Digital Transformations of Classrooms in Virtual Reality

immersive VR has the potential to take online and remote learning closer to real-world settings . The effects of such digitaltransformations on learners, particularly for VR, have not been evaluated indepth . This work investigates the interaction-related effects of sittingpositions of learners, visualization styles of peer-learners and teachers, and eye tracking data .…

What If Memory Information is Stored Inside the Neuron Instead of in the Synapse

Memory information in the brain is commonly believed to be stored in the synapse . Recent electrophysiology research has raised the possibility that memory information may actually be stored inside theneuron itself . Drawing on information theory and communications system engineering perspectives, we examine the problem of how memory information can be transmitted reliably between neurons .…

Design and Analysis of Wideband Full Duplex FR2 IAB Networks

This paper develops a 3GPP inspired design for the in-band-full-duplex (IBFD)Integrated Access and Backhaul (IAB) networks in the frequency range 2 (FR2)band . Self-interference (SI) is usually more than 100dB higher than the signal-of-interest . The SI is canceled in three stages, where antenna isolation formsthe first stage and is assumed to be achieved .…

Near Optimal Detection for Both Data and Sneak Path Interference in Resistive Memories with Random Cell Selector Failures

Resistive random-access memory is one of the most promising candidates for the next generation of non-volatile memory technology . However, its crossbarstructure causes severe “sneak-path” interference, which also leads to strong inter-cell correlation . We propose a near-optimal data detectionscheme that can approach the performance bound of the optimal detection scheme .…

Image Compression with Encoder Decoder Matched Semantic Segmentation

The proposed EDMS framework can get up to35.31% BD-rate reduction over the HEVC-based (BPG) codec, 5% bitrate, and 24% encoding time saving compare to the state-of-the-art semantic-based imagecodec . The proposedEDMS framework is based on a special convolution neural network to enhance the inaccurate semantic segment in the decoder without requiring extra bits .…

Exploiting Web Images for Fine Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators . Labels noise and hard examples in web images are two obstacles for training robust fine-grained recognition models . In this paper, we propose a novel approach for removing irrelevantsamples from real-world web images during training, while employing useful hardexamples to update the network .…

A novel reconstruction technique for two dimensional Bragg scatter imaging

We introduce a new reconstruction technique for two-dimensional BraggScattering Tomography . Our method uses a combination of ideas frommultibang control and microlocal analysis to construct an objective function which can regularize the BST artifacts . We then test our algorithm in avariety of Monte Carlo (MC) simulated examples of practical interest in airportbaggage screening and threat detection .…

Optimal renormalization of multi scale systems

Aperturbatively renormalizable model is stable for long times and includes all the complex effects present in the 3D Euler dynamics . We find that, in each application, the renormalization coefficients display algebraic decay within the resolution, and that the parameter which controls the time-decay of the memory is problem-dependent .…

Low rank signal subspace parameterization projection and signal estimation

The paper contains several theoretical results related to the weightednonlinear least-squares problem for low-rank signal estimation . It is shown how the obtained results help to describe thetangent plane, prove optimization problem features and construct stable algorithms . For the latter, astable algorithm for constructing the projection onto a subspace of time seriesthat satisfy a given GLRR is proposed and justified .…

A symmetric fractional order reduction method for direct nonuniform approximations of semilinear diffusion wave equations

We introduce a symmetric fractional-order reduction (SFOR) method to construct numerical algorithms on general nonuniform temporal meshes forsemilinear fractional diffusion-wave equations . By using the novel orderreduction method, the governing problem is transformed to an equivalent coupled system . The linearized L1 scheme and Alikhanov scheme are then proposed on general time meshes .…

Factorization method with one plane wave from model driven and data driven perspectives

The factorization method by Kirsch (1998) provides a necessary and sufficient condition for characterizing the shape and position of an unknown scatterer . It can be regarded as adomain-defined sampling method and does not require forward solvers . Inparticular, the proposed scheme can be interpreted as a model-driven and data-driven method, because it essentially depends on the scattering model and a priori given the data .…

Implementing WordNet Measures of Lexical Semantic Similarity in a Fuzzy Logic Programming System

This paper introduces techniques to integrate WordNet into a Fuzzy LogicProgramming system . Proximity equations are key syntactic structures which, in addition to a weak unificational algorithm, make a flexible query-answering process possible in this kind ofprogramming language . This addition widens the scope of FuzzY Logic Programming, allowing certain forms of lexical reasoning, and reinforcingNatural Language Processing applications .…

Towards efficient models for real time deep noise suppression

State-of-the-art models can achieve outstanding results interms of speech quality and background noise reduction . The main challenge is to obtain compact enough models, which are resource efficient during inferencetime . We show interesting tradeoffs between computational complexity and the achievable speech quality, measured on real recordings using a highly accurate MOS estimator .…

Corrective Information Does Not Necessarily Curb Social Disruption

The authenticity ofinformation on a social networking service (SNS) is unknown, and falseinformation can be easily spread . Many studies have been conducted on methods to control the spread of misinformation on socialnetworking sites . This study models the impact of the reduction of misinformation and the diffusion of corrective information on society .…

Generating a Doppelganger Graph Resembling but Distinct

Deep generative models, since their inception, have become increasingly morecapable of generating novel and perceptually realistic signals . With the emergence of deep models for graph structured data, natural interests seek extensions of these models for graphs . In this work, we propose an approach to generating a doppelganger graph that can hardly beused to reverse engineer the original one, in the sense of a near zero edgeoverlap .…

A Methodology for the Development of RL Based Adaptive Traffic Signal Controllers

This article proposes a methodology for the development of adaptive trafficsignal controllers using reinforcement learning . Our methodology addresses the lack of standardization in the literature that renders the comparison ofapproaches in different works meaningless . The proposedmethodology comprises all the steps necessary to develop, deploy andevaluate an adaptive traffic signal controller — from simulation setup toproblem formulation and experimental design to the design process .…

Communication Efficient Variance Reduced Decentralized Stochastic Optimization over Time Varying Directed Graphs

We consider decentralized optimization over time-varying directed networks . Network nodes can access only their local objectives, and aim to minimize a global function by exchanging messages with theirneighbors . This is the first decentralized optimization framework that achievessuch a convergence rate and applies to settings requiring sparsifiedcommunication.…

How Much Communication Resource is Needed to Run a Wireless Blockchain Network

The consensus mechanisms (CMs) play a pivotal role in blockchain, are communication resource-demanding and largely determines blockchain securitybound and other key performance metrics such as transaction throughput, latency and scalability . Most blockchain systems are designed in a stable wiredcommunication network running in advanced devices under the assumption ofsufficient communication resource provision .…

Are Top School Students More Critical of Their Professors Mining Comments on RateMyProfessor com

Student reviews and comments on RateMyProfessor.com reflect realisticlearning experiences of students . Such information provides a large-scale datasource to examine the teaching quality of the lecturers . We uncover interesting insights about the characteristics of college students and professors . Our study proves that student reviews andcomments contain crucial information and can serve as essential references forenrollment in courses and universities .…

Towards Robust Visual Information Extraction in Real World New Dataset and Novel Solution

Visual information extraction (VIE) has attracted considerable attention owing to its various advanced applications such as documentunderstanding, automatic marking and intelligent education . In this paper, we propose a robust visual information extraction system (VIES) towards real-worldscenarios . We construct a fully-annotated dataset called EPHOIE(https://github.com/HCIILAB/EPHOIE),…

BF a language for general purpose neural program synthesis

Most state of the art decision systems based on Reinforcement Learning (RL)are data-driven black-box neural models . It is often difficult to incorporate expert knowledge into the models or let experts review andvalidatethe learned decision mechanisms . We propose a new programming language, BF++, designed specifically for neural program synthesis .…

Deep Learning for General Game Playing with Ludii and Polygames

Combinations of Monte-Carlo tree search and Deep Neural Networks have produced state-of-the-art results for automated game-playing in many board games . Ludii is a general game system that already contains over 500 different games . Polygames is a frameworkwith training and search algorithms, which has already produced superhuman players for several board games.…

Symbiotic System Design for Safe and Resilient Autonomous Robotics in Offshore Wind Farms

Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience . 80% of the O&M cost relates to deploying personnel, the offshore wind sector looks to robotics and Artificial Intelligence for solutions . We propose a symbiotic system; reflecting the lifecycle learning and co-evolutionwith knowledge sharing for mutual gain of robotic platforms and remote humanoperators .…

Neural Relational Inference with Efficient Message Passing Mechanisms

Many complex processes can be viewed as dynamical systems of interacting agents . In many cases, only the state sequences of individual agents are observed, while the interacting relations and the dynamical rules are unknown . This paper introduces efficient message passing mechanisms to the graph neural networks withstructural prior knowledge to address these problems .…

ARTH Algorithm For Reading Text Handily An AI Aid for People having Word Processing Issues

“ARTH” is the short form of Algorithm for Reading Handily . The objective of this project is to solve one of the major problems faced by the people having word processing issues like trauma, or mild mentaldisability . ARTH is a self-learning set of algorithms that is an intelligent way of fulfilling theneed for “reading and understanding the text effortlessly” which adjusts according to the needs of every user .…

Explainable Artificial Intelligence Approaches A Survey

The lack of explainability of a decision from an Artificial Intelligence (AI)based “black box” system/model is a key stumbling block for adopting AI in many high stakes applications . While many popular ExplainableArtificial Intelligence (XAI) methods or approaches are available to facilitate a human-friendly explanation of the decision .…