NIR Calibration-Model Services Spectroscopy and Chemometrics News Weekly 46, 2020 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry foodindustry Analysis Lab Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "Near infrared absorption spectroscopy for the quantification of unsulfated alcohol in sodium lauryl ether sulfate" LINK "Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection" LINK "Near infrared spectroscopy (NIRS) based high-throughput online assay for key cell wall features that determine sugarcane bagasse digestibility") LINK "Authentication of barley-finished beef using visible and near infrared spectroscopy (Vis-NIRS) and different discrimination approaches" LINK "Energetic Distribution of States in Irradiated Low-Density ...

Abstract--Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial systems, industrial applications, and the demand for privacy protection. This paper reviews recent advances on binary neural network (BNN) and 1-bit CNN technologies that are well suitable for front-end, edge-based computing. We introduce and summarize existing work and classify them based on gradient approximation, quantization, architecture, loss functions, optimization method, and binary neural architecture search. We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing. ITH the rapid development of information technology, cloud computing with centralized data processing cannot the performance of binary neural networks. To better review meet the needs of applications that require the processing these methods, we six aspects including gradient approximation, of massive amounts of data, nor can they be effectively used quantization, structural design, loss design, optimization, when privacy requires the data to remain at the source. Finally, we will also edge computing has become an alternative to handle the data review object detection, object tracking, and audio analysis from front-end or embedded devices.

The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other. Traditionally, a deterministic coach agent will choose the most suitable strategy and formation for each adversary's strategy. Therefore, the role of a coach is of great importance to the game. In this sense, this paper proposes an end-to-end approach for the coaching task based on Reinforcement Learning (RL). The proposed system processes the information during the simulated matches to learn an optimal policy that chooses the current formation, depending on the opponent and game conditions. We trained two RL policies against three different teams (balanced, offensive, and heavily offensive) in a simulated environment. Our results were assessed against one of the top teams of the VSSS league, showing promising results after achieving a win/loss ratio of approximately 2.0.

Just as businesses tap the value of machine learning, so too can charitable and non-profit organizations. There are a wide variety of ways people are applying machine learning for social good. Predict Align Prevent applies machine learning to identify children at risk for maltreatment. In the U.S., between 1,500 and 3,000 infants and children die due to abuse and neglect each year. Children aged 0-3 years are at the greatest risk. Those most vulnerable are commonly not visible to the professionals.

Processing sequential multi-sensor data becomes important in many tasks due to the dramatic increase in the availability of sensors that can acquire sequential data over time. Human Activity Recognition (HAR) is one of the fields which are actively benefiting from this availability. Unlike most of the approaches addressing HAR by considering predefined activity classes, this paper proposes a novel approach to discover the latent HAR patterns in sequential data. To this end, we employed Latent Dirichlet Allocation (LDA), which is initially a topic modelling approach used in text analysis. To make the data suitable for LDA, we extract the so-called "sensory words" from the sequential data. We carried out experiments on a challenging HAR dataset, demonstrating that LDA is capable of uncovering underlying structures in sequential data, which provide a human-understandable representation of the data. The extrinsic evaluations reveal that LDA is capable of accurately clustering HAR data sequences compared to the labelled activities.

Several deep learning architectures have been proposed over the last years to deal with the problem of generating a written report given an imaging exam as input. Most works evaluate the generated reports using standard Natural Language Processing (NLP) metrics (e.g. BLEU, ROUGE), reporting significant progress. In this article, we contrast this progress by comparing state of the art (SOTA) models against weak baselines. We show that simple and even naive approaches yield near SOTA performance on most traditional NLP metrics. We conclude that evaluation methods in this task should be further studied towards correctly measuring clinical accuracy, ideally involving physicians to contribute to this end.

MarketsandResearch.biz has added an exhaustive research study of Global Machine Learning as a Service (MLaaS) Market 2020 by Company, Regions, Type and Application, Forecast to 2025 that represents a basic overview of the market in which historical information related to the market such as market size, status, competitor segment, key vendors, top regions, product types, and end industries has been provided. The report highlights new business opportunities and existing marketing strategies through insights regarding SWOT analysis, market valuation, competitive spectrum, regional share, and revenue predictions. The market is suitably segmented and sub-segmented so that it can shed light on every aspect of the global Machine Learning as a Service (MLaaS) market such as the type of product, application, and region. It also reveals the competition landscape of the companies and the flow of the global supply and consumption. The report offers a granular analysis of insights into various developments, historical data, current scenario, and future predictions.

See list and speakers following the plenary agenda, below. The Athens Roundtable is committed to advancing legal stakeholder education in AI and the law. The Roundtable is being held with the intention that attendees qualify for continuing legal education in their areas of professional practice. Attendance is upon invitation only. If you wish to attend, please request an invitation at aiathens@thefuturesociety.org.

Latest added Artificial Intelligence in Healthcare Market research study by MarketDigits offers detailed product outlook and elaborates market review till 2026. The market Study is segmented by key regions that is accelerating the marketization. At present, the market is sharping its presence and some of the key players in the study are Intel Corporation, IBM, Google, Microsoft, General Vision, GENERAL ELECTRIC, Siemens Healthcare. The study is a perfect mix of qualitative and quantitative Market data collected and validated majorly through primary data and secondary sources. This report studies the Artificial Intelligence in Healthcare Market size, industry status and forecast, competition landscape and growth opportunity.

Posted by Jason Mayes, Developer Relations Engineer for TensorFlow.js The TensorFlow YouTube channel has a new show called "TensorFlow.js In this program, we highlight amazing tech demos from the TensorFlow.js Our next show will be on 11th December 9AM PT over on the TensorFlow YouTube channel, but if you missed the previous ones, you can find past episodes on this playlist. Do you love great tech demos that push the boundaries of what is possible for a given industry?