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Hodgson, Jonathan
St. Joseph's University
Philadelphia, Pennsylvania
 
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Dr. Jonathan Hodgson is a Professor of Mathematics and Computer Science at Saint Joseph's University in Philadelphia, PA, where he teaches a wide variety of courses at both the undergraduate and master's level. Previously, he was on the faculty at Adelphi University and, prior to that, at the University of Pennsylvania. Dr. Hodgson started his career as a mathematician working in the field of topology. He holds a Ph.D. in Mathematics from the University of Cambridge. Dr. Hodgson became involved with computers, originally with the idea of drawing pictures of knots on a Tektronix machine using Plot-90. In the 1980s, he developed an interest in artificial intelligence, particularly problem solving and logic programming, both of which are his areas of major research interest.

Dr. Hodgson has published papers on differential topology, problem solving, and the use of Hypertext Markup Language (HTML) tags for flagging semantic content in Web pages. He is currently the convenor of WG17, the ISO/IEC JTC1 working group on Prolog standardization.

 
 
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  An innovative approach for ad hoc network establishment in disaster environments by the deployment of wireless mobile agents
Su X., Zhang M., Bai Q.  ACM Transactions on Autonomous and Adaptive Systems 13(4): 1-22, 2019. Type: Article

This paper considers the challenges to mobile wireless robots (WRs) in disaster environments: the unknown environment; the complexity of the environment, namely obstacles in the environment; and the limited capability and number of robots. The goa...

Jul 6 2021  
  Evaluating the effects of missing values and mixed data types on social sequence clustering using t-SNE visualization
Lazar A., Jin L., Spurlock C., Wu K., Sim A., Todd A.  Journal of Data and Information Quality 11(2): 1-22, 2019. Type: Article

The authors discuss how one can compensate for missing values when clustering joint categorical sequences. For example, one might have a set of sequences with both nominal values, such as family size, and binary values, for example, marriage statu...

Apr 21 2021  
  Towards the use of machine learning algorithms to enhance the effectiveness of search strings in secondary studies
Cairo L., Monteiro M., de F. Carneiro G., Brito e Abreu F.  SBES 2019 (Proceedings of the XXXIII Brazilian Symposium on Software Engineering, Salvador, Brazil,  Sep 23-27, 2019) 22-26, 2019. Type: Proceedings

The authors propose the use of text mining to enhance the creation of search strings for constructing so-called secondary studies--that is to say, survey articles. The primary measures of success in this endeavor should be recall (that is, re...

Mar 26 2021  
  Machine learning for the developing world
De-Arteaga M., Herlands W., Neill D., Dubrawski A.  ACM Transactions on Management Information Systems 9(2): 1-14, 2018. Type: Article

The authors describe the ways in which machine learning for the developing world (referred to as ML4D) requires a different viewpoint from the kind of machine learning that concerns itself with the developed world. They assert that the aim of ML4D...

May 11 2020  
   Fairness-aware machine learning: practical challenges and lessons learned
Bird S., Hutchinson B., Kenthapadi K., K c man E., Mitchell M.  WWW 2019 (Companion Proceedings of The 2019 World Wide Web Conference, San Francisco, CA,  May 13-17, 2019) 1297-1298, 2019. Type: Proceedings

This is a timely paper in light of recent stories about bias in artificial intelligence (AI) systems, such as the COMPAS system used in Florida to predict recidivism. The tutorial’s aim is to describe what the authors call a “fairness-...

May 7 2020  
   Intelligent environments 2018: workshop proceedings of the 14th International Conference on Intelligent Environments
Amft O., Novais P., Tobe Y., Chatzigiannakis I.,  IOS PRESS, Amsterdam, the Netherlands, 2018. 604 pp. Type: Book (978-1-614998-73-0)

This book presents workshop proceedings from the 14th International Conference on Intelligent Environments, held in Rome in June 2018. It consists of nine tutorials and some 60 papers. This review cannot go into a detailed report of each paper; in...

Apr 21 2020  
  The seven tools of causal inference, with reflections on machine learning
Pearl J.  Communications of the ACM 62(3): 54-60, 2019. Type: Article, Reviews: (3 of 3)

There are three obstacles to meeting the increasing expectations for artificial intelligence (AI), according to this article: the lack of adaptability or robustness; the lack of explainability; and “the lack of understanding of cause-effect ...

Mar 23 2020  
  Automatic language identification in texts: a survey
Jauhiainen T., Lui M., Zampieri M., Baldwin T., Lindén K.  Journal of Artificial Intelligence Research 65(1): 675-782, 2019. Type: Article

One might think that automatic language identification (LI) is straightforward--surely, distinguishing English from Polish is easy. This review shows that the problem is much harder than one might expect. For example, distinguishing Modern St...

Mar 19 2020  
  Speech emotion recognition: two decades in a nutshell, benchmarks, and ongoing trends
Schuller B.  Communications of the ACM 61(5): 90-99, 2018. Type: Article

The two decades referred to in the subtitle essentially span the time since the publication of Picard’s foundational Affective computing [1], which began the study of emotion recognition by computers. This paper can therefore be viewe...

Mar 18 2020  
   Quantitative intertextuality: analyzing the markers of information reuse
Forstall C., Scheirer W.,  Springer Publishing Company, Incorporated, New York, NY, 2019. 189 pp. Type: Book (978-3-030234-13-3)

Intertextuality is a term used to denote the idea that there are relationships between texts. This includes the conscious modeling of one text on another, allusions to phrases from other texts, and even plagiarism. The goal of this book is to intr...

Nov 26 2019  
 
 
 
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