Reviewerly/exHarmony huggingface dataset:
Construction of Control Rights Allocation Index of Listed Companies Based on Neural Network and Machine Learning

Control power is a core issue that every listed company pays great attention to. The company’s shareholding structure directly affects the allocation of control rights. Therefore, the shareholding structure of listed companies is analyzed, and various factors related to the allocation of company control rights are discussed. It is very important to build indicators of control allocation of listed companies and improve the governance model of listed companies. Based on this, this article proposes to use neural networks and machine learning techniques to build related models and solve related problems. This article takes the control allocation index of listed companies on the SSE and SZSE platforms under good securities’ market conditions as the research object and takes the stock holding allocation of listed companies as a reference for the control allocation index. Combine sliding removal technology and approximate entropy with sample entropy, select the sliding window and sliding step size as 21 data, keep the sliding window unchanged, and calculate the approximate entropy and sample entropy of the sequence after removing 21 data for each sliding value to analyze the correlation between the rate of return, complexity, and effectiveness. The results of the study show that the mean and median of the majority shareholder’s equity pledge behavior are 0.249 and 0, respectively, and the mean and median of the majority shareholder’s equity pledge ratio are 0.147 and 0, respectively, indicating that 24.9% of the companies in the sample have major shareholder equity. Pledge is limited by sample data, and the proportion of major shareholders’ equity pledge is moderate, which means that there is a certain gap in the quality of internal control between companies.
Behavior and usability analysis for multimodal user interfaces

Multimodal interfaces offer ever-changing tasks and challenges for designers to accommodate newer technologies, and as these technologies become more accessible, newer application scenarios emerge. Prototype development and user evaluation are important steps in the creation of solutions to these challenges. Furthermore, playful interactions and games are shown to be important settings to study social signals of interaction people. Research in multimodal analysis brings together people with diverse skills and specializations on the integration of tools in different modalities, to collect and annotate data, and to exchange ideas and skills, and this special issue is a reflection of that collective effort.
Career Strategies Approach for the Digitalised World Requirements

In the conditions of global digitalization of business, which influence on the competitiveness of entrepreneurs, on complete regions and national economies, it is of great importance to implement graduates career strategies. In the paper authors offer the approach of development a successful graduates career strategy, aimed in the systematic implementation of the formation of professional competences of young people. In the research the questionnaire was conducted among students regarding their motivation for study and employment, and in-depth interviews based on open questions were used to identify the main factors influencing the career development of managers and specialists who are associated with digitalization. The social features of the development of graduates career strategies are revealed. One of the main social barriers to the implementation of career growth strategies is the social stereotypes that exist in society and guide young people in ineffective career scenarios. The authors proposed an effective model for the development of the successful personal career strategy based on the development of professional digital competencies.
Number Theoretic Transform: Generalization, Optimization, Concrete Analysis and Applications

Number theoretic transform (NTT) is a basic mathematic operation, and is particularly fundamental to the practical implementations of cryptographic algorithms based on lattices with algebraic structures. In this work, we make a systematic and comprehensive study of NTT and its variants. We first review the NTT technique and the recent advances raised in the implementations of practical lattice-based cryptography. We clarify the relationship of some existing NTT variants, and prove their computational equivalence. We then make the generalizations of NTT, analyze their exact computational complexity, and derive the optimal bounds. Finally, we show the applications of our results to some prominent practical lattice-based algorithms.
Architectural Dependency Analysis: Addressing the Elephant in the Room

everal tools have been developed to help manage software evolution and maintenance and assist with architecture conformance and quality analysis.Most advances in automation and tooling have been variations on providing static analysis metrics at the level of code, object-oriented design, and module view of an architecture, although some have focused on version control history to understand the impact of change or discover architecture smells. 1,2Our experience shows that a multiview architectural dependency analysis approach is more effective in conducting change impact analysis than approaches limited to the module view of an architecture. 3,4The focus on syntactic dependencies related to data and control flow in a module view precludes dependencies from other perspectives, such as those that express semantic transformation associated with performance, availability, safety and testing (for instance, understanding the impact of splitting a module or using a module as a standard interface
The influence of informatization on the systemic pedagogical traditions of the information and educational environment higher military education

The article examines the features of the influence of informatization on the systemic pedagogical traditions of military education in Russia. The authors believe that in the information and educational environment of military universities, under the influence of the introduction of elements of e-learning, information and information and communication educational technologies, there is a "partial" transformation of the traditional pedagogical system, which means its further functioning, taking into account the didactic innovations of electronic pedagogy and continuously improving information dangers , risks and threats in the global educational IT space.&#x0D; The practice of informatization of the educational environment of the Military University and other military universities shows that due to the introduction and saturation of information and pedagogical methods of e-learning, IT and ICT educational technologies in the information and educational environment of military universities, a fundamental adjustment of the systemic pedagogical traditions of military educational institutions and the established reproductive -informative learning model&#x0D; The renewal of the traditional pedagogical system comes into conflict and dangerous conflict with the "network" generation of "independent" learning content for an individual, whose consciousness, under the influence of the Internet, found itself in conditions of free access to universal knowledge, but at the same time completely without methodological, ideological, psychological, moral and political and legal conviction in its reliability, outside the philosophy of security and psychological protection.
Research Note: An Investigation of Cybercrime Victims’ Reporting Behavior

Abstract Even though the dark number is especially high for cybercrimes, it is unclear by whom and to whom cybercrime is reported. Therefore, this study will answer the following questions: (1) who reports cybercrime incidents, and (2) to whom do victims report cybercrime? Analyses based on survey data including 334 recent cybercrime victims, indicate that 73.4% of victims do not report the incident. Cybercrime victims who do report are significantly older. In addition, 40.4% of the reporting victims notified the police, while others contacted a wide diversity of organizations. Based on these results, awareness raising campaigns should especially encourage young victims to report cybercrime incidents to the police. It would moreover be useful to appoint and promote a more centralized reporting channel.
Dynamic Graph based Method for Mining Text Data

An improved graph based association rules mining (ARM) approach to extract association rules fromtext databases is proposed in this paper. The text document in the proposed technique is read only once to lookfor the terms whose occurrences are greater than some threshold value, these terms are stored in a file with theirfrequencies, then they are represented as nodes in a weighted directed graph where edges represent relationsbetween these terms, the edges will denote the associations between terms while the edges' weights denote thestrength or confidence of these rules. The proposed method is called Dynamic Graph based Rule Mining fromText (DGRMT) because the graph is built level by level according the length of a sentence (number of frequentterms). Weighted subgraph mining is used to ensure the efficiency and throughput of the proposed technique;only the most frequent subgraphs are extracted. The proposed technique is validated and evaluated using realworld textual data sets and compared with one of the best graph based rule mining technique, which is algorithmfor Generating Association Rules based on Weighting scheme(GARW). The results determine that the proposed approach is better than GARW on almost all textual datasets.
Risk Analysis in Movie Recommendation System Based on Collaborative Filtering

The main aim of any project or software is to provide customer satisfaction at the end. The problem in Movie Recommendation System is unusual recommendation of movies to users, i.e., liking and disliking of recommended movie. This problem or risk impacts customer satisfaction, as a result of which the software will not be used much by the end users, and the company will be in huge loss. So, this paper provides you a detail description of recommendation system, steps of how to analyze risks and how to tackle or mitigate them using genetic algorithm based on collaborative filtering approach. Single and multiobjectives were implemented.
Mining method of public opinion related topic in network multimedia data

Social media has become the platform for rumors rapid propagation, more and more users adopt the pictures and videos to express their opinions in order to avoid being detected by text-based approaches, which has greatly affected the efficiency of online public opinion monitoring. For tackling the above-mentioned problem, this paper mainly studies how to extract the related opinions from the multimedia network data. Firstly, three typical events in Sina weibo are selected as the research targets, in which the web crawler is designed to collect the multimedia data. Secondly, the text detection algorithm based on connectionist text proposal network (CTPN) is employed to perform the text localization, and then a fusion method by combining DenseNet and connectionist temporal classification (CTC) is employed to perform text extraction. Finally, an effective algorithm by combining multi granularity-latent Dirichlet allocation (MG-LDA) and jieba is proposed to accurately identify the related topics from the extracted text. The experimental results show that the proposed method can accurately extract the texts from multimedia with different formats, resolutions and colors, and can also extract the texts with different rotating angles. Our research provides the solid foundations for online public opinion monitoring.
3D software simulator for primary care training

"Medical software simulators are used to teach specific procedures that allow the user to follow only a strict sequence of steps without the possibility of alternative, avoiding considering the consequence of an error and then potentially admitting its tolerance. Usually these applications are a state machine implementation where learners must make a specific action to obtain a specific result. In our work we propose a brand-new approach with a “open world” serious game medical simulator, based on Agent Based Model Paradigm. Starting by these concepts, a user can learn and test his skills in a dynamic environment that changes in real time based on his actions. We provide a configurable starting set of conditions (patient heath state, available medical instruments and drugs) to create, potentially, infinite scenarios; alongside these boundary values the game permits to configure real time events that influence patient in an unpredictable way by the user side."
Online Proctoring Put to the Test

Abstract Traditionally, universities are places where students and professors physically interact. Knowledge and skills of students are generally tested in oral and written forms, on campus. In times of COVID-19, however, a new common in education is called for as students are unable to be physically present on campus. Both education and assessments need to take place at a distance. This development creates opportunities. However, education at a distance also bears risks, such as the risk of fraud. Universities are responsible for safeguarding the quality of education, assessments, and diplomas. Only students who actually master the learning objectives should receive a diploma. Online proctoring systems can help to achieve this. A challenging issue is that some students oppose these systems from the perspective of privacy and data protection. In view of the key focus of this book, moving towards a new common while preserving the best of the old common, it is thus important to put online proctoring systems to the test and consider their impact on human rights.
Mining Bug Report Repositories to Identify Significant Information for Software Bug Fixing

Most studies relating to bug reports aims to automatically identify necessary information from bug reports for software bug fixing. Unfortunately, the study of bug reports focuses only on one issue, but more complete and comprehensive software bug fixing would be facilitated by assessing multiple issues concurrently. This becomes a challenge in this study, where it aims to present a method of identifying bug reports at severe level from a bug report repository, together with assembling their related bug reports to visualize the overall picture of a software problem domain. The proposed method is called “mining bug report repositories”. Two techniques of text mining are applied as the main mechanisms in this method. First, classification is applied for identifying severe bug reports, called “bug severity classification”, while “threshold-based similarity analysis” is then applied to assemble bug reports that are related to a bug report at severe level. Our datasets are from three opensource namely SeaMonkey, Firefox, and Core:Layout downloaded from the Bugzilla. Finally, the best models from the proposed method are selected and compared with two baseline methods. For identifying severe bug reports using classification technique, the results show that our method improved accuracy, F1, and AUC scores over the baseline by 11.39, 11.63, and 19% respectively. Meanwhile, for assembling related bug reports using threshold-based similarity technique, the results show that our method improved precision, and likelihood scores over the other baseline by 15.76, and 9.14% respectively. This demonstrate that our proposed method may help increasing chance to fix bugs completely.
AirSim Drone Racing Lab.

Autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control. We introduce AirSim Drone Racing Lab, a simulation framework for enabling fast prototyping of algorithms for autonomy and enabling machine learning research in this domain, with the goal of reducing the time, money, and risks associated with field robotics. Our framework enables generation of racing tracks in multiple photo-realistic environments, orchestration of drone races, comes with a suite of gate assets, allows for multiple sensor modalities (monocular, depth, neuromorphic events, optical flow), different camera models, and benchmarking of planning, control, computer vision, and learning-based algorithms. We used our framework to host a simulation based drone racing competition at NeurIPS 2019. The competition binaries are available at our github repository.
Peningkatan Nilai Akurasi Prediksi Algortima Backpropogation (Kasus: Jumlah Pengunjung Tamu pada Hotel berbintang di Sumatera Utara)

The research objective is to analyze and test whether the number of foreign tourist arrivals in Indonesia can be predicted using artificial intelligence techniques. The research data were obtained from the Indonesian Central Bureau of Statistics for the tourism category. The data used in the study is data on the number of tourist visits from 2003 - 2018. The artificial intelligence technique used in this study is the artificial neural network technique using the backpropagation method. The study will explore backpropagation parameters such as learning rate and network architecture where the results of the study are expected to help provide information for decision-makers to attract more foreign tourists to Indonesia because this has an impact on improving the economy in Indonesia. The research results show that from 4 architectural models tested (7-2-1, 7-5-1, 7-10-1 and 7-2-10-1) with a learning rate of 0.1; 0.01; 0.001; 0.2; 0.02; 0.002; 0.3; 0.03; and 0.003 where the 7-10-1 model with learning rate = 0.02 is obtained which is the best prediction model with the Root Mean Squared Error is 0.094 and the relative error is 4.49% +/- 10.21%. The accuracy of the truth obtained is 96%.
A Fall Risk Evaluation and Feedback System for Older Adults

Falls are widespread among older adults causing serious injuries and threatening their quality of life. An approach to estimate fall risk, and to prevent falls, is the Timed-Up-and-Go (TUG) test. The TUG test has established validity and reliability. However, as a clinical test, it is not accessible for personal use. To enhance its reach, the authors developed a prototype called Fall Risk Evaluation and Feedback System (FREFS). The prototype is a Kinect-based depth sensor system with interfaces that support older adults in completing the TUG test and receiving personalized test results. The personalized feedback feature is novel that existing prototypes do not include. This study's goal was to gain knowledge of the user experience of FREFS. This research applied methods of observation, interviews, and collected responses on the System Usability Scale (SUS). Results show participants perceived the system as usable, with SUS score of 84.3, but also revealed issues. First, users were unsure how to deal with the TUG tests results when the results showed high fall risk. Second, clearer instructions and reduction of information overload specifically for these age groups were needed. Third, a communication approach embedded into the system would be required (i.e., a link to a chatbot feature or a button to connect to a real person). Overall, the study demonstrated that such a prototype cannot be fully automated; it needs a sociotechnical system solution that includes human communication.
Cross-Domain Text Classification Based on BERT Model

Diversity of structure and classification are difficulties for information security data. With the popularization of big data technology, cross-domain text classification becomes increasingly important for the information security domain. In this paper, we propose a new text classification structure based on the BERT model. Firstly, the BERT model is used to generate the text sentence vector, and then we construct the similarity matrix by calculating the cosine similarity. Finally, the k-means and mean-shift clustering are used to extract the data feature structure. Through this structure, clustering operations are performed on the benchmark data set and the actual problems. The text information can be classified, and the effective clustering results can be obtained. At the same time, clustering evaluation indicators are used to verify the performance of the model on these datasets. Experimental results demonstrate the effectiveness of the proposed structure in the two indexes Silhouette coefficient and Calinski-Harabaz.
A Refined Structure Preserving Image Abstraction Framework as a Pre-Processing Technique for Desire Focusing on Prominent Structure and Artistic Stylization

This work identifies the strong dominant features by its location and extracts the image features for the purpose of automatic desire focusing on prominent structure and artistic stylization of images. At the pre-processing level, dataset image is treated using refined structure preserving image abstraction framework which can deliver the best effectual structure preserved abstracted results by utilizing visual attributes from 2D color image. The presented framework efficiently conserves the structural characteristics in the foreground of an input image by exhaustively amalgamate the series of non-photorealistic rendering image filters over meticulous investigational work and it also reduces the background substance of an image. The framework assesses image and object space details to generate structure preserved image abstraction thus distinguishing the accentuated elements of an enhanced structures using Harris key-point feature detector and chooses the 100 major unique dominant feature locations among available features. This work automatically selects the unique location from the extracted features using polynomial region of interest and unselected image regions and its background are blurred using Gaussian motion blurring with point spread function. Deblurring the selected region using wiener filtering to get the desire focusing on prominent structure followed by color quantization and flow-based bilateral filtering is applied over focused structural region to achieve artistic stylization. Efficiency of the framework has been validated by carrying out the trials on the selected Flickr repository, David Mould and Ruixing Wang dataset. In addition, user’s visual opinion and the image quality estimation methods were also utilized to appraise the proposed pre-processing framework. This work lists the structure preserving image abstraction framework applications, limitation, execution difficulties and future work in the field of Non-photorealistic rendering domain.
Remarks and Prospects on Explainable Fuzzy Systems

Since 2016, there is an increasing interest on research topics such as fairness, accountability and interpretability, in the entire community of researchers in Artificial Intelligence. However, there were researchers working hard on these research topics much earlier. For example, Michalski published his comprehensibility postulate in the 1980s while Zadeh introduced the notions of fuzzy sets and systems in the 1960s and the paradigm of computing with words in the 1990s. Moreover, Zadeh was talking about precisiated natural language in 2004. All these pioneer contributions to explainable Artificial Intelligence should be recognized properly. Explainable fuzzy systems go a step ahead of interpretable fuzzy systems and are ready to act as a cornerstone in the context of explainable Artificial Intelligence where interdisciplinary research fields (e.g., Computational Linguistics, Argumentation Technology, Human-machine Interaction, Philosophy, Math, Computer Science, Neuroscience, and so on) meet. Explainable fuzzy systems provide users with interactive explanations in natural language, the preferred modality among humans, with visualization as a complementary modality. Fuzzy Logic endows explainable fuzzy systems with a solid mathematical background to model properly the inherent uncertainty, imprecision and vagueness of human language.