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Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things

InfoMat 2020年第6期2卷 1131-1162页

作者： Qiongfeng Shi Bowei Dong Tianyiyi He Zhongda Sun Jianxiong Zhu Zixuan Zhang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore117576Singapore Center for Intelligent Sensors and MEMS National University of SingaporeSingapore117608Singapore Hybrid-Integrated Flexible(Stretchable)Electronic Systems Program National University of SingaporeSingapore117608Singapore NUS Suzhou Research Institute(NUSRI) Suzhou215123China NUS Graduate School for Integrative Science and Engineering National University of SingaporeSingapore117456Singapore

The past few years have witnessed the significant impacts of wearable electronics/photonics on various aspects of our daily life,for example,healthcare monitoring and treatment,ambient monitoring,soft robotics,prosthetics,flexible display,communication,human-machine interactions,and so *** to the development in recent years,the next-generation wearable electronics and photonics are advancing rapidly toward the era of artificial intelligence(AI)and internet of things(IoT),to achieve a higher level of comfort,convenience,connection,and ***,this review provides an opportune overview of the recent progress in wearable electronics,photonics,and systems,in terms of emerging materials,transducing mechanisms,structural configurations,applications,and their further integration with other ***,development of general wearable electronics and photonics is summarized for the applications of physical sensing,chemical sensing,humanmachine interaction,display,communication,and so *** self-sustainable wearable electronics/photonics and systems are discussed based on system integration with energy harvesting and storage ***,technology fusion of wearable systems and AI is reviewed,showing the emergence and rapid development of intelligent/smart *** the last section of this review,perspectives about the future development trends of the next-generation wearable electronics/photonics are provided,that is,toward multifunctional,self-sustainable,and intelligent wearable systems in the AI/IoT era.

关键词： artificial intelligence energy harvesting human-machine interface internet of things wearable electronics wearable photonics

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Comparing spoken dialog corpora collected with recruited subjects versus real users

Comparing spoken dialog corpora collected with recruited sub...

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8th SIGdial Workshop on Discourseand Dialogue

作者： Ai, Hua Raux, Antoine Bohus, Dan Eskenazi, Maxine Litman, Diane Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Language Technologies Institute Carnegie Mellon University Pittsburgh PA 15213 United States Computer Science Department Carnegie Mellon University Pittsburgh PA 15213 United States Dept. of Computer Science LRDC University of Pittsburgh Pittsburgh PA 15260 United States Microsoft Research Redmond WA United States

ISBN: (纸本)9789074029322

Empirical spoken dialog research often involves the collection and analysis of a dialog corpus. However, it is not well understood whether and how a corpus of dialogs collected using recruited subjects differs from a corpus of dialogs obtained from real users. In this paper we use Let's Go Lab, a platform for experimenting with a deployed spoken dialog bus information system, to address this question. Our first corpus is collected by recruiting subjects to call Let's Go in a standard laboratory setting, while our second corpus consists of calls from real users calling Let's Go during its operating hours. We quantitatively characterize the two collected corpora using previously proposed measures from the spoken dialog literature, then discuss the statistically significant similarities and differences between the two corpora with respect to these measures. For example, we find that recruited subjects talk more and speak faster, while real users ask for more help and more frequently interrupt the system. In contrast, we find no difference with respect to dialog structure. © 2007 Association for Computational Linguistics.

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A CRCD experience: Integrating machine learning concepts into introductory engineering and science programming courses

A CRCD experience: Integrating machine learning concepts int...

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2003 ASEE Annual Conference and Exposition: Staying in Tune with Engineering Education

作者： Georgiopoulos, Michael Russell, Ingrid Castro, Jose Wu, Annie Kysilka, Marcella Demara, Ronald Gonzalez, Avelino Gelenbe, Erol Mollaghasemi, Mansooreh Sch. of Elec. Eng. and Comp. Science University of Central Florida Department of Computer science University of Hartford Intelligent Systems Focus Group IEEE-CS/ACM Computer Engineering Program University of Central Florid Education Foundations Department University of Central Florida Sch. of Elec. Eng. and Comp. Science Coll. of Eng. and Computer Science University of Central Florida Indust. Eng. and Mgmt. Sci. Dept. University of Central Florida

Machine Learning has traditionally been a topic of research and instruction in computer science and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as electrical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. The project involves the development of a model for the integration of Machine Learning into the undergraduate curriculum of those engineering and science disciplines mentioned above. The goal is increased exposure to Machine Learning technology for a wider range of students in science and engineering than is currently available. Our approach of integrating Machine Learning research into the curriculum involves two components. The first component is the incorporation of Machine Learning modules into the first two years of the curriculum with the goal of sparking student interest in the field. The second is the development of new upper level Machine Learning courses for advanced undergraduate students. The paper will describe the first phase of the project, that of the integration of Machine Learning concepts into introductory engineering and science programming courses through appropriately designed programming projects.

关键词： Engineering education

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The impact of social performance visualization on students

The impact of social performance visualization on students

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12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012

作者： Falakmasir, Mohammad Hassan Hsiao, I.-Han Mazzola, Luca Grant, Nancy Brusilovsky, Peter Intelligent Systems Program University of Pittsburgh Pittsburgh PA United States School of Information Sciences University of Pittsburgh Pittsburgh PA United States Faculty of Communication Sciences University of Lugano Lugano Switzerland Department of Computer Information Science Community College of Allegheny County West Mifflin PA United States

ISBN: (纸本)9780769547022

Over the last 10 years two major research directions explored the benefits of visualizing student learning progress. One stream of research on learning performance visualization attempts to build a visual presentation of students' learning progress, targeting the needs of instructors and academic advisors. The other stream of research on Open Student Modeling (OSM) attempts to visualize the state of individual student's knowledge and present the visualization directly to the student. The results of the studies in that area show that, presenting students with basic representation of their knowledge will result in facilitating their metacognitive activities and promoting self-reflection and awareness. This paper tries to study the impact of a more sophisticated form of performance visualization on students. We believe that our visualization tool can positively influence students by granting them the opportunity to get a view of their performance in the content of the class progress. Moreover, we tried to boost their motivation by building a positive sense of competition using a representation of average class performance. In this paper we present study comparing two groups of students, one using the visualization and another without visualization. The results of the study shows that: 1) the students are likely to use the social visualization tool during the whole semester to monitor their progress in comparison with their peers;2) the visualization tool encourages students to use the learning materials in a more continuous manner during the whole semester and 3) students will achieve a higher success rate in answering self-assessment quizzes. © 2012 IEEE.

关键词： Students

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Causal discovery using a Bayesian local causal discovery algorithm

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Studies in Health Technology and Informatics 2004年第PT 1期107卷 731-735页

作者： Mani, Subramani Cooper, Gregory F. Center for Biomedical Informatics Intelligent Systems Program University of Pittsburgh PA 15213 United States Department of Computer Science University of Wisconsin Milwaukee Milwaukee WI 53201 United States

This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework. BLCD initially uses heuristic greedy search to derive the Markov Blanket (MB) of a node that serves as the 'locality' for the identification of pair-wise causal relationships. BLCD takes as input a dataset and outputs potential causes of the form variable X causally influences variable Y. Identification of the causal factors of diseases and outcomes, can help formulate better management, prevention and control strategies for the improvement of health care. In this study we focused on investigating factors that may contribute causally to infant mortality in the United States. We used the U.S. Linked Birth/Infant Death dataset for 1991 with more than four million records and about 200 variables for each record. Our sample consisted of 41,155 records randomly selected from the whole dataset. Each record had maternal, paternal and child factors and the outcome at the end of the first year-whether the infant survived or not. Using the infant birth and death dataset as input, BLCD output six purported causal relationships. Three out of the six relationships seem plausible. Even though we have not yet discovered a clinically novel causal link, we plan to look for novel causal pathways using the full sample. © 2004 IMIA. All rights reserved.

关键词： Bayesian networks

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Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics

arXiv

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arXiv 2023年

作者： Murali, Nihal Puli, Aahlad Yu, Ke Ranganath, Rajesh Batmanghelich, Kayhan Intelligent Systems Program University of Pittsburgh United States Department of Computer Science New York University United States Department of Electrical and Computer Engineering Boston University United States

Deep Neural Networks (DNNs) are prone to learning spurious features that correlate with the label during training but are irrelevant to the learning problem. This hurts model generalization and poses problems when deploying them in safety-critical applications. This paper aims to better understand the effects of spurious features through the lens of the learning dynamics of the internal neurons during the training process. We make the following observations: (1) While previous works highlight the harmful effects of spurious features on the generalization ability of DNNs, we emphasize that not all spurious features are harmful. Spurious features can be "benign" or "harmful" depending on whether they are "harder" or "easier" to learn than the core features for a given model. This definition is model and dataset dependent. (2) We build upon this premise and use instance difficulty methods (like Prediction Depth (Baldock et al., 2021)) to quantify "easiness" for a given model and to identify this behavior during the training phase. (3) We empirically show that the harmful spurious features can be detected by observing the learning dynamics of the DNN’s early layers. In other words, easy features learned by the initial layers of a DNN early during the training can (potentially) hurt model generalization. We verify our claims on medical and vision datasets, both simulated and real, and justify the empirical success of our hypothesis by showing the theoretical connections between Prediction Depth and information-theoretic concepts like V-usable information (Ethayarajh et al., 2021). Lastly, our experiments show that monitoring only accuracy during training (as is common in machine learning pipelines) is insufficient to detect spurious features. We, therefore, highlight the need for monitoring early training dynamics using suitable instance difficulty metrics. © 2023, CC BY.

关键词： Deep neural networks

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An Approach to Formation Control of UAVs Based on Applying Adapted Kohonen Neural Network

An Approach to Formation Control of UAVs Based on Applying A...

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Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

作者： Mikhail Khachumov Intelligent Control Laboratory Ailamazyan Program Systems Institute of RAS Pereslavl-Zalessky Russia Federal Research Center” Computer Science and Control” of RAS Moscow Russia

The paper gives a statement and considers the solution of an urgent scientific problem of formation control for a group of unmanned aerial vehicles (UAVs) operating in an unstable environment. To construct the reference trajectories and allocate UAVs to positions of a given structure, an original approach is proposed based on the adapted Kohonen neural network with a set of metrics, including Euclidean, Mahalanobis and Euclidean-Mahalanobis distances. To implement the movement of a UAV group in a complex environment, we apply the principles of intelligent-geometric control, which allows to combine flexible intelligent and precise geometric control methods within one concept. When moving along a given route, UAVs use a set of allowable control strategies. The developed modeling scheme is designed to take into account wind loads and possibly dangerous rapprochement of vehicles. To reflect the dynamics of UAVs, a special module is used that contains transfer functions integrated into a single stabilization system with an autopilot. The proposed approach has shown promise when simulating a series of formation control problems.

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Rapid modeling and analysis with QGENIE

Rapid modeling and analysis with QGENIE

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International Multiconference on computer science and Information Technology, IMCSIT

作者： Marek J. Drużdżel Faculty of Computer Science Białystok Technical University (BTU) Bialystok Poland Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA

QGENIE is a specialized interface to GENIE, a decision modeling environment developed by the Decision systems Laboratory, University of Pittsburgh. QGENIE allows for rapid construction of graphical models in which all variables are propositional, almost no numerical probabilities are displayed at the user interface, and degrees of truth of propositions are displayed by means of node colors. All numerical parameters, such as prior probability distributions over variables and strengths of influences between variables, are entered by means of graphical sliders. While the underlying computations are all numerical and based on Bayesian updating, QGENIE makes the impression of a qualitative, ¿order of magnitude¿ type system that aids rapid model building and an approximate analysis of systems.

关键词： Bayesian methods User interfaces Probability distribution Random variables Business Brain modeling computer science Graphical models Predictive models Information technology

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Computational argument as a diagnostic tool: The role of reliability

Computational argument as a diagnostic tool: The role of rel...

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2009 AAAI FAll Symposium

作者： Lynch, Collin Ashley, Kevin D. Pinkwart, Niels Aleven, Vincent Intelligent Systems Program Univ. of Pittsburgh Pittsburgh PA United States ISP and School of Law Univ. of Pittsburgh Pittsburgh PA United States Computer Science Institute Clausthal Univ. of Technology Clausthal Lower Saxony Germany HCII Carnegie Mellon Univ. Pittsburgh PA United States

ISBN: (纸本)9781577354406

Formal and computational models of argument are ideally suited for education in ill-defined domains such as law, public policy, and science. Open-ended arguments play a central role in these areas but students of the domains may not have been taught an explicit model of argument. Computational models of argument may be ideally suited to act as argument tutors guiding students in the formation of arguments and argument analysis according to an explicit model. In order to achieve this it is important to establish that the models can be understood and evaluated reliably, an empirical question. In this paper we report ongoing work on the diagnostic utility of argument diagrams produced in the LARGO tutoring system. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Students

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A multivariate probabilistic method for comparing two clinical datasets 12

A multivariate probabilistic method for comparing two clinic...

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2nd ACM SIGHIT International Health Informatics Symposium, IHI'12

作者： Sverchkov, Yuriy Visweswaran, Shyam Clermont, Gilles Hauskrecht, Milos Cooper, Gregory F. Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Department of Biomedical Informatics University of Pittsburgh Pittsburgh PA 15260 United States Department of Computer Science University of Pittsburgh Pittsburgh PA 15260 United States Department of Critical Care Medicine University of Pittsburgh Pittsburgh PA 15260 United States

ISBN: (纸本)9781450307819

We present a novel method for obtaining a concise and mathematically grounded description of multivariate differences between a pair of clinical datasets. Often data collected under similar circumstances reect fundamentally different patterns. For example, information about patients undergoing similar treatments in different intensive care units (ICUs), or within the same ICU during different periods, may show systematically different outcomes. In such circumstances, the multivariate probability distributions induced by the datasets would differ in selected ways. To capture the probabilistic relationships, we learn a Bayesian network (BN) from the union of the two datasets. We include an indicator variable that represents the dataset from which a given patient record is obtained. We then extract the relevant conditional distributions from the network by finding the conditional probabilities that differ most when condi- tioning on the indicator variable. Our work is a form of explanation that bears some similarity to previous work on BN explanation;however, while previous work has mostly focused on justifying inference, our work is aimed at explaining multivariate differences between distributions. Copyright © 2012 ACM.

关键词： Bayesian networks

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