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Cullen, J., and A. Bryman. "The Knowledge Acquisition Bottleneck: Time for Reassessment?" Expert Systems 5, no. 3 (1988): 216–25. http://dx.doi.org/10.1111/j.1468-0394.1988.tb00065.x.
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Wagner, Christian. "Breaking the Knowledge Acquisition Bottleneck Through Conversational Knowledge Management." Information Resources Management Journal 19, no. 1 (2006): 70–83. http://dx.doi.org/10.4018/irmj.2006010104.
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Aussenac-Gilles, Nathalie, and Fabien Gandon. "From the knowledge acquisition bottleneck to the knowledge acquisition overflow: A brief French history of knowledge acquisition." International Journal of Human-Computer Studies 71, no. 2 (2013): 157–65. http://dx.doi.org/10.1016/j.ijhcs.2012.10.009.
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Hoppenbrouwers, Stijn, Bart Schotten, and Peter Lucas. "Towards Games for Knowledge Acquisition and Modeling." International Journal of Gaming and Computer-Mediated Simulations 2, no. 4 (2010): 48–66. http://dx.doi.org/10.4018/jgcms.2010100104.
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Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known “knowledge acquisition bottleneck”. Based on the authors’ work in conceptual modelling and method engineering, this paper casts methods for knowledge modelling in the framework of games. The resulting games-for-modelling approach is illustrated by a first prototype of such a game. The authors’ long-term goal is to lower the threshold for formal knowledge acquisition and modelling.
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Sauer, Christopher, Thilo Breitsprecher, Christof Küstner, Benjamin Schleich, and Sandro Wartzack. "SLASSY—An Assistance System for Performing Design for Manufacturing in Sheet-Bulk Metal Forming: Architecture and Self-Learning Aspects." AI 2, no. 3 (2021): 307–29. http://dx.doi.org/10.3390/ai2030019.
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Substantial efforts have been made to integrate manufacturing- and design-relevant knowledge into product development processes. A common approach is to provide the relevant knowledge to the design engineers using a knowledge-based system (KBS) that, in turn, becomes the engineering assistance system. Keeping the knowledge up to date is a critical issue, making knowledge acquisition a bottleneck of developing and maintaining KBS. This article presents a robust metamodel optimization and performance estimation architecture for developing and maintaining a KBS useful for design-for-manufacturing from the context of sheet-bulk metal forming. It is shown that the presented KBS or engineering assistance system helps achieve performing design-for-manufacturing, integrating both design and manufacturing knowledge. Using the presented approach helps overcome the bottleneck of knowledge acquisition and knowledge update through its self-learning component based on data mining and knowledge discovery.
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Lin, Shun-Chieh, Chia-Wen Teng, and Shian-Shyong Tseng. "Capturing Evolutional Knowledge Using Time Interval Tracing." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 4 (2007): 373–80. http://dx.doi.org/10.20965/jaciii.2007.p0373.
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Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.
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Zhao, Guo Zhen, and Wan Li Zuo. "Semi-Supervised Word Sense Disambiguation via Context Weighting." Advanced Materials Research 1049-1050 (October 2014): 1327–38. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1327.
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Word sense disambiguation as a central research topic in natural language processing can promote the development of many applications such as information retrieval, speech synthesis, machine translation, summarization and question answering. Previous approaches can be grouped into three categories: supervised, unsupervised and knowledge-based. The accuracy of supervised methods is the highest, but they suffer from knowledge acquisition bottleneck. Unsupervised method can avoid knowledge acquisition bottleneck, but its effect is not satisfactory. With the built-up of large-scale knowledge, knowledge-based approach has attracted more and more attention. This paper introduces a new context weighting method, and based on which proposes a novel semi-supervised approach for word sense disambiguation. The significant contribution of our method is that thesaurus and machine learning techniques are integrated in word sense disambiguation. Compared with the state of the art on the test data of the English all words disambiguation task in Sensaval-3, our method yields obvious improvements over existing methods in nouns, adjectives and verbs disambiguation.
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Ren, Yong Chang, Tao Xing, and Ping Zhu. "An Attribute Reduction Algorithms of Expert System Knowledge Acquisition." Applied Mechanics and Materials 48-49 (February 2011): 187–91. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.187.
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Knowledge acquisition is the bottleneck of construction expert system, to provide an accurate inference of knowledge is the key decision-making plan. This article use the rough sets theory, through the rough sets reduction eliminate redundant condition attribute, to achieve the streamlining of the knowledge library. First study the knowledge acquisition, in exposition knowledge hierarchical structure foundation, has given the conceptualization, formal, the knowledge library refinement and so on three knowledge acquisition; and then study attributes reduction algorithms, in the research sets difference and the attribute importance, the reduction algorithms inferential reasoning process's foundation, has given the attribute reduction algorithms six steps. Finally, according to the attributes reduction algorithms and the steps, to estimate the expert system to the function analytic method construction software cost, the composition technology complexity factor of 14 factors reduction. The results showed that the use of rough sets theory to reduce the attributes, can simplify the structure of complex systems, and can effectively maintain the knowledge library structure and performance.
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Lv, Zhan Min, Wei Yan, Jun Liang He, and Dao Fang Chang. "Knowledge-Based System for Major-Pieces Lifting Project Using MRM." Applied Mechanics and Materials 170-173 (May 2012): 3260–65. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3260.
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Knowledge-based system (KBS) for major-pieces lifting projects can be useful for improving the level of decision-making. And it is important to use the knowledge acquired from domain experts to finish all kinds of projects for lifting major-pieces. Knowledge acquisition plays a critical role in constructing a KBS for major-pieces lifting project, also it is commonly regarded as a major obstacle and bottleneck in the process of designing and implementing a KBS. This paper presents a KBS model based on matrix representation and mapping (MRM) approach to facilitate the effectiveness of knowledge acquisition in constructing a KBS. A case study on practical application is employed to illustrate how the KBS works.
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Jensen, Isabel Nadine, Roumyana Slabakova, Marit Westergaard, and Björn Lundquist. "The Bottleneck Hypothesis in L2 acquisition: L1 Norwegian learners’ knowledge of syntax and morphology in L2 English." Second Language Research 36, no. 1 (2019): 3–29. http://dx.doi.org/10.1177/0267658318825067.
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The Bottleneck Hypothesis (Slabakova, 2008, 2013) proposes that acquiring properties of the functional morphology is the most challenging part of learning a second language. In the experiment presented here, the predictions of this hypothesis are tested in the second language (L2) English of Norwegian native speakers. Two constructions are investigated that do not match in English and Norwegian: One involving functional morphology, subject–verb (SV) agreement, which is obligatory in the L2 but non-existent in the first language (L1), and one involving syntax, verb-second (V2) word order, which is obligatory in the L1, but restricted to specific contexts in the L2. The results of an acceptability judgement task indicate that the participants struggled more with identifying ungrammatical SV agreement than ungrammatical word order. We conclude that the findings lend tentative support to the Bottleneck Hypothesis.
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KODRATOFF, Y., and S. MOSCATELLI. "MACHINE LEARNING FOR OBJECT RECOGNITION AND SCENE ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (1994): 259–304. http://dx.doi.org/10.1142/s0218001494000139.
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Learning is a critical research field for autonomous computer vision systems. It can bring solutions to the knowledge acquisition bottleneck of image understanding systems. Recent developments of machine learning for computer vision are reported in this paper. We describe several different approaches for learning at different levels of the image understanding process, including learning 2-D shape models, learning strategic knowledge for optimizing model matching, learning for adaptive target recognition systems, knowledge acquisition of constraint rules for labelling and automatic parameter optimization for vision systems. Each approach will be commented on and its strong and weak points will be underlined. In conclusion we will suggest what could be the “ideal” learning system for vision.
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Yang, Jie, Chenzhou Ye, and Xiaoli Zhang. "An Expert System Shell for Fault Diagnosis." Robotica 19, no. 6 (2001): 669–74. http://dx.doi.org/10.1017/s0263574701003460.
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Traditional expert systems for fault diagnosis have a bottleneck in knowledge acquisition, and have limitations in knowledge representation and reasoning. A new expert system shell for fault diagnosis is presented in this paper to develop multiple knowledge models (object model, rules, neural network, case-base and diagnose models) hierarchically based on multiple knowledge. The structure of the expert system shell and the knowledge representation of multiple models are described. Diagnostic algorithms are presented for automatic modeling and hierarchical reasoning. It will be shown that the expert system shell is very effective in building diagnostic expert systems.
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Tao, Gui Ming. "Research on Fault Knowledge Acquisition Base of Circuit Simulation." Advanced Materials Research 490-495 (March 2012): 1763–66. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1763.
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To resolve bottle-net problem of knowledge acquisition of fault diagnoses expert system. This paper make use of circuit simulation software Multisim2001module modeling and simulating, acquiring fault data of electric control circuit of missile launching vehicle, acquiring correspondence relation of function-fault, establishing knowledge base of intelligent diagnostic expert system, affording a new thought to resolve knowledge acquisition of expert system. The fault diagnosis expert system is based on the knowledge base, but many fault diagnosis expert systems face the same problem: the knowledge of fault and fault maintenance is very poor. This causes many of intelligent fault diagnosis systems are still in the theoretical research or prototype design stage, and its use value is low, therefore, how to gain more significant and enough knowledge has turned to be the bottleneck which confine the engineering operational capability of fault diagnosis system[1] . This article will build an electrical system dynamic operational model of some equipment by circuit simulation. And it will do the direct current analysis, transient analysis etc. for the circuit by the software’s powerful analyze ability, include the setting of some devices’ voltage value, current value and test point, and gain the simulation parameters of normal conditions and fault conditions. Then lead these data into the database so as to the system to call and search, and confirm the fault node finally [2, 3]. The function simulation which is mentioned in this article is an ingredient of the equipment system simulation; structure simulation and movement simulation are also like it. And it contains system-level simulation, part-level simulation and component-level simulation in the hierarchical division. Because of the limited space, this article just considerate the electricity circuit simulation’s application in the fault knowledge acquisition, which is belong to the function simulation.
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Усамов, И. Р., Б. Ж. Юхигов, and З. А. Магазиева. "EXPERT SYSTEMS SHELL: AN EFFECTIVE KNOWLEDGE ACQUISITION MODEL." Вестник ГГНТУ. Гуманитарные и социально-экономические науки, no. 2(28) (August 26, 2022): 80–90. http://dx.doi.org/10.34708/gstou.2022.61.19.010.
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Экспертная система - это интеллектуальная компьютерная программа, которая использует знания и процедуры вывода для решения проблем, которые считаются достаточно сложными, чтобы для их решения хватило человеческого опыта. С момента внедрения первых экспертных систем в начале 1970-х годов и последовавших за этим успехов в сочетании с преимуществами, которые человечество получило от этих ранних экспертных систем и их преемников, можно было бы ожидать, что пять десятилетий спустя произойдет бум использования экспертных систем для выполнения более специализированных задач в конкретных областях применения. Причины этого могут быть обусловлены сущностью технических факторов, финансовых последствий, культурных и религиозных убеждений и ограничений, связанных с внедрением экспертных систем, особенно в слаборазвитых странах, либо отсутствием достаточных знаний о существовании оболочек экспертных систем и предоставляемых ими средств, утилит и инструментов, которые делают разработку новых экспертных систем своего рода «довольно легким делом». В этой статье основное внимание уделяется базовой концепции оболочек экспертных систем с целью выделения их базового использования и создания некоторой формы углубленного общего представления о некоторых компонентах и утилит, которые они предоставляют, предлагая более эффективную модель получения знаний, которая позволила бы устранить узкое место в приобретении знаний при разработке экспертных систем, а также выделить функции, выполняемые оболочками экспертных систем, в попытке подчеркнуть их возможность повторного использования в различных других конкретных областях применения внутри, либо за пределами одной и той же области. An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that are considered complex enough to have enough human experience to solve them. Since the introduction of the first expert systems in the early 1970s and the subsequent successes combined with the benefits that humanity received from these early expert systems and their successors. one would expect that five decades later there would be a boom in the use of expert systems to perform more specialized tasks in specific applications. The reasons for this may be due to the nature of technical factors, financial consequences, cultural and religious beliefs and restrictions associated with the introduction of expert systems, especially in underdeveloped countries, or the lack of sufficient knowledge about the existence of expert system shells and the tools, utilities and tools provided by them that make the development of new expert systems a kind of “pretty easy thing”. This article focuses on the basic concept of expert system shells in order to highlight their basic use and create some form of in-depth general understanding of some of the components and utilities they provide, offering a more effective knowledge acquisition model that would eliminate the bottleneck in knowledge acquisition in the development of expert systems, as well as highlight the functions performed by expert system shells, in an attempt to emphasize their reusability in various other specific applications within, or outside the same area.
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GALEA, MICHELLE, QIANG SHEN, and JOHN LEVINE. "Evolutionary approaches to fuzzy modelling for classification." Knowledge Engineering Review 19, no. 1 (2004): 27–59. http://dx.doi.org/10.1017/s0269888904000189.
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An overview of the application of evolutionary computation to fuzzy knowledge discovery is presented. This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent reasoning systems, and in the data mining of databases where the aim is the discovery of new knowledge. The different strategies utilizing evolutionary algorithms for knowledge acquisition are abstracted from the work reviewed. The simplest strategy runs an evolutionary algorithm once, while the iterative rule learning approach runs several evolutionary algorithms in succession, with the output from each considered a partial solution. Ensembles are formed by combining several classifiers generated by evolutionary techniques, while co-evolution is often used for evolving rule bases and associated membership functions simultaneously. The associated strengths and limitations of these induction strategies are compared and discussed. Ways in which evolutionary techniques have been adapted to satisfy the common evaluation criteria of the induced knowledge—classification accuracy, comprehensibility and novelty value—are also considered. The review concludes by highlighting common limitations of the experimental methodology used and indicating ways of resolving them.
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Musen, Mark A., and Johan van der Lei. "Knowledge Engineering for Clinical Consultation Programs: Modeling the Application Area." Methods of Information in Medicine 28, no. 01 (1989): 28–35. http://dx.doi.org/10.1055/s-0038-1635543.
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Abstract:Developers of computer-based decision-support tools frequently adopt either pattern recognition or artificial intelligence techniques as the basis for their programs. Because these developers often choose to accentuate the differences between these alternative approaches, the more fundamental similarities are frequently overlooked. The principal challenge in the creation of any clinical consultation program - regardless of the methodology that is used - lies in creating a computational model of the application domain. The difficulty in generating such a model manifests itself in symptoms that workers in the expert systems community have labeled “the knowledge-acquisition bottleneck” and “the problem of brittleness”. This paper explores these two symptoms and shows how the development of consultation programs based on pattern-recognition techniques is subject to analogous difficulties. The expert systems and pattern recognition communities must recognize that they face similar challenges, and must unite to develop methods that assist with the process of building of models of complex application tasks.
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Lin, Yun Feng, and Xiao Ping Hu. "The Application of Data Mining Technology in Mechanical Fault Diagnosis." Key Engineering Materials 460-461 (January 2011): 821–26. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.821.
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This article first introduced the survey of mechanical fault diagnosis technology development and the data mining technology theory. Then its application situation at present and the main questions that exist are elaborated. Its development trend is analyzed. The application feasibility of using data mining technology in mechanical fault diagnosis is discussed. Next, the naissance, the development and the future development tendency of data mining technology are introduced. The four algorithms are analyzed and the framework is built too. Intelligent Diagnosis is a major development direction of the fault diagnosis. Knowledge acquisition is the bottleneck of Intelligent Diagnosis development. It comprehensive use of many kinds of advanced technology, discover valuable and hidden knowledge from the large amounts of data mining.
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Ming-qian, Wang. "RESEARCH ON SHIP ENGINE ROOM MONITORING AND FAULT DIAGNOSIS SYSTEM BASED ON LABVIEW." EPH - International Journal of Science And Engineering 7, no. 3 (2021): 1–11. http://dx.doi.org/10.53555/ephijse.v7i3.186.
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The structure of marine engine room system is complex, with many fault sources and abundant symptom parameters. There is no simple correspondence between faults and symptoms, but they are interrelated and intricate. The existing fault diagnosis of ship engine room is mostly based on expert system, but the bottleneck of knowledge acquisition, lack of learning ability and the complexity and efficiency of the expert system are restricting its further development. In order to overcome the above shortcomings of expert system, the support vector machine (SVM) is introduced into the expert system, and the support vector machine (SVM) is used to solve the problem of small sample self-learning ability and high-dimensional space self-adaptation ability. The expert system is used to achieve the automatic knowledge acquisition and rapid logical reasoning. Knowledge base management and maintenance, symbolic reasoning and related explanations, and give full play to the advantages of both SVM and expert system. Finally, the fault diagnosis of marine diesel engine booster system is introduced as an example. The MATLAB Script is used to call the MATLAB SVM analysis toolkit, and the combination of SVM and expert system is used to diagnose the fault.
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Welty, Chris, Lora Aroyo, Flip Korn, Sara M. McCarthy, and Shubin Zhao. "Rapid Instance-Level Knowledge Acquisition for Google Maps from Class-Level Common Sense." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 9 (October 4, 2021): 143–54. http://dx.doi.org/10.1609/hcomp.v9i1.18947.
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Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting an expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple approach to acquiring and reasoning with class-level attributes from the crowd that represent broad common sense associations between categories. We pick a very real industrial-scale data set and problem: how to augment an existing knowledge graph of places and products with associations between them indicating the availability of the products at those places, which would enable a KG to provide answers to questions like, "Where can I buy milk nearby?" This problem has several practical challenges, not least of which is that only 30% of physical stores (i.e. brick & mortar stores) have a website, and fewer list their product inventory, leaving a large acquisition gap to be filled by methods other than information extraction (IE). Based on a KG-inspired intuition that a lot of the class-level pairs are part of people's general common sense, e.g. everyone knows grocery stores sell milk and don't sell asphalt, we acquired a mixture of instance- and class- level pairs (e.g. , , resp.) from a novel 3-tier crowdsourcing method, and demonstrate the scalability advantages of the class-level approach. Our results show that crowdsourced class-level knowledge can provide rapid scaling of knowledge acquisition in this and similar domains, as well as long-term value in the KG.
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Kota, Sridhar, Arthur G. Erdman, and Donald R. Riley. "Development of Knowledge Base for Designing Linkage-Type Dwell Mechanisms: Part 2—Application." Journal of Mechanisms, Transmissions, and Automation in Design 109, no. 3 (1987): 316–21. http://dx.doi.org/10.1115/1.3258796.
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Knowledge acquisition is a big bottleneck in any expert system development. Several “rules-of-thumb” are developed for designing linkage-type dwell mechanisms. These rules of thumb are based on path curvature theory (Part 1 of this paper) and systematic synthesis, analysis and classification of straight-line, circular-arc and symmetrical coupler-curve generating linkages. The methods of representing the domain-specific knowledge in an expert system are discussed here. “Frames” for structural representation of knowledge and “production rules” to control the reasoning during the design process are proposed here. Frames and rules, in the light of dwell-mechanism synthesis are presented. Finally, a conceptual design example illustrates the various stages that the actual expert system goes through in the design process when these concepts are fully developed and programs are written out in LISP. These concepts are developed with an eye toward future development of a general expert system for type and dimensional synthesis of mechanisms.
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Xiao, Lin, Jiyan Wu, Xiangwang Hu, Jian Sun, and Ye Tian. "Collaborate or Compete? Time-Varying Incentives versus Tolling in Parallel Bottleneck." Journal of Advanced Transportation 2023 (May 12, 2023): 1–22. http://dx.doi.org/10.1155/2023/6402531.
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Incentive-based traffic demand management (IBTDM) schemes redistribute traffic demand across space and time. The relationship between IBTDM and traditional tolling programs impacts financial sustainability and decision making of both programs. This study aims to examine if an IBTDM and a tolling program can coexist and should collaborate or compete to establish a financially sustainable mobility ecosystem. We investigate a duopoly case with an IBTDM and a tolling program in a parallel bottleneck model with fixed demand. It is assumed that the IBTDM program has a user acquisition cost and is constrained by a total budget. Then, the collaboration and competition cases between two programs are investigated, respectively. We found that both programs can attract substantial amounts of commuters and thus can coexist with each other, while the IBTDM program could attract more commuters. Overall, the tolling agency will not cooperate with the IBTDM company if its goal is to raise revenue. Additionally, the IBTDM company held an advantage as it can determine an optimal incentive scheme with incomplete information when competing. This study fills the knowledge gap on the coexistence of incentive and tolling programs, which helps to optimize the profiles of incentive and tolling, as well as to make strategic decisions on competition and cooperation.
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Martinez, D., O. Lopez de Lacalle, and E. Agirre. "On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation." Journal of Artificial Intelligence Research 33 (September 25, 2008): 79–107. http://dx.doi.org/10.1613/jair.2395.
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This article focuses on Word Sense Disambiguation (WSD), which is a Natural Language Processing task that is thought to be important for many Language Technology applications, such as Information Retrieval, Information Extraction, or Machine Translation. One of the main issues preventing the deployment of WSD technology is the lack of training examples for Machine Learning systems, also known as the Knowledge Acquisition Bottleneck. A method which has been shown to work for small samples of words is the automatic acquisition of examples. We have previously shown that one of the most promising example acquisition methods scales up and produces a freely available database of 150 million examples from Web snippets for all polysemous nouns in WordNet. This paper focuses on the issues that arise when using those examples, all alone or in addition to manually tagged examples, to train a supervised WSD system for all nouns. The extensive evaluation on both lexical-sample and all-words Senseval benchmarks shows that we are able to improve over commonly used baselines and to achieve top-rank performance. The good use of the prior distributions from the senses proved to be a crucial factor.
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Asai, Masataro, Hiroshi Kajino, Alex Fukunaga, and Christian Muise. "Classical Planning in Deep Latent Space." Journal of Artificial Intelligence Research 74 (August 9, 2022): 1599–686. http://dx.doi.org/10.1613/jair.1.13768.
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Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.
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LENTING, JACQUES H. J., and PETER J. BRASPENNING. "THE FRAME PROBLEM FROM AN ENGINEERING PERSPECTIVE." International Journal of Software Engineering and Knowledge Engineering 03, no. 02 (1993): 257–65. http://dx.doi.org/10.1142/s0218194093000124.
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Since its introduction in 1969, the phrase “frame problem” has been attributed various interpretations. Most researchers in the field of Artificial Intelligence define the frame problem as the problem of finding an effective representation for reasoning about change. Logicians use the phrase to refer to a much less general technical problem within logic whereas philosophers tend to interpret the phrase as the more general problem of determining (ir)relevance. All in all, this discrepancy has led to considerable confusion about the meaning of the phrase. We contend that most of this confusion can be avoided, if the original (robotics) context of the frame problem is adhered to. We present an engineering view on the frame problem that allows us to strip the frame problem from associated problem notions like qualification and ramification. The problem that remains is intimately related to the knowledge acquisition bottleneck in knowledge engineering.
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Taylor, Daniel J., Jessica R. Dietch, Kristi Pruiksma, et al. "Developing and Testing a Web-Based Provider Training for Cognitive Behavioral Therapy of Insomnia." Military Medicine 186, Supplement_1 (2021): 230–38. http://dx.doi.org/10.1093/milmed/usaa359.
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ABSTRACT Introduction Chronic insomnia is a common and debilitating disease that increases risk for significant morbidity and workplace difficulties. Cognitive behavioral therapy for insomnia (CBT-I) is the first-line treatment, but there is a critical lack of behavioral health providers trained in CBT-I because, in part, of a bottleneck in training availability and costs. The current project developed and evaluated a web-based provider training course for CBT-I: CBTIweb.org. Materials and Methods Subject matter experts developed the content for CBTIweb.org. Then, trainees completed alpha testing (n = 24) and focus groups, and the site was improved. Next, licensed behavioral health providers and trainees completed beta testing (n = 41) and the site underwent another round of modifications. Finally, to compare CBTIweb.org to an in-person workshop, licensed behavioral health providers were randomly assigned to CBTIweb.org (n = 21) or an in-person workshop (n = 23). All participants were CBT-I naïve and completed the following assessments: Computer System Usability Questionnaire, Website Usability Satisfaction Questionnaire, Website Content Satisfaction Questionnaire, and Continuing Education knowledge acquisition questionnaires. Results Alpha and beta testers of CBTIweb.org reported high levels of usability and satisfaction with the site and showed significant within-group knowledge acquisition. In the pilot comparison study, linear fixed-effects modeling on the pre-/postquestionnaires revealed a significant main effect for time, indicating a significant increase in knowledge acquisition from 69% correct at baseline to 92% correct at posttraining collapsed across in-person and CBTIweb.org groups. The interaction effect of Time by Condition was nonsignificant, indicating equivalence in knowledge gains across both groups. Conclusion CBTIweb.org appears to be an engaging, interactive, and concise provider training that can be easily navigated by its users and produce significant knowledge gains that are equivalent to traditional in-person workshops. CBTIweb.org will allow for worldwide dissemination of CBT-I to any English-speaking behavioral health providers. Future research will work on translating this training to other languages and extending this web-based platform to the treatment of other sleep disorders (e.g., nightmares) and populations (e.g., pediatric populations with insomnia).
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Bolshina, Angelina Sergeevna, and Natalia Valentinovna Loukachevitch. "Weakly Supervised Word Sense Disambiguation Using Automatically Labelled Collections." Proceedings of the Institute for System Programming of the RAS 33, no. 6 (2021): 193–204. http://dx.doi.org/10.15514/ispras-2021-33(6)-13.
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State-of-the-art supervised word sense disambiguation models require large sense-tagged training sets. However, many low-resource languages, including Russian, lack such a large amount of data. To cope with the knowledge acquisition bottleneck in Russian, we first utilized the method based on the concept of monosemous relatives to automatically generate a labelled training collection. We then introduce three weakly supervised models trained on this synthetic data. Our work builds upon the bootstrapping approach: relying on this seed of tagged instances, the ensemble of the classifiers is used to label samples from unannotated corpora. Along with this method, different techniques were exploited to augment the new training examples. We show the simple bootstrapping approach based on the ensemble of weakly supervised models can already produce an improvement over the initial word sense disambiguation models.
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Asai, Masataro, and Hiroshi Kajino. "Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 592–600. http://dx.doi.org/10.1609/icaps.v29i1.3526.
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While classical planning has been an active branch of AI, its applicability is limited to the tasks precisely modeled by humans. Fully automated high-level agents should be instead able to find a symbolic representation of an unknown environment without supervision, otherwise it exhibits the knowledge acquisition bottleneck. Meanwhile, Latplan (Asai and Fukunaga 2018) partially resolves the bottleneck with a neural network called State AutoEncoder (SAE). SAE obtains the propositional representation of the image-based puzzle domains with unsupervised learning, generates a state space and performs classical planning. In this paper, we identify the problematic, stochastic behavior of the SAE-produced propositions as a new sub-problem of symbol grounding problem, the symbol stability problem. Informally, symbols are stable when their referents (e.g. propositional values) do not change against small perturbation of the observation, and unstable symbols are harmful for symbolic reasoning. We analyze the problem in Latplan both formally and empirically, and propose “Zero-Suppressed SAE”, an enhancement that stabilizes the propositions using the idea of closed-world assumption as a prior for NN optimization. We show that it finds the more stable propositions and the more compact representations, resulting in an improved success rate of Latplan. It is robust against various hyperparameters and eases the tuning effort, and also provides a weight pruning capability as a side effect.
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Weng, Cheng-Hsiung. "Knowledge discovery of digital library subscription by RFC itemsets." Electronic Library 34, no. 5 (2016): 772–88. http://dx.doi.org/10.1108/el-06-2015-0086.
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Purpose The paper aims to understand the book subscription characteristics of the students at each college and help the library administrators to conduct efficient library management plans for books in the library. Unlike the traditional association rule mining (ARM) techniques which mine patterns from a single data set, this paper proposes a model, recency-frequency-college (RFC) model, to analyse book subscription characteristics of library users and then discovers interesting association rules from equivalence-class RFC segments. Design/methodology/approach A framework which integrates the RFC model and ARM technique is proposed to analyse book subscription characteristics of library users. First, the author applies the RFC model to determine library users’ RFC values. After that, the author clusters library users’ transactions into several RFC segments by their RFC values. Finally, the author discovers RFC association rules and analyses book subscription characteristics of RFC segments (library users). Findings The paper provides experimental results from the survey data. It shows that the precision of the frequent itemsets discovered by the proposed RFC model outperforms the traditional approach in predicting library user subscription itemsets in the following time periods. Besides, the proposed approach can discover interesting and valuable patterns from library book circulation transactions. Research limitations/implications Because RFC thresholds were assigned based on expert opinion in this paper, it is an acquisition bottleneck. Therefore, researchers are encouraged to automatically infer the RFC thresholds from the library book circulation transactions. Practical implications The paper includes implications for the library administrators in conducting library book management plans for different library users. Originality/value This paper proposes a model, the RFC model, to analyse book subscription characteristics of library users.
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Lefever, Els, and Véronique Hoste. "Parallel corpora make sense." International Journal of Corpus Linguistics 19, no. 3 (2014): 333–67. http://dx.doi.org/10.1075/ijcl.19.3.02lef.
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We present a multilingual approach to Word Sense Disambiguation (WSD), which automatically assigns the contextually appropriate sense to a given word. Instead of using a predefined monolingual sense-inventory, we use a language-independent framework by deriving the senses of a given word from word alignments on a multilingual parallel corpus, which we made available for corpus linguistics research. We built five WSD systems with English as the input language and translations in five supported languages (viz. French, Dutch, Italian, Spanish and German) as senses. The systems incorporate both binary translation features and local context features. The experimental results are very competitive, which confirms our initial hypothesis that each language contributes to the disambiguation of polysemous words. Because our system extracts all information from the parallel corpus, it offers a flexible language-independent approach, which implicitly deals with the sense distinctness issue and allows us to bypass the knowledge acquisition bottleneck for WSD.
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Taylor, D., B. Bunnell, C. Calhoun, et al. "1186 Developing And Testing A Web-based Provider Training For Cognitive Behavioral Therapy Of Insomnia." Sleep 43, Supplement_1 (2020): A453. http://dx.doi.org/10.1093/sleep/zsaa056.1180.
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Abstract Introduction Chronic insomnia is a common, debilitating disorder and a risk factor for significant medical morbidity, mental health problems, and workplace difficulties. Cognitive behavioral therapy for insomnia (CBT-I) is the gold standard treatment for insomnia. However, few providers are trained in CBT-I, in part due to a bottleneck in training availability and the time and cost associated with current training platforms. To address this training deficit, our team developed and evaluated CBTIweb.org, a web-based provider training course for CBT-I. Methods Feedback from alpha- and beta-testing of CBTIweb.org was collected and used to optimize course content and functionality. Then, a comparison study was conducted in which licensed providers were randomized to complete either the online CBTIweb.org course (n=21) or an in-person CBT-I training (n=23). During all phases of development, providers completed a Computer System Usability Questionnaire (CSUQ), investigator-developed website usability and content questionnaires, and pre/post-training competency assessments. Results Independent samples t-tests indicated significant improvements in CSUQ, and website usability and content questionnaires responses from alpha- to beta-testing (all ps < .05). Linear mixed-effects modeling revealed significant within-subject increases in knowledge acquisition (F(34.7) = 65.4, p < 0.001; baseline = 69% correct, post-training = 92% correct) when collapsed across in-person and web-based groups. The interaction group by time interaction was non-significant (F(34.7) = 1.7, p = 0.204), indicating similar gains in knowledge (i.e., equivalence) between the in-person and the CBTIweb.org training formats. Conclusion Alpha and beta testers of CBTIweb.org reported high levels of satisfaction while also noting areas for improvement, which were used to update the site. Findings suggest the final CBTIweb.org product successfully trained clinicians compared to an in-person workshop, given knowledge acquisition improvements. CBTIweb.org is an efficient and effective training platform for clinicians to gain knowledge and competence in the most effective treatment for insomnia. Support W81XWH-17-1-0165
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Aboras, Afnan. "The Acquisition of Definiteness and Specificity in English: A Case Study with Saudi-Arabic Learners of English Using an Online Task." English Language and Literature Studies 10, no. 1 (2020): 13. http://dx.doi.org/10.5539/ells.v10n1p13.
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Definiteness with Arabic learners has been explored by many researchers such as Jaensch and Sarko (2009) and Sarko (2009). The majority of previous studies have used an offline task and focused on identifying the types of errors which learners were committing. Conversely, the present study will use an online reaction time task to investigate the learners’ accuracy in judging [±definite and ±specific] in a series of sentences. The aim of the study is to ascertain the accuracy of participants in judging grammatical and ungrammatical sentences in terms of definiteness and specificity in English, and also to identify which factors have the greatest effect on this accuracy. The study will examine the process of article acquisition from the perspective of universal grammar using the following hypotheses: The Representational Deficit hypothesis (RDH) by Hawkins and Chan (1997), the Feature Reassembly hypothesis by Lardiere (2009) and the bottleneck hypothesis by Slabakova (2008, 2009, 2015). Thirty-two Saudi learners have completed a grammatical judgment task that was designed using OpenSesame to incorporate a reaction time test along with two vocabulary tests (Yes/No and Lex30) and a proficiency test. The results showed no effect on definiteness and specificity with the Saudi-Arabic learners. Moreover, the findings demonstrated that there is no difference in reaction time which could be attributed to [±definite and ±specific]. Receptive vocabulary knowledge and proficiency affected the learners’ accuracy in judging article use in English, but no such effect was found for the learners’ productive vocabulary knowledge. Additionally, L1 negative transfer has been observed in Saudi-Arabic learners of English particularly with low-level learners.
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Khan, Usman Ali. "Synthesizing a three Layer Architectural Framework for Adaptive Intelligent User Interfaces for the Disabled Community." International Journal of Automation and Smart Technology 14, no. 1 (2024): 11. http://dx.doi.org/10.5875/s2rfmt87.
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Knowledge acquisition has been a bottleneck in the design for efficient User Interfaces for Disabled Community. The best suited interfaces have been oriented towards adaptability that is the User Interface needs to adapt and configure itself according to the needs and likes of the user. Such user interfaces can arouse interest in the mind of the disabled and motivate them to become an asset to the nation, and use their intellect to contribute to the society. This study pioneers an innovative approach, aiming to develop User Interfaces (UIs) for the Disabled Community. The research introduces a comprehensive three-layer architectural framework employing Learning, Trust, and Knowledge Models. Unlike conventional approaches, this Architectural Framework is designed to learn not only from the Disabled but also from their Assistants/Guides, employing principles of knowledge engineering. While the former offers static context, the latter contributes dynamic insights by observing behaviors, responses, and actions of the Disabled. This symbiotic relationship forms the foundational basis for developing intelligent adaptive UI’s that not only accommodate but actively engage and motivate the Disabled Community. The proposed research methodology involves a meticulous gathering of data through extensive user studies and interactions. By using the Human Computer Interface technology, the study aims to develop a robust framework capable of comprehensively understanding the preferences, needs, and nuances of disabled users. The expected outcomes include the development of a prototype adaptive UI, validated through rigorous testing and feedback from the disabled community. Additionally, the study anticipates contributing a comprehensive set of guidelines and best practices for designing intelligent adaptive UIs, ensuring comfort and ease for impaired users, boosting their confidence and encouraging greater involvement in socio-development activities.
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Feinerer, Ingo. "Efficient large-scale configuration via integer linear programming." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, no. 1 (2013): 37–49. http://dx.doi.org/10.1017/s0890060412000376.
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AbstractConfiguration of large-scale applications in an engineering context requires a modeling environment that allows the design engineer to draft the configuration problem in a natural way and efficient methods that can process the modeled setting and scale with the number of components. Existing configuration methods in artificial intelligence typically perform quite well in certain subareas but are hard to use for general-purpose modeling without mathematical or logics background (the so-called knowledge acquisition bottleneck) and/or have scalability issues. As a remedy to this important issue both in theory and in practical applications, we use a standard modeling environment like the Unified Modeling Language that has been proposed by the configuration community as a suitable object-oriented formalism for configuration problems. We provide a translation of key concepts of class diagrams to inequalities and identify relevant configuration aspects and show how they are treated as an integer linear program. Solving an integer linear program can be done efficiently, and integer linear programming scales well to large configurations consisting of several thousands components and interactions. We conduct an empirical study in the context of package management for operating systems and for the Linux kernel configuration. We evaluate our methodology by a benchmark and obtain convincing results in support for using integer linear programming for configuration applications of realistic size and complexity.
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CRAWFORD, STUART L., and STEVEN K. SOUDERS. "A COMPARISON OF TWO NEW TECHNIQUES FOR CONCEPTUAL CLUSTERING." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 03 (1990): 409–28. http://dx.doi.org/10.1142/s0218001490000253.
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Clustering, pattern recognition, and classification are important components of many artificial intelligence systems, especially those designed to classify new observations. Often, one has access to a history of old observations that have been previously classified and, under these circumstances, the old data may be used as a training set from which one may obtain rules for the subsequent classification of new data. The techniques used to obtain these rules may be traditional statistical methods or modern computer-intensive techniques. Sometimes, however, the history of old observations has not been previously classified. Under these circumstances, the analyst simply wishes to uncover structure in the data and ascertain whether the structure is apparent or real. When the analyst is searching for clusters, statistical clustering methodologies are often used. Although effective at locating clusters, such approaches leave the interpretation of the clusters as a task for the human analyst. A relatively new class of “conceptual” clustering techniques have emerged from the discipline of machine learning. These techniques attempt to both locate and explain clusters among the data. In this way, the explanations of cluster membership may be used to construct rules for the subsequent classification of new data. The generation of interpretable rules, whether by the use of classification algorithms or conceptual clustering algorithms is of considerable importance in reducing the knowledge acquisition “bottleneck” that often impedes progress towards the building of rule-based systems. In this paper, two new techniques for conceptual clustering are introduced and compared.
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Reif, Kathryn E., Guy H. Palmer, Massaro W. Ueti, et al. "Dermacentor andersoni Transmission of Francisella tularensis subsp. novicida Reflects Bacterial Colonization, Dissemination, and Replication Coordinated with Tick Feeding." Infection and Immunity 79, no. 12 (2011): 4941–46. http://dx.doi.org/10.1128/iai.05676-11.
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ABSTRACTTicks serve as biological vectors for a wide variety of bacterial pathogens which must be able to efficiently colonize specific tick tissues prior to transmission. The bacterial determinants of tick colonization are largely unknown, a knowledge gap attributed in large part to the paucity of tools to genetically manipulate these pathogens. In this study, we demonstrated thatFrancisella tularensissubsp.novicida, for which a complete two-allele transposon mutant library has been constructed, initially infects the midguts of 100% of acquisition-fedDermacentor andersoninymphs, with stable colonization and replication during a subsequent molt. Increased dissemination to and marked replication within the salivary gland was closely linked to a second (transmission) feed and culminated in secretion of bacteria into the saliva and successful transmission. Simultaneous testing of multiple mutants resulted in total bacterial levels similar to those observed for single mutants. However, there was evidence of a bottleneck during colonization, resulting in a founder effect in which the most successful mutant varied when comparing individual ticks. Thus, it is essential to assess mutant success at the level of the tick population rather than in individual ticks. The ability ofF. tularensissubsp.novicidato recapitulate the key physiological events by which bacteria colonize and are transmitted by ixodid ticks provides a new genome-wide approach to identify the required pathogen molecules and pathways. The identification of specific genes and, more importantly, conserved pathways that function at the tick-pathogen interface will accelerate the development of new methods to block transmission.
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Shan, Xin. "Multisensor Cross-Media Data Mining Method Assisted by Expert System." Journal of Sensors 2023 (January 31, 2023): 1–12. http://dx.doi.org/10.1155/2023/3303790.
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The information generated by the multifaceted and multilevel processing of the data of multiple sensors is more meaningful than the information obtained by a single sensor and provides accurate information and decision-making basis for various application systems. How to apply data mining theory to the multisensor cross-media field has become a research hotspot. Through the analysis of multisensor cross-media data, it is of great significance to dig out the important rules, information, or knowledge hidden in it and use it for cross-media retrieval engine. The rule acquisition is the “bottleneck” problem of the expert system. This paper adopts the data mining method based on the rough set to acquire the rules and improves the basic algorithm of attribute reduction. Using the attribute reduction algorithm and the heuristic value reduction algorithm, the calculation is simplified and the reduction efficiency is improved. In the presentation, according to the characteristics of cross-media and the application requirements of expert systems, this paper takes the case representation based on features as the basis and classifies cases according to feature attributes. In case retrieval, according to the hierarchical structure of case features, the entire case database is organized into a multilevel hierarchical index structure. In this paper, a cross-media retrieval engine is constructed from the perspective of classifier design, and the Euclidean distance is used as the similarity matching model of image content. The mutual retrieval of images and audios preliminarily forms the design process of retrieval from one media type to another and establishes a corresponding cross-media index. The experimental results show that the algorithm has better processing effect and higher accuracy than other algorithms. Different k-nearest neighbor values were selected in the experiment, and it reached about 96% in the test environment of libsvm toolbox, which is better than the processing results of LE and LLE algorithms.
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McNeese, Michael D., and Brian S. Zaff. "Knowledge as Design: A Methodology for Overcoming Knowledge Acquisition Bottlenecks in Intelligent Interface Design." Proceedings of the Human Factors Society Annual Meeting 35, no. 17 (1991): 1181–85. http://dx.doi.org/10.1177/154193129103501701.
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Matricano, Diego, Elena Candelo, Mario Sorrentino, and Aurora Martínez-Martínez. "Absorbing in-bound knowledge within open innovation processes. The case of Fiat Chrysler Automobiles." Journal of Knowledge Management 23, no. 4 (2019): 786–807. http://dx.doi.org/10.1108/jkm-10-2018-0625.
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Purpose The purpose of this paper is to investigate the way companies involved in Open Innovation Processes (OIPs) routinize the procedure through which they can absorb in-bound knowledge, i.e. knowledge that comes from the outside and, in particular, from the crowd. In-bound knowledge passes through the phases of acquisition, assimilation, transformation and exploitation. Thus, companies need to define mechanisms and paths – related to their potential and realized absorptive capacity –to manage and exploit it. Design/methodology/approach The present paper is based on a longitudinal case study, an OIP launched by Fiat Chrysler Automobiles (FCA) that has already been implemented for three times. Multiple direct interviews with FCA top managers have allowed rebuilding the routinized procedure through which the company absorbs in-bound knowledge. Findings To routinize the procedure of absorbing in-bound knowledge, the company has settled specific mechanisms and paths and has established some bottlenecks over the process of acquisition, assimilation, transformation and exploitation of in-bound knowledge. These mechanisms and path, as well as these bottlenecks, are identified and descripted in the paper. Research limitations/implications Beyond the limitations linked to the use of a single case study, another limitation might be the reference to a big company in a specific industry. Anyway, with due caution, achieved findings can be referred to other industries as well. Originality/value This paper contributes to exploring if and how companies managing OIPs routinize the procedure through which they can absorb in-bound knowledge.
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Hao, L. N., Wen Lin Chen, X. F. Zhang, and Wan Shan Wang. "Rough Set Data Analysis System and Its Applications in Machinery Fault Diagnosis." Materials Science Forum 471-472 (December 2004): 850–54. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.850.
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The characteristics of fault diagnosis are as follows. First, features extraction is the key of improving diagnosis efficiency and correct rate. Secondly, fault diagnosis method based on rule reasoning has a wide application, but rule acquisition is one of the bottlenecks. Thirdly, rule modification is a key question of solving the real-time rule acquisition in the dynamic environments, and a primary question of knowledge base modification of expert system, etc. In this paper, Rough Set Theory (RST) was used to solve the key problems of machinery fault diagnosis, and a Rough Set Data Analysis System (RSDAS) was developed. RSDAS was used to implement rule generation automation & rule modification based on RST such as indiscernibility relation and knowledge reduction method, depicted importance of different attributes in knowledge representation, and reduced knowledge representation space. The method of fault diagnosis using RSDAS was summarized. The experiment results approved the feasibility and the high precision of RSDAS. Therefore, we can use RADAS to machinery fault diagnosis.
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Yau'Mee Hayati Hj Mohamed Yusof, Awanis Romli, Ruzaini Abdullah Arshah, and Azira Abdul Aziz. "A Sensitivity Analysis for Roles Selection in Hybrid Multi-Criteria Decision Making." Journal of Advanced Research in Applied Sciences and Engineering Technology 31, no. 1 (2023): 210–25. http://dx.doi.org/10.37934/araset.31.1.210225.
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The process of retaining leadership succession at higher education institutions (HEI) is crucial since it has entailed the process of selecting the ideal candidate. The goal is to guide universities toward maintaining organizations' excellence in their academic leadership and management (ALM) positions. Due to the lack of established standards for assessing the competency of possible successors at their home institutions, many ALM of Malaysia HEIs is difficult to identify the proper replacement for their posts. This study aims to propose a multi-criteria tacit knowledge acquisition framework (MC-TKAF) for supporting talent development intervention programs in Malaysia HEIs. It will be based on cognitive apprenticeship, socialization, and informal learning theory which is mostly used in acquiring knowledge from expertise to overcome talent bottlenecks among novices. Fuzzy Delphi will be used as the primary methodology in this study to gather agreement regarding the appropriate indicator to measure tacit knowledge competency among ALMs at Malaysian HEIs. There are three phases: Phase 1 involves analysing the current tacit knowledge acquisition (TKA) and identifying the appropriate parameters to build the intended framework. Phase 2 involves using the results of Phase 1 to create a new framework of tacit knowledge acquisition (TKAF) that is appropriate for the HEI environment. Phase 3's final objective is to assess the viability of the Talent Development Intervention Program's (TDIP) Tacit Knowledge Acquisition Framework (TKAF) utilizing the Multi-Criteria Decision Making (MCDM) approach. This paper's goal is to offer the hybrid MCDM approach as a talent performance indicator for the multi-criteria tacit acquisition framework. The final Phase 3 of the study design will essentially be the subject of this paper. The built-in indicators in this document may be utilized as a guide for the HEI sectors to create talent performance metrics that are appropriate for each TKA applied.
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Stelitano, Giovanni, Mario Cocorullo, Matteo Mori, Stefania Villa, Fiorella Meneghetti, and Laurent Roberto Chiarelli. "Iron Acquisition and Metabolism as a Promising Target for Antimicrobials (Bottlenecks and Opportunities): Where Do We Stand?" International Journal of Molecular Sciences 24, no. 7 (2023): 6181. http://dx.doi.org/10.3390/ijms24076181.
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The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) infections is one of the most crucial challenges currently faced by the scientific community. Developments in the fundamental understanding of their underlying mechanisms may open new perspectives in drug discovery. In this review, we conducted a systematic literature search in PubMed, Web of Science, and Scopus, to collect information on innovative strategies to hinder iron acquisition in bacteria. In detail, we discussed the most interesting targets from iron uptake and metabolism pathways, and examined the main chemical entities that exhibit anti-infective activities by interfering with their function. The mechanism of action of each drug candidate was also reviewed, together with its pharmacodynamic, pharmacokinetic, and toxicological properties. The comprehensive knowledge of such an impactful area of research will hopefully reflect in the discovery of newer antibiotics able to effectively tackle the antimicrobial resistance issue.
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Carnevali, L., E. Ippoliti, F. Lanfranchi, S. Menconero, M. Russo, and V. Russo. "CLOSE-RANGE MINI-UAVS PHOTOGRAMMETRY FOR ARCHITECTURE SURVEY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 217–24. http://dx.doi.org/10.5194/isprs-archives-xlii-2-217-2018.
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The survey of historical façades contains several bottlenecks, mainly related to the geometrical structure, the decorative framework, the presence of natural or artificial obstacles, the environment limitations. Urban context presents additional restrictions, binding by ground acquisition activity and leading to building data loss. The integration of TLS and close-range photogrammetry allows to go over such stuff, not overcoming the shadows effect due to the ground point of view. In the last year the massive use of UAVs in survey activity has permitted to enlarge survey capabilities, reaching a deeper knowledge in the architecture analysis. In the meanwhile, several behaviour rules have been introduced in different countries, regulating the UAVs use in different field, strongly restricting their application in urban areas. Recently very small and light platforms have been presented, which can partially overcome these rules restrictions, opening to very interesting future scenarios. This article presents the application of one of these very small RPAS (less than 300 g), equipped with a low-cost camera, in a close range photogrammetric survey of an historical building façade in Bologna (Italy). The suggested analysis tries to point out the system accuracy and details acquisition capacity. The final aim of the paper is to validate the application of this new platform in an architectonic survey pipeline, widening the future application of close-range photogrammetry in the architecture acquisition process.
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Shao, Jianming, Yong Li, Guoping Zhang, Shunkai Sun, Qi Xu, and Yingxiao Chi. "Research on Equipment Management Decision Model based on Semantic Analysis." Highlights in Science, Engineering and Technology 15 (November 26, 2022): 22–27. http://dx.doi.org/10.54097/hset.v15i.2198.
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In recent years, the industrial automation of cigarette enterprises has made great progress, and the underlying data acquisition, centralized control and condition monitoring systems have been established. The management has also established a large number of application systems. There are a large number of equipment data resources in these industrial automation systems and application systems, but incremental data value mining is difficult to really solve the problem. The exploration of big data and machine learning has gradually encountered bottlenecks and ceilings, Lack of integration of domain knowledge, it is difficult to mine high-value application scenarios. From the point of view of "semantic analysis" and "equipment management", it is necessary to establish a unified method of data collection and application, which is the basis of effective data collection and management. From the perspective of "semantic analysis" and "equipment management", it is necessary to establish a unified method of data collection and application.
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Gong, Liheng, Xiao Zhang, and Ling Li. "An Artificial Intelligence Fusion Model for Cardiac Emergency Decision Making: Application and Robustness Analysis." JMIR Medical Informatics 8, no. 7 (2020): e19428. http://dx.doi.org/10.2196/19428.
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Background During cardiac emergency medical treatment, reducing the incidence of avoidable adverse events, ensuring the safety of patients, and generally improving the quality and efficiency of medical treatment have been important research topics in theoretical and practical circles. Objective This paper examines the robustness of the decision-making reasoning process from the overall perspective of the cardiac emergency medical system. Methods The principle of robustness was introduced into our study on the quality and efficiency of cardiac emergency decision making. We propose the concept of robustness for complex medical decision making by targeting the problem of low reasoning efficiency and accuracy in cardiac emergency decision making. The key bottlenecks such as anti-interference capability, fault tolerance, and redundancy were studied. The rules of knowledge acquisition and transfer in the decision-making process were systematically analyzed to reveal the core role of knowledge reasoning. Results The robustness threshold method was adopted to construct the robustness criteria group of the system, and the fusion and coordination mechanism was realized through information entropy, information gain, and mutual information methods. Conclusions A set of fusion models and robust threshold methods such as the R2CMIFS (treatment mode of fibroblastic sarcoma) model and the RTCRF (clinical trial observation mode) model were proposed. Our study enriches the theoretical research on robustness in this field.
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Li, Jing Hua, Ying Hui Wang, Zong You Li, et al. "Exploration of Intelligent Development of Medical Heritage." Advances in Science and Technology 105 (April 2021): 272–81. http://dx.doi.org/10.4028/www.scientific.net/ast.105.272.
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With the rapid development of science and technology, more and more new methods and technologies have been added to the traditional Chinese Medicine Inheritance model, which makes the process of inheritance of famous doctors have more means, and the results of inheritance are more objective, rigorous and intelligent. In the process of inheriting the informationization of famous doctors, there are some bottlenecks, such as data acquisition difficulties, data processing difficulties, algorithm application difficulties, analysis and summary difficulties. Integration of artificial intelligence with big data, deep learning algorithm and knowledge atlas technology has brought technological innovation to the informationization of famous doctors' inheritance. Under this wave, the team of the Intelligent Research and Development Center of Traditional Chinese Medicine, Institute of Traditional Chinese Medicine Information, Chinese Academy of Traditional Chinese Medical Sciences, has developed a series of professional application systems in the field of traditional Chinese medicine around the planning of famous doctors' inheritance and excavation, and has developed ancient Chinese medicine, such as Today's Medical Records Cloud Platform, Medical Records Big Data Analysis Platform, Cloud Medical Records APP, Famous Medical Heritage Workstation. To a certain extent, it can solve the problems of inefficient collection of medical records, lack of objective data support and information barriers in the summary of famous doctors' experience under the limitation of traditional model, so as to promote the inheritance of famous doctors' experience and enhance the teaching ability and efficiency of teachers and apprentices.
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de Almeida, Ulisses Barres, Benno Bodmann, Paolo Giommi, and Carlos H. Brandt. "The Brazilian Science Data Center (BSDC)." International Journal of Modern Physics: Conference Series 45 (January 2017): 1760075. http://dx.doi.org/10.1142/s2010194517600758.
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Astrophysics and Space Science are becoming increasingly characterised by what is now known as “big data”, the bottlenecks for progress partly shifting from data acquisition to “data mining”. Truth is that the amount and rate of data accumulation in many fields already surpasses the local capabilities for its processing and exploitation, and the efficient conversion of scientific data into knowledge is everywhere a challenge. The result is that, to a large extent, isolated data archives risk being progressively likened to “data graveyards”, where the information stored is not reused for scientific work. Responsible and efficient use of these large data-sets means democratising access and extracting the most science possible from it, which in turn signifies improving data accessibility and integration. Improving data processing capabilities is another important issue specific to researchers and computer scientists of each field. The project presented here wishes to exploit the enormous potential opened up by information technology at our age to advance a model for a science data center in astronomy which aims to expand data accessibility and integration to the largest possible extent and with the greatest efficiency for scientific and educational use. Greater access to data means more people producing and benefiting from information, whereas larger integration of related data from different origins means a greater research potential and increased scientific impact. The project of the BSDC is preoccupied, primarily, with providing tools and solutions for the Brazilian astronomical community. It nevertheless capitalizes on extensive international experience, and is developed in full cooperation with the ASI Science Data Center (ASDC), from the Italian Space Agency, granting it an essential ingredient of internationalisation. The BSDC is Virtual Observatory-complient and part of the “Open Universe”, a global initiative built under the auspices of the United Nations.
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"The knowledge acquisition bottleneck: time for reassessment?" Knowledge-Based Systems 2, no. 3 (1989): 198–99. http://dx.doi.org/10.1016/0950-7051(89)90055-5.
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Li, Qian, and Hui Gao. "How External Knowledge Acquisition Contribute to Innovation Performance: A Chain Mediation Model." SAGE Open 13, no. 4 (2023). http://dx.doi.org/10.1177/21582440231219203.
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It has become a common way for enterprises to use external knowledge to reduce the development bottleneck caused by internal resource constraints. How does external knowledge develop inside enterprises and then promote the innovation performance? Drawing upon resource-based view and dynamic capability theory, this study proposes a chain mediation model of how firms’ opportunity identification and R&D, as two distinctive microfoundations of dynamic capabilities, mediate the relationship between external knowledge acquisition and firms’ innovation performance. Through the questionnaire survey of Chinese high-tech enterprises, the results show that both external market knowledge acquisition and external technological knowledge acquisition influence the level of opportunity identification and internal R&D activities, so that the internal knowledge base of the enterprise can be updated or reconstructed, and the innovation performance can be improved. This further confirms that enterprises transform external knowledge with the role of dynamic capabilities, which enriches the research on open innovation from the perspective of dynamic capabilities.
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Schubert, Lenhart. "NLog-like Inference and Commonsense Reasoning." Linguistic Issues in Language Technology 9 (January 1, 2014). http://dx.doi.org/10.33011/lilt.v9i.1329.
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Recent implementations of Natural Logic (NLog) have shown that NLog provides a quite direct means of going from sentences in ordinary language to many of the obvious entailments of those sentences. We show here that Episodic Logic (EL) and its Epilog implementation are well-adapted to capturing NLog-like inferences, but beyond that, also support inferences that require a combination of lexical knowledge and world knowledge. However, broad language understanding and commonsense reasoning are still thwarted by the “knowledge acquisition bottleneck”, and we summarize some of our ongoing and contemplated attacks on that persistent difficulty.
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Wu, Shiyu, Dilin Liu, and Zan Li. "Testing the Bottleneck Hypothesis: Chinese EFL learners’ knowledge of morphology and syntax across proficiency levels." Second Language Research, October 11, 2022, 026765832211285. http://dx.doi.org/10.1177/02676583221128520.
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This study tests the Bottleneck Hypothesis (BH) that functional morphology presents the greatest difficulty in second language acquisition by examining Chinese English as a foreign language (EFL) learners’ knowledge of both functional morphological properties and core syntactic properties across three language proficiency levels. Specifically, this study compares Chinese EFL learners’ grasp of subject–verb agreement (a functional morphological property) vs. their grasp of the syntactic formation rules (properties) of WH-questions including WH-movement, WH- do-insertion (i.e. the insertion of the auxiliary do), and WH- do-inversion (subject–auxiliary inversion): three core syntactic transformation rules. Analyses of the experimental results using generalized mixed-effects models yield complex results that generally support the BH. While subject–verb agreement was found to be persistently more difficult than WH-movement and WH- do-inversion, it was shown to pose essentially the same level of difficulty as WH- do-insertion due to the fact that the latter also involves the application of the functional features of SV-agreement and tense, i.e. the high level of difficulty of WH- do-insertion likely lies in its accompanying application of functional features. Possible explanations for the complex results and their implications are discussed.