Reviewerly/exHarmony huggingface dataset:
Short-Term Forecasting for Three Phrase Current on Distribution Network by Applying Multiple-Layer Perceptron Algorithm

On the trail progressing towards the era of whirlwind technological advances in the field of Information Technology (IT) and media during Industry 4.0, never before has the Internet been integrated into such an essential component in our society, and along with the decrease in the cost of telecommunication equipment, Internet of Things (IoT) has become the mainstay of future development. Therefore, the installation of 3G modem and lighting cables for over 1000 switchgear as well as 110 kV and 22 kV substation has been initiated across Vietnam with a view of conducting distant monitor and information gathering. After a procedure of real-time information gathering, the quality and information storage considered to be essential as a huge amount of data are collected for analyzing useful information on customers. In order to take advantage of this plentiful data resource, an automatic tool for analysis is prerequisite. A procedure of electrical system basing on improved parallel neuron network with smart identification, quick diagnosis, and great stability will be elaborated in this research paper. This study has successfully developed an improved model of parallel neuron network for the stable identification problem. The suggested model has achieved the main target of improving the layers’ accuracy.
Examination of Cybercrime and Its Effects on Corporate Stock Value

Cybercrime is a prevalent and serious threat to publicly traded companies. Defending company information systems from cybercrime is one of the most important aspects of technology management. Cybercrime often results in stolen assets and lost business, but also damages a company’s reputation, which in turn may affect the company's stock market value. This is a serious concern to company managers, financial analysts, investors, and creditors. This paper examines the impact of cybercrime on stock prices of a sample of publicly traded companies. Stock prices were negatively affected in all time-periods examined, significantly so in one period.
TECHNICAL EDUCATION, TECHNICAL SKILLS DEVELOPMENT AND METHODS IN REMOTE LEARNING

The technical education at all school levels is currently a very topical issue that is being widely discussed by professionals.It is necessary and imperative to improve the technical literacy of pupils to better prepare them for life in the new societal context.The change and development of technical thinking and skills also require fundamental changes in the pedagogical and psychological approaches in the training of the young generation.The aim of this paper is to present a part of a longitudinal research that studies the technical skills of pupils at the primary school level, the changes in the application of educational methods and strategies in the time of an unexpected pandemic, as well as the accompanied and extended remote learning in the selected Slovak schools.The research was carried out within the framework of the VEGA grant task No. 1/0383/19, "Analysis of the state of play in the technical education and the development of technical skills of pupils at the primary school level.
Comparative Analysis of Web Crawling Algorithms for Improvement in Web Crawler

World Wide Web (WWW) is a massive resource of information contains data in zeta bytes. Large number of users surfing internet for various purposes need a quick response from web. Search engines are preferable, easily available and reliable source to retrieve data very rapidly with minimum resources. User’s viewpoint diverges according to topic and time. Effectual relevant prophecy can assist in reducing irrelevant and replicas. Search engine uses Web Crawler to fulfill this purpose. It is a program that automatically passes through hypertext structure of the web and access webpage and hyperlink within it. Webpage is considered as a node and hyperlink as an edge of web and accessing it is basically a graph traversing. To make the traversing accurate and productive, correctly designed web crawling algorithm is required. Web crawler and crawling techniques are secret methods which are not exposed and their structure are kept covered as it is a hidden formula for search engine success. In this paper comparative analyses of thirteen web crawling algorithms have been discussed, pros and cons of these crawling algorithms have also been identified.
The Effect of EFL Students’ Engagement on the Learning Interaction and Learning Performance Relationship During Indonesian Online Education Migration

The Indonesian education system had been shifted into online education due to the widespread of COVID-19. There had been numerous studies revealing some factors which were influential in online education, for instance, factors leading to student loyalty in open and online education (Daud et al., 2020), an empirical study on service recovery satisfaction in open and online education (Rushidi et al., 2020), or pre-service teaching practice for online and distance education students (Abdullah & Mirza, 2020). However, a lack of research revealed other factors such as learning interaction, students’ engagement, and learning performance in online education. This research addressed the gap and represented a pioneer study revealing the role of students’ engagement as mediation to relate learning interaction and learning performance in the context of Indonesia. We designed this research quantitatively using non-probability purposive sampling to empirically test the research model and collected 700 responses from undergraduate students in January 2021. A web-based survey was used to collect relevant data. Google Forms generated the survey, a free online application to build surveys, and distributed it via Whatsapp group to the 700 participants. We found that students’ engagement suggested complementary mediation or partial mediation to the relationship between learning interaction and learning performance. The findings had implications on how the success of online education was highly determined by the degree of students’ engagement as the third party to enhance the students’ effective communication.
Active vibration control method of double crystal monochromator of P2 protein crystallography beam line at SSRF

The angular stability of the double-crystal monochromator (DCM) directly affects the positional stability of the focused spot. This paper describes the active vibration control (AVC) method of the DCM of P2 Experimental Station at SSRF based on the adaptive filtering algorithm and the AVC system improves the performance of by optimizing the filter weight estimation algorithm. The simulation results show that the amplitude of the crystal in the pitch direction is greatly reduced by the AVC method, the RMS value of the angular displacement in the pitch direction of the crystal is 1.23 nrad, which is 91.93% lower than the passive vibration control (the RMS value of the angular displacement in the pitch direction is 15.25 nrad); the RMS value of the angular displacement in the pitch direction of the optimized AVC system is 0.233 nrad, which is 98.47% lower than that of the passive vibration control method. The AVC method can effectively ensure the angular stability of the DCM in the pitch direction, which is of great significance for the key equipment of the new generation of synchrotron radiation light source to achieve nano-radian stability.
A Recommendation Model Based on Visitor Preferences on Commercial Websites Using the TKD-NM Algorithm

In recent years, recommendation models have been widely implemented in various areas. In order to construct recommendation models, much research makes use of machine learning techniques. This research also proposes a recommendation model using a novel machine learning technique called the “TKD-NM” algorithm. It is the combination of the TF-IDF, KMeans, and Decision Tree incorporated with the Nelder-Mead algorithm. TF-IDF was applied to form word vectorization from webpage headings. KMeans was utilized for clustering webpage headings while the Decision Tree algorithm was employed to investigate the performance of KMeans. Nelder-Mead was applied to find the optimum values of word vectorization. The dataset analyzed in the research was collected from a specific commercial website. Visitor preferences on the website were considered as the dataset in the research. The recommendation lists were retrieved from webpages in the same cluster. The prediction accuracy of the TKD-NM algorithm was approximately 97.31% while the prediction accuracy of the baseline model was only 88.87%.
Research on Intelligent Service Technology of Network Electromagnetic Space Situation Cloud Based on Cloud-Edge-Terminal Synergy

Based on the current technology of network electromagnetic space situation awareness service by the limitations of traditional system framework and fixed working mode cannot adapt to the harsh environment within strong antagonistic conditions, limited tactical edge computing resources and intermittent network communication, the author focuses on the maintenance of traction network electromagnetic space security demand, based on the network electromagnetic situation global multi-dimensional cognition, combines with artificial intelligence machine learning and big data cloud computing of cutting edge technology for fusion development, aims at intelligent service of network electromagnetic space situation, puts forward to the network electromagnetic space situation intelligent service within edge and cloud computing synergy, builds cloud-edge-terminal regional network electromagnetic space situation cloud information service technology architecture to provide technical support for realizing the goal of network electromagnetic space security maintenance including global awareness coverage, prediction analysis, effective response and deterrence of the regional network electromagnetic space environment.
EXPERIMENTAL STUDY ON MARKER INK STAIN TEST ON POLYMER BASED FLOORING MATERIAL

The phenomenon that letters and symbols by markers on concrete slab seep on the surface of polymer based flooring materials after several years of completion becomes a problem.
Detection of Special Scams by Emotion Analysis

Telephone scams called "special scams" have become a major problem, with people being defrauded of nearly 30 billion yen every year in Japan. In this study, we used emotion analysis techniques based on speech analysis to detect whether victims were being deceived by analyzing multiple emotional parameters of victims in a special scam. In the verification, we first clarified the characteristics of the emotions generated in victims by two scam methods. Next, we improved the accuracy of detecting the risk of a victim being deceived by limiting the analysis section.
Cross-modal semantic autoencoder with embedding consensus

Cross-modal retrieval has become a topic of popularity, since multi-data is heterogeneous and the similarities between different forms of information are worthy of attention. Traditional single-modal methods reconstruct the original information and lack of considering the semantic similarity between different data. In this work, a cross-modal semantic autoencoder with embedding consensus (CSAEC) is proposed, mapping the original data to a low-dimensional shared space to retain semantic information. Considering the similarity between the modalities, an automatic encoder is utilized to associate the feature projection to the semantic code vector. In addition, regularization and sparse constraints are applied to low-dimensional matrices to balance reconstruction errors. The high dimensional data is transformed into semantic code vector. Different models are constrained by parameters to achieve denoising. The experiments on four multi-modal data sets show that the query results are improved and effective cross-modal retrieval is achieved. Further, CSAEC can also be applied to fields related to computer and network such as deep and subspace learning. The model breaks through the obstacles in traditional methods, using deep learning methods innovatively to convert multi-modal data into abstract expression, which can get better accuracy and achieve better results in recognition.
A variational Bayes moving horizon estimation adaptive filter with guaranteed stability

This paper addresses state estimation of linear systems with special attention on unknown process and measurement noise covariances, aiming to enhance estimation accuracy while ensuring stability. To this end, the full information estimation problem over a finite interval is first addressed. Then, a novel adaptive variational Bayesian (VB) moving horizon estimation (MHE) method is proposed, exploiting VB inference, MHE, and Monte Carlo integration with importance sampling for joint estimation of the unknown process and measurement noise covariances, along with the state trajectory over a moving window of fixed length. Further, it is proved that the proposed adaptive VB MHE filter ensures mean-square boundedness of the estimation error with any number of importance samples and VB iterations, as well as for any window length. Finally, simulation results on a target tracking example demonstrate the effectiveness of the VB MHE filter with enhanced estimation accuracy and convergence properties compared to the conventional non-adaptive Kalman filter and other existing adaptive filters.
CRAMPING OUR STYLE? GENDER AND PHILOSOPHICAL WRITING

In this article, I argue that we need to reconsider some of the stylistic principles that (explicitly or implicitly) govern writing in analytic philosophy. I suggest that the rules are (i) much more difficult to justify than might be thought at first sight; and (ii) may possibly be gendered, given what we know about the reading preferences and writing styles of men and women.
Median-Tree: An Efficient Counterpart of Tree-of-Shapes

Abstract Representing an image through a tree structure as provided with a morphological hierarchy enables efficient image analysis and processing methods operating directly on the tree structure. Max-tree and min-tree can be built with efficient algorithms but they only focus on brighter and darker components of the image respectively. Conversely, the Tree-of-Shapes is a self-complementary image representation that provides access to all regional extrema of the image (both brighter and darker components), but its computation is more time-consuming. In this paper, we introduce a new, simple and efficient tree structure called median-tree. It relies on a median image that is straightforwardly constructed by subtracting the median pixel value from an image to decompose it into positive and negative parts. The median tree can then be obtained by applying the efficient max-tree algorithms available in the literature on this median image. We show through theoretical and experimental studies that the median-tree offers similar characteristics to the Tree-of-Shapes, but comes with a considerably lower construction complexity.
Multi-object Tracking Using Hidden Markov Model with SIFT Feature

Solving the problems of occlusion and illumination in the multi-object tracking task is the focus in current research. In the case of occlusion, improving the performance of the tracker can improve the tracking effect. This paper proposes an improved Hidden Markov Model for multi-object tracking. Firstly, we extract the SIFT feature points of the previous frame of image as the observation status and input it into the improved model. Secondly, we perform iterative training on each object to estimate the parameters of each model. Finally, the hidden status with the maximum probability of occurrence of the model is obtained according to the observed status and convergent parameters. The hidden status can reflect the SIFT points in the current frame to accomplish the tracking of object. In addition, we setup comparison experiment with other advanced trackers on MOT17 dataset. Experiment results show that our method achieves the best performance in MOTA and IDS, and have good robustness to occlusion problems.
Related-Tweak Impossible Differential Cryptanalysis of Reduced-Round TweAES

We consider the related-tweak impossible differential cryptanalysis of TweAES. It is one of the underlying primitives of Authenticated Encryption with Associated Data (AEAD) scheme ESTATE which was accepted as one of second-round candidates in the NIST Lightweight Cryptography Standardization project. Firstly, we reveal several properties of TweAES, which show what kinds of distinguishers are more effective in recovering keys. With the help of automatic solver Simple Theorem Prover (STP), we achieve many 5.5-round related-tweak impossible differentials with fixed input differences and output differences that just have one active byte. Then, we implement 8-round key recovery attacks against TweAES based on one of these 5.5-round distinguishers. Moreover, another 5.5-round distinguisher that has four active bytes at the end is utilized to mount a 7-round key recovery attack against TweAES, which needs much lower attack complexities than the 6-round related-tweak impossible differential attack of TweAES in the design document. Our 8-round key recovery attack is the best one against TweAES in terms of the number of rounds and complexities so far.
PAMA-TTS: Progression-Aware Monotonic Attention for Stable SEQ2SEQ TTS with Accurate Phoneme Duration Control

Sequence expansion between encoder and decoder is a critical challenge in sequence-to-sequence TTS. Attention-based methods achieve great naturalness but suffer from unstable issues like missing and repeating phonemes, not to mention accurate duration control. Duration-informed methods, on the contrary, seem to easily adjust phoneme duration but show obvious degradation in speech naturalness. This paper proposes PAMA-TTS to address the problem. It takes the advantage of both flexible attention and explicit duration models. Based on the monotonic attention mechanism, PAMA-TTS also leverages token duration and relative position of a frame, especially countdown information, i.e. in how many future frames the present phoneme will end. They help the attention to move forward along the token sequence in a soft but reliable control. Experimental results prove that PAMA-TTS achieves the highest naturalness, while has on-par or even better duration controllability than the duration-informed model.
A-optimal active learning

Abstract In this work we discuss the problem of active learning. We present an approach that is based on A-optimal experimental design of ill-posed problems and show how one can optimally label a data set by partially probing it, and use it to train a deep network. We present two approaches that make different assumptions on the data set. The first is based on a Bayesian interpretation of the semi-supervised learning problem with the graph Laplacian that is used for the prior distribution and the second is based on a frequentist approach, that updates the estimation of the bias term based on the recovery of the labels. We demonstrate that this approach can be highly efficient for estimating labels and training a deep network.
The Impact on the Test Coverage Caused by the Introduction of Adaptive Behavior in a Legacy Web Application: A Case Study

Self-Adaptive systems (ASs) are an alternative to get IT professionals free from details of operation and maintenance. Web applications can incorporate characteristics of an AS in order to make them autonomous. However, one of the challenges of software engineering is to transform a legacy system into an AS. Furthermore, transforming a legacy system into an AS can lead to the insertion of faults. This way, the code coverage information produced by software tests assist in activities related to the evolutionary maintenance of the system. In this work, we analyzed, through the comparison of coverage metrics, how the introduction of adaptive behaviors in a real world legacy web application impacted on code coverage. For each implemented scenario, a set of test cases was created in the legacy version of the web application. Then, evolutionary versions were developed with the introduction of adaptive behavior. The versions (legacy and adaptive) were organized into branches of the version control system so that adaptive implementations have evolved in two technological ways. The coverage metrics for each version of the web application were collected to allow data analysis. Beyond the legacy version, we analyzed the coverage of six versions of the web application with adaptive behavior. The results indicate that the impact on the total code coverage is relevant, where even relatively small changes can considerably affect, either positively or negatively, the percentage of coverage of the changed unit.
Learning Sentiments using Co-occurrence Analysis

As part of natural language processing, sentiment analysis intends to investigate the author's emotions while writing a given text. This paper proposes a new method that uses a co-occurrence graph that can be automatically extended in a background reading (and learning) process, when a person's additional text source is available. The proposed method applies the concepts of PageRank to find the sentiment value of a considered node depending on the sentiment value of predecessor nodes that point to it and iteratively process those values in the same manner. The experiment results showed that 1) the methods described converge and 2) from a few, initially labelled positive (good) and negative (bad) words precise sentiment values for other words can be obtained, which corresponds to the authors emotions.
Developing a Minimum Dataset for a Mobile-based Contact Tracing System for the COVID-19 Pandemic

Context: Contact tracing is a cornerstone community-based measure for augmenting public health response preparedness to epidemic diseases such as the current coronavirus disease 2019 (COVID-19). However, there is no an agreed data collection tool for the unified reporting of COVID-19 contact tracing efforts at the national level. Objectives: The purpose of this research was to determine the COVID-19 Contact Tracing Minimal Dataset (COV-CT-MDS) as a prerequisite to develop a mobile-based contact tracing system for the COVID-19 outbreak. Methods: This study was carried out in 2020 by a combination of literature review coupled with a two-round Delphi survey. First, the probable data elements were identified using an extensive literature review in scientific databases, including PubMed, Scopus, ProQuest, Science Direct, and Web of Science (WOS). Then, the core data elements were validated using a two-round Delphi survey. Results: Out of 388 articles, 24 were eligible to be included in the study. By the full-text study of the included articles and after the Delphi survey, the designed COV-CT-MDS was categorized into two clinical and administrative data sections, nine data classes, and 81 data fields. Conclusions: COV-CT-MDS is an efficient and valid tool that could provide a basis for collecting comprehensive and standardized data on COVID-19 contact tracing. It could also provide scientific teamwork for health care authorities, which may lead to the enhanced quality of documentation, research, and surveillance outcomes.