Reviewerly/exHarmony huggingface dataset:
Realtime Behavior-Based Continual Authentication of Users in Virtual Reality Environments

In this work, we present a realtime interactive system that uses the trajectories of the device controllers and headset of a virtual reality (VR) system to perform continual authentication of users interacting in VR. Our system identifies a user within .21 ± .04 seconds of provision of a trajectory for a ball-throwing application. Given an interaction session authorized for a user within an organization, our system continually authenticates the current user by computing matches for the positions and orientations of the points on trajectories of the right hand controller, left hand controller, and headset for the user against controller and headset trajectories in a library of users. The system combines the position and orientation matches using a neural network pre-trained to predict confidences on matches computed within the library, and provides a speech interface to output the identified user as the library user with the maximum value of predicted confidence. The system uses the identity of the user authorized to use a session and a confidence threshold to detect if the user is genuine, a malicious user internal to the organization, or an external intruder. Our system can be readily ported into virtual reality applications that require realtime behavioral authentication of users for secure access.
Image-Based Service Recommendation System: A JPEG-Coefficient RFs Approach

Online shopping platforms are growing at an unprecedented rate all over the world. These platforms mostly rely on search engines, which are still primarily based on the knowledge-base and use keywords matching for finding similar products. However, customers want an interactive setup that is convenient and reliable for querying related products. In this paper, we propose a novel idea of searching for products in an online shopping system using an image-based approach. A user can provide, select, or click an image, and similar image-based products will be presented to the user. The proposed recommendation system is based on content-based image retrieval and is composed of two major phases; Phase 1 and Phase 2. In Phase 1, the proposed approach learns the class/type of the product. In Phase 2, the proposed recommendation system retrieves closely matched similar products. For Phase 1, the proposed approach creates a model of products using Machine Learning (ML). The model is then used to find the category of the test products. From the ML perspectives, we employ the Random Forests (RF) classifier, and for feature extraction, we use the JPEG coefficients. The dataset used for proof of concepts includes 20 categories of products. For image-based recommendation, the proposed RF model is evaluated for Phase 1 and Phase 2. In Phase 1, the evaluation of the proposed model generates a 75% accurate model. For performance enhancements, the RF model has been integrated into the Deep Learning (DL) setup achieving 84% accurate predictions. Based on the custom evaluation approach for Phase 2, the proposed recommendation approach achieves 98% correct recommendations, thus demonstrating its efficacy for the product recommendation in practical applications.
Detecting Duplicate Questions in Stack Overflow via Deep Learning Approaches

Stack Overflow is a popular question and answer website based on the software programming. Different users often ask the same questions in different ways, resulting in a large number of duplicate questions in Stack Overflow. Generally, the users with high reputation manually analyze and mark duplicate questions, which is time consuming and low efficiency. Therefore, the automatic duplicate question detection approach is demanded. We first investigate the application of deep learning models to software engineering task. Then, three deep learning models (i.e., CNN, RNN and LSTM) are applied to demonstrate whether they are effective to duplicate question detection task in Stack Overflow. In this paper, we explore three deep learning approaches DQ-CNN, DQ-RNN and DQ-LSTM based on CNN, RNN and LSTM to detect duplicate questions. The effectiveness of DQ-CNN, DQ-RNN and DQ-LSTM is evaluated by six different question groups. The experimental results show that DQ-LSTM outperforms DupPredictor, Dupe, DupePredictorRep-T and DupeRep in terms of recall-rate@5, recall-rate@10 and recall-rate@20 except for Ruby question group.
CG-GAN: An Interactive Evolutionary GAN-Based Approach for Facial Composite Generation

Facial composites are graphical representations of an eyewitness's memory of a face. Many digital systems are available for the creation of such composites but are either unable to reproduce features unless previously designed or do not allow holistic changes to the image. In this paper, we improve the efficiency of composite creation by removing the reliance on expert knowledge and letting the system learn to represent faces from examples. The novel approach, Composite Generating GAN (CG-GAN), applies generative and evolutionary computation to allow casual users to easily create facial composites. Specifically, CG-GAN utilizes the generator network of a pg-GAN to create high-resolution human faces. Users are provided with several functions to interactively breed and edit faces. CG-GAN offers a novel way of generating and handling static and animated photo-realistic facial composites, with the possibility of combining multiple representations of the same perpetrator, generated by different eyewitnesses.
The effect of performance appraisal on job performance in governmental sector: The mediating role of motivation

The purpose of this paper is to assess the impact of performance appraisal on job performance focusing on the role of Motivation in the governmental sector in Qatar.While many organizations consider performance appraisal (PA) an important tool to measure and evaluate employees' performance, PA can be used as an effective tool to maintain the capabilities and effectiveness of the workforce.In general, PA can be a crucial tool to motivate employees and leverage their job performance and thus can be considered as a competitive advantage over rivals.This paper has identified main factors of PA from the previous pieces of literature and prioritized the most influencing factors affecting PA such as purpose (PAP), criteria (PAC), leadership (PAL), and methods of evaluations (PAE) on employee's motivation as well as their relationships with job performance.Besides, the study sought to fill the literature gap on the performance management field in the context of Qatar for future research.In the course of this study, descriptive research design was adopted in the form of a survey.The survey was distributed, and 294 responses were collected.The conclusion of the finding was that the PAP and PAE had impacts on employee's motivation and job performance, while PAC and PAL did not.In addition, the findings illustrate a positive correlation between employee's motivation and performance.Human Resource managers of organizations may use findings to better support employees' motivation and performance.
Grid-Connected Photovoltaic Systems with Single-Axis Sun Tracker: Case Study for Central Vietnam

Recently, the demand for small grid-connected photovoltaic (PV) systems has been rapidly increasing; this is due to the reduction in the costs of grid-connected storage systems as compared to those of the stand-alone ones. Notably, the performance of the solar tracking system is not only depending on the types but also the region that they are set up and used. To understand how solar tracking systems work globally, we need to know their performance in each country and even the different parts of a country. In this study, two grid-connected PV systems with 250 W solar modules were used to investigate the efficient improvement of a single-axis sun tracking system in Central Vietnam. First, a mechanical tracking device with a linear actuator and a controller was designed and then its performance was comprehensively investigated with a grid-connected PV system. In addition to evaluating the energy gain by the tracking system, this study also considered the energy consumption of the linear actuator with its controller and a small grid-connected PV system; this has generally been omitted in previous works. Experimental results indicate that the total energy consumption of the tracking system was approximately 2–8% of the energy generated by the grid-connected PV system. The maximum overall energy generation was confirmed to have increased by up to 30.3% on a sunny day upon using the proposed tracking system; further, the net energy gain by using the sun tracker was ascertained to be 15.2% in average weather conditions. Based on the success of this work, we will keep experimenting with other parts of Vietnam. Also, we will collaborate with colleagues in other countries to create a guideline for understanding and using the solar tracker regionally and globally.
Opinion-Mining on Marglish and Devanagari Comments of YouTube Cookery Channels Using Parametric and Non-Parametric Learning Models

YouTube is a boon, and through it people can educate, entertain, and express themselves about various topics. YouTube India currently has millions of active users. As there are millions of active users it can be understood that the data present on the YouTube will be large. With India being a very diverse country, many people are multilingual. People express their opinions in a code-mix form. Code-mix form is the mixing of two or more languages. It has become a necessity to perform Sentiment Analysis on the code-mix languages as there is not much research on Indian code-mix language data. In this paper, Sentiment Analysis (SA) is carried out on the Marglish (Marathi + English) as well as Devanagari Marathi comments which are extracted from the YouTube API from top Marathi channels. Several machine-learning models are applied on the dataset along with 3 different vectorizing techniques. Multilayer Perceptron (MLP) with Count vectorizer provides the best accuracy of 62.68% on the Marglish dataset and Bernoulli Naïve Bayes along with the Count vectorizer, which gives accuracy of 60.60% on the Devanagari dataset. Multilayer Perceptron and Bernoulli Naïve Bayes are considered to be the best performing algorithms. 10-fold cross-validation and statistical testing was also carried out on the dataset to confirm the results.
A deep learning AlexNet model for classification of red blood cells in sickle cell anemia

Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively.
Multimodal Violence Detection in Videos

Effective tools for detection of violence are highly demanded, specially when dealing with video streams. Such tools have a wide range of applications, from forensics and law enforcement to parental control over the ever increasing amount of videos available online. Prior studies showed that deep learning has great potential in detecting violence, but focuses on detecting violence in general, or only specific cases of violent behavior. While the concept of violence is broad and highly subjective, simpler concepts such as fights, explosions, and gunshots, convey the idea of violence while being more objective. Even though different concepts relate to this same broader idea of violence, they differ widely in relation to whether or not they convey the idea of movement, the presence of a specific object, or even if they generate distinctive sounds. In this study, we propose to analyze different concepts related to violence and how to better describe these concepts exploring visual and auditory cues in order to reach a robust method to detect violence.
Global software development governance: Challenges and solutions

Abstract Global software development (GSD) has become a rising software development model in the last few years. Although much research has been performed in terms of GSD management, GSD governance research is scarce at the present time and presents multiple challenges that need to be addressed. In this paper, a systematic mapping study has been conducted, the aim of which was to discover the main issues dealt with in GSD governance literature, as well as to point out the particular research gaps that are still present in this domain. This allowed us to find out new challenges to be addressed, along with possible solutions to these. The results reveal a lack of research in key aspects such as tools, culture, people, and information, all of which provides a great opportunity for future approaches while also highlighting new opportunities in GSD governance research.
Spin squeezing by one-photon–two-atom excitation processes in atomic ensembles

It has been shown elsewhere that two spatially separated atoms can jointly absorb one photon, whose frequency is equal to the sum of the transition frequencies of the two atoms. We describe this process in the presence of an ensemble of many two-level atoms, and show that it can be used to generate spin squeezing and entanglement. This resonant collective process allows to create a sizeable squeezing already at the single-photon limit. It represents a novel way for generating manybody spin-spin interactions, yielding a two-axis twisting-like interaction among the spins, which is very effcient for the generation of spin squeezing. We perform explicit calculations for ensembles of magnetic molecules coupled to a superconducting coplanar cavities. This system represents an attractive on-chip architecture for the realization of improved sensing.
Generalized Guerra’s interpolation schemes for dense associative neural networks

In this work we develop analytical techniques to investigate a broad class of associative neural networks set in the high-storage regime. These techniques translate the original statistical–mechanical problem into an analytical–mechanical one which implies solving a set of partial differential equations, rather than tackling the canonical probabilistic route. We test the method on the classical Hopfield model – where the cost function includes only two-body interactions (i.e., quadratic terms) – and on the “relativistic” Hopfield model — where the (expansion of the) cost function includes p-body (i.e., of degree p) contributions. Under the replica symmetric assumption, we paint the phase diagrams of these models by obtaining the explicit expression of their free energy as a function of the model parameters (i.e., noise level and memory storage). Further, since for non-pairwise models ergodicity breaking is non necessarily a critical phenomenon, we develop a fluctuation analysis and find that criticality is preserved in the relativistic model.
ICD-10 Coding of Spanish Electronic Discharge Summaries: An Extreme Classification Problem

Objective: Medical coding is used to identify and standardize clinical concepts in the records collected from healthcare services. The tenth revision of the International Classification of Diseases (ICD-10) is the most widely-used coding with more than 11,000 different diagnoses, affecting research, reporting, and funding. Unfortunately, ICD-10 code sets tend to follow biased, unbalanced, and scattered distributions. These distribution attributes, along with high lexical variability, severely restrict performance when coded clinical records are used to infer code sets in uncoded records. To improve that inference, we explore a combination of example-based methods optimized to capture codes with different appearance frequencies in data sets. Materials and Methods: The proposed exploration has been carried out on Spanish hospital discharge reports coded by experts, excluding all sentences without any biomedical concept. Representations based on semantic and lexical features are explored, using both global and label-specific attributes. In turn, algorithms based on binary outputs, groups of subsets and extreme classification are compared. Lists of codes together with their confidence values (certainty probabilities) are suggested by each method. Results: Diverse spectral behaviors are shown for each method. Binary classifiers seem to maximize the capture of more popular codes, while extreme classifiers promote infrequent ones. In order to exploit such differences, ensemble approaches are proposed by weighting every output code according to the method, confidence value and appearance frequency. The rule-based combination reaches a 46% Precision at 10 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P \text{@} 10$ </tex-math></inline-formula> ), which means a 15% improvement over the best individual proposal. Conclusion: Assembling methods based on weighting each code according to training frequency and performance can achieve better overall Precision scores on extreme distributions, such as ICD-10 coding.
When Explanations Lie: Why Many Modified BP Attributions Fail

Attribution methods aim to explain a neural network's prediction by highlighting the most relevant image areas. A popular approach is to backpropagate (BP) a custom relevance score using modified rules, rather than the gradient. We analyze an extensive set of modified BP methods: Deep Taylor Decomposition, Layer-wise Relevance Propagation (LRP), Excitation BP, PatternAttribution, DeepLIFT, Deconv, RectGrad, and Guided BP. We find empirically that the explanations of all mentioned methods, except for DeepLIFT, are independent of the parameters of later layers. We provide theoretical insights for this surprising behavior and also analyze why DeepLIFT does not suffer from this limitation. Empirically, we measure how information of later layers is ignored by using our new metric, cosine similarity convergence (CSC). The paper provides a framework to assess the faithfulness of new and existing modified BP methods theoretically and empirically. For code see: this https URL
Constant Curvature Graph Convolutional Networks

Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical, that provide specific inductive biases useful for certain real-world data properties, e.g. scale-free, hierarchical or cyclical. However, the popular graph neural networks are currently limited in modeling data only via Euclidean geometry and associated vector space operations. Here, we bridge this gap by proposing mathematically grounded generalizations of graph convolutional networks (GCN) to (products of) constant curvature spaces. We do this by i) introducing a unified formalism that can interpolate smoothly between all geometries of constant curvature, ii) leveraging gyro-barycentric coordinates that generalize the classic Euclidean concept of the center of mass. Our class of models smoothly recover their Euclidean counterparts when the curvature goes to zero from either side. Empirically, we outperform Euclidean GCNs in the tasks of node classification and distortion minimization for symbolic data exhibiting non-Euclidean behavior, according to their discrete curvature.
Towards Good Practices for Data Augmentation in GAN Training.

Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance of using more data in GAN training. Yet it is expensive to collect data in many domains such as medical applications. Data Augmentation (DA) has been applied in these applications. In this work, we first argue that the classical DA approach could mislead the generator to learn the distribution of the augmented data, which could be different from that of the original data. We then propose a principled framework, termed Data Augmentation Optimized for GAN (DAG), to enable the use of augmented data in GAN training to improve the learning of the original distribution. We provide theoretical analysis to show that using our proposed DAG aligns with the original GAN in minimizing the JS divergence w.r.t. the original distribution and it leverages the augmented data to improve the learnings of discriminator and generator. The experiments show that DAG improves various GAN models. Furthermore, when DAG is used in some GAN models, the system establishes state-of-the-art Frechet Inception Distance (FID) scores.
Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization

State-of-the-art argument mining studies have advanced the techniques for predicting argument structures.However, the technology for capturing non-tree-structured arguments is still in its infancy.In this paper, we focus on non-tree argument mining with a neural model.We jointly predict proposition types and edges between propositions.Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges.Experimental results show that both TSP and PLBA boost edge prediction performance compared to baselines.
Resource-Efficient Quantum Computing by Breaking Abstractions

Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge.Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing applications that might save years of engineering effort.Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity.In this review, we point out that greater efficiency of quantum computing systems can be achieved by breaking the abstractions between these layers.We review several works along this line, including two hardware-aware compilation optimizations that break the quantum Instruction Set Architecture (ISA) abstraction and two error-correction/informationprocessing schemes that break the qubit abstraction.Last, we discuss several possible future directions.