Reviewerly/exHarmony huggingface dataset:
Auto-weighted Mutli-view Sparse Reconstructive Embedding

With the development of multimedia era, multi-view data is generated in various fields. Contrast with those single-view data, multi-view data brings more useful information and should be carefully excavated. Therefore, it is essential to fully exploit the complementary information embedded in multiple views to enhance the performances of many tasks. Especially for those high-dimensional data, how to develop a multi-view dimension reduction algorithm to obtain the low-dimensional representations is of vital importance but chanllenging. In this paper, we propose a novel multi-view dimensional reduction algorithm named Auto-weighted Mutli-view Sparse Reconstructive Embedding (AMSRE) to deal with this problem. AMSRE fully exploits the sparse reconstructive correlations between features from multiple views. Furthermore, it is equipped with an auto-weighted technique to treat multiple views discriminatively according to their contributions. Various experiments have verified the excellent performances of the proposed AMSRE.
Research on Technical Architecture and Application of Big Data Cloud Platform for Electric Power Measurement

Abstract This paper studies the technical architecture and composition of the cloud computing platform. The data types and data source related to the big data of the power energy measurement are discussed. The design of the large data application service scene of the power energy measurement is discussed in combination with the technical targets and business objectives. Finally, the development of cloud platform and its application in business field are prospected.
From monitoring of seismic fields to the automatic forecasting of earthquakes

Purpose The purpose of this paper is to offer two Web-based platforms for systematic analysis of seismic processes. Both platforms are designed to analyze and forecast the state of the environment and, in particular, the level of seismic hazard. The first platform analyzes the fields representing the properties of the seismic process; the second platform forecasts strong earthquakes. Earthquake forecasting is based on a new one-class classification method. Design/methodology/approach The paper suggests an approach to systematic forecasting of earthquakes and examines the results of tests. This approach is based on a new method of machine learning, called the method of the minimum area of alarm. The method allows to construct a forecast rule that optimizes the probability of detecting target earthquakes in a learning sample set, provided that the area of the alarm zone does not exceed a predetermined one. Findings The paper presents two platforms alongside the method of analysis. It was shown that these platforms can be used for systematic analysis of seismic process. By testing of the earthquake forecasting method in several regions, it was shown that the method of the minimum area of alarm has satisfactory forecast quality. Originality/value The described technology has two advantages: simplicity of configuration for a new problem area and a combination of interactive easy analysis supported by intuitive operations and a simplified user interface with a detailed, comprehensive analysis of spatio-temporal processes intended for specialists. The method of the minimum area of alarm solves the problem of one-class classification. The method is original. It uses in training the precedents of anomalous objects and statistically takes into account normal objects.
An Improved Algorithm for Dense Object Detection Based on YOLO

The YOLO v3 (you only look once) algorithm based on CNN (convolutional neural network) is currently the state-of-the-art algorithm that achieves the best performance in real-time object detection. However, this algorithm still has the problem of large detection errors in dense object scenes. This paper analyses the reason for the large error, and proposes an improved algorithm by optimizing confidence adjustment strategy for overlapping boxes and using dynamic overlap threshold setting. Experiments show that the improved algorithm has better performance in dense scenes while has little difference in other scenarios compared to the original algorithm.
Practical and Theoretical Grounds of a Student’s Autonomous Learning Activities in Professional Education

Human capital is becoming a major factor in the development of economy and society. The focus of modern education is the individual’s ability to on-going professional education and self-realization. Labor market requires from an employee not only formed competences, but also soft skills. According to the authors, a student’s autonomous learning activities allow them to form independence and self-management of learning activities, which allows for further professional and personal self-realization. Therefore, the authors have conducted an empirical and theoretical study to prove their hypothesis. The obtained results give grounds for a student’s autonomous learning activities.
Learning Classifiers for Target Domain with Limited or No Labels

In computer vision applications, such as domain adaptation (DA), few shot learning (FSL) and zero-shot learning (ZSL), we encounter new objects and environments, for which insufficient examples exist to allow for training models from scratch, and methods that adapt existing models, trained on the presented training environment, to the new scenario are required. We propose a novel visual attribute encoding method that encodes each image as a low-dimensional probability vector composed of prototypical part-type probabilities. The prototypes are learnt to be representative of all training data. At test-time we utilize this encoding as an input to a classifier. At test-time we freeze the encoder and only learn/adapt the classifier component to limited annotated labels in FSL; new semantic attributes in ZSL. We conduct extensive experiments on benchmark datasets. Our method outperforms state-of-art methods trained for the specific contexts (ZSL, FSL, DA).
QExpy: A python-3 module to support undergraduate physics laboratories

QExpy is an open source python-3 module that was developed in order to simplify the analysis of data in undergraduate physics laboratories. Through the use of this module, students can focus their time on understanding the science and the data from their experiments, rather than on processing their data. In particular, the module allows users to easily propagate uncertainties from measured quantities using a variety of techniques (derivatives, Monte Carlo), as well as to plot and fit functions to data. The interface is designed to be pedagogical so that students with no prior programming experience can be eased into using python in their introductory physics laboratories.
Aspects of Linguistic Complexity: A German – Norwegian Approach to the Creation of Resources for Easy-To-Understand Language

Our project draws on linguistic resources for German and Norwegian in support of initiatives that try to make public language more accessible. We focus on Leichte Sprache for German and Klart Sprak for Norwegian. The former refers to usage forms of German which are easily understood also by users of German with a lower competence in text processing, the latter refers to a governance project which has as its aim to aid institutions in their effort to communicate with the public. While different in their goals both initiatives seek to increase the general access to public information. A central concern is to identify the factors which affect language complexity and set up linguistic resources such as parallel text corpora, linguistic rules and terminology databases. Based on these results and resources, we are building software that supports authors and translators, in close collaboration with them.
Metrics and Indicators of Information Security Incident Management: A Systematic Mapping Study

The number of threats and vulnerabilities has increased rapidly in recent years. For this reason, organizations are in need of providing improvements in their computer security incident management (CSIM), in order to safeguard their intellectual capital. Therefore, the identification and use of both metrics and indicators are a crucial factor to manage security incidents. In this context, organizations try to improve their level of CSIM based on standards or only according to their criteria based on their experience. This article aims at carrying out a systematic mapping study of academic articles conducted in this research area, in order to present a document that describes metrics and indicators of security incidents in organizations. The results of this work show and describe several key indicators and metrics related to the cost, quality, and service (time) involved in dealing with such incidents. Also, it is expected that this study serves as a strategic reference for organizations.
Development of Automated System for Remote Control and Diagnostics of Solar Power Plant Parameters with Dual-Axis Solar Tracker

The article provides a description of a hardware and software system of a solar power plant with a dual-axis solar tracking system. The development allows you to create statistical databases for the generation of electricity by a solar power plant, and monitor the photovoltaic system parameters in the online mode on graphics and tables. The analysis of foreign scientific and technical literature on the development of systems that allow forming the databases of parameters of solar stations has been carried out. The logic of the remote monitoring system and diagnosis, the structural and wiring schemes of the complex are considered in detail. The principal features of the system as well as the advantages and disadvantages of the used components are indicated. In the paper graphical dependencies of power generation of stationary solar panels and the solar module with a tracker have been constructed and analyzed according to the data obtained from the hardware and software system.
Approximate search in dissimilarity spaces using GA

Nowadays, the metric space properties limit the methods of indexing for content-based similarity search. The target of this paper is a data-driven transformation of a semimetric model to a metric one while keeping the data indexability high. We have proposed a genetic algorithm for evolutionary design of semimetric-to-metric modifiers. The precision of our algorithm is near the specified error threshold and indexability is still good. The paper contribution is a proof of concept showing that genetic algorithms can effectively design semimetric modifiers applicable in similarity search engines.
Adaptive Agents for Adaptive Tactical Training: The State of the Art and Emerging Requirements

Military tacticians require practice to learn their craft. Practice requires adaptive opponents capable of responding to trainee actions in ways that are realistic and instructionally productive. Current agents are generally too brittle, too scripted and unresponsive to support adaptive training in this way. What is required to develop adaptive agents are (1) real-time feeds of simulator data that are sufficiently rich and realistic to support agent development and execution; (2) agent architectures capable of generating realistic and instructive behaviors from these data; and (3) a testbed that can deliver data and performance measures in sufficient volume to enable modelers to accelerate agent development by applying emerging analytics and machine learning. The 711th Human Performance Wing/RHA has invested in precisely these capabilities over four years, engaging eight of the leading developers of intelligent agents. In this paper, we describe these capabilities, and, importantly, the data requirements these solutions impose on simulators and operational systems that can employ these technologies in the future.
Verifying Effectiveness on Physical Activity and Nutrition Program for Obesity Management of Children from Low Income Family

The purpose of this study is to verify the effectiveness of physical activity and nutrition programs to prevent obesity in children from low-income families. For 423 local children"s centers in 50 metropolitan areas, the program was tested for its effectiveness after running two hours of physical activity and one hour of nutrition/food education per week for eight weeks (July 30 to September 21). The statistical analysis program used the SPSS Statistics V21.0 (IBM), and the statistical verification set a significant level (α=.05). Changes in pre- and post-obesity levels, knowledge, attitudes and behaviors of physical activity and nutrition used the descriptive, frequency analysis, and Paired samples test methods. According to the analysis, the “healthy care playground” program, which was conducted together with physical activity and nutrition education, was effective in managing obesity in children from low-income families. Along with changes in body composition (during abdominal obesity rate, muscle mass, skeletal muscle mass, and basic metabolic rate), it also showed positive changes in knowledge, attitude and behavior about physical activities.
Effectiveness and Cost-Benefit Ratio of Weekly User Group Sessions

Early user involvement is a central part of a user-centered design process. In a project to create a UX design for an engineering tool for railways, weekly one-hour user group sessions have been used to elicit domain insights and to gather user feedback for proposed design options. This paper explores the effectiveness of such weekly user group sessions, by answering three research questions: RQ1: How much did the participating users effectively influence the UX design? RQ2: What was the impact of the participating users on efficiency improvements? RQ3: What was the return-on-investment (ROI) for the weekly group sessions? During 18 weekly user group sessions, 64 design decisions were made. Out of the 64 decisions, in 24 cases (38%) the participating users have chosen a different design option than the design option, initially selected by the UX team. Out of the 24 decisions, 14 decisions led to low, 10 to medium and 0 to high efficiency improvements. Comparing the hourly effort of preparing and holding such weekly sessions (cost of investment) with the avoided rework (gain of investment), the ROI is 12%. The conclusion is that weekly user group sessions have been a worthwhile approach to make the user supported design choices early in the development process.
A Survey of the Constraints Encountered in Dynamic Vision-Based Sign Language Hand Gesture Recognition

Vision-based hand gesture recognition has received attention in the recent past and much research is being conducted on the topic. However, achieving a robust real time vision-based sign language hand gesture recognition system is still a challenge, because of various limitations (The term limitation in this study is used interchangeably to mean constraint or challenge in respect to the problems that can or are encountered in the process of implementing a vision-based hand gesture recognition system.). These limitations include multiple context and interpretations of gestures as well as well as complex non-rigid characteristics of the hand. This paper exposes the constraints encountered in the image acquisition via camera, image segmentation and tacking, feature extraction and gesture classification phase of vision-based sign language hand gesture recognition. It also highlights the various algorithms that have been used to address the problems. This paper will be useful to new as well as experienced researchers in this field. The paper is envisaged to act as a reference point for new researchers in vision-based hand gesture recognition in the journey towards achieving a robust system that is able to recognize full sign language.
A Multimodal Chatbot System for Enhancing Social Skills Training for Security Guards

Chatbots are typically used in dialogue systems for various purposes such as customer service and information acquisition. This paper explores enhancement of social skills training for security guards with the use of chatbots. More specifically, we designed a chatbot using text and voice as input to study the acceptance and the impact of the system to training security guards in deal with stress situations. The result of a pilot experiment and a survey are presented and discussed. Finally, we discuss possible improvements and future work.
Towards Understanding Negative Votes in a Question and Answer Social Network

Online community question answering (CQA) social networking sites thrive when community members actively participate in the network. To influence participation, some CQA sites such as Stack Overflow reward members with incentives such as reputation, badges, and privileges. One way people earn such rewards is by posting helpful question and answers which get upvoted. An upvote is an indication of how useful a post is to the reader. An increase in a post’s upvotes could lead to the user who created the post accruing reputation and earning rewards in the community. To encourage people to write good posts, Stack Overflow has guidelines on their site for writing good question and answer posts. Despite these measures by Stack Overflow to encourage quality posts from community members, there are still several posts in the community with negative scores. A negative score indicates that the post received more downvotes than upvotes. If one consistently receives negative scores on their posts, it could influence them to stop using the network. Thus, it is important to identify the characteristics of posts with negative scores and explore how they differ from posts with positive scores. To contribute to research in this area, we identified the time of day posts were created, the sentiments and emotions used in writing the posts, the length of posts, and the topics that the posts were tagged with for posts with negative scores and those with positive scores. The result of our analysis on these two groups of posts suggests that posts created between 6.01 pm and 12 am received significantly higher scores than posts written at other times of the day. In addition, posts with positive scores were significantly longer in length than posts with negative scores. Furthermore, posts with positive scores were significantly more analytical and clout and less authentic compared to posts with negative scores.
Investigating the association between university teachers’ professional autonomy and their innovation performance

The purpose of this paper is to investigate both - theoretical and practical aspects in association between foreign language teachers’ professional autonomy and their innovation performance under the current educational circumstances. The findings demonstrate that the development of innovative university teachers is possible in the framework of the autonomy-oriented approach that gets the teachers to be involved in intensive analysis and project activity, based on an analysis and a review of teachers’ personal learning and professional experience in a wide range of educational context.
Traffic Sign Image Enhancement in Low Light Environment

In order to improve the contrast and sharpness of traffic sign images obtained under low light natural environment, we propose an improved enhancement method based on discrete wavelet transform to improve image contrast. We convert the original RGB image to the HSV color space, and use the discrete wavelet transform (DWT) to decompose the luminance component (V). In the low-frequency component use multi-scale Retinex algorithm estimate the illuminance to enhance the contrast of images, the high-frequency component enhances the detail information through the multi-scale detail boosting method. Finally, adjust the saturation component (S) by a piecewise exponential transformation method to make the image color more suitable for human observation. Experimental results demonstrate that our method can better display image details while reducing the halo effect, and effectively improve the contrast and sharpness of low-light images compared with existing algorithms through subjective and objective analysis.
Managing CRM with Fabric Hyperledger blockchain technology

In this chapter we describe an application of the blockchain technology for managing CRM through security “by design” according to the “Defense In Depth” methodology, where protection is obtained through multiple protection layers applied independently. The use of Hyperledger blockchain technology natively supports encryption anonymization and decentralization, allowing the CRM to be built on a zero trust system support where there is no need of any trust in the infrastructure hosting software components. The provided CRM service will be available safely even in case of cyberattack to the infrastructure and cyberattacks on servers, to critical bugs in the infrastructure, and to external pressures or dishonest employees. The use of blockchain allows the encryption of data in the client application under user control. The data are available and accessible only to authorized users and unintelligible to others. The native digital signatures allow the precise identification of the sender, who cannot dissociate from any action. Data integrity is natively ensured on the blockchain by encryption and hashing functions, which allow the prevention of any dishonest data alteration with encrypted data saved on a back end based on blockchain. We implement this scheme with a permissioned blockchain using “Fabric” by Hyperledger.
Wellbeing Technology: Beyond Chatbots

Digitalization has revolutionized almost every sector of our life, including both the private and the professional one, just as the relationship between man and machine/computer is radically changing our lives. Among many sectors, the authors have chosen to explore how digitalization and new technologies are mutating the healthcare, focusing in particular on the exploitation of chatbots and on the forms of interactions with them. Human computer interfaces are becoming more important in a world whose complexity is increased by technology and in which certain groups of people risk being excluded. Fragile categories, health support services, are common keywords because one of the most important games is played on them and on the concept of social innovation through technology. In this context, social differences can be smoothed out, but can also be magnified. In sectors like education or healthcare, such a level of inequality could be reached, that there could be A-class citizens, who would have access to technological prostheses, thus increasing their capacities in some way, and B-class citizens who would be excluded. The authors start giving an overview of the main technologic innovations in the field of healthcare and explaining how they can magnify the risk of exclusion. Then the focus is shifted first on the interaction between people and chatbots used for generic purposes, and then on the central theme of health-bots. Finally, in the conclusions, the authors give a reading about the main interaction forms that can help people using these systems.
Inception and ResNet: Same Training, Same Features

Deep convolutional neural networks (CNNs) are the dominant technology in computer vision today. Unfortunately, it’s not clear how different from each other the best CNNs really are. This paper measures the similarity between two well-known CNNs, Inception and ResNet, in terms of the features they extract from images. We find that Inception’s features can be well approximated as an affine transformation of ResNet’s features and vice-versa.The similarity between Inception and ResNet features is surprising. Convolutional neural networks learn complex non-linear features of images, and the architectural differences between the systems suggest that these functions should take different forms. Instead, they seem to have converged on similar solutions. This suggests that the selection of the training set may be more important than the selection of the convolutional architecture.