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 .…

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 .…

Boosting Memory Access Locality of the Spectral Element Method with Hilbert Space Filling Curves

We propose an algorithm based on Hilbert space-filling curves to reorder meshelements in memory for use with the Spectral Element Method . We reorder mesh-related data via Hilbertcurves to achieve sizable reductions in execution time under several meshconfigurations in shared-memory systems .…

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 .…

Are Word Embedding Methods Stable and Should We Care About It

WordEmbedding Methods (WEMs) are a class of representation learning methods thatgenerate dense vector representation for each word in the given text data . A representation learning method is considered stable if it consistentlygenerates similar representation of the given data across multiple runs .…

Emergence of Lie symmetries in functional architectures learned by CNNs

In this paper we study the spontaneous development of symmetries in the early layers of a Convolutional Neural Network (CNN) during learning on natural images . Our architecture is built in such a way to mimic the early stages of biological visual systems .…

Risk score learning for COVID 19 contact tracing apps

Digital contact tracing apps for COVID-19 need to estimate the risk that a user was infected during aparticular exposure . Machine learning methods can be used to optimize the parameters of the risk score model . This can beparticularly useful when the risk factors of the disease change, e.g.,…

Cetacean Translation Initiative a roadmap to deciphering the communication of sperm whales

Machine learning will be thecornerstone of future collection, processing, and analysis of multimodalstreams of data in animal communication studies . Cetaceans are unique non-humanmodel species as they possess sophisticated acoustic communications, bututilize a very different encoding system that evolved in an aquatic rather thanterrestrial medium .…

Spherical Multi Modal Place Recognition for Heterogeneous Sensor Systems

In this paper, we propose a robust end-to-end multi-modal pipeline for placerecognition where the sensor systems can differ from the map building to the query . Our approach operates directly on images and LiDAR scans without requiring any local feature extraction modules .…

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 .…

Welfare Measure for Resource Allocation with Algorithmic Implementation Beyond Average and Max Min

In this work, we propose an axiomatic approach for measuring the performance/welfare of a system consisting of concurrent agents in aresource-driven system . Our approach provides a unifying view on popular systemoptimality principles, such as the maximal average/total utilities and themax-min fairness .…

Optimal Pose and Shape Estimation for Category level 3D Object Perception

We consider a category-level perception problem, where one is given 3D sensordata picturing an object of a given category . We show thatrotation estimation can be decoupled from the estimation of the objecttranslation and shape . We wrap our optimal solver in a robust estimation scheme based ongraduated non-convexity .…

Hercule Representing and Reasoning about Norms as a Foundation for Declarative Contracts over Blockchain

Hercule represents acontract via regulatory norms that capture the involved parties’ expectation . It computes the states of norms (hence, of contracts) from events in the blockchain . Hercule’s novelty and significance lie in that itoperationalizes declarative contracts over semistructured databases .…

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 .…

EXTRACTOR Extracting Attack Behavior from Threat Reports

The knowledge on attacks contained in Cyber Threat Intelligence (CTI) reports is very important to effectively identify and quickly respond to cyber threats . However, this knowledge is often embedded in large amounts of text, and therefore difficult to use effectively .…

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 .…

Exploring Deep Learning for Joint Audio Visual Lip Biometrics

Audio-visual (AV) lip biometrics is a promising authentication technique that combines the benefits of both the audio and visual modalities in speechcommunication . DeepLip outperforms traditional speaker recognition models in context modeling, with an equal error rate of 0.75% and 1.11% on the testdatasets, respectively.…

Jump on the Bandwagon Characterizing Bandwagon Phenomenon in Online NBA Fan Communities

Understanding user dynamics in online communities can provide valuable insights for human behavior analysis . We observe that better teams attract more bandwagon fans, but they do not necessarily come fromweak teams . We find that bandwagon users write shorter comments but receivebetter feedback, and use words that show less attachment to their affiliatedteams .…

Characterization of the Firm Firm Public Procurement Co Bidding Network from the State of Ceará Brazil Municipalities

Fraudulent activity associated with public procurement contracts accounts for losses of billions of eurosevery year . Analysts use network science methods to study the co-biding relationships between firms that participate in publictenders issued by the $184$ municipalities of the State of Cear\’a (Brazil) between 2015 and 2019 .…

GupShup An Annotated Corpus for Abstractive Summarization of Open Domain Code Switched Conversations

Code-switching is the communication phenomenon where speakers switch betweendifferent languages during a conversation . We develop the first code-switched conversation summarization dataset – GupShup, which contains over 6,831 conversations in Hindi-English . We present a detailed account of the data collection and annotation processes .…

SAILFISH Vetting Smart Contract State Inconsistency Bugs in Seconds

SSAILFISH outperforms five state-of-the-art smart contract analyzers (SECURITY,MYTHRIL, OYENTE, SEREUM and VANDAL ) in terms of performance, and precision . It discovered 47 previously unknown vulnerable smart contracts outof 89,853 smart contracts from ETHERSCAN . Intotal, SSAFISH discovered 47 unknown .…

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 .…

Budgeted Influence and Earned Benefit Maximization with Tags in Social Networks

Given a social network, where each user is associated with a selection cost, the problem of Budgeted Influence Maximization asks to choose a subset of them (known as seed users) within an allocated budget whose initial activation leads to the maximum number ofinfluenced nodes .…

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.…

ALGAMES A Fast Augmented Lagrangian Solver for Constrained Dynamic Games

ALGAMES (AugmentedLagrangian GAME-theoretic Solver) is a solver that handlestrajectory-optimization problems with multiple actors and general nonlinear state and input constraints . It is able to reliably solve complex problems likeramp merging with three vehicles three times faster than a state-of-the-art DDP-based approach .…

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 .…

Modeling and control of decision making of miners in blockchain

To maintain blockchain-based services with ensuring its security, it is animportant issue how to decide a mining reward so that the number of minersparticipating in the mining increases . We propose a dynamical model ofdecision-making for miners using an evolutionary game approach .…

Co BERT A Context Aware BERT Retrieval Model Incorporating Local and Query specific Context

BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval . Co-BERT has been proposed to exploit several BERT architectures to calibrate the query-documentrepresentations using pseudo relevance feedback before modeling the relevance of a group of documents jointly .…

Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning

The surging usage of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS) This paper analyses the impact of False Data Injection (FDI) and Distributed Denial of Services (DDoS) attacks on the operation of EVCS .…

Deep Learning in Beyond 5G Networks with Image based Time Series Representation

Beyond Five-Generation (B5G) networks vision the use of machine learning (ML) methods to predict the networkconditions and performance indicators . In this paper, we propose a new ML approach to accomplishpredictions in B5G networks . We analyze different techniques to transformtime-series of network measures into image representation, e.g.,…

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 .…

SurviveCovid 19 A collaborative healthcare game towards educating people about safety measures and vaccination for Covid 19

SurviveCovid-19++ is a collaborative multiplayer desktop based game . The game essentially revolves around four roles – doctor, sanitation worker, citizen and law enforcer, delivering their duties, following safety measures . We have performed apreliminary evaluation of the game through a qualitative and quantitative usersurvey .…

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 .…

Embodying Pre Trained Word Embeddings Through Robot Actions

Previous studies have shown that robots can use words that are not included in the action-description paired datasets by using pre-trained word embeddings . We propose a promising neural network model with which to acquire a groundedrepresentation of robot actions and linguistic descriptions thereof .…

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 .…