Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Arkansas Gov. Sarah Huckabee Sanders' proposal to restrict the public's access to records about her administration, travel and security stumbled at the outset of a special legislative session that convened Monday, with lawmakers trying to rework the legislation in the face of growing criticism that it erodes the state's open records law. The House and Senate ended the day without any action on the legislation, one of several items Sanders placed on the agenda for the special session she announced Friday. The Senate scuttled plans to hold a committee hearing Monday night on the bill, as lawmakers worked on revising the proposed changes to the state's Freedom of Information Act.

Alexa AI is co-organizing three special sessions -- themed sessions within the main conferences -- at two major 2022 conferences on speech-related technologies, and two of those sessions are currently seeking paper submissions. At the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), in May, Alexa AI is co-organizing a special session on federated learning, a machine learning paradigm in which distributed computers process privacy-protected data locally, but the results of the distributed computations are amalgamated into a single joint model. At Interspeech, in September, Alexa AI is co-organizing two special sessions, and both are still open for submissions. One session is on machine learning and signal processing in the context of multiple networked smart devices. This session will address topics such as synchronization, arbitration (deciding which device should respond to a query), and privacy.

This year the 34th edition of the IEA/AIE (International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems), abbreviated as IEA/AIE 2021, was held in Kula Lumpur, https://ieaaie2021.wordpress.com/ The IEA/AIE conference is a longstanding conference, held every year since 1988, which focuses on artificial intelligence and its applications. Over many years, the IEA/AIE conference has been held worldwide in more than twenty different countries. The IEA/AIE 2021 conference is sponsored by the International Society of Applied Intelligence (ISAI) in cooperation with Springer, University Teknologi Malaysia, the i-SOMET incorporated Association, Association for the Advancement of Artificial Intelligence (AAAI) / Assoc. This year, 145 papers were submitted to the conference.

The paper surveys the topic of tensor decompositions in modern machine learning applications. It focuses on three active research topics of significant relevance for the community. After a brief review of consolidated works on multi-way data analysis, we consider the use of tensor decompositions in compressing the parameter space of deep learning models. Lastly, we discuss how tensor methods can be leveraged to yield richer adaptive representations of complex data, including structured information. The paper concludes with a discussion on interesting open research challenges.

Distributed artificial intelligence solutions on the edge of the network is important research topic within the 6G development. The EdgeAI Special Interest Group, which is coordinated together with the 6G and FCAI Flagships, is delighted to present a special session on AI for 6G within the 6G Summit in Levi, Lapland. This special session highlights the current state of the art of edge-driven artificial intelligence research in Finland with invited research talks. As an international guest assistant professor Aaron Ding from TU Delft, Netherlands, shares his view to building EdgeAI solutions towards 6G era. The session ends with a panel of the speakers.

Understanding the Quality of Experience (QoE) for visual media has been very important for optimal compression and content delivery for diverse video formats, and it has been a hot topic of research for the last decades. Researchers mostly studied this problem from a signal processing perspective using image and video processing tools, while learning-based methods have been increasing momentum recently. The popularisation of deep learning-based methods affected the whole signal and image processing community as a disruptive force, and visual QoE estimation is no different than others. Use of learning-based methods and especially deep learning methods open a new path for understanding the human visual system in the perception process and other QoE parameters. The objectives of this special session are twofold: first, to develop new metrics reaching beyond the performance of the legacy signal processing approaches for visual QoE estimation, and second, to understand the stages of human perception for visual media better utilising the learning-based methods and different analysis methods such as ablation studies.

In management, business, economics, scien ce, engineering, and research domains, L arge Scale Global Optimization (LSGO) plays a predominant and vital role. Though LSGO is applied in many of the application domains, it is a very troublesome and a perverse task . The Congress o n Evolutionary Comp utation (CEC) began a n LSGO competition to come up with algorithms with a bunch of standard benchmark unconstrained LS GO functions . Therefore, in this paper, we propos e a hybrid meta - heuristic algorithm, which combines a n I mproved and M odified Harmony Search (IMHS), along with a M odified Differential Evolution (MDE) with an alternate selection strategy . Harmony Search (HS) does the job of exploration and exploitation, and Differe ntial Evolution does the job of giving a perturbation to the exploration of IMHS, as harmony search suffers from being stuck at the basin of local optimal . To judge the performance of the suggested algorithm, we compare the proposed algorithm with ten excellent met a - heuristic algorithms on fifteen LSGO benchmark functions, which have 1000 continuous decision variables, of the CEC 2013 LSGO special session . The experimental results consistently show that our proposed hybrid meta - heuristic performs statistically on par with some algorithms in a few problems, while it turned out to be the best in a couple of problems.

The talk is Wednesday afternoon, Jan. 30. Here is the paper: Envisioning AI for K-12: What should every child know about AI?

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic.

The Third International Conference on Artificial Intelligence Planning Systems (AIPS-96) was held in Edinburgh, Scotland, from 29 to 31 May 1996. The main gathering of researchers in AI and planning and scheduling, the conference promoted the practical applications of planning technologies. Details of the conference papers and sessions are provided as well as information on the Defense Advanced Research Projects Agency-Rome Laboratory Planning Initiative. Previous conferences were held at the University of Maryland in June 1992 (AIPS-92), organized by Jim Hendler and Drew McDermott, and the University of Chicago in June 1994 (AIPS-94), organized by Kristian Hammond. The generation of plans and related fields, such as scheduling, resource allocation, and reasoning about action, have a long research tradition in AI.