Reviewerly/exHarmony huggingface dataset:
Analysis of digitizing archive applications to reduce paper usage

Abstract Various data and information in the form of documents will accompany every activity in an institution. The existence of a reliable management to create, store, record, distribute, and maintain documents is needed to generate dynamic information traffic. In addition, the application of document processing can reduce the use of paper in every activity of an institution which will indirectly reduce excessive forest exploitation. The purpose of this research is to analyse digitizing archive application as an integrated archive management application to reduce the use of paper in every activity of an institution. Qualitative descriptive methods was used as a research methodology. The result of this study based of the analysis of the digitizing archive application that has been done in terms of data and procedures can be concluded that digitizing archive application can reduce the use of paper as a document in various institutional activities.
Detection of Spammers Using Modified Diffusion Convolution Neural Network

Social network brings convenience to our life, but it also provides a platform for spammers to spread malicious information and links. Most of the existing methods for identifying spammers mainly rely on the user’s behavior information to learn classification models. But, privacy and information security issues make it impossible to monitor all behavioral of users. In addition, owing to the diversity and variability of spammers’ strategies, it is difficult to distinguish them from legitimate users only by their own behavior. To solve this challenge, in this paper we propose a novel spammer detecting method using DCNN (Diffusion Convolution Neural Network) which is a graph-based model. And DCNN model can learn behavior information from other users through the graph structure (i.e., social network relationships). However, the original DCNN model is a general classification model. In order to make the original DCNN model more effective for detecting spammers, we modify it by using attenuation Coefficient and social Regularization, which is called DCNN+CR model. The experimental results on real-world Twitter dataset show that the proposed DCNN+CR model outperforms existing methods, especially in terms of accuracy and F1-score.
On Software Implementation of Gabidulin Decoders

This work compares the performance of software implementations of different Gabidulin decoders. The parameter sets used within the comparison stem from their applications in recently proposed cryptographic schemes. The complexity analysis of the decoders is recalled, counting the occurrence of each operation within the respective decoders. It is shown that knowing the number of operations may be misleading when comparing different algorithms as the run-time of the implementation depends on the instruction set of the device on which the algorithm is executed.
Difficulties and Solutions for Public Health Data Governance Under the Normalization of Epidemic Prevention and Control

Abstract COVID-19 has become the world’s largest public health emergency in recent years. For the needs of epidemic prevention, governments of various countries encourage public and private institutions to actively use digital technology to carry out joint prevention and control work. The use of data governance to prevent and defuse major risks and improve the ability to respond to public health emergencies is an important part of promoting the modernization of the national governance system and governance capabilities in the new era. This article believes that the establishment of a data security governance mechanism in public health emergencies should be based on application scenarios, with legality as the basic requirement, establish an accountability system, adhere to the principles of transparency, purpose restriction, confidentiality, and minimum necessity, and standardize digital technology design based on this Standards and effective supervision of public health data.
Towards Knowledge Graphs Federations: Issues and Technologies

In recent decades, knowledge graph plays an increasingly significant role in intelligent information services. However, for some domains, knowledge graphs are constructed for special purposes and disconnected with each other, which fails to take advantage of knowledge from different knowledge graphs. To this end, we are motivated to propose the concept of knowledge graph federation. In this keynote, we first discuss the issues about knowledge graph federation, and then we introduce two technologies of automatic knowledge graph construction, i.e., relation extraction and entity alignment. The issues in this keynote will provide guidelines for the development of knowledge graph technology.
An Attention-Based Approach to Rule Learning in Large Knowledge Graphs

This paper presents a method for rule learning in large knowledge graphs. It consists of an effective sampling of large knowledge graphs (KGs) based on the attention mechanism. The attention-based sampling is designed to reduce the search space of rule extraction and thus to improve efficiency of rule learning for a given target predicate. An implementation ARL (Attention-based Rule Learner) of rule learning for KGs is obtained by combining the new sampling with the advanced rule miner AMIE+. Experiments have been conducted to demonstrate the efficiency and efficacy of our method for both rule learning and KG completion, which show that ARL is very efficient for rule learning in large KGs while the precision is still comparable to major baselines.
Research on State Estimation of Outdoor Mobile Robot Based on Multiple Sensor Combination

Multi-sensor information fusion technology is the core technology to achieve accurate and reliable vehicle positioning. For the selection of sensor types and quantities, this article proposes a variety of combinations. Through the extended Kalman filter algorithm, many experiments were carried out to analyze different results and their reasons, and obtain the state estimation effect of different sensor combinations in outdoor situations. This test provides a certain test result reference for future outdoor automatic driving technology.
AN INTEGRATIVE APPROACH TO ASSESSING THE RESULTS OF PRACTICAL TRAINING OF UNIVERSITY STUDENTS (ON THE EXAMPLE OF THE DIRECTION 44.04.01 "PEDAGOGICAL EDUCATION")

The practical training of a university student takes a significant place from the standpoint of the formation and assessment of competencies (indicators). The article substantiates the need to use an integrative approach to assessing the results of practical training of students (on the example of the direction 44.04.01 "Pedagogical education"). Approaches and methods used: integrative approach; theoretical analysis of literature; modeling; questioning of teachers of practice bases, universities and students. Integration at the stage of giving the final mark for the practice is explained by the need to take into account several aspects: the mark given to the student on the basis of practice for the work done (lessons, extracurricular activities, performance of other types of work); assessment for the prepared documentation; protection grade; assessment of competence, as well as the opinion of the student. Using the example of educational technological (design and technological) practice, indicators for evaluating the results of practice are highlighted, points are determined using the rating, a scale for converting the scores into a standard mark is presented. The described approach is positively assessed by the heads of practice bases, heads of practices from the university and students. The described approach can be carried over to other areas of training; will allow to implement uniform requirements for students, increase the objectivity of the final grade for practice.
Deep User Representation Construction Model for Collaborative Filtering

Model-based collaborative filtering (CF) methods can be divided into user-item methods and item-item methods. In most cases, both of them can be seen as modeling the user-item interaction and the only difference between them is that they adopt different ways to build user representations. User-item methods obtain user representations by directly assigning each user a real-valued vector and do not consider users’ historical item information. However, users’ historical item information can reflect users’ preferences to some extent and can alleviate the problem of data sparsity. Ignoring this information may lead to incomplete construction of user representations and vulnerability to data sparsity. Although existing item-item methods address this problem by using the users’ historical items to build the user representations, they always use the same vector to represent the same historical item for different users, which may limit the expressiveness and further improvement of the models. In this paper, we propose Deep User Representation Construction Model (DURCM) to construct user presentations in a more effective and robust way. Specially, different from existing item-item methods that directly use historical item vectors to build user representations, we first adopt a conversion module to convert a user’s historical item vectors into personalized item vectors, which enables that even the same item has different expressions for different users. Second, we design a special attention module to automatically assign weights to these personalized item vectors when constructing the users’ final representations. We conduct comprehensive experiments on four real-world datasets and the results verify the effectiveness of our proposed methods.
The Development of the 3D Role-Playing Game on PC with an Assistive System for Deuteranopia

The purposes of the study were 1) to design and develop the 3D role-playing game on PC with an assistive system for green vision impaired people, 2) to assess the quality of the game created by the experts, and 3) to evaluate the satisfaction of the samples towards the game. This game was qualitatively evaluated by three experts in gaming development through a purposive sampling. Thirty participants were recruited via accidental sampling method to evaluate a satisfaction towards the game. The quality evaluation results was found to be at a satisfactory level (mean = 3.90/SD = 0.12). The participants rated their satisfaction towards the game at the very satisfactory level (mean = 3.64/SD = 0.05). It can be summarized that the 3D role-playing game on PC with an assistive system for green vision impaired people is appropriate to be used for the visually impaired. The assistive system can also be applied to further develop games for the vision-impaired people. and relaxing users, which can be compared to being on a vacation.
A Parallelized Frequent Temporal Pattern Mining Algorithm on a Time Series Database

For many years, time series data have been considered as one of the most popular significant data forms in our daily life. Time series exist in many application domains such as finance, medicine, geology, meteorology, and telecommunication. Among the time series mining tasks, frequent temporal pattern discovery is an interesting task because this task brings us a deep insight view of relationships between many objects and events through time. However, it is challenging when a combinatorial explosion occurs with many longer time series. It is more challenging if more informative patterns are required from a time series database. In this paper, we propose a parallel algorithm, called PTP, to cope with the frequent temporal pattern mining task. Our PTP is developed with multithreading on a frequent temporal pattern tree where each branch is processed in parallel. In addition, our PTP maintains the details of each frequent temporal pattern not only from its frequent occurrences in an individual time series but also from its frequent inter-time series associations. As a result, frequent temporal patterns discovered by PTP are more informative with explicit and exact temporal information, showing the relationships among the objects/events corresponding to the time series. Through the experimental results on real time series, PTP outperforms the brute force algorithm and the existing non-parallel algorithm in terms of both time and space. The found frequent temporal patterns can be further analyzed for other tasks such as prediction, classification, and clustering.
On the Formally Self-Dual Binary [30, 15, 8] Codes

We classify all binary linear [30, 15, 8] codes with dual distance 8 using the software package QextNewEdition. There are exactly 42 such codes and all of them are formally self-dual even codes. This result closes the long standing open problem about the existence of a formally self-dual odd code with these parameters.
Analysis and Visualization

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Analysis of Polysemy using Variance Values for Word Embeddings by BERT

Word embeddings output by BERT depends on the context of the sentence in which the word appears. Therefore, we collect examples of word w and calculate their variance values from word w's embeddings obtained by BERT, and it is considered that the variance values correspond to the polysemy of word w. We collected examples of polysemous words (head)", "(meaning)", "(nuclear)", "(record)", "(language)", "(chest)") and monosemous words (production)", "(politics)", "(consciousness)", "(protest)", "(scores)") and compared the variance values. We expected that variance values for polysemous and monosemous words would be large and small, respectively. We also investigated which BERT position has the most influence on polysemy. However, we found that it is difficult to measure polysemy using the previous variance values, and we considered the cause of this difficulty.
A Semi-structured Data Classification Model with Integrating Tag Sequence and Ngram

Many collaboratively building resources, such as Wikipedia, Weibo and Quora, exist in the form of semi-structured data and semi-structured data classification plays an important role in many data analysis applications. In addition to content information, semi-structured data also contain structural information. Thus, combining the structure and content features is a crucial issue in semi-structured data classification. In this paper, we propose a supervised semi-structured data classification approach that utilizes both the structural and content information. In this approach, generalized tag sequences are extracted from the structural information, and nGrams are extracted from the content information. Then the tag sequences and nGrams are combined into features called TSGram according to their link relation, and each semi-structured document is represented as a vector of TSGram features. Based on the TSGram features, a classification model is devised to improve the performance of semi-structured data classification. Because TSGram features retain the association between the structural and content information, they are helpful in improving the classification performance. Our experimental results on two real datasets show that the proposed approach is effective.
Banking and Financial Services Industry in the Wake of Industrial Revolution 4.0

The banking and financial services industry today stands at the crossroads between the traditional methods of business and the evolving modern methods of banking and providing financial services. Technological advancement in the field of finance has led to a whole new form of doing business which is a radical departure from the conventional methods of doing business. The players in the field of banking and finance are facing challenges and competition from entities which never existed in the traditional sphere. The banking and financial industry is not only facing stiff competition from these new age players; it is also facing challenges to find fresh talent who can tide off this industry. This chapter in its existing form shall present an overview of the banking and financial industry of today in the wake of Industrial Revolution 4.0 and identify some associated challenges.
Determining accurate hand positions in three-dimensional space using twin-axis electrogoniometer sensors

Abstract Electrogoniometers are low price sensors which are easily attached to the body in any environment. Although electrogoniometer sensors are typically used for angular measurements, they can also be used to determine position. This study aimed to accurately determine hand position during the performance of five daily life activities using two electrogoniometer sensors to measure shoulder and elbow angles simultaneously. The measurement of joint angles involves some errors which are divided into intrinsic and extrinsic errors. These errors cause considerable inaccuracies in the estimated hand positions. To overcome this issue, the errors identified are compensated for in two phases, the angular phase and the positional phase, in which a polynomial function and an Elman neural network are used for error compensation, respectively. The derived hand trajectories and the decrease of the root mean square error at every stage of the error compensation, along with the variance accounted for of the Elman networks for each task, show the effectiveness of the proposed method.
EFEITOS DA GINÁSTICA CEREBRAL EM PACIENTES ONCOLÓGICOS COM SINTOMAS DE DEPRESSÃO

Cancer is characterized by the disordered growth of cells that tend to spread quickly through the body, which can cause a great physiological and psychological impact on the individual's life, including depression. Therefore, it is necessary to build a multidisciplinary relationship between health professionals who work with these patients. Objective: The objective of this research is to evaluate whether physical therapy through brain gymnastics is able to reduce the symptoms of depression in patients diagnosed with cancer. Methods: Experimental study carried out at the Instituto Lar Esperanca e Vida Mateus Loureiro Ticle in the municipality of Lavras - MG. The Patient Health Questionnaire-9 (PHQ-9) questionnaire was used to assess the degree of depression, applied before and after ten weeks (two weekly meetings) of a brain exercise program. All patients with symptomatology favorable to depression were included according to the PHQ-9 questionnaire and those with cognitive impairment were excluded. The Wilcoxon test was used with significance at 5% probability for data analysis. Results: The sample included 16 individuals with a mean age of 57.2 ± 7.2 years, divided into asymptomatic and symptomatic groups for depression. Breast cancer was the most prevalent (37.5% of patients). There was a significant improvement in the symptoms of depression (p = 0.008) between the initial and final mean scores of the PHQ-9 questionnaire. Conclusion: Brain gymnastics significantly reduced the symptoms of depression, being, therefore, a possibility of effective treatment to alleviate these symptoms in cancer patients.
Generation of Geiger-Muller detector dataset for simulation of radioactive source localization algorithm

Abstract Detector dataset in digital data is required to simulate and evaluate a particle filter (PF) algorithm. The PF is used to estimate the position and intensity of unknown hotspots within a region of interest (ROI) from sparse sampling points around the ROI. The dataset will emulate GM measured data at these sampling points. GM radiation detection model, Poisson random variable generator, and Gaussian noise generator were implemented to generate the datasets. Next, using the datasets as input to the PF code, the corresponding output is evaluated for various configuration of hotspot locations and radiation intensities. The resulting detector datasets and the particle filter output were analyzed; and the results are presented in the paper.
¿Cómo promover la Ciudadanía Digital?

There are several attempts that have been made in Mexico in recent years and through the different six-year periods of Government, to insert Information and Communication Technologies (ICT) in the teaching and learning process at the levels of basic education. The foregoing was attempted through the creation of multiple Federal Government programs for that purpose. One of these programs establishes digital competencies and within these can be located the competence of Digital Citizenship. The present work shows a small outline of the competition of digital citizenship in new students of Educational Institutions of the State of Tabasco. Digital citizenship does not have a single focus, there are multiple edges that can be addressed. Finally, some proposals for various actions are exposed, through which the acquisition of this digital competence can be achieved. Teachers, as active agents in the teaching and learning process, can be important actors through the promotion of the proposed activities and practices.
A Lightweight Multi-label Image Classification Model Based on Inception Module

Convolutional Neural Networks (CNNs) have shown enormous potential for solving multi-label image classification problems. In recent years, a lot of experimentation is done with various state-of-the-art CNN architectures. The CNN architectures have evolved to become deeper and more complex in these years. These architectures are big due to a greater number of layers and trainable parameters. However, there are many real-time applications which demand fast and accurate classification. Keeping this in consideration, a simple model inspired by Inception V7 is proposed for multi-label image classification in this work. The proposed model consists of six convolution layers including three inception blocks with one million parameters approximately, which are very few as compared to many state-of-the-art CNN models. This makes the model deployable in lightweight devices for some real-time applications. The comparison experiments with other deep state-of-the-art CNNs were carried out on image datasets from multiple domains including general benchmark datasets, medical datasets, and agricultural datasets. The model exhibits better performance on many datasets making it feasible to use in various domains for multi-label image classification.
Information Community as Natural the Phase of the Historical Development of World Civilization

Vladimir Sergeevich Grodsky* Author Affiliations Samara State Polytechnic University, Russia Received: January 16, 2021 | Published: January 22, 2021 Corresponding author: Vladimir Sergeevich Grodsky, Samara State Polytechnic University, Russia DOI: 10.26717/BJSTR.2021.33.005384
On a diversity of perspectives and world views: Learning under Bayesian vis-á-vis DeGroot updating

In an influential recent paper, Mailath–Samuelson formalize learning and reasoning through “model-based inference” and Bayesian updating. In this announcement, we substitute DeGroot’s heuristic for Bayesian updating by (i) furnishing a plausible interaction matrix that agents use to weigh each other’s beliefs, and by (ii) using this matrix to derive properties of the process for the DeGroot updating of beliefs by agents and oracles. The alternative argumentation that we provide facilitates bridging the literature on networks and that on model-based learning and inference; and it identifies productive and ongoing directions for further investigation.