Reviewerly/exHarmony huggingface dataset:
A Data Fusion Model Based on Multi-source Non-real-Time Intelligence

In order to solve the problem of target fusion in complex battle environment, a data fusion model based on multi-source intelligence is established. Weight quantification analysis and fusion of non-real-time intelligence source are realized based on this model, the evidence reasoning method is used to solve the uncertainty in decision-making, and the accuracy of data fusion is improved by integrating non-real-time and real-time intelligence source. In general, this model considers more sufficient and comprehensive influencing factors, and the computational process of this model is intelligent.
Cloud Computing for Enabling Big Data Analysis

Every day billions of people access web sites, blogs, and social media. Often they use their mobile devices and produce huge amount of data that can be effectively exploited for extracting valuable information concerning human dynamics and behaviors. Such data, commonly referred as Big Data, contains rich information about user activities, interests, and behaviors, which makes it intrinsically suited to a very large set of applications. For getting valuable information and knowledge from such data in a reasonable time, novel scalable frameworks and data analysis techniques on Cloud systems have been developed. This paper aims at describing some recent Cloud-based frameworks and methodologies for Big Data processing that can be used for developing and executing several data analysis applications, including trajectory mining and sentiment analysis. The paper is organized in two main parts. The first part focuses on tools for developing and executing scalable data analysis applications on Clouds. The second part presents data analysis methodologies for extracting knowledge from large datasets.
Link Prediction Based on Heuristics and Graph Attention

Recent years have seen a surge in many deep learning research approaches to predict links in structured network data, however, these approaches seem to falter in its applicability. This paper seeks to propose a new model, named HLPGAM (Heuristics Link Prediction Graph Attention Mechanism), which combines probabilistic heuristics and attention mechanism to learn a more suitable way of predicting links in a given structured-network without relying on sophisticated feature engineering based on the statistical properties of a given node. The paper first aligns graphs and performs graph2Vec conversion using graph convolutions operation then it overcomes entity classification and link prediction limitation via an attention mechanism, i.e. to replace the normalization with data-dependent attention weights. For the entity classification problem, the experimental results have demonstrated that the HLP-GAM model can act as a competitive, end-to-end trainable graph-based encoder. For link prediction, the HLP-GAM model outperformed direct optimization of the factorization model and achieved competitive results on standard link prediction benchmarks. Our model achieves much better performance than other algorithms when he experimented both based on AIFB and AM dataset.
Development and Implementation of Gamified Technologies in the Life Long Learning System (Based on a Multidisciplinary University)

Active use of gamification in training, development and socialization has established itself among the best educational and managerial practices. Specialists note that this phenomenon, apart from being a practical implementation of theoretical educational technologies, also reflects a change in the approach to socialization of a new generation of students and specialists. The key objective of the educational process is to shape skills and competence of students as they prepare for their future professional activities. The potential of gamification is primarily based on the basic need of individuals for development. In the conditions of transition to the economy of knowledge and even of entertainment, educational institutions are facing increasingly more requirements in the field of developing Life Long Learning technologies. In response to this challenge, universities start creating educational programs that combine gamification opportunities and requirements to continuity of the educational process for target audiences.
Statistical Reasoning of Zero-Inflated Right-Skewed User-Generated Big Data A/B Testing

A/B testing serves as an ultimate standard for decision making in the technology industry. Compared with extensive studies on machine learning algorithms, system design and user research of relevance related products, like recommender systems, search ranking, etc., there are very few works on discussion of related A/B testing methods. In particular, some very important online KPIs, like user expenditure for online e-commerce and user active minutes for social media or video streaming platforms, typically involve zero-inflated and right-skewed data as a significant number of users may not even engage while a few of them engage heavily. Therefore, a deep understanding of zero-inflated right-skewed data testing methods is crucial. In this paper, we did an extensive and detailed survey on this topic, while we extended several statistical estimators and hypothesis testing methods for zero-inflated right-skewed data as well. We compared these statistical methods both theoretically and empirically in a large sample setting to align with the huge amount of data we collect in industrial A/B testing. Our theoretical analysis and simulation results on both synthetic and Twitter real data challenged the superiority of several methods which claimed with small samples or specific underlying distributions. Moreover, we analyzed two common pitfalls of testing zero-inflated right-skewed data in practice, which helps better decision making with A/B testing on data with such structures.
The influence of e-conferences and e-meetings on increasing communication skills among leaders in Jordanian mining and manufacturing industries

This study aims to examine the influence of e-communication (e-meetings and e-conferences) on the communication skills of managers and leaders (decision making, problem solving, consistency, productivity and better work flow). Quantitative approach was employed to achieve the aim; 150 managers and leaders responded to a self-administered questionnaire, which was tackled later using SPSS.The results of the study confirmed an undeniable influence of e-communications on communication skills of managers and leaders within Jordanian mining and manufacturing industries; the most influenced variable was decision-making process associated with the real-time data that e-communication means can provide to decision makers (data, information, figures and numbers), which facilitated a faster ability to take decisions and avoid risks among organizations. The study also demonstrated that electronic meetings and conferences are based on transmitting voice over internet protocol (VoIP), which allows voice to be transmitted over the Internet, which is key to facilitating real-time electronic meetings. The study recommended adopting more electronic means of communication as an approach to avoid loss of time and efforts in face-to-face communication.
An Improved K Means Algorithm for Unstructured Data

An important significantly applied data mining approach is the clustering. Since the development in technology has increased, rapid generation of large unstructured datasets arises. Traditional clustering does not operate efficiently because of its large processing time. As a result, MapReduce k mean algorithms are developed to process larger unstructured data but it may suffer from disadvantage related to poor quality and inefficiency. In this research, an additional LGC (Local Gravitational Clustering) is integrated with MapReduce k mean known as MapRedLGC module is proposed, which plays a key role in partitioning the data point into clusters on the basis of Euclidean distances. LGC views the data points as mass objects related with produced local resultant force (LRF) by the corresponding neighbours. Significant difference exists in the LRF data points across the centre and boundary, which are analyzed by investigating the centrality (CE) and coordination (CO) factors. The simulation results of proposed method are compared with traditional algorithm in terms of elapsed time, intra-cluster distances and SSE, in which an observed result shows that the proposed technique possess better performances.
DISTANCE LEARNING IN THE CONTEXT OF COVID-19 PANDEMIC

1 Рeoples’ Friendship University of Russia (RUDN University) (RUSSIAN FEDERATION)2 Plekhanov Russian University of Economics (RUSSIAN FEDERATION)
Hybrid Recommender Systems for Next Purchase Prediction Based on Optimal Combination Weights

Recommender systems (RS) play a key role in e-commerce by pre-selecting presumably interesting products for customers. Hybrid RSs using a weighted average of individual RSs’ predictions have been widely adopted for improving accuracy and robustness over individual RSs. While for regression tasks, approaches to estimate optimal weighting schemes based on individual RSs’ out-of-sample errors exist, there is scant literature in classification settings. Class prediction is important for RSs in e-commerce, as here item purchases are to be predicted. We propose a method for estimating weighting schemes to combine classifying RSs based on the variance-covariance structures of the errors of individual models’ probability scores. We evaluate the approach on a large real-world e-commerce data set from a European telecommunications provider, where it shows superior accuracy compared to the best individual model as well as a weighting scheme that averages the predictions using equal weights.
Creative Composition Problem: A Knowledge Graph Logical-Based AI Construction and Optimization Solution

Abstract Contribution of this work is to Define the Creative Composition Problem (CCP) for Human Well-being Optimization by Construction of Knowledge Graph using Knowledge Representation and logic-based Artificial Intelligence reasoning-planning where the computation of the Optimal Solution is achieved by Dynamic Programming or Logic Programming. The Creative Composition Problem is embedded within Cecilia: an architecture of a digital companion artificial intelligence agent system composer of dialogue scripts for Well-being and Mental Health. Where Cecilia Framework is instantiated in Well-being and Mental Health domain for optimal well-being development of first year university students. We define the ‘The Problem of Creating a Dialogue Composition (PCDC)’ and we propose a feasible and optimal solution of it. CCP is instantiated in this applied domain to solve PCDC optimizing the Mental Health and Well-being of the student. CCP as PCDC is applied to optimize maximizing the mental health of the student but also maximizing the smoothness, coherence, enjoyment and engagement each time the dialogue session is composed. Cecilia helps students to manage stress/anxiety to attempt the prevention of depression. Students can interact through the digital companion making questions and answers. While the system “learns” from the user it allows the user to learn from herself. Once the student discovers elements that were unnoticed by her, she will find a better way to improve when discovering her points of improvement.
A Log-likelihood Regularized KL Divergence for Video Prediction With a 3D Convolutional Variational Recurrent Network

The use of latent variable models has shown to be a powerful tool for modeling probability distributions over sequences. In this paper, we introduce a new variational model that extends the recurrent network in two ways for the task of video frame prediction. First, we introduce 3D convolutions inside all modules including the recurrent model for future frame prediction, inputting and outputting a sequence of video frames at each timestep. This enables us to better exploit spatiotemporal information inside the variational recurrent model, allowing us to generate high-quality predictions. Second, we enhance the latent loss of the variational model by introducing a maximum likelihood estimate in addition to the KL divergence that is commonly used in variational models. This simple extension acts as a stronger regularizer in the variational autoencoder loss function and lets us obtain better results and generalizability. Experiments show that our model outperforms existing video prediction methods on several benchmarks while requiring fewer parameters.
An Adaptive End-to-end Codec based on Multi-source Fusion for UAV Equipped with 5G NSA Network

Abstract This paper proposed an adaptive end-to-end codec based on multi-source fusion. Our codec adjusts the coding scheme according to the channel condition adaptively, selects deep recursion when the channel is good to obtain sufficient and effective information, and shallow recursion to ensure the transmission of key frames when the channel condition is poor. Besides, communication information is also imported into the codec through parallel branches to avoid confusion caused by multiple sources. We test on the DJI M210 UAV equipped with 5G network to verify the usability of the adaptive end-to-end codec both quantitatively and qualitatively. Under the same target detection level, our codec provides higher compression rate, and adjusts the compression rate to adapt to different conditions.
Structuring Rich Agriculture using Pervasive Computing

Abstract Now a day almost all humans carry gadgets which are smaller in size but has maximum utilization in term of functionality i.e., mobile or smart watches. Cloud Computing is proven one of the best hope for farmers for better cultivation of crops by using digital compilation. In almost every year, more than 40% downfall in the cultivation has observed due to unfavourable conditions for instance changes in the climate, the storage of water and shortage of labour due to increased urbanized population. The solution of this problem turn onto the form of a new technology named ‘eplant’. With the minimized input the best possible crops production for the best health is the focus of this technique. Thus the term “Pervasive Computing” came into existence. “Pervasive Computing” is also known as “Ubiquitous Computing”. This paper is about introducing this new technique in farming. Data sensors are used to provide all necessary resources for better crop development through and different compatible actuators are also used as per the environmental conditions. The role of sensors is the collection of the data about soil, the climate and for specific plants conditions. In order to minimize the hard work and to maximize the profit of the farmer this technique provided desired parameters by different calculations.
Team Building Technologies in Engineering Education

The paper discussed the effective team-building technologies in engineering education. The relevance of the research is due to academic and industry based research the emphasized the need for competitive engineers who possess professional competencies and personal qualities as well as engineering graduates who need to work on solving complex problems in teams composed of professionals from many disciplines. The paper describes supported by examples how teamwork can be integrated into the curriculum in conjunction with engineering projects and topics. The research emphasize that students need to understand principles and attributes of effective teamwork, practice teamwork, receive feedback on teamwork, and adopt practices of self-monitoring and self-regulation in their teams. The study offers a methodological training system that includes active teaching methods and a more pragmatic approach in teaching students the skills necessary to function as effective and productive team members. Teamwork skills are increasingly being sought when hiring engineering graduates and employers assume they raise the status of an organization in the market and increase its profit by improving the quality of solving interdisciplinary tasks with minimal time costs and maximum productivity. It also motivates employees and facilitates bonding within the company, opportunity to work together in close proximity leads to strong relationships.
Cosine similarity distance pruning algorithm Based on graph attention mechanism

In recent years, graph neural network has been widely used. Attention mechanism is introduced into the graph neural network to make it more applicable. Both GAT and AGNN prove that attention mechanism plays an important role in graph neural network. Attention mechanism algorithms such as gat and AGNN directly use a self-learning variable to do the point product after calculating the connection (or similarity calculation) of node and neighbor features (without further processing of the calculation results). Finally, we get an aggregation of neighbor information. A cosine similarity distance pruning algorithm based on graph attention mechanism (CDP-GA) is proposed to optimize the attention matrix of nodes and their adjacent nodes. By calculating the cosine similarity between node features and neighbor features (the feature here is obtained by linear transformation), the similarity of nodes is regarded as the distance between nodes (or the weight of edges). And we think that the aggregation degree of node information is inversely proportional to the distance between nodes (similar to the heat conduction formula). In the method, we prune the neighborhood of the node according to the cosine similarity to get the final attention coefficient matrix. In this way, the attention mechanism in the graph neural network is further refined, and the loss of aggregation neighbor information is reduced. In the experiments of three datasets, our model is compared with the experimental classification of GAT and AGNN and the experiment of correlation graph neural network algorithm. Finally, it is proved that the algorithm is better than three known datasets.
Pre-service Librarians’ Perspective on the Role of Participatory Design in Libraries with Youth

Participatory Design (PD) is a design methodology that incorporates the end users in the design process. An active area of research in PD focuses on designing new technology with children. As more libraries are offering different technologies to their patrons—especially to children—there is an increasing need to think about how to best incorporate such technologies into library services. Recent interest in the field has encouraged librarians to situate themselves as designers, and to find creative solutions to the problems that arise in library settings. However, design methods are not widely utilized in the field of library and information science. In this study, we have interviewed pre-service librarians and children participants who did participatory design in libraries within a service-learning course. These participatory design sessions focused on incorporating new technologies into library programming. Our study indicates that PD is a possible method for capturing the ethos of librarianship. Pre-service librarians found that PD allowed them to effectively enact values of service, such as democratic participation and creativity, while children valued being heard from adults in the library and helping their community.
Creation of a strategic planning system for the socio-economic and innovative development of organizations of higher education

The article reveals the theoretical and methodological provisions for the formation of the composition of technological elements of the strategic planning system, their interaction with the resource components of the organization of higher education (on the example of Voronezh and St.Petersburg universities) to achieve generalizing and specific targets of quantitative and qualitative nature of the strategic the goal of long-term development of a higher education institution. The concepts of “strategy”, “strategic vision”, “mission of the organization of higher education” are defined. The possibility of optimizing resource consumption in the process of implementing complex measures within the framework of federal, regional, sectoral and local programs and projects of social, economic and innovative development of higher education organizations is substantiated. The priority directions in the development strategy of the analyzed universities are highlighted. The necessity of including such technological elements as strategic analysis, mission, strategic vision, strategic goal, generalizing (basic) strategy, functional (private) strategies in the system of strategic planning of socio-economic and innovative development of the studied universities is substantiated.
Rademacher Complexity in Simplex/l<sub>∞</sub> Set

Abstract When the size of the neural network is too large, calculating the bound of neural network is a difficult problem. Therefore, “size-independent” is what needs to look for here. This paper follows the path of “Size-Independent Sample Complexity of Neural Network ”, and tries to get a better expression of Rademacher Complexity of neural networks.
Wearable Devices Information Security: An Overview

Wearable technology has become a subject of research and analysis. However, the importance of the information carried across these devices incentivizes malicious individuals to look for ways to attack them and intercept such information for various illicit purposes, it makes information security a major concern for users and manufacturers. The purpose of this research is to analyze wearable devices security through different parameters centered on two different models found after a thorough research based on scientific articles, where the authors demonstrate their importance and effectiveness in the analysis of computer security. The first model selected was the DREAD model, which analyzes the Damage, Reproducibility, Exploitability, Affectability, and Discoverability of each attack examined. The second model chosen is the STRIDE model, this one allows to classify attacks within the main categories of threats, which are: denial of services, elevation of privileges, repudiation, among others. The analysis performed shows the injection of code attack as the one that represents a higher level of risk for wearable devices. On the other hand, the threat of information disclosure ends up being to which these devices are most susceptible because it houses the most attacks analyzed.
A Pedestrian Detection Method Combining I-HOG and Sobel-ULBP Features

The previous Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) feature fusion pedestrian detection method has disadvantages such as sensitivity to noise, serious spectral information loss, high feature dimension and much redundant information, etc. This paper proposes a pedestrian detection method based on the feature fusion of Improved Histogram of Oriented Gradient (I-HOG) and Sobel Uniform Pattern LBP (Sobel-ULBP). By inserting Gaussian filtering algorithm to complete the pre-processing of the image to be extracted, the image transition is uniform and smooth, details are eliminated, and the noise generated by compression and transmission is removed. Subsequently, Sobel edge detection is performed on the image to reduce the influence of pedestrian background and clothing texture and other information, and highlight the pedestrian silhouette. Because the optimized I-HOG features and Sobel-ULBP texture feature obtained by calculation have higher feature dimensions. Therefore, the principal component analysis algorithm (PCA) is used to reduce the dimensions of the two features, and then the two features are fused in series, and finally the fused features are sent to the support vector machine (SVM) for training to obtain a pedestrian classifier. A simulation experiment was carried out on the INRIA pedestrian database, and the results show that the method has improved recall rate and pedestrian recognition rate.