Reviewerly/exHarmony huggingface dataset:
SWOT analysis of subscriptions to printed and electronic periodicals

The rapid development of digital technology has led to the need of replenishing library collections with both traditional and electronic resources. Against this backdrop, a topical question has emerged: In what proportion should the library collections be replenished with printed and electronic resources? This topic is of particular importance in the context of forming collections of periodicals. The purpose of the study was to assess the potential of print and electronic periodicals and identify criteria for selecting the optimal approach to replenishment of library collections. For the first time, an attempt was made to use the strategic planning method - SWOT analysis. The study contained three stages, the description and the results of each are presented in the article. The analysis resulted in assessing the potential of electronic and printed periodicals to provide information services and proposing criteria for choosing the format of subscriptions to periodicals.
Optimal deep recurrent neural network for sentiment grade classification

Purpose Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not. Design/methodology/approach A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity. Findings By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms. Originality/value The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted.
Local and Non-local Context Graph Convolutional Networks for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) for skeleton-based action recognition have achieved considerable progress recently. However, there are still two unresolved shortages. One is that the input data lacks high-level motion information of discriminant features. The other is that the access to the long-range action features is limited by the local sampling scale. In this work, we propose a new model called local and non-local context graph convolutional networks (LnLC-GCN). The first innovation is a motion enhanced graph containing high-level motion information which is served as the multi-stream input. Secondly, to overcome the limitations of local receptive field, we present a local and non-local context module based on the global context mechanism. Moreover, we use two optimization strategies of front-end fusion and non-local context feedback to further improve the accuracy of LnLC-GCN. For validation of the performance, numerous experiments were deployed on three public datasets, NTU-RGB+D 60 & 120 and Kinetics-Skeleton, strongly demonstrating that our approach achieves state-of-the-art.
Determining The E-Learning Implementation Gap According to The Standards of The European Association of Distance Teaching Universities: A Case Study in A Number of Public and Private Colleges in Baghdad

The study aims to determine the e-learning implementation gap following the standards of the European Associationof Distance Teaching Universities, which are strategic management, curriculum design, academic program design, academicprogram delivery, staff support and student support by identifying the gap and diagnosing the strengths and weaknesses of thecolleges investigated. The problem of the study was the emergence of many problems and challenges facing e-learning inhigher education institutions. Therefore, the need to adopt international and approved standards to work on appeared, where elearninghas become an essential part of the educational process and inherent in traditional education even after the demise ofthe Corona pandemic. The case study method was adopted to achieve the objectives of the study and to reach the The requiredscientific facts, as well as the use of the checklist as a tool for collecting data and information. The study cases were a numberof public and private colleges investigated in Baghdad, namely (College of Administration and Economics - BaghdadUniversity, College of Administration and Economics - Al-Mustansiriya University, Al-Esraa University College and Collegeof Administration and Economics - Uruk National University). The results showed that the College of Administration andEconomics, University of Baghdad ranked first in terms of the percentage of implementation, followed by the College ofAdministration and Economics, Al-Mustansiriya University, and then the Al-Esraa University College, which amounted to(69%, 64%, 61%) respectively, but this showed the opposite in the College of Administration and Economics, University ofUruk, with a large gap in implementation that amounted to (81%). The study concluded with a number of recommendations زThe most prominent of which was the interest in spreading the culture of e-learning in higher education institutions throughconferences or workshops that are held face to face or electronically for the purpose of conferencing and to increase awarenessand interest in it as it has become an integral part of the educational process and cannot be dispensed with at present and in thefuture.
Efficient Tabu Search Algorithm for the Cyclic Inspection Problem

Unmanned aerial vehicles (UAVs) are a powerful tool for remote monitoring of objects requiring early anomaly detection, emergency intervention, or measurement data collection. We consider the problem of determining the optimal path of a UAV performing remote inspection of objects. The UAV inspects objects repeatedly (infinitely many times) every specified period of time. We propose an effective heuristic algorithm based on the tabu search method. In its construction, we used some properties of the problem under consideration.
One-Shot Imitation Learning on Heterogeneous Associated Tasks via Conjugate Task Graph

One-shot imitation learning is one of the crucial topics in robot learning with the pursuit of higher intelligence. Recently, conjugate task graph (CTG) network has been applied to generalize the imitation of homogeneous tasks based on a single video demonstration, where a standard optimization method is utilized to update the parameters of graph neural network. Nevertheless, when dealing with heterogeneous associated tasks, the standard algorithm needs to be improved to acquire higher learning accuracy. Given a set of heterogeneous tasks containing N sets of homogeneous tasks, we propose an N -Step Alternating Optimization in CTG (NSAO-CTG) to accomplish a superior learning, where each step incorporates the nodes and edges corresponding to a new set of homogeneous tasks. Furthermore, NSAO-CTG with a novel update rule for the node localizer and edge classifier (NSAO-CTG+) is proposed for execution based on the association information between tasks. Extensive experiments demonstrate the effectiveness of the proposed method in one-shot imitation learning of heterogeneous associated tasks.
Indicating and Assessing Quality Criteria for Cause-Effect Models

It seems to be a common understanding in the TRIZ community that cause-effect models built for the same problems by different analysts may differ a lot and also that it requires some practice to build “good” cause-effect models. This paper aims at identifying reasonable quality criteria and feasible ways of applying them to models developed using Cause-Effect Chains Analysis (CECA) and Root Conflict Analysis (RCA+). The paper starts with considerations about human factors in causal modeling and a review of the relevant TRIZ literature. Then various approaches to quality are briefly presented, and the proposed assessment method is described. Although the most important semantic level has not been addressed, the rules of syntactical correctness have been formulated, which may be used for automated model validation in software applications.
A Study on Pedestrian Re-identification Based on Transfer Learning

In recent years, with the continuous development of deep learning-based image classification and recognition, artificial intelligence has become the most representative keyword of our time, intelligent video surveillance technology has also become a current research hotspot. Intelligent video surveillance system is mainly developed on computer vision, pattern recognition and machine learning techniques. It first captures videos or images by the surveillance camera, then filters redundant data, and finally extract effective features from the data for accurate detection and analysis. Pedestrian detection and Person re-identification (ReID) are the core of intelligent video surveillance systems. However, many negative factors such as the lack of large-scale datasets, the minor differences between pedestrians and the complex surveillance environment, still pose considerable challenges to the pedestrian re-identification task. In this paper, we proposed a deep convolutional neural network for video pedestrian re-identification algorithms. The proposed framework is developed based on the motion trajectory of pedestrian. Concretely, we first design a good deep convolutional neural network and train it using transfer learning technique, then we employ it and a weighted probability-based decision mechanism to recognize pedestrians in a series of video frames. To verify the effectiveness of the proposed method, we conduct relative experiments on the public dataset. The experimental results show that the proposed method achieves good performance in pedestrian re-identification.
Research of Tri-modal Mandarin Emotion Recognition Based on Speech, Facial Expression and Body Gesture

In order to overcome the problem that the accuracy and efficiency of Mandarin emotion recognition is low based on singal-modal, a tri-modal Mandarin emotion recognition method based on the feature-level fusion of three important modal of speech, facial expression and body gesture is proposed. Firstly, different emotional features are extracted from speech, facial expressions and body gestures, and then the three modal emotion features are fused by IAGA method. Finally, SVM classifier is used to construct prediction model and emotion recognition is completed on it. The CHEAVD Chinease multi-modal emotion dataset is utilized to evaluate our proposed methods. Compared with the traditional mono-modal and dual-modal emotion recognition, the experimental results show that the dual-modal fusion has higher emotion recognition rate than the mono-modal, and the tri-modal fusion has higher emotion recognition rate than the dual-modal, which verifies the effectiveness of the tri-modal Mandarin emotion recognition
Information and Probability Models of Students’ Independent Work in Modern Educational Technology

The paper presents mathematical models of students’ independent work, which is the dominant component of the educational process, both in its volume and in its didactic significance. These models are based on probability theory and set theory. As part of the research of the independent work of students in the conditions of the information and educational environment at university, a pedagogical experiment was conducted at Kuban State Technological University and Krasnodar Cooperative Institute (branch) of the Russian University of Cooperation, both located in the city of Krasnodar, Russia. The undergraduate students involved in the research entered university in the period from 2014 to 2016. As a result of the analysis of empirical data, a matrix reflecting the relationship between the success of students’ classroom work and independent work has been obtained. The authors have developed information and probability models that reflect the relationship between classroom and independent work of students.
Predicting the Concrete Security of LWE Against the Dual Attack Using Binary Search

The dual attack is widely used in the concrete security estimation of the learning with errors (LWE) problem. Predicting the concrete security of LWE against the dual attack, i.e., the minimal cost of the dual attack, is a constrained optimization problem. However, there is no complete theoretical analysis. We fill in this gap by proving that, for almost all LWE instances used in the design of public-key cryptographic schemes, the cost of the dual attack can be considered as a U-shape function. Therefore, we can predict the minimal cost with binary search. We use the binary search to predict the concrete security of all LWE-based algorithms in NIST-PQC and the experimental results demonstrate the accuracy of the binary search.
Improved Prosodic Clustering for Multispeaker and Speaker-Independent Phoneme-Level Prosody Control

This paper presents a method for phoneme-level prosody control of F0 and duration on a multispeaker text-to-speech setup, which is based on prosodic clustering. An autoregressive attention-based model is used, incorporating multispeaker architecture modules in parallel to a prosody encoder. Several improvements over the basic single-speaker method are proposed that increase the prosodic control range and coverage. More specifically we employ data augmentation, F0 normalization, balanced clustering for duration, and speaker-independent prosodic clustering. These modifications enable fine-grained phoneme-level prosody control for all speakers contained in the training set, while maintaining the speaker identity. The model is also fine-tuned to unseen speakers with limited amounts of data and it is shown to maintain its prosody control capabilities, verifying that the speaker-independent prosodic clustering is effective. Experimental results verify that the model maintains high output speech quality and that the proposed method allows efficient prosody control within each speaker’s range despite the variability that a multispeaker setting introduces.
Design and Application of Security Monitoring and Management Platform Based on Internet of Things Technology

Through the integration of communication technology, information technology and wearable equipment, precise positioning equipment, physical sign monitoring equipment, video monitoring equipment, and safety monitoring equipment, a safety monitoring management platform based on the Internet of Things technology is designed. The platform adopts a three-tier logical architecture, A smart security monitoring and management platform with data collection, processing, and analysis functions is built, which solves the problem of incompatibility of various subsystems. The prevention functions of real-time monitoring, data mining, and security linkage make it possible to achieve the expected goal of no blind spots in the prevention area and no loopholes in the prevention methods. The results of its application in an elderly care institution show that the platform can issue safety warnings in a timely manner, effectively reducing the incidence of accidents, and significantly improving the management efficiency of elderly care institutions and the quality of life of the elderly.
Building a Weighted Performance Indicator Concept utilized The Respondent’s Opinion Approach

This study discusses building the concept of weighting performance indicators based on respondents' opinions. The opinion of respondents has the power to provide an assessment. So far, the performance appraisal is determined based on the Balanced Scorecard method, AHP, Topics, and others where the dimensions of this method are limited. In companies, performance appraisals are carried out by the HR Department. The specified indicator is sometimes too high, therefore it is considered achieved. The most flexible approach in which the determination of indicators is determined by the respondent who will implement the rule. Weighted performance indicators are constructed by developing an association rule method and ranking method. Performance appraisal structures in the form of multi values and multidimensional can be built using this concept. Items that meet the support value and minimum weight are determined based on the higher frequency. This concept is a new proposal from a mining method developed to produce a performance appraisal model that can be applied to various needs
An Empirical Evaluation of Algorithms for Data Labeling

The lack of labeled data is a major problem in both research and industrial settings since obtaining labels is often an expensive and time-consuming activity. In the past years, several machine learning algorithms were developed to assist and perform automated labeling in partially labeled datasets. While many of these algorithms are available in open-source packages, there is a lack of research that investigates how these algorithms compare to each other for different types of datasets and with different percentages of available labels. To address this problem, this paper empirically evaluates and compares seven algorithms for automated labeling in terms of their accuracy. We investigate how these algorithms perform in twelve different and well-known datasets with three different types of data, images, texts, and numerical values. We evaluate these algorithms under two different experimental conditions, with 10% and 50% labels of available labels in the dataset. Each algorithm, in each dataset for each experimental condition, is evaluated independently ten times with different random seeds. The results are analyzed and the algorithms are compared utilizing a Bayesian Bradley-Terry model. The results indicate that the active learning algorithms using the query strategies uncertainty sampling, QBC and random sampling are always the best algorithms. However, this comes with the expense of increased manual labeling effort. These results help machine learning practitioners in choosing optimal machine learning algorithms to label their data.
Personalized dynamic recommendation integrating high-level similarity social relationships

With the development of the Internet, people have communicated more closely. People are more likely to find more similar partners online and communicate offline. Therefore, the demand for such activity recommendation is growing, but too many activities will inevitably make users confused and difficult to choose. Personalized recommendation system can help users filter out the content that they are not interested in or have a low evaluation, and recommend the content that they may be interested in to the participating users. However, the existing research focuses on the analysis of personal participation records, while the impact of dynamic interaction between participants on user participation preferences has not been explored, and the sparse user trust matrix and other problems hinder the improvement of recommendation performance.Therefore, it is important to integrate the social relationship dynamically into the personalized recommendation system. Secondly, considering that users trust different friends in different fields, in order to infer the social relations of users in specific fields more accurately, we will use network representation technology to embed the social trust relations in specific fields into low-dimensional space. We propose a personalized dynamic recommendation method integrating high-level similarity social relationships. The results show the superiority of the proposed model by comparing with the state-of-the-art models.
Control to Manipulate Robotic Arms Using Augmented Reality

This work presents an application for mobile devices in augmented reality, as a tool for teaching technological learning in the handling of industrial robotic arms, advanced control algorithm techniques are implemented, the simulation is performed from the application allowing the user, select the different desired trajectories, visualize the behavior of the robotic arm in the monitoring of the proposed path, in addition to animations to understand and become familiar with the operation of the same, as well as the visualization of control errors in each simulation performed. The application developed is focused on training and assistance of industrial robotic arms allowing users to interact and manage the structure of the robot in the educational sector, within an environment of friendly and intuitive augmented reality, providing new technologies with digital information immersion simulation programs. The tests conducted on the mobile application demonstrate the intuition and ease of use with the user, in understanding the operation and functioning of programmable robotic manipulators.
Natural Deduction for Intuitionistic Euler-Venn Diagrams

We present preliminary results for a proof system based on Natural Deduction for intuitionistic Euler-Venn diagrams. These diagrams are our building blocks to visualise intuitionistic arguments, that is, arguments avoiding classical Boolean arguments, for example “proof by contradiction” or the “law of the excluded middle”. We have previously presented semantics for these diagrams and a proof system in the style of Sequent Calculus. Within this proof system, proof search consists of gradually decomposing the diagram to prove. While such a style is easy to automate, it is not very intuitive for human reasoners. Since formalising human reasoning was the main intention behind Natural Deduction, we choose this style for a proof system that is easier to apply and to understand than the existing system.