Mixed Gibbs Sampling Detector in High Order Modulation Large Scale MIMO Systems

A neighborhood restricted Mixed Gibbs Sampling (MGS) based approach is proposed for low-complexity high-order modulation large-scale Multiple-Scale Multiple-InputMultiple-Output (LS-MIMO) detection . Numerical simulation results considering 64-QAM demonstrated that the proposed detectionmethod can substantially improve the MGS algorithm convergence, whereas noextra computational complexity per iteration is required .…

Verified Double Sided Auctions for Financial Markets

Double sided auctions are widely used in financial markets to match demand and supply . We extend notions of double sided auctions to incorporate multiple quantity trade requests . We establish new uniqueness theorems that enable automatic detection ofviolations in an exchange program by comparing its output with that of averified program .…

Multi Metric Optimization using Generative Adversarial Networks for Near End Speech Intelligibility Enhancement

The intelligibility of speech severely degrades in the presence of environmental noise and reverberation . We propose a novel deeplearning based system for modifying the speech signal to increase itsintelligibility . We train adeep neural network (DNN) model to simultaneously optimize multiple advanced speech metrics, including both intelligibility- and quality-related ones, which results in notable improvements in performance and robustness .…

Remote smartphone based speech collection acceptance and barriers in individuals with major depressive disorder

The ease of in-the-wild speech recording using smartphones has sparked interest in the combined application of speech, remote measurement technology (RMT) and advanced analytics as a research and healthcare tool . For this to be realised, feasibility must be established not only from ananalytical perspective, but also the acceptability of the approach to the user .…

Labeling Schemes for Deterministic Radio Multi Broadcast

We consider the multi-broadcast problem in arbitrary connected radio networks . Each source node has a distinct piece of information that it wants to share with all nodes in the network . We set out todetermine the shortest possible labels so that the problem can be solveddeterministically in the labeled radio network by some universal deterministic algorithm .…

Learning to Boost the Efficiency of Modern Code Review

Modern Code Review (MCR) is a standard in all kinds of organizations thatdevelop software . The goal of this thesis is to boost the efficiency of MCR by developing AI techniques that can partially replace or assist human reviewers . The envisioned techniques distinguish from existing MCR-related AImodels in that we interpret these challenges as graph-learning problems .…

IITP in COLIEE ICAIL 2019 Legal Information Retrieval usingBM25 and BERT

The Competition on Legal Information Extraction/Entailment (COLIEE-2019) run in association with the International Conference on Artificial Intelligence andLaw (ICAIL)-2019 has come up with few challenging tasks . We obtain encouraging results in all these four sub-tasks (i.e. Task1, Task2, Task3 and Task4), which will be able toprovide few automated systems to the judicial system .…

A Gradual Type System for Elixir

Elixir is a functional programming language with dynamic typing . We propose agradual type system that makes it possible to perform type-checking on asignificant fragments of the language . An important feature of the type system is that it does not require any syntactic change to Elixir .…

Dual Metric Learning for Effective and Efficient Cross Domain Recommendations

Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications . Existing cross-domain models typically require large number of overlap users, which can be difficult to obtain in some applications . We develop a novel latentorthogonal mapping to extract user preferences over multiple domains while preserving relations between users across different latent spaces .…

A Graph guided Multi round Retrieval Method for Conversational Open domain Question Answering

In recent years, conversational agents have provided a natural and convenient access to useful information in people’s daily life . The main challenge is how to well capture and fullyexplore the historical context in conversation to facilitate effectivelarge-scale retrieval . We propose a novel graph-guided retrieval method to model the relations amonganswers across conversation turns .…

SoK Design Tools for Side Channel Aware Implementions

Traditional approaches for leakage detection measure the physical properties of the device and fail to provide root cause analysis . An alternative approach that is gaining traction is to automate leakage detection by modeling the device . We survey the proposed tools to determine the current knowledge levelacross the domain and identify open problems .…

A Surface Geometry Model for LiDAR Depth Completion

LiDAR depth completion is a task that predicts depth values for every pixel on the corresponding camera frame . Most of the existing state-of-the-art solutions are based on deepneural networks . In this letter, a novel non-learning depth completionmethod is proposed by exploiting the local surface geometry that is enhanced by an outlier removal algorithm .…

H 2 Korn s Inequality and the Nonconforming Elements for The Strain Gradient Elastic Model

Specht triangle and NZT tetrahedron analyzed as two typical representatives for robust nonconforming elements . We establish a new H2 Korn’s inequality and its discrete analog, which simplify construction of nonconformable elements for a linear straingradient elastic model . We construct new regularized interpolation estimate and theenriching operator for both elements, and prove the error estimates undermineimal smoothness assumption on the solution .…

Towards Fortifying the Multi Factor Based Online Account Ecosystem

Chain Reaction Attack exploits weakest point in Online Account Ecosystem . Vulnerability stems from defective multi-factor authentication (MFA), specifically the ones with SMS-based verification, and dependencies among accounts on different platforms . We propose countermeasures including the online account exposedinformation protection mechanism and the built-in authentication to fortify thesecurity of Online Account ecurity of Online Accounts and proposes feasible countermeasures .…

KazakhTTS An Open Source Kazakh Text to Speech Synthesis Dataset

This paper introduces a high-quality open-source speech synthesis dataset for Kazakh . The dataset consists of about 91 hours of transcribed audio recordings spoken by two professional speakers . It is the first publicly available large-scale dataset developed to promote Kazakh text-to-speech (TTS) applications in both academia and industry .…

SelectVisAR Selective Visualisation of Virtual Environments in Augmented Reality

SelectVisAR is a selective visualisation system of virtual environments in augmented reality . The design uses a human-centric approach to informationfiltering, selectively visualising only parts of the virtual environment . The system enables an augmented reality spectator to perceive a co-present virtual reality user in the context of four distinct visualisation conditions: Interactive, Proximity, Everything,and Dollhouse .…

A Decentralized Shared CAV System Design and Application

In this study, we propose a novel heuristic two-step algorithm for sharedridehailing in which users can share their rides with only one more user . Thealgorithm, which is centrally formulated, starts with matching users and creating a set of passenger pairs in step 1 and is followed by solving an assignment problem to assign passenger pairs to the vehicles .…

CTU Depth Decision Algorithms for HEVC A Survey

High-Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encodingtime-complexity . The Coding Tree Unit (CTU) is the main building block used in HEVC . In the HEVC standard, frames are divided into CTUs with the predeterminedsize of up to 64×64 pixels .…

Exact imposition of boundary conditions with distance functions in physics informed deep neural networks

The challenges in satisfying Dirichlet boundary conditions in meshfree and particle methods are well-known . This issue is also pertinent in the development of physics informed neural networks (PINN) for the solution of partial differential equations . We introduce geometry-aware trial functions inartifical neural networks to improve the training in deep learning for partialdifferential equations.…

Uncovering audio patterns in music with Nonnegative Tucker Decomposition for structural segmentation

Recent work has proposed the use of tensor decomposition to model repetitions and to separate tracks in loop-based electronic music . Nonnegative Tucker Decompositon (NTD) touncover musical patterns and structure in pop songs in their audio form . The resultingfeatures also turn out to be efficient for structural segmentation, leading to experimental results on the RWC Pop data set which are potentially challenging state-of-the-art approaches that rely on extensive example-based learningschemes .…

Comparison of remote experiments using crowdsourcing and laboratory experiments on speech intelligibility

Many subjective experiments have been performed to develop objective speechintelligibility measures . The novel coronavirus outbreak has made it verydifficult to conduct experiments in a laboratory . One solution is to performremote testing using crowdsourcing . Because we cannot control the listening conditions, it is unclear whether the results are entirely reliable .…

Ripple Simplified Large Scale Computation on Heterogeneous Architectures with Polymorphic Data Layout

Ripple library provides a unified view of the computational space across multiple dimensions and multiple GPUs . It allows polymorphic datalayout, and provides a simple graph interface to describe an algorithm fromwhich inter-GPU data transfers can be optimally scheduled . We describe the abstractions provided by Ripple to allow complex computations to be describedsimply, and to execute efficiently across many GPUs with minimal overhead .…

H_ infty Almost Output and Regulated Output Synchronization of Heterogeneous Multi agent Systems A Scale free Protocol Design

This paper studies scale-free protocol design for H_\infty almost output and regulated output synchronization of heterogeneous multi-agent systems . The collaborative linear protocol designs are based on localizedinformation exchange over the same communication network, which do not require knowledge of the directed network topology and spectrum of the associatedLaplacian matrix .…

Quantifying the Need for Attorney Pro Bono Services in Connection with the Social Determinants of Health

The paper estimates the need for additional attorney hours annually to address the legal needs of indigent clients throughout the United States inmatters that comprise the so-called social determinants of health (SDoH) The result will inform stakeholders such as policy makers and private donors sothey can allocate resources appropriately and design programs to close thedo-called justice gap .…

Ponzi Scheme Detection in EthereumTransaction Network

Paper mainly focuses on the Ponzi scheme, atypical fraud, which has caused large property damage to the users in Ethereum . It proposes a detecting model based on graph convolutionalnetwork (GCN) to precisely distinguishPonzi contracts . Experiments on differentreal-world . datasets demonstrate that our proposed model has promising results compared with .…

GzScenic Automatic Scene Generation for Gazebo Simulator

Scenic is mainly designed for autonomous vehicle simulation and doesnot support the most popular general-purpose simulator: Gazebo . GzScenic automatically generates both the models required forrunning Scenic on the scenarios, and the models that are required for running the simulation. GzSCenic generates concrete scenes that can be rendered by simulators.…

On the Effectiveness of Various Machine Learning Algorithms for THz Channel Estimation

Terahertz communication is one of the most promising wireless communicationtechnologies . THz frequenciessuffer however from high signal attenuation and signal degradation, which makesthe THz channel modeling and estimation fundamentally hard . We apply different machinelearning algorithms for channel estimation, including neural networks (NN),logistic regression (LR), and projected gradient ascent (PGA) Numerical results show that PGA algorithm yields most promising performance at SNR=0dB with NMSE of -12.8 dB .…