## Revisiting the size effect in software fault prediction models

In object oriented (OO) software systems, class size has been acknowledged as having an indirect effect on the relationship between certain characteristics, captured via metrics, and faultproneness . In particular, size appears to have a more significant mediation effect on CBO andFan-out than other metrics, although the evidence is not consistent in allexamined systems .…

## Android OS CASE STUDY

Android is a mobile operating system based on a modified version of the Linux kernel and other open source software . It is an operating system forlow powered devices that run on battery and are full of hardware like GlobalPositioning System (GPS) receivers, cameras, light and orientation sensors, Wi-Fi and LTE (4G telephony) connectivity and a touch screen .…

## Auto Response Generation in Online Medical Chat Services

Telehealth helps to facilitate access to medical professionals by enabling remote medical services for the patients . The benefits of telehealth have been even more apparent sincethe beginning of the COVID-19 crisis, as people have become less inclined tovisit doctors in person during the pandemic .…

## VCGAN Video Colorization with Hybrid Generative Adversarial Network

Hybrid GenerativeAdversarial Network (VCGAN) addresses two prevalent issues in the videocolorization domain: Temporal consistency and unification of colorizationnetwork and refinement network into a single architecture . The hybrid VCGAN strikes a good balance between color vividness and videocontinuity . To improve the consistency of farframes, we propose a dense long-term loss that smooths the temporal disparity of every two remote frames.…

## Represent Items by Items An Enhanced Representation of the Target Item for Recommendation

Item-based collaborative filtering (ICF) has been widely used in industrial applications such as recommender system and online advertising . In this paper, we propose an enhanced representation of the target item which distills relevant information from the co-occurrence items . With the enhanced representation, CER has strongerrepresentation power for the tail items compared to the state-of-the-art ICFmethods.…

## Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging

The consideration of predictive uncertainty in medical imaging with deeplearning is of utmost importance . We apply $\sigma$ scaling with a single scalarvalue; a simple, yet effective calibration method for both types ofuncertainty . The performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-artconvolutional network architectures .…

## DADgraph A Discourse aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension

Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC models as models must handle complex dialogue discourse structure . We present a discourse-aware dialogue graphneural network, DADgraph, which explicitly constructs the dialogue graph using discourse dependency links and discourse relations .…

## Intelligent Internal Temperature Control of Food in Standard Convection Ovens

Typical convection ovens employ an open-loop control system that requires a person to estimate the amount of time needed to cook foods in order to achieve desired internal temperatures . This approach, however, can result in undesired results with the final food temperatures being too high or low .…

## Leaving My Fingerprints Motivations and Challenges of Contributing to OSS for Social Good

Growing interest in open source software has also been attributed to developers deciding to use their technical skills to benefit a commonsocietal good . Researchers conducted 21semi-structured interviews with OSS for Social Good (OSS4SG) contributors . They found that OSS4SG contributors focus less on benefiting themselves by padding their resumewith new technology skills and are more interested in leaving their mark on society at statistically significant levels .…

## Learning from Event Cameras with Sparse Spiking Convolutional Neural Networks

Convolutional neural networks (CNNs) are now the de facto solution for computer vision problems thanks to their impressive results and ease oflearning . But implementation on conventional hardware (CPU/GPU) results in high power consumption, making integration on embedded systems difficult .…

## Android OS CASE STUDY

Android is a mobile operating system based on a modified version of the Linux kernel and other open source software . It is an operating system forlow powered devices that run on battery and are full of hardware like GlobalPositioning System (GPS) receivers, cameras, light and orientation sensors, Wi-Fi and LTE (4G telephony) connectivity and a touch screen .…

## Contextualized Keyword Representations for Multi modal Retinal Image Captioning

Medical image captioning automatically generates a medical description to describe the content of a given medical image . A traditional medical imagecaptioning model creates a . medical description only based on a single medical image input . A newend-to-end deep multi-modal medical image caption .…

## Model Guided Road Intersection Classification

Understanding complex scenarios from in-vehicle cameras is essential for safely operating autonomous driving systems in densely populated areas . Intersection areas are one of the most critical as they concentrate aconsiderable number of traffic accidents and fatalities . Detecting and understanding the scene configuration of these usually crowded areas is then ofextreme importance for both autonomous vehicles and modern ADAS aimed at preventing road crashes and increasing the safety of vulnerable road users .…

## Cloud computing as a platform for monetizing data services A two sided game business model

The role of the cloud should be reshaped from being a passive virtual market to become an active platform for monetizing the big data through Artificial Intelligence (AI) services . The objective is to enable the cloud to help big data service providers reach a wider set of customers and cloud users to be exposed to a larger and richer variety of data to run their data analytic tasks .…

## Deep Learning Empowered Predictive Beamforming for IRS Assisted Multi User Communications

The realization of practical intelligent reflecting surface (IRS) systems critically depends on properbeamforming design exploiting accurate channel state information (CSI) However, channel estimation (CE) in IRS-MUC systems requires a significantlylarge training overhead due to the numerous reflection elements involved inIRS .…

## Efficient Hyperparameter Optimization for Physics based Character Animation

Physics-based character animation has seen significant advances in recent years with the adoption of Deep Reinforcement Learning (DRL) However,DRL-based learning methods are usually computationally expensive . Tuninghyperparameters for these methods often requires repetitive training of controlpolicies . In this work, we propose a novel Curriculum-based Multi-Fidelity Bayesian Optimization framework .…

## A Sliding Window Approach to Automatic Creation of Meeting Minutes

Meeting minutes record any subject matters discussed, decisions reached andactions taken at meetings . Sliding window approach aims to tackle issues associated with thenature of spoken text, including lengthy transcripts and lack of documentstructure . Approach combines a sliding window and a neuralabstractive summarizer to navigate through the transcripts to find salient content .…

## Index Modulation Based Coordinate Interleaved Orthogonal Design for Secure Communications

In this paper, we propose a physical layer security scheme that exploits anovel index modulation (IM) technique for coordinate interleaved orthogonaldesigns (CIOD) Utilizing the diversity gain of CIOD transmission, the proposed scheme, named CIOD-IM, provides an improved spectral efficiency by means of IM .…

## ECLIPSE Envisioning Cloud Induced Perturbations in Solar Energy

ECLIPSE is a spatio-temporal neural network architecture that models cloud motion from sky images to predict both future segmented images and corresponding irradiance levels . It is based on the analysis of sequences of ground-taken sky images . It reduces temporal delay while generating visually realistic futures .…

## Wise SrNet A Novel Architecture for Enhancing Image Classification by Learning Spatial Resolution of Feature Maps

VGG models used two sets of fully connected layers for the classification part of their architectures, which significantly increasesthe number of models’ weights . ResNet and next deep convolutional models used the Global Average Pooling (GAP) layer to compress the feature map and feed it to the classification layer .…

## Stochastic Recurrent Neural Network for Multistep Time Series Forecasting

Time series forecasting based on deep architectures has been gaining popularity in recent years due to their ability to model complex non-lineartemporal dynamics . The recurrent neural network is one such model capable of handling variable-length input and output . In our model design, the transition function of therecurrent neural network, which determines the evolution of the hidden states, is stochastic rather than deterministic as in a regular recurrent neuralnetwork .…

## Android OS CASE STUDY

Android is a mobile operating system based on a modified version of the Linux kernel and other open source software . It is an operating system forlow powered devices that run on battery and are full of hardware like GlobalPositioning System (GPS) receivers, cameras, light and orientation sensors, Wi-Fi and LTE (4G telephony) connectivity and a touch screen .…

## Consistency issues in Gaussian Mixture Models reduction algorithms

Many approximate GMR algorithms have been proposed in the past decades, although none of them provides optimality guarantees . We discuss the importance of the choice of the dissimilarity measure and the issue of consistency of all steps of a reduction algorithm with the chosen measure .…

## Motion based Extrinsic Calibration of a 3D Lidar and an IMU

This work presents a novel extrinsic calibration estimation algorithm between a 3D Lidar and an IMU using an Extended Kalman Filter . We experimentally validate our method on data collected in our lab . The steps include, datacollection by moving the LIDar Inertial sensor suite randomly along all degreesof freedom .…

Channel sensing consists of probing the channel from time to time to check whether or not it is active – say of an incoming message . When communication is sparse with information being sent once in a long while,channel sensing becomes a significant source of energy consumption .…

## To mock a Mocking bird Studies in Biomimicry

This paper dwells on certain novel game-theoretic investigations inbio-mimicry . The model is used to study the situation where multi-armedbandit predators with zero prior information are introduced into the ecosystem . The prey can be either nutritious or toxic to the predator, but the prey may signal (possibly) deceptively without revealing its true “type” The model uses a model to study a panmictic ecosystem occupied by species of prey with a relatively short lifespan, which evolve mimicry signals over generations .…

## Designing Optimal Key Lengths and Control Laws for Encrypted Control Systems based on Sample Identifying Complexity and Deciphering Time

There has been no systematic methodology of constructing cyber-physical systems that can achieve desired control performance while being protected against eavesdropping attacks . We propose a systematic method for designing the both of an optimal key length and an optimal controller to maximize both of the controlperformance and the difficulty of the identification .…

## On the Nature of Issues in Five Open Source Microservices Systems An Empirical Study

There is a limited evidence-based and thorough understanding of the types of issues faced by microservices system developers and causes that trigger the issues . Technicaldebt (321), Build (145), Security (137) and Serviceexecution and communication (119) are prominent . “General programming errors”, “Poor security management”, “invalidconfiguration and communication”, and “Legacy versions, compatibility anddependency” are the predominant causes for the leading four issue categories .…

## Weakly Supervised Multi task Learning for Concept based Explainability

In ML-aided decision-making tasks, the human-in-the-loop prefers high-level concept-based explanations instead of low-levelexplanations based on model features . We leverage multi-task learning to train a neural network that learns to predict a decision task based on the predictions of aprecedent explainability task .…

## Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization

Classical approaches in cluster analysis are typically based on a featurespace analysis . Many applications lead to datasets with additionalspatial information and ground truth with spatially coherent classes . We propose several approaches with different optimization techniques, where the TV regularization is either performed as a subsequent postprocessing step or included into the clustering algorithm .…

## Efficient training of physics informed neural networks via importance sampling

Physics-Informed Neural Networks (PINNs) are a class of deep neural networksthat are trained, using automatic differentiation, to compute the response of systems governed by partial differential equations (PDEs) The training of PINNs is simulation-free, and does not require any training dataset to be obtained from numerical PDE solvers .…

## Fleet management for ride pooling with meeting points at scale a case study in the five boroughs of New York City

Introducing meeting points to ride-pooling (RP) services has been shown to increase satisfaction level of both riders and service providers . Drivers may choose to walk to a meeting point for a cost reduction . Driversmay also get matched with more riders without making additional stops .…

## Performance and Energy Aware Bi objective Tasks Scheduling for Cloud Data Centers

Cloud computing enables remote execution of users tasks . Increasing use of computing servers exacerbates issues of energy consumption, operating costs, and environmental pollution . An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters .…

## Coresets for k median clustering under Fréchet and Hausdorff distances

We give algorithms for computing coresets for clustering of polygonal curves and point sets . The size of the coreset is independent of the number of inputcurves/point sets to be clustered . We characterize a general condition on the restricted space of clustercenters .…

## An Adaptive Learning based Generative Adversarial Network for One To One Voice Conversion

Voice Conversion (VC) deals with conversion of vocal style of one speaker to another speaker while keeping the linguistic contentsunchanged . VC task is performed through a three-stage pipeline consisting of speech analysis, speech feature mapping, and speech reconstruction . ALGAN-VC framework consists of some approaches to improve speech quality and voice similarity between source and target speakers .…

## StegaPos Preventing Crops and Splices with Imperceptible Positional Encodings

We present a model for differentiating between images that are authenticcopies of ones published by photographers and ones manipulated by cropping, splicing or downsampling after publication . The modelcomprises an encoder that resides with the photographer and a matching decoder that is available to observers .…

## OPTION OPTImization Algorithm Benchmarking ONtology

Different platforms for benchmarking optimization algorithms use different data models and formats, which drastically inhibits identification of relevant data sets, their interpretation, and their interoperability . We report in this paper on the development of such an ontology, which we nameOPTION (OPTImization algorithm benchmarking ONtology) Our ontology providesthe vocabulary needed for semantic annotation of the core entities involved inthe benchmarking process, such as algorithms, problems, and evaluationmeasures .…

## Math Operation Embeddings for Open ended Solution Analysis and Feedback

Feedback on student answers and even during intermediate steps in solving questions is an important element in math education . Such feedback can help students correct their errors and ultimately lead toimproved learning outcomes . Most existing approaches for automated studentsolution analysis and feedback require manually constructing cognitive models and anticipating student errors for each question .…

## Prior free Strategic Multiagent Scheduling with focus on Social Distancing

The algorithm takes input from citizens and schedules the store’s time-slots based on their importance to visit the facility . We show that it reduces the socialcongestion significantly using users’ visit data from a store . The problem becomes NP-complete as soon as the multi-slot demands are indivisibleand provide a polynomial-time mechanism that is truthful, individuallyrational, and approximately optimal .…

## What About the Precedent An Information Theoretic Analysis of Common Law

In common law, the outcome of a new case is determined mostly by precedent cases, rather than by existing statutes . Answering this question is crucial for guaranteeing fair and consistent judicial decision-making . We are the first to approach this question computationally by comparing twolongstanding jurisprudential views .…

## Highly efficient and energy dissipative schemes for the time fractional Allen Cahn equation

In this paper, we propose and analyze a time-stepping method for the timefractional Allen-Cahn equation . The key property of the proposed method is itsunconditional stability for general meshes, including the graded mesh commonly used for this type of equations .…

## Normal Driven Spherical Shape Analogies

This paper introduces a new method to stylize 3D geometry . The key observation is that the surface normal is an effective instrument to capturedifferent geometric styles . This formulation can deform a 3D shape intodifferent styles within a single framework .…

## Comparing Hand Gestures and the Gamepad Interfaces for Locomotion in Virtual Environments

Hand gesture is a new and promising interface for locomotion in virtualenvironments . Finger Distance gesture and the Finger Number gesture are very promising interfaces for virtual locomotion . FingerTapping gesture needs further improvements before it can be used for virtualwalking .…

## On Measure Quantifiers in First Order Arithmetic Long Version

First-order arithmetic with measure quantifiers is capable of offormalizing simple results from probability theory and, most importantly, ofrepresenting every recursive random function . Weshow: We introduce arealizability interpretation of this logic in which programs have access to anoracle from Cantor space .…

## Two Server Verifiable Homomorphic Secret Sharing for High Degree Polynomials

Homomorphic secret sharing (HSS) allows multiple input clients to secret-share their data among multiple servers . Each server is able to locally compute a function on its shares to obtain a partial result . The degree of the outsourced polynomials can be as high as a polynomial in the system’s security parameter .…

## The Design of the User Interfaces for Privacy Enhancements for Android

We present the design and design rationale for the user interfaces for Privacy Enhancements for Android (PE for Android) These UIs are built around core ideas that developers should explicitly declare the purpose of sensitive data is being used, and these permission-purpose pairs should be besplit by first party and third party uses .…

## Case Study on Using Colours in Constructing Emotions by Interactive Digital Narratives

This article addresses the possibility of supporting the construction of emotions in the participants of Interactive Digital Narratives (IDN) by means of colours . The article uses goal models for expressing protostories . The core of the article consists of the case study where two colour synes-thetes wereasked to choose colours for eight emotions .…

## Move Schedules Fast persistence computations in sparse dynamic settings

The standard procedure for computing the persistent homology of a filteredsimplicial complex is the matrix reduction algorithm . In practice, the quadratic scaling in the number oftranspositions often makes this maintenance procedure slower than simply computing the decomposition from scratch .…

## Seeking Quality Diversity in Evolutionary Co design of Morphology and Control of Soft Tensegrity Modular Robots

Evolutionary algorithms (EAs) represent a valid tool to overcome this issue . Designing optimal soft modular robots is difficult, due to non-trivialinteractions between morphology and controller . ViE-NEAT shows comparable performance to MAP-Elites on goal reaching, although it does not exploit any map .…

## Low rank Tensor Estimation via Riemannian Gauss Newton Statistical Optimality and Second Order Convergence

In this paper, we consider the estimation of a low Tucker rank tensor from anumber of noisy linear measurements . The general problem covers many specificexamples arising from applications, including tensor regression, tensorcompletion, and tensor PCA/SVD . We propose a Riemannian Gauss-Newton (RGN)method with fast implementations for low Tucker Rank tensor estimation .…