction, which can quickly encrypt the transmission information and effectively identify malicious nodes. Simulation results show that the proposed mechanism can effectively protect user privacy against tampering and eavesdropping attacks.
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Ruan, Jianzhong, Todd E. Sparks, Ajay Panackal, F. W. Liou, Kunnayut Eiamsa-ard, Kevin Slattery, Hsin-Nan Chou, and Mary Kinsella. "Automated Slicing for a Multiaxis Metal Deposition System." Journal of Manufacturing Science and Engineering 129, no. 2 (September 12, 2006): 303–10. http://dx.doi.org/10.1115/1.2673492.
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A multiaxis adaptive slicing algorithm for multiaxis layered manufacturing, which can generate optimal slices to achieve deposition without support structures, is presented in this paper. Different from current adaptive slicing, this technique varies not only layer thickness but also in slicing/building direction. Aware of potential problems of previous research on slicing, the work in this paper focuses on innovative geometry reasoning and analysis tool-centroidal axis. Similar to medial axis, it contains geometry and topological information but is significantly computationally cheaper. Using a centroidal axis as a guide, the multiaxis slicing procedure is able to generate a three-dimensional layer or change slicing direction as needed automatically to build the part with better surface quality. This paper presents various examples to demonstrate the feasibility and advantages of centroidal axis and its usage in the multiaxis slicing process.