Reviewerly/exHarmony huggingface dataset:
Cybersecurity of Business Intelligence Analytics Based on the Processing of Large Sets of Information with the Use of Sentiment Analysis and Big Data

Purpose: The research aims to characterize newest soultions, especially with the respect to cybersecurity aspects of Business Intelligence analytics based on the processing of large sets of information with the use of sentiment analysis and Big Data. Design/Methodology/Approach: The working hypothesis refers to assumption that current regulations and security solutions for Business Intelligence analytics based on the processing of large sets of information with the use of sentiment analysis and Big Data is under extreme preassure to meet evergrowing challenges. There are more and more dends form the legal regulators as well as from the market and that creates a lot of problems with data protection. The article uses legal and comparative analysis as well as structural and functional analysis. Additionally, the interpretation method is also present. Findings: Article indicates that the aforementioned issues with the respect to growing importance of internet including the Internet of Things and Internet of Everything are becoming of more and more importance and cannot go with appropriate level of cybersecurity since the data they collect is of the great importance. The trends immanent to Industry 4.0 require from business more effort and customer orientation. Growing population and access to Internet demands larger scales of business operations. Practical Implications: As a result of conducting the research, it is possible to identify threats and present some recommendations for cybersecurity of Business Intelligence. Originality/Value: This is a complete research for Cybersecurity of Business Intelligence analytics based on the processing of large sets of information with the use of sentiment analysis and Big Data.
Assessment of the quality of a binary classifier in research

The article deals with the basic principles of the assessment of the quality of a binary classifier using the potential of the ROC analysis and describes a ROC analysis algorithm and the interpretation of the obtained results. The work discusses the methodology for the calculation of the main parameters of the ROC curve, as well as the instructions for the assessment of their statistical significance.
Study on College English Online Teaching Model in Mixed Context Based on Genetic Algorithm and Neural Network Algorithm

College English classroom teaching evaluation is an important basis for understanding teaching level and improving teaching quality. The traditional college English classroom teaching evaluation is mainly carried out through questionnaires and scales, but this method is time-consuming and laborious, inevitably introduces subjective errors, and reduces the accuracy and credibility of the evaluation results. In recent years, the rise and development of wisdom education not only provides a more convenient and efficient modern education form but also brings new ideas for classroom teaching evaluation. A subjective and objective fusion statistical evaluation model based on multidirectional genetic variation method and optimized neural network is proposed. The algorithm avoids subjective errors and improves the accuracy and reliability of the evaluation results, and a comprehensive evaluation model is constructed. Finally, according to different evaluation indexes, a systematic visualization scheme is designed to generate students’ classroom learning evaluation report and teachers' classroom teaching evaluation report, respectively, and visualize them on the web.
Named Entity Recognition Architecture Combining Contextual and Global Features

Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations, ...) within a document into predefined categories. Correctly identifying these phrases plays a significant role in simplifying information access. However, it remains a difficult task because named entities (NEs) have multiple forms and they are context dependent. While the context can be represented by contextual features, the global relations are often misrepresented by those models. In this paper, we propose the combination of contextual features from XLNet and global features from Graph Convolution Network (GCN) to enhance NER performance. Experiments over a widely-used dataset, CoNLL 2003, show the benefits of our strategy, with results competitive with the state of the art (SOTA).
Performance Comparison of cGAN Models for Channel Estimation in One-Bit Massive MIMO System

In massive multiple-input multiple-output (MIMO) systems, deep learning (DL) methods exhibit a prominent role because they reduce the analytical complexity and improve the performance of wireless communication systems. Channel estimation becomes more challenging for a massive MIMO system in a highly unbalanced and movable environment. This paper investigates a DL-based channel estimation technique, namely conditional generative adversarial network (cGAN), with various generator model architectures to forecast more realistic and accurate channels. cGAN not only predicts the channels from quantized observations but also calculates the adaptive loss function. The simulation results confirm the accuracy of the DL-based channel estimation method. These results of this work have been compared with those of other previously reported DL methods in terms of mean square error.
Relation Selective Graph Convolutional Network for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) have made significant progress in the skeletal action recognition task. However, the graphs constructed by these methods are too densely connected, and the same graphs are used repeatedly among channels. Redundant connections will blur the useful interdependencies of joints, and the overly repetitive graphs among channels cannot handle changes in joint relations between different actions. In this work, we propose a novel relation selective graph convolutional network (RS-GCN). We also design a trainable relation selection mechanism. It encourages the model to choose solid edges to work and build a stable and sparse topology of joints. The channel-wise graph convolution and multiscale temporal convolution are proposed to strengthening the model’s representative power. Furthermore, we introduce an asymmetrical module named the spatial-temporal attention module for more stable context modeling. Combining those changes, our model achieves state-of-the-art performance on three public benchmarks, namely NTU-RGB+D, NTU-RGB+D 120, and Northwestern-UCLA.
Detecting and Analyzing Collusive Entities on YouTube

YouTube sells advertisements on the posted videos, which in turn enables the content creators to monetize their videos. As an unintended consequence, this has proliferated various illegal activities such as artificial boosting of views, likes, comments, and subscriptions. We refer to such videos (gaining likes and comments artificially) and channels (gaining subscriptions artificially) as “collusive entities.” Detecting such collusive entities is an important yet challenging task. Existing solutions mostly deal with the problem of spotting fake views, spam comments, fake content, and so on, and oftentimes ignore how such fake activities emerge via collusion. Here, we collect a large dataset consisting of two types of collusive entities on YouTube— videos submitted to gain collusive likes and comment requests and channels submitted to gain collusive subscriptions. We begin by providing an in-depth analysis of collusive entities on YouTube fostered by various blackmarket services . Following this, we propose models to detect three types of collusive YouTube entities: videos seeking collusive likes, channels seeking collusive subscriptions, and videos seeking collusive comments. The third type of entity is associated with temporal information. To detect videos and channels for collusive likes and subscriptions, respectively, we utilize one-class classifiers trained on our curated collusive entities and a set of novel features. The SVM-based model shows significant performance with a true positive rate of 0.911 and 0.910 for detecting collusive videos and collusive channels, respectively. To detect videos seeking collusive comments, we propose CollATe , a novel end-to-end neural architecture that leverages time-series information of posted comments along with static metadata of videos. CollATe is composed of three components: metadata feature extractor (which derives metadata-based features from videos), anomaly feature extractor (which utilizes the time-series data to detect sudden changes in the commenting activity), and comment feature extractor (which utilizes the text of the comments posted during collusion and computes a similarity score between the comments). Extensive experiments show the effectiveness of CollATe (with a true positive rate of 0.905) over the baselines.
Proof-of-Vax: Studying User Preferences and Perception of Covid Vaccination Certificates

Abstract Digital tools play an important role in fighting the current global COVID-19 pandemic. We conducted a representative online study in Germany on a sample of 599 participants to evaluate the user perception of vaccination certificates. We investigated five different variants of vaccination certificates based on deployed and planned designs in a between-group design, including paper-based and app-based variants. Our main results show that the willingness to use and adopt vaccination certificates is generally high. Overall, paper-based vaccination certificates were favored over app-based solutions. The willingness to use digital apps decreased significantly by a higher disposition to privacy and increased by higher worries about the pandemic and acceptance of the coronavirus vaccination. Vaccination certificates resemble an interesting use case for studying privacy perceptions for health-related data. We hope that our work will educate the currently ongoing design of vaccination certificates, give us deeper insights into the privacy of health-related data and apps, and prepare us for future potential applications of vaccination certificates and health apps in general.
Performance and Preferences: Interactive Refinement of Machine Learning Procedures

Problem-solving procedures have been typically aimed at achieving well-defined goals or satisfying straightforward preferences. However, learners and solvers may often generate rich multiattribute results with procedures guided by sets of controls that define different dimensions of quality. We explore methods that enable people to explore and express preferences about the operation of classification models in supervised multiclass learning. We leverage a leave-one-out confusion matrix that provides users with views and real-time controls of a model space. The approach allows people to consider in an interactive manner the global implications of local changes in decision boundaries. We focus on kernel classifiers and show the effectiveness of the methodology on a variety of tasks.
Distance learning in Higher Education during the first pandemic lockdown: The point of view of students with special educational needs

The study investigates the perspective on distance learning (DL) of a sample of students with disability. Participants (N= 198; 62% females) completed an online questionnaire. The results highlight that students perceive both advantages and barriers, which vary as a function of the type of disability. This seems to suggest that DL potentials should be evaluated in relation to the specific vulnerabilities and educational needs associated with each type of disability, which might be accomplished by adopting the Universal Design for Learning framework. Also, it may be that the impact of DL depends on the discipline as well as on the teachers’ digital competences, which can make a great difference in the quality of the online lesson and in the overall didactic experience of students with SEN.
Deep-learning-based deflectometry for freeform surface measurement

We propose a deep-learning based deflectometric method for freeform surface measurement, in which a deep neural network is devised for freeform surface reconstruction. Full-scale skip connections are adopted in the network architecture to extract and incorporate multi-scale feature maps from different layers, enabling the accuracy and robustness of the testing system to be greatly enhanced. The feasibility of the proposed method is numerically and experimentally validated, and its excellent performance in terms of accuracy and robustness is also demonstrated. The proposed method provides a feasible way to achieve the general measurement of freeform surfaces while minimizing the measurement errors due to noise and system geometry calibration.
Traffic incident detection based on a global trajectory spatiotemporal map

Abstract Traffic incidents endanger the smooth running of vehicles. Congestion caused by traffic incidents has caused a waste of time and fuel and seriously affected transportation efficiency. At present, most methods use manual judgment or image features to detect traffic incidents, but these methods lack timeliness, leading to secondary incidents. For dangerous road sections such as ramp-free and long downhills, this paper proposes an algorithm to quickly detect traffic incidents based on a spatiotemporal map of vehicle trajectories. First, a vehicle dataset from the monitoring perspective is constructed, and an improved YOLOv4 detection algorithm is used to detect images organized as batches. Based on the detection result, the multi-object tracking method of vehicle speed prediction in key frames is used to obtain the vehicle trajectory. Then according to the vehicle trajectory obtained in a single scene, the vehicle trajectory is reidentified and associated in the continuous monitoring scene to construct a long-distance vehicle trajectory spatiotemporal map. Finally, according to the distribution and generation status of the trajectory in the spatiotemporal map, traffic incidents such as vehicle parking, vehicle speeding, and vehicle congestion are analyzed. Experimental results show that the proposed method greatly increases the speed of vehicle detection and tracking and obtains high mAP, MOTA, and MOTP indicators. The global spatiotemporal map constructed by trajectory reidentification can achieve high detection rates for traffic incidents, reduce the average elapsed time, and avoid the problems of the inaccuracy of analyzing image features.
SOD2G: A Study on a Social-Engineering Organizational Defensive Deception Game Framework through Optimization of Spatiotemporal MTD and Decoy Conflict

Existing moving target defense (MTD) and decoy systems are conceptually limited in avoiding and preventing attackers’ social-engineering real-time attacks by organization through either structural mutations or induction and isolation only using static traps. To overcome the practical limitations of existing MTD and decoy and to conduct a multi-stage deception decision-making in a real-time attack-defense competition, the current work presents a social-engineering organizational defensive deception game (SOD2G) as a framework, consi dering hierarchical topologies and fingerprint characteristics by organization. The present work proposed and applied deception concepts and zero-sum-based two-player game models as well as attacker and defender decision-making process based on deceivable organizational environments and vulnerability information. They were designed in consideration of limited organizational resources so that they could converge in the positive direction to secure organizational defender dominant share and optimal values of the defender deception formulated by both scenario and attribute. This framework could handle incomplete private information better than existing models and non-sequentially stratified, and also contributed to the configuration of the optimal defender deception strategy. As the experimental results, they could increase the deception efficiency within an organization by about 40% compared to existing models. Also, in the sensitivity analysis, the proposed MTD and decoy yielded improvements of at least 60% and 30% in deception efficiency, respectively, compared to the existing works.
Statistical Models For Sunshine Duration Related To Precipitation And Relative Humidity

Energy usage leads to environmental problems especially climate change and air pollution.In urban areas, extensive consumption of fossil energy sources deteriorates urban air quality and causes health problems such as asthma and respiratory diseases.The usage of renewable energy sources mainly solar energy instead of fossil energy sources provide the healthier and cleaner environment.Some statistical approaches have been used to predict the solar radiation for many years.The formula which is named as Angström-Prescott is widely used for the estimation.This approach uses the value of relative sunshine duration which is computed by using the daily measurement of sunshine duration and the daylength.Sunshine duration has been measured at meteorological stations for a long time.But in some cases such as remote areas or non-exist stations, sunshine duration is forecasted by using statistical methods.In atmospheric environment, sunshine duration is affected from other climate parameters such as cloud cover, wind speed, relative humidity, precipitation, air temperature, pressure.This paper offers to gain the relationships for sunshine duration via precipitation and relative humidity.To construct the statistical models, cloud cover was entered the models firstly because of its association with sunshine duration.Many studies were shown that sunshine duration was highly correlated to the cloud cover.The observed daily mean values of sunshine duration and climate elements were the data of Samsun meteorological station measured by Turkish State Meteorological Service.To acquire the statistical models for sunshine duration, data was arranged as monthly mean values and then linear regression analysis method was operated.Also the graphs of time series of climate variables were visually created for a clear interpretation.According to the findings, sunshine duration can be estimated bu using statistical models over cloud cover, precipitation and relative humidity.The contributions of precipitation and relative humidity change for different time scales.
Evaluating the Performance Parameters of Cryptographic Algorithms for IOT-based Devices

Nowadays, terabytes of digital data are generated and sent online every second. However, securing this extent of information has always been a challenging task. Cryptography is a fundamental method for securing data, as it makes data unintelligible for attackers, offering privacy to authorized clients. Different cryptographic algorithms have different speeds and costs that make them suitable for different applications. For instance, banking applications need outrageous security amenities, as they utilize superior algorithms having greater requirements, while gaming applications focus more on speed and cost reduction. Consequently, cryptographic algorithms are chosen based on a client's prerequisites. This study compared DES, AES, Blowfish, and RSA, examining their speed, cost, and performance, and discussed their adequacy for use in wireless sensor networks and peer-to-peer communication.
Using analog computers in today's largest computational challenges

Abstract. Analog computers can be revived as a feasible technology platform for low precision, energy efficient and fast computing. We justify this statement by measuring the performance of a modern analog computer and comparing it with that of traditional digital processors. General statements are made about the solution of ordinary and partial differential equations. Computational fluid dynamics are discussed as an example of large scale scientific computing applications. Several models are proposed which demonstrate the benefits of analog and digital-analog hybrid computing.
Text Analytics in Bulgarian: An Overview and Future Directions

Abstract Text analytics is becoming an integral part of modern business and economic research and analysis. However, the extent to which its application is possible and accessible varies for different languages. The main goal of this paper is to outline fundamental research on text analytics applied on data in Bulgarian. A review of key research articles in two main directions is provided – development of language resources for Bulgarian and experimenting with Bulgarian text data in practical applications. By summarizing the results of a large literature review, we draw conclusions about the degree of development of the field, the availability of language resources for the Bulgarian language and the extent to which text analytics has been applied in practical problems. Future directions for research are outlined. To the best of the author’s knowledge, this is the first study providing a comprehensive overview of progress in the field of text analytics in Bulgarian.
Learn2Write: Augmented Reality and Machine Learning-Based Mobile App to Learn Writing

Augmented reality (AR) has been widely used in education, particularly for child education. This paper presents the design and implementation of a novel mobile app, Learn2Write, using machine learning techniques and augmented reality to teach alphabet writing. The app has two main features: (i) guided learning to teach users how to write the alphabet and (ii) on-screen and AR-based handwriting testing using machine learning. A learner needs to write on the mobile screen in on-screen testing, whereas AR-based testing allows one to evaluate writing on paper or a board in a real world environment. We implement a novel approach to use machine learning for AR-based testing to detect an alphabet written on a board or paper. It detects the handwritten alphabet using our developed machine learning model. After that, a 3D model of that alphabet appears on the screen with its pronunciation/sound. The key benefit of our approach is that it allows the learner to use a handwritten alphabet. As we have used marker-less augmented reality, it does not require a static image as a marker. The app was built with ARCore SDK for Unity. We further evaluated and quantified the performance of our app on multiple devices.
Pedagogical experiences in a virtual exchange project using high-immersion virtual reality for intercultural language learning

Social Virtual Reality (VR) applications enable real-time interpersonal conversation and allow users to perform activities together. They have the potential of changing the ways learners practise speaking a foreign language. Following a previous study (Jauregi Ondarra, Gruber, &amp; Canto, 2020), we designed the present study to explore how presence, immersion, and interactivity affect overall social experience. Students from Germany and the Netherlands engaged in High-immersion VR (HiVR) virtual exchange sessions, using Spanish as a lingua franca at A2 level. International dyads carried out four interaction tasks in AltspaceVR, using head-mounted devices. To examine students’ HiVR virtual exchange experiences, different sources of data were gathered: questionnaires, reflection diaries, recordings, and focus group interviews. The preliminary results, based on the surveys and reflection journals, show that students liked to use a social VR app to communicate in the target language with peers from other countries, as they felt completely immersed and co-present in the social interactive VR space. This might enhance engagement and lower anxiety levels.