Intelligent Learning Systems
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2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
FUZZ-IEEE 2021 will represent a unique meeting point for scientists and engineers, both from academia and industry, to interact and discuss the latest enhancements and innovations in the field. The topics of the conference will cover all the aspects of theory and applications of fuzzy sets, fuzzy logic and associated approaches (e.g. aggregation operators such as the Fuzzy Integral), as well as their hybridizations with other artificial and computational intelligence techniques.
The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.
The IEEE Global Engineering Education Conference (EDUCON) 2020 is the eleventh in a series of conferences that rotate among central locations in IEEE Region 8 (Europe, Middle East and North Africa). EDUCON is one of the flagship conferences of the IEEE Education Society. It seeks to foster the area of Engineering Education under the leadership of the IEEE Education Society.
The PCIC provides an international forum for the exchange of electrical applications technology related to the petroleum and chemical industry. The PCIC annual conference is rotated across North American locations of industry strength to attract national and international participation. User, manufacturer, consultant, and contractor participation is encouraged to strengthen the conference technical base. Success of the PCIC is built upon high quality papers, individual recognition, valued standards activities, mentoring, tutorials, networking and conference sites that appeal to all.
The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06), 2006
Traditional hypermedia techniques add a new dimension to reading, but do not enable precise pedagogical strategies to be followed during the composition of an adaptive course. This paper investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment. The predetermination of a set of selection rules based on the assessment results of the learner can ...
Proceedings of the 2012 IEEE Global Engineering Education Conference (EDUCON), 2012
Over the last decade, two new facets of the Web emerged. On one hand, the Semantic Web provides data structures in order to break meanings barriers between distributed and heterogeneous data sources presents on the Web. On the other hand, the Social Web helps in clustering Web users into communities, and let them be prosumers of their Web experience. Both ...
2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService), 2017
American college students are increasingly put in situations involving unarmed or armed attacks. Recent incidents demonstrate that college campuses are targets for mass shootings. As such tragic incidents become ever more frequent, it is critical for students to know how to react. However, current self-defense courses in higher education offer experience-based learning without the benefit of much research to measure ...
Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05), 2005
Few intelligent learning systems exist which are dynamic and able to provide personalized learning materials to satisfy individual students' requirements. We have developed an agent-based learning system that incorporates learning objects to facilitate personalization, and is based on a learning style theory as the pedagogic foundation for adaptivity. In this paper, we present our novel approach to the incorporation of ...
2014 IEEE 14th International Conference on Advanced Learning Technologies, 2014
Diagnostic assessment is a vital and effective strategy in any teaching- learning process such that it provides a pre-learning assessment of the learners state of knowing with regard to a given knowledge concept. Current intelligent learning systems still do not integrate effective techniques for evaluating prior knowledge that can be used effectively to diagnose gaps that will inhibit future learning ...
Brain-like Intelligence Inside - Towards Autonomously Interacting Systems
Intelligent Transportation Systems Society: Changing how the world moves
IROS TV 2019-Collaborative Robotics & Intelligent Systems Institute CoRIS at Oregon State University
Adaptive Learning and Optimization for MI: From the Foundations to Complex Systems - Haibo He - WCCI 2016
Intelligent Systems for Deep Space Exploration: Solutions and Challenges - Roberto Furfaro
IROS TV 2019- Shantou University- Institute of Robotics and Intelligent Manufacturing
Robotics History: Narratives and Networks Oral Histories: Ruzena Bajcsy
IROS TV 2019- Rutgers University- Center for Accelerated Real Time Analytics
Continuously Learning Neuromorphic Systems with High Biological Realism: IEEE Rebooting Computing 2017
IROS TV 2019- Macau- Episode 2- Robots Connecting People
Yuan-ting Zhang AMA EMBS Individualized Health
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
History of Robotics and Automation: Ruzena Bajcsy
IROS TV 2019- Welcome to IROS 2019- Opening Ceremony
ICASSP 2011 Trends in Machine Learning for Signal Processing
Learning to Dribble on a Real Robot by Success and Failure
A Conversation About Mind/Brain Research and AI Development: IEEE TechEthics Interview
Mind/Brain Research and AI Development: How Do They Inform Each Other? - IEEE TechEthics Panel
Roberto Saracco: Far Futures Panel - Symbiotic Autonomous Systems - TTM 2018
Traditional hypermedia techniques add a new dimension to reading, but do not enable precise pedagogical strategies to be followed during the composition of an adaptive course. This paper investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment. The predetermination of a set of selection rules based on the assessment results of the learner can increase the system adaptation and affects even more the relevance of the approach. Nevertheless, only the expert tutor is entitled to formulate with certainty a correspondence between learner's profile and the characteristics of learning objects. A methodology is introduced then for producing a decision model that imitated the way decided by the designer. An intelligent mechanism based on a non-symbolic approach system is used for an adaptation of the teaching contents to the learners' performances
Over the last decade, two new facets of the Web emerged. On one hand, the Semantic Web provides data structures in order to break meanings barriers between distributed and heterogeneous data sources presents on the Web. On the other hand, the Social Web helps in clustering Web users into communities, and let them be prosumers of their Web experience. Both visions encompass many online applications, including Education. On the question of what is really the next stage of Web developments for Education, authors usually focus on a single of these two facets of the Web. As few studies have tried to combine them for an educational purpose this paper aims at giving an insight on the pioneers' works and the opportunities raised by mixing the Social and the Semantic Web for education.
American college students are increasingly put in situations involving unarmed or armed attacks. Recent incidents demonstrate that college campuses are targets for mass shootings. As such tragic incidents become ever more frequent, it is critical for students to know how to react. However, current self-defense courses in higher education offer experience-based learning without the benefit of much research to measure how well instructors make corrections that actually increase the students ability to defend and escape attacks. Thus, in this paper, we propose to design a revolutionary self- defense education system that transfers self-defense education from an instructor experience-based model into a science-based model assisted with instructor experience. The system is based on modern technology with a combination of sensors, big data, and mobile applications that will target functions and strictly control all of the learning, lab applications assessment, and real-time feedback.
Few intelligent learning systems exist which are dynamic and able to provide personalized learning materials to satisfy individual students' requirements. We have developed an agent-based learning system that incorporates learning objects to facilitate personalization, and is based on a learning style theory as the pedagogic foundation for adaptivity. In this paper, we present our novel approach to the incorporation of learning style theory and learning objects, and evaluation indicates that the approach is able to provide personalized learning materials and improve the adaptivity in learning systems.
Diagnostic assessment is a vital and effective strategy in any teaching- learning process such that it provides a pre-learning assessment of the learners state of knowing with regard to a given knowledge concept. Current intelligent learning systems still do not integrate effective techniques for evaluating prior knowledge that can be used effectively to diagnose gaps that will inhibit future learning and for making recommendations for learning and tutoring to fill them. In this paper, we present a mechanism for pre- assessment of previous learning upon which the recommendation for a new or appropriate learning level is based. Our approach is based on message passing procedure between agents in a multi-agent system. We have tested the pre- assessment technique with a prototype based on the Jason Agent Speak language, and using learning materials from a structured query language (SQL) revision module.
Technological progress in recent years has allowed the design of new intelligent learning systems in smart environments aiming to facilitate users' lives. As a consequence, besides making use of traditional sensors for monitoring the quantities of interest, such systems can also benefit from information obtained from the users' smart devices, which can now be considered as additional sensing tools. In this article, we present the design of a novel system based on the fog computing paradigm that can improve the services offered to users on a smart campus by using different smart devices, i.e., smartphones, smartwatches, tablets, smartcameras and so on. In particular, we will describe a system in which several smart devices will collect sensory and context information, whilst the cloud will aggregate and analyze this data to extract information of particular interest. The main challenge of this project is to create an intelligent platform that allows new software modules to be added without having to re-design the entire architecture, and that can provide new services to campus users or improve existing ones.
While much research has been devoted to learning and machine intelligence, the field is still in its infancy. In particular, a technology that will allow for heuristic exploitation of information domain regularities to reduce the time required for knowledge acquisition while concomitantly resulting in an increase in the reliability of the acquired knowledge is still lacking. Unfortunately, contemporary learning mechanisms such as neural network architectures are inherently incapable of such performance. The objective of this paper is to present a new way of looking at learning and machine intelligence which has applicability in many fields such as in robotics, intelligent agents, data fusion, and cooperative sensing. In particular, we propose to construct a new architecture, that is, a transformational architecture for learning, intelligent fusion and transference of knowledge. A System of Systems (SoS) approach is used to realize machine intelligence. Random differences are learned by the system, generalized, and made available for subsequent replay in design transformations. Cross-domain symmetries can play a major role in design generation in particular and in the design of SoSs in general. The fundamental theory of randomization is the science, which underpins the practice. This strategy is employed in the design of the Knowledge Amplification by Structural Expert Randomization or KASER system.
A soft expert system is one that is qualitatively fuzzy. In this paper, we present such a system known as the ldquoknowledge amplification by structural expert randomizationrdquo system or KASER. This system facilitates reasoning using a domain specific expert and commonsense knowledge. It accomplishes this through object-classed predicates and an associated inference engine. The KASER addresses the high cost associated with the bottleneck of knowledge acquisition. Further, it also enables the entry of a basis of rules and provides for the automatic extension of that basis through domain symmetries. We will demonstrate the learning features of the KASER by comparing its capabilities with an evolutionary programming system that tries to learn the game of chess. In this paper, we concentrate on the evolutionary chess player and also describe the learning capabilities of the KASER, found through other tests. While this EP system may be able to play chess, the KASER provides knowledge as to why certain moves are employed as it learns the game. This powerful characteristic allows the KASER to learn supra-linearly, rather than through exhaustive searches. Thus, the KASER can be applied for many other scenarios in which learning through knowledge acquisition is employed.
This paper discusses the limitations of PBL environments and introduces the student adaptivity technology into PBL environments to improve the effectiveness and efficiency of the learning process. A web-based prototype is implemented by using PHP, MySQL and Apache, and uses the accounting as subject domain. With the system, students work on the real world costing calculation problems, and the system evaluates students' performance results on the problems to provide adaptation to the students.
This work aims to purpose the creation of a model to develop intelligent learning systems with self-regulated contents. Its main goal is to support self-oriented learning in workplace, to improve performance levels and skills. Our target is mainly software industries, where the novelty of knowledge and learning needs are critical success factors for the companies' competitiveness, and the learning cycles are being shortened. Learning in workplace environment is a recent research phenomenon, which has been taking place along with the evolutional trend of e-Learning. The innovative component is based on technological and behavioral standards arising mainly from the Social Semantic Web. To approach these challenges one main issue is to find out if the organization's intrinsic knowledge is recognized and organized. Besides, organizations also need to recognize the practical use of this kind of systems. Both issues have been surveyed in a case study.
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