Reviewerly/exHarmony huggingface dataset:
Research on Lucene Based Full-Text Query Search Service for Smart Distribution System

Big data is one of the newest and biggest technological changes in the IT industry. Data search, data association and analysis are the essential needs by big data utilities. There are all kinds of literary data in distribution network which are not utilized and analyzed. Full-text query can incorporate straight words and return mass data set according to the search which provides the convenience for data management and decision support. In this paper, a full-text retrieval system based on Lucene architecture integrated with Hadoop architecture for distribution network big data center is established which could provide literary and full-text query across all distribution network management systems such as Distribution management system(DMS), Intelligent system for integrated grid operation (OS2), Customer service system etc. The proposed application could assist system operators to fasten the information retrieval and provide the convenience and efficiency of data analysis by the big data technology.
Modulation Classification in MIMO Systems With Distribution Test Ensemble

In classification of signal modulation types in MIMO systems, it is difficult to achieve both high accuracy and high computational efficiency at the same time. State-of-the-art likelihood based methods incur massive increase in computational complexity when the number of transmitting antennas and the order of modulation increase. To make modulation classification feasible in time critical systems, we propose a low complexity algorithm with an ensemble of distribution tests. Three goodness-of-fit and a novel variance based distribution tests are employed to examine the mismatch between unknown signal and different modulation hypotheses. The results from all tests are combined by a multilayer perceptron classifier for improved robustness under a variety of channel conditions including AWGN channel and slow fading channels. The resulting solution achieves performance close to the maximum likelihood classifier at high SNR. Yet, it requires much lower computational complexity in all cases.
Variable Importance Analysis in Imbalanced Datasets: A New Approach

Decision-making using machine learning requires a deep understanding of the model under analysis. Variable importance analysis provides the tools to assess the importance of input variables when dealing with complex interactions, making the machine learning model more interpretable and computationally more efficient. In classification problems with imbalanced datasets, this task is even more challenging. In this article, we present two variable importance techniques, a nonparametric solution, called mh-χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and a parametric method based on Global Sensitivity Analysis. The mh-χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> employs a multivariate continuous response framework to deal with the multiclass classification problem. Based on the permutation importance framework, the proposed mh-χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> algorithm captures the dissimilarities between the distribution of misclassification errors generated by the base learner, Conditional Inference Tree, before and after permuting the values of the input variable under analysis. The GSA solution is based on the Covariance decomposition methodology for multivariate output models. Both solutions will be assessed in a comparative study of several Random Forest-based techniques with emphasis in the multiclass classification problem with different imbalanced scenarios. We apply the proposed techniques in two real application cases in order first, to quantify the importance of the 35 companies listed in the Spanish market index IBEX35 on the economic, political and social uncertainties reflected in economic newspapers in Spain during the first quadrimester of 2020 due to the COVID-19 pandemic and second, to assess the impact of energy factors on the occurrence of spike prices on the Spanish electricity market.
Acrophobia treatment with virtual reality assisted acceptance and commitment therapy: case reports

Exposure has an essential place in the treatment of acrophobia and technology may increase the success of exposure therapies in many ways.We predicted that the integration of acceptance and commitment therapy (ACT) with Virtual Reality (VR) technology would be beneficial in the treatment of acrophobia and would improve clients' compliance with therapy.This report presents two examples of VR-assisted ACT treatment for acrophobia.The process consists of six sessions (one initial interview and five sessions of interventions) for individual therapy.In the first two sessions, functional behavioral analysis and case formulation with clients were performed and skills to be used during exposure sessions taught.From the second session, application of VR-assisted exposure interventions with ACT principles was started.Results showed a significant improvement for both clients.At the end of the treatment, they were both able to complete the virtual behavioral approach task and presented decreased avoidance strategies and increased value-based behaviors.VR-assisted ACT is promising, especially to facilitate exposure treatments.
Dynamic Contract Design for Systemic Cyber Risk Management of Interdependent Enterprise Networks

The interconnectivity of cyber and physical systems and Internet of things has created ubiquitous concerns of cyber threats for enterprise system managers. It is common that the asset owners and enterprise network operators need to work with cybersecurity professionals to manage the risk by remunerating them for their efforts that are not directly observable. In this paper, we use a principal–agent framework to capture the service relationships between the two parties, i.e., the asset owner (principal) and the cyber risk manager (agent). Specifically, we consider a dynamic systemic risk management problem with asymmetric information where the principal can only observe cyber risk outcomes of the enterprise network rather than directly the efforts that the manager expends on protecting the resources. Under this information pattern, the principal aims to minimize the systemic cyber risks by designing a dynamic contract that specifies the compensation flows and the anticipated efforts of the manager by taking into account his incentives and rational behaviors. We formulate a bi-level mechanism design problem for dynamic contract design within the framework of a class of stochastic differential games. We show that the principal has rational controllability of the systemic risk by designing an incentive compatible estimator of the agent’s hidden efforts. We characterize the optimal solution by reformulating the problem as a stochastic optimal control program which can be solved using dynamic programming. We further investigate a benchmark scenario with complete information and identify conditions that yield zero information rent and lead to a new certainty equivalence principle for principal–agent problems. Finally, case studies over networked systems are carried out to illustrate the theoretical results obtained.
Hyperion: A Visual Analytics Tool for an Intrusion Detection and Prevention System

Intrusion detection and prevention systems (IDPSs) are at the core of protecting an enterprise's network.In general, IDPSs use pre-defined rules to detect potential attacks.As the size of an organization grows and new types of intrusions appear, the quantity and complexity of the rules also increase.Moreover, IDPSs generate an overwhelming number of logs that are challenging to handle and analyze.For a more effective and integrative analysis and management of the rules and logs, we propose a novel visual analytics tool, Hyperion.Hyperion interactively visualizes rules to help users understand how the IDPS rules are managed and applied to the enterprise's network entities.Hyperion also provides effective visualizations to enable users to visually analyze the type, period, traffic, and frequency of attacks in addition to a traditional count-based timeline visualization.Finally, Hyperion enables users to interactively simulate the effect of a change in parameters of a detection rule.These features can help streamline the security control cycle consisting of rule application, information collection, log analysis, and rule revision.
Research trends review on RSA scheme of asymmetric cryptography techniques

One of the cryptography classifications is asymmetric cryptography, which uses two different keys to encrypt and decrypt the message. This paper discusses a review of RSA scheme of asymmetric cryptography techniques. It is trying to present the domains of RSA scheme used including in public network, wireless sensor network, image encryption, cloud computing, proxy signature, Internet of Things and embedded device, based on the perspective of researchers’ effort in the last decade. Other than that, this paper reviewed the trends and the performance metrics of RSA scheme such as security, speed, efficiency, computational complexity and space based on the number of researches done. Finally, the technique and strengths of the proposed scheme are also stated in this paper.
Deep Learning-based Online Alternative Product Recommendations at Scale

Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customers’ needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12% in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.
AAFM: Adaptive Attention Fusion Mechanism for Crowd Counting

CNN-based crowd counting methods have achieved great progress in recent years. However, most of these CNN-based crowd counting methods do not make full use of contextual information, which contains high-level semantic features and low-level detail features from different receptive fields of CNN. But rich contextual information is important to solve the scale variation problem of crowd counting. So the precision of previous CNN-based crowd counting methods is decreased. To solve this problem, we propose an adaptive attention fusion mechanism (AAFM). AAFM can use multi-scale features from different receptive fields of CNN effectively. It integrates the convolution network for feature learning and the attention mechanism for multi-scale features fusion. We apply the first 13 convolution layers of VGG-16 as the encoder module to extract the base features. Then, the base features are fed into the decoder module. The decoder module mainly contains Density Regression Branch (DRB) and Feature Fusion Branch (FFB). DRB uses multiple convolution layers for feature learning and multi-scale feature extraction. FFB uses attention modules for modeling multi-scale features and element-wise multiply for features fusion. Therefore, AAFM can obtain rich contextual information into the encoder-decoder framework for generating high-quality crowd density maps and accurate counting. We perform experiments on ShanghaiTech, UCF-CC-50, and UCF-QNRF datasets, and AAFM achieves promising results.
Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs

Many physical systems have underlying safety considerations that require that the policy employed ensures the satisfaction of a set of constraints. The analytical formulation usually takes the form of a Constrained Markov Decision Process (CMDP). We focus on the case where the CMDP is unknown, and RL algorithms obtain samples to discover the model and compute an optimal constrained policy. Our goal is to characterize the relationship between safety constraints and the number of samples needed to ensure a desired level of accuracy---both objective maximization and constraint satisfaction---in a PAC sense. We explore two classes of RL algorithms, namely, (i) a generative model based approach, wherein samples are taken initially to estimate a model, and (ii) an online approach, wherein the model is updated as samples are obtained. Our main finding is that compared to the best known bounds of the unconstrained regime, the sample complexity of constrained RL algorithms are increased by a factor that is logarithmic in the number of constraints, which suggests that the approach may be easily utilized in real systems.
Research on Behavior-Based Data Leakage Incidents for the Sustainable Growth of an Organization

With the continuously increasing number of data leakage security incidents caused by organization insiders, current security activities cannot predict a data leakage. Because such security incidents are extremely harmful and difficult to detect, predicting security incidents would be the most effective preventative method. However, current insider security controls and systems detect and identify unusual behaviors to prevent security incidents but produce many false-positives. To solve these problems, the present study collects and analyzes data leaks by insiders in advance, analyzes information leaks that can predict security incidents, and evaluates risk based on behavior. To this end, data leakage behaviors by insiders are analyzed through an analysis of previous studies and the implementation of an in-depth interview method. Statistical verification of the analyzed data leakage behavior is performed to determine the validity and derive the levels of leakage risk for each behavior. In addition, by applying the N-gram analysis method to derive a data leakage scenario, the levels of risk are clarified to reduce false-positives and over detection (i.e., the limitations of existing data leakage prevention systems) and make preemptive security activities possible.
Introducing Multi-Convexity in Path Constrained Trajectory Optimization for Mobile Manipulators

Mobile manipulators have a highly non-linear and non-convex mapping between the end-effector path and the manipulator's joints and position and orientation of the mobile base. As a result, trajectory optimization with end-effector path constraints takes the form of a difficult non-linear optimization problem. In this paper, we present the first multi-convex approximation to this difficult optimization problem that eventually reduces to solving a sequence of globally valid convex quadratic programs (QPs). The proposed optimizer rests on two novel building blocks. First, we introduce a set of auxiliary variables in which the non-linear constraints that arise out of manipulator kinematics and its coupling with the mobile base have a multi-affine form. Projecting the auxiliary variables to the space of actual configuration variables of the mobile manipulator involves a non-convex optimization. Thus, the second building block involves computing a convex surrogate for this non-convex projection. We show how large parts of the proposed optimizer can be solved in parallel providing the possibility of exploiting multi-core CPUs. We validate our trajectory optimization on different benchmark examples. Specifically, we highlight how it solves the cyclicity problem and provides a holistic approach where a diverse set of trajectories can be obtained by trading-off different aspects of manipulator and mobile base motion.
Case Study of the Experience Capturer Evaluation Tool in the Design Process of an Industrial HMI

In the absence of user experience evaluation tools for industrial human–machine interfaces (HMI), a specific tool called eXperience Capturer (XC) has been created. It is a multi-method user-centred tool that evaluates the pragmatic and experiential aspects of employees’ interaction with industrial HMIs during the three phases of experience. In this article, a case study is shown where the XC tool is used in an industrial HMI design process. The results show that evaluation using the XC tool facilitates the creation of a new design that improves the experience of employees during interaction, increasing their autonomy, competence, closeness to the system, safety and stimulation.
Aspectos psicosociales de la pandemia COVID-19 en la población de la ciudad de Mendoza

espanolIntroduccion: En marzo de 2020 la Organizacion Mundial de la Salud (OMS) declaro a la enfermedad SARS-CoV-2 coronavirusuna pandemia; desde ese momento se lleva a cabo un experimento psicologico notablemente grande en el mundo: elaislamiento social.Objetivos: Analizar los aspectos psicosociales del confinamiento y las restricciones impuestas por la pandemia en la ciudadde Mendoza (Argentina).Material y metodos: Mediante la utilizacion de las redes sociales (WhatsApp, Instagram, Facebook, e-Mail) se realizo de maneraanonima por medio de un formulario, una encuesta disenada para valorar los aspectos psicosociales y conductuales delconfinamiento obligatorio por la situacion de pandemia, y como afecto a la poblacion. Fue realizada en la ciudad de Mendozacapital y gran Mendoza utilizando la poblacion que asiste al instituto de cardiologia y medicina del deporte.Resultados: Contestaron la encuesta 915 personas en 10 dias de enviado el link; el 49% de los participantes se encontrabaentre los 20 y 40 anos y la mayoria eran mujeres. El 80,7% de la poblacion encuestada realizo ejercicio durante el tiempo deconfinamiento, pero solo el 3,8% mantuvo la cantidad de horas previas de ejercicio semanal. El 44% de las personas aumentoel tiempo que pasaba frente a una pantalla y el 61% altero sus habitos alimentarios saludables. Al menos el 60% de la poblacionencuestada refirio sintomas compatibles con depresion, ansiedad, tristeza, falta de voluntad o desesperanza.Conclusiones: El tiempo de confinamiento sin permitir actividades al aire libre, a pesar de la prohibicion de contacto social,se asocia con cambios en el estilo de vida saludable. EnglishBackground: In March 2020 the World Health Organization (WHO) declared SARS-CoV-2 coronavirus disease a pandemic,and since then the largest psychological experiment in the world is being carried out: social isolation.Objectives: The aim of this study was to analyze the psychosocial aspects of confinement and the restrictions imposed by thepandemic in the city of Mendoza, ArgentinaMethods: An anonymous survey using social networks (WhatsApp, Instagram, Facebook and e-mail) was designed to assessthe psychosocial and behavioral aspects of mandatory confinement due to the pandemic and its impact in the population thatattends the Institute of Cardiology and Sports in the city of Mendoza and Greater Mendoza.Results: The survey was answered by 915 persons within 10 days of sending the link; 49% of participants were between 20 and40 years old with a majority of female gender. In 80.7% of cases they performed physical exercise during the confinement, butonly 3.8% maintained the previous number of hours of weekly activity, 44% increased their time in front of a screen and 61%altered their healthy eating habits. At least 60% of the surveyed population reported symptoms compatible with depression:anxiety, sadness, unwillingness or hopelessness.Conclusions: Confinement without allowing outdoor activities, notwithstanding the proscription of social contact, is associatedwith changes in healthy lifestyle.
An Efficient High-Quality Medical Lesion Image Data Labeling Method Based on Active Learning

The rapid development of artificial intelligence has allowed deep learning technology to change our lives and has brought considerable convenience, but deep learning cannot succeed without a sufficient quantity and quality of data. In medical systems, due to the special nature of medical data resources, labeling and screening require professional input from doctors at considerable cost. However, if these data cannot be used effectively, then resources are wasted. To solve this problem, this paper proposes an effective high-quality medical lesion image data labeling method based on active learning, which labels the most representative and high-quality medical image data with artificial assistance. First, we generated subregions for all unlabeled images and predicted their classifications. Second, multifactor calculations were performed on all images. Finally, the values of multiple factors were used to sort all images, and the top-ranked images were selected and labeled with artificial assistance. The above steps were repeated until a suitable number of datasets had been labeled. The experimental results showed that a model trained on the labeled high-quality dataset could achieve the same quality as the model trained on all the data and save a considerable amount of time on manual labeling, which demonstrates the effectiveness of the method. The method ensures that the labeled data are valuable, high quality and rich in information to reduce the labeling workload and avoid wasting data resources.
Aggregating quantum networks

Quantum networking allows the transmission of information in ways unavailable in the classical world. The information encoded in a single qubit can now be split and transmitted in a coherent way over different routes. This aggregation allows information to be transmitted in a fault tolerant way between different parts of the quantum network (or the future internet), even when that is not achievable with a single path approach. It is a quantum phenomenon not available in conventional telecommunication networks either. We show how the multiplexing of independent quantum channels allows a distributed form of quantum error correction to protect the transmission of quantum information between nodes or users of a quantum network. Combined with spatial-temporal single-photon multiplexing, we observe a significant drop in network resources required to transmit that quantum signal, even when only two channels are involved. This work goes far beyond the concepts of channel capacities and shows how quantum networking may operate in the future.