Reviewerly/exHarmony huggingface dataset:
The impact of using emoji in online advertisements on consumer behavior. Case: Banking sector in Lebanon

Abstract Communication is an essential element in life. It is key to understanding each other, forming relationships, and sharing knowledge, information and ideas. “Social media is modifying the ways we communicate and is providing marketers with interactive means to effectively reach consumers. This has given rise to those digital images known as Emoji, which is a trending topic that is shaping our lives and sculpting its way to related social sciences. Many businesses have been employing emoji in their communication messages, yet little research was conducted in this area. This research will build new insights and will investigate the role of emoji in influencing behaviors in terms of engagement, satisfaction, loyalty, and perception. Abductive approach to theory was used. Secondary data was collected to explore and explain the concept of online marketing psychology using emoji language. The idea is then tested via survey, to modify the existing theory, and to generate a new idea regarding banks via interview as well. Research questions investigated the use of emoji in online communication as an effective way to engage consumers, whether emoji lead to better customer satisfaction and loyalty, and how serious organizations can benefit from employing them to break the perceived “coldness”. Results approved the effectiveness of emoji use in marketing, and they’re no longer limited to “modern” businesses, rather they’re starting to encompass almost all businesses in different industries. The research ends with a set of recommendations that’ll guide businesses through this trend.”
MODELLING OF VIRTUAL LEARNING ENVIRONMENT: PRIORITIES OF A NEW LEARNING POLICY STRATEGY

The research explores problem contexts of the modelling of virtual learning environment. School communities are facing learning difficulties under the rapidly evolving global conditions. Considering the tendencies in changing learning processes, it is essential to reflect upon and provide a basis for the contours of the modelling of a virtual learning environment. While in search of new learning conceptualization strategies, it is very important to properly understand the inclusive and activating context of open learning space, as well as to properly assess the content elements and anticipate the possible features of expression. The process of modelling of the virtual learning environment is analysed through reflexive approaches. The contemporary school is now moving onto the logic of organizing learning services, revealing new trends of expression: skills, information, knowledge, networking relations between learners and the creation of learning values. Virtual learning environment is positioned as an inevitable vision. This vision aims for the learner to be in the centre of learning, while learning itself would be understood as a social process, where learners create a confidence-based culture, design a course of personalized learning for themselves, apply different methods and styles of learning in regard to the objective of the learning object. The goal of the new learning policy strategy is to establish learning environments that would create conditions for people of different interests and capabilities to receive customized education, adapt to various life situations and aims, as well as combine formal learning and self-education. Keywords: virtual learning environment, modelling, new learning strategies, contexts of educational content, competences, priorities of educational policy.
Design of an Automatic Monitoring Model for Real-Time Data Flow in Network Based on Fuzzy Clustering Analysis for Health Analysis

Abstract The current automatic monitoring model of real-time data flow in network has poor data clustering ability, which leads to a poor data flow clustering effect and a high memory occupation rate when the model is applied. To solve this problem, based on the data processing point of view, this paper designs an automatic monitoring model for real-time data flow in network based on fuzzy clustering analysis. The threshold sampling method is used to complete the dynamic sampling of real-time data in network. The Principal Component Analysis (PCA) method sorts out the eigenvectors in the data flow and solves them. According to the collected data flow samples, fuzzy clustering analysis algorithm combined with feature vector is used to realize the automatic monitoring of real-time data flow in network. So far, the design of automatic monitoring model for real-time data flow in network based on fuzzy clustering analysis is completed. The experimental results show that the fuzzy clustering model is better than the current model.
Global Reconstructed and Contrastive Prototypical Network for Few-shot Learning

Few-shot learning aims to learn classification with only a few labeled samples. Prototypical network has become the focus of few-shot learning, but the prototypical network has some problems such as the lack of representativeness of the extracted feature space and the deviation of the prototype representation due to too few samples. In response to these problems, we propose Global Reconstructed and Contrastive Prototypical Network (GR-CPN), a method based on the losses of global reconstruction and Contrastive learning. The contributions of our work can be summarized as follows: 1) Inspired by the prototypical network, our approach combine image reconstruction and contrastive learning to learn the embedding space, in which embedding feature strengthens the representation of the object itself rather than just the distinguishability between samples of different classes; 2) We label query samples with pseudo-label to expand the support set so that intra-class bias caused by too few support samples for each class is reduced in the embedding space. The results of experiments on miniImagenet and FC100 datasets show that our GR-CPN is a simple, high-efficiency approach that can extract the representative features of the object and outperforms the state-of-the-art.
Digital Tools for Competitive Engineering Training

Nowadays, the development of international industrial companies is based on digital technologies. The implementation of digital technologies into the Russian economy is one of the national priorities, which lead to the modernization of industrial complex. The transformation of Russian industry in accordance with the new standards of the digital economy leads to the modernization of higher education. In these conditions, the training of engineers who will be able to develop innovative technologies and implement them into modern industrial processes is of great importance. The study aims at defining conditions of engineering training, developing teaching methodology based on the integrated use of digital tools, which intensify the training process and increase the quality of engineering education at the technological university. The article presents a complex of digital tools for training future petrochemical engineers. The authors analyze the modern industrial conditions and propose the mechanisms of infusing complex digital tools in engineering educational programs. The complex provides the development of engineering competences according to professional standards and requirements of industrial employers.
A Knowledge-based Question-Answering Method for Military Critical Information Under Limited Corpus

In the military field, question-answering (QA) to analysis, decision-making and other critical information is an important application of command and decision-making. Nowadays, in natural language processing, current knowledge-based intelligent QA systems have achieved significant improvement of efficiency and accuracy, while at the same time the quantity and scale of the corpus are required as well. However, in the field of military, the question and answer corpus of military critical information related to analysis and decision-making is commonly very limited. In this paper, we propose a knowledge-based military critical information QA method, aiming at realizing intelligent QA under the condition of the limited corpus. After constructing the corpus of military critical information questions, a template matching method based on the naive Bayes classifier is adopted to identify the categories of critical information questions. According to the question category, query statements are generated by filling question semantic information extracted in advance into query statement templates. Then, through querying the critical information knowledge graph, the QA for military critical information under the condition of the limited corpus can be achieved.
Certificateless Multi-Party Authenticated Encryption Mitigating Ephemeral Key Leakage

We propose two secure modifications of a multi-party authenticated encryption scheme with aggregation, mitigating ephemeral leakage attacks on narrowband Internet of Things devices and sensor equipment, used in different type of connected infrastructures. Our schemes are provably secure in a stronger security model where a set of nodes in a 5G-based architecture can produce authenticated and encrypted signcryption messages, aggregated into one verifiable cipher text. We provide benchmarks from a proof of concept implementation, showing the feasibility of our solutions.
Characterising the area under the curve loss function landscape

Abstract One of the most common metrics to evaluate neural network classifiers is the area under the receiver operating characteristic curve (AUC). However, optimisation of the AUC as the loss function during network training is not a standard procedure. Here we compare minimising the cross-entropy (CE) loss and optimising the AUC directly. In particular, we analyse the loss function landscape (LFL) of approximate AUC (appAUC) loss functions to discover the organisation of this solution space. We discuss various surrogates for AUC approximation and show their differences. We find that the characteristics of the appAUC landscape are significantly different from the CE landscape. The approximate AUC loss function improves testing AUC, and the appAUC landscape has substantially more minima, but these minima are less robust, with larger average Hessian eigenvalues. We provide a theoretical foundation to explain these results. To generalise our results, we lastly provide an overview of how the LFL can help to guide loss function analysis and selection.
Analysis of the methods for competing recursion in recursive riles in the logic programming language Prolog

One of the developing trends in programming is the logic programming associated with the implementation of tools for creating artificial intelligence. One of such programming languages is the nonprocedural declarative logic programming language Prolog. This article is dedicated to the use of recursive rules in Prolog software. The goal of this work lies in analysis of the methods for completing recursive calls in recursive rules, as well as in explication of the use of such methods on the examples of programs with recursion. The author explores the specialized literature on the topic, generalized and systematizes the data, as well as tested the programs and the progress of their implementation. Recursive rule in the Prolog software sets an infinite cycle of repetition of predicates. For completing the recursive cycle, it is necessary to set a condition within the program that would end the cycle. The article examines the variants of organizing recursions with the completion of infinite cycle. The examples used in the article allows using them as the basis for programming in language Prolog for solving similar tasks. The acquired results are valuable for further development of the use of recursive predicates in logic programming languages.
Three-dimensional reconstruction of railway driver based on monocular camera

This paper uses OpenPose to obtain the two-dimensional information of the human body posture, then uses the continuity of the driver’s skeletal joints in time .And selects the mean filter to smooth the data,so as to complet the correction of the data. Next use the camera imaging principle and the human skeleton model to restore the driver ’ s posture information to the three-dimensional coordinate system. Finally, according to the external constraints of the cab environment and camera placement, as well as the internal constraints of the human posture, the driver ’ s three-dimensional posture is determined. Correction, through the quantitative analysis of the experimental results, this article found that combining the internal and external constraints, the driver’s three-dimensional posture reconstruction has achieved good results.
Data Mining Implementation Using Frequent Pattern Growth on Transaction Data for Determining Cross-selling and Up-selling (Case Study: Cascara Coffee)

The development and competition of the coffee shop business is increasingly popular nowadays. Cascara Coffee is one of the business shops that must have the right marketing strategy so that the shop's business can survive. This paper presents a study on the use of the FP-Growth association algorithm to process transaction data in order to provide best association parameter in cross-selling and up-selling recommendations for coffee sales. The data used is sales of Cascara Coffee in one year as much as 16,579. Based on the experimental results, the highest lift ratio value obtained is 2,789. In addition, the best association rule is to use a minimum support parameter of $0,20x{10^{ - 2}}$ and a minimum confidence of 0.3. The association rules can be a recommendation for the company to carry out cross-selling and up-selling marketing strategies.
Orbital Table Tennis Serving System: A Face Detection-driven Approach

Table tennis serving (TTS) machine can effectively reduce the burden of coach and improve the training effect. However, most of the existing TTS systems are generally fixed on the table to serve the balls, and they are pre-programmed and lack of flexibility. To address above problems, we develop an orbital TTS system, which can flexibly move with the human body. Specifically, an improved face detection method is designed to track the human position by introducing the attention mechanism. Moreover, a single branch connection is adopted to reduce the running memory requirement, and a multi-threading scheme is employed to reduce the computing time. Practical experiments show that the developed TTS system has a better flexibility and a higher serving ball quality.
Research on Hue-Preserving Color Image Enhancement without Gamut Problem

This paper gives theoretical analysis for the change of saturation of a RGB color vector under hue preserving transformation, and presents non-ideal performance of existing hue-Preserving image enhancing methods. Then it proposes a basic framework which is used to extend existing gray enhancement technologies to color images. By taking histogram equalization technology as example, a new hue preserving method for color image enhancement is studied. When the proposed method is applied to underexposed images, hue and saturation are preserved because underexposed images often have enough saturation; when it is applied to overexposed images, hue is preserved and saturation is raised. The method is simple but more effective. One of its novelties lies in its treatment of the maximum of RGB components, so that it does not generate gamut problem. The experimental results with many test images show that the method is superior to the other ones subjectively and objectively. Compared with the other methods, its better performance consists of three points: the first one is that saturation of the output images is high; the second one is that it brings less color levels merged and keeps more color levels; the last one is that the color levels kept have a more uniform distribution in the RGB cube. Moreover, the proposed method doesn&#x2019;t need any color space conversion, so its computational complexity is less than ones of the methods based on HSI, YIQ, YCrCB etc.
TWO TACTICS FOR THE IMPLEMENTATION OF THE POLICY OF "INDIGENOUSIZATION" IN THE FIELD OF EDUCATION AMONG NORTHERN NATIONAL MINORITIES. (TO THE HISTORY OF THE NORTHERN NATIONALITIES DEPARTMENT OF THE HERZEN PEDAGOGICAL INSTITUTE IN THE 1930S)

The article is devoted to the history of the Department of Northern Nationalities, created in 1929 as part of the Herzen Pedagogical Institute in Leningrad in connection with the implementation of the strategy of "leveling the historical curve" in the field of ethno-national politics - achieving de facto equality of the peoples of the USSR. This strategy was carried out in the 1920s-1930s through the "indigenousization" of peoples in a certain territory, assigned to them within the framework of national republics, territories, districts and village councils, which required an increase in the status of the native language, the formation of national managerial personnel and intelligentsia. Taking into account the tasks of socialist modernization, the "indigenization" of the school and the education system among small peoples in the remote regions of the North, Siberia and the Far East could not be carried out "mechanically" - through the direct involvement of the local population in the process of administrative management and teaching, because many peoples simply did not have a written language. In relation to them, another scenario of “indigenization” was chosen - “functional”. The training of the intelligentsia from among the northern peoples first began to be carried out through the workers' faculty, created in 1925. at the Leningrad University and transformed in 1930. to the Institute of the Peoples of the North. However, the implementation of broad plans for the modernization of remote regions depended on the creation of an education system in them, covering the entire population. A necessary condition for this was the large- scale organization of professional pedagogical education. The history of the first decade of the Northern branch of the Leningrad State Pedagogical Institute named after A.I. Herzen, recreated on the basis of a comparison of historical sources, demonstrates what problems and difficulties had to be overcome on this path.
Image Data Enhancement Method Based on Generative Adversarial Network for Millimeter-wave Body Scanner

A large number of serious artifact image data sets are necessary for the training of image recognition algorithm of millimeter wave body scanner. It is very difficult to obtain serious artifact images in practical work. The generative adversarial network is introduced for data enhancement by building an appropriate loss function. This method can create conditions for training multi-class classification networks and improve the effectiveness of millimeter wave body image evaluation.
Step Detection using SVM on NURVV Trackers

This paper introduces a Machine Learning approach (ML) for classifying step detection during human running activities. First, we use a signal processing strategy to label Inertial Measurement Unit (IMU) data (i.e. acceleration and angular speed) in terms of foot contact, ground vs air. This is done by performing Exploratory Data Analysis (EDA), that includes Principal Component Analysis (PCA) for interpretability, on a collection of IMU data sets obtained via multiple runners using a NURVV Run wearable device. Once we are in the presence of a supervised learning problem, by leveraging ML techniques - such as Support Vector Machine (SVM) - we can optimize models to detect if the foot is in the air or on the ground solely based on IMU data. Unlike in this first instance where we rely on signal processing, this algorithm is designed to not need any post-processing, i.e. if the embedded system has enough resources it should be able to run in real-time. Since the raw IMU data is affected by factors such as the position of trackers on the shoes, running speed, runner technique and terrain, a single model doesn’t generalise well. Therefore, we implement an ensemble SVM model, that relies on the confidence that each separate SVM model has on the output of its own classification to extract, through hard-voting, the classification of the sample. We present promising initial results from applying this approach to unseen test data.
Accelerator for Sparse Convolutional Neural Networks Based on Shift Units

With the development of convolutional neural networks (CNNs), deeper and wider networks improve accuracy while increasing deployment difficulty. Design lightweight algorithms and high-efficiency hardware accelerators has therefore become a research hotspot. In this article, the sparsity and quantized weights of CNNs are explored in the quest to obtain lightweight models that decrease the number of weights to 28% and bit widths to 25% compared with original models. To avoid unbalanced computation, we model sparse CNNs and propose a data flow with high processing element (PE)-utilization ratio and low DRAM-access amount. We design a sparse CNN accelerator based on shift units, in which the zero weights are skipped to save execution time and energy. The layout was taped out in TSMC 28 nm and packaged with QFP144. Passing the functional tests, the design achieves 256.1GOPS performance and gains 1.133TOPS/W efficiency. Compared with similar designs, our design achieves 46.7% less EDP.
LE-CapsNet: A Light and Enhanced Capsule Network

Capsule Network (CapsNet) classifier has several advantages over CNNs, including better detection of images containing overlapping categories and higher accuracy on transformed images. Despite the advantages, CapsNet is slow due to its different structure. In addition, CapsNet is resource-hungry, includes many parameters and lags in accuracy compared to CNNs. In this work, we propose LE-CapsNet as a light, enhanced and more accurate variant of CapsNet. Using 3.8M weights, LECapsNet obtains 76.73% accuracy on the CIFAR-10 dataset while performing inference 4x faster than CapsNet. In addition, our proposed network is more robust at detecting images with affine transformations compared to CapsNet. We achieve 94.37% accuracy on the AffNIST dataset (compared to CapsNet’s 90.52%).
The Generic Framework of Privacy Preserving Data Mining Phases: Challenges &amp; Future Directions

The recent era is witnessing great advances in technologies, communication, and storage methods. This gave the ability to business organizations to collect and store huge volumes of data about their business and individuals. In addition, data mining has gained more attention and usability in many business fields to extract useful information and insights, discovering unknown patterns from such massive, collected data. However, in many domains, these data include private information about people and cannot be utilized and shared directly due to several individuals’ privacy issues. Hence, Privacy Preserving Data Mining (PPDM) has received a huge consideration in the current research to utilize the private collected data in different data mining tasks, while protecting the individuals’ privacy. Various review studies for PPDM have been proposed in the literature, presenting different types of techniques used to preserve data privacy. However, no systematic description for the involved steps that are performed thoroughly in each PPDM technique was presented. In this paper, we present a general deduced framework for PPDM, clarifying the different phases performed through the associated processes. Besides, based on the phase at which the technique can be applied, a classification for the different types of PPDM techniques proposed in the literature is provided. Finally, concerns that need to be addressed and future research directions in the PPDM research field are highlighted.