Use of the Internet of Medical Things (IoMT) in hospitals is growing. IP addressable medical technologies help deliver personalized care more quickly, give healthcare professionals access to real-time data to improve diagnosis and treatment plans, and streamline processes to help save hospitals money. But their wider use increases the risk of a breach and the complex environment in which they ...

The second annual workshop on Naval Applications of Machine Learning (NAML) was held February 13-15, 2018, at the Space and Naval Warfare (SPAWAR) Systems Center Pacific (SSC Pacific), a U.S. Navy research laboratory in San Diego, California, USA. The workshop events included invited speakers, demonstrations, discussion sessions, and oral and poster presentations. The workshop cochairs were Josh Harguess and Katie Rainey, both from SSC Pacific. The poster presentations were coordinated by Chris Ward also from SSC Pacific. This article discusses the motivation, goals, and impact of the workshop and highlights some of the topics covered.

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Sixth AAAI Conference on Human Computation and Crowdsourcing was held on the campus of the University of Zurich in Zurich, Switzerland on 5 July 2018. There were three full-day workshops in the program: CrowdBias: Disentangling the Relation between Crowdsourcing and Bias Management; Subjectivity, Ambiguity, and Disagreement in Crowdsourcing; Work in the Age of Intelligent Machines; a three-quarter day workshop, Advancing Human Computation with Complexity Science; and Project Networking; and a quarter day Project Networking workshop. This report contains summaries of three of the events.  

The AAAI-18 workshop program included 15 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 2–7, 2018, at the Hilton New Orleans Riverside in New Orleans, Louisiana, USA. This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the Artificial Intelligence for Cyber Security workshop; the AI for Imperfect-Information Games; the Declarative Learning Based Programming workshop; the Engineering Dependable and Secure Machine Learning Systems workshop; the Health Intelligence workshop; the Knowledge Extraction from Games workshop; the Plan, Activity, and Intent Recognition workshop; the Planning and Inference workshop; the Preference Handling workshop; the Reasoning and Learning for Human-Machine Dialogues workshop; and the the AI Enhanced Internet of Things Data Processing for Intelligent Applications workshop.

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s 12th International Conference on Web and Social Media (AAAI-18) was held at Stanford University, Stanford, California USA, on Monday, June 25, 2018. There were fourteen workshops in the program: Algorithmic Personalization and News: Risks and Opportunities; Beyond Online Data: Tackling Challenging Social Science Questions; Bridging the Gaps: Social Media, Use and Well-Being; Chatbot; Data-Driven Personas and Human-Driven Analytics: Automating Customer Insights in the Era of Social Media;  Designed Data for Bridging the Lab and the Field: Tools, Methods, and Challenges in Social Media Experiments; Emoji Understanding and Applications in Social Media; Event Analytics Using Social Media Data; Exploring Ethical Trade-Offs in Social Media Research; Making Sense of Online Data for Population Research; News and Public Opinion; Social Media and Health: A Focus on Methods for Linking Online and Offline Data; Social Web for Environmental and Ecological Monitoring and The ICWSM Science Slam. Workshops were held on the first day of the conference. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers, and users on topics of current interest. Organizers from nine of the  workshops submitted reports, which are reproduced in this report. Brief summaries of the other five workshops have been reproduced from their website descriptions.

This paper describes the discipline of distance metric learning, a branch of machine learning that aims to learn distances from the data. Distance metric learning can be useful to improve similarity learning algorithms, and also has applications in dimensionality reduction. We describe the distance metric learning problem and analyze its main mathematical foundations. We discuss some of the most popular distance metric learning techniques used in classification, showing their goals and the required information to understand and use them. Furthermore, we present a Python package that collects a set of 17 distance metric learning techniques explained in this paper, with some experiments to evaluate the performance of the different algorithms. Finally, we discuss several possibilities of future work in this topic.

As everywhere else, Artificial Intelligence has the potential to transform HR. We look at key trends for 2019 and assess key impact areas for AI in HR. There's a great deal of talk and hype around Artificial Intelligence (AI) and all it can achieve. From talking cars to machines that can almost read our minds, the possibilities are endless, and the excitement, palpable. Regardless of how soon all of this becomes a reality, the facts seem to point towards a future that's brimming with potential.

YANGON – Myanmar's first-ever training course for Japanese-language teachers is opening as part of Prime Minister Shinzo Abe's plan to invite more Asian youths to work in Japan. The initial phase of the training program starts this month at the Yangon University of Foreign Languages for students majoring in Japanese and for teachers from private Japanese-language schools, the Japan Foundation said. The foundation, a government-backed institution that carries out international cultural exchange programs, picked Myanmar as the third country in which to offer such training courses, after India and Vietnam, following Abe's speech at an international conference in Tokyo in 2017 where he said Japan would choose three locations in Asia to nurture Japanese-language teachers. Noriyuki Matsukawa, executive director of the Japan Foundation Center for Japanese Language Testing, said the yearlong program aims to support Myanmar's human resources through Japanese-language learning, recruit a new kind of teacher and improve current teachers' skills. "Myanmar has high demand for Japanese-language proficiency," he said, adding that the number of people in Myanmar taking the Japanese-Language Proficiency Test nearly tripled from 13,099 in 2016 to 37,786 in 2018.

There are no hard and fast rules for learning such a complex topic. The beauty of online learning is that you get to choose what you lack and what excite you. For this part 1 of the series, I will review the maths and python fundamental courses that I had taken. Please note that these are my personal opinion which might or might not resonate with you. I like to give special mention to Data Science A-Z by Kirill Eremenko and the SuperDataScience Team.

Phylogeny is the field of modelling the temporal discrete dynamics of speciation. Complex models can nowadays be studied using the Approximate Bayesian Computation approach which avoids likelihood calculations. The field's progression is hampered by the lack of robust software to estimate the numerous parameters of the speciation process. In this work we present an R package, pcmabc, based on Approximate Bayesian Computations, that implements three novel phylogenetic algorithms for trait-dependent speciation modelling. Our phylogenetic comparative methodology takes into account both the simulated traits and phylogeny, attempting to estimate the parameters of the processes generating the phenotype and the trait. The user is not restricted to a predefined set of models and can specify a variety of evolutionary and branching models. We illustrate the software with a simulation-reestimation study focused around the branching Ornstein-Uhlenbeck process, where the branching rate depends non-linearly on the value of the driving Ornstein-Uhlenbeck process. Included in this work is a tutorial on how to use the software.