Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network Based Facial Expression Recognition Using Image Filtering Techniques

Facial expressions are indicative of one's mood that serves to communicate his/her the status of mind.Facial expression recognition poses problems when we attempt to use Convolutional Neural Network (CNN) architectures.The choice of a particular CNN architecture/model and the design of its internal architecture are two problems that need to be addressed.We have chosen a simple CNN architecture comprising two convolutional layers and two pooling layers.To improve its performance the input facial images displaying emotions are subject to two kinds of filtering: One by Gaussian filter and another by LoG.The classification of seven emotions by the CNN model on the filtered facial images from Extended Cohn-Kanade (CK+) dataset comprising 981 images has led to the attainment of 100% recognition accuracy thus vindicating the effectiveness of the proposed approach.
Experimental Demonstrations of Native Implementation of Boolean Logic Hamiltonian in a Superconducting Quantum Annealer

Experimental demonstrations of quantum annealing with native implementation of Boolean logic Hamiltonians are reported. As a superconducting integrated circuit, a problem Hamiltonian whose set of ground states is consistent with a given truth table is implemented for quantum annealing with no redundant qubits. As examples of the truth table, NAND and NOR are successfully fabricated as an identical circuit. Similarly, a native implementation of a multiplier comprising six superconducting flux qubits is also demonstrated. These native implementations of Hamiltonians consistent with Boolean logic provide an efficient and scalable way of applying annealing computation to so-called circuit satisfiability problems that aim to find a set of inputs consistent with a given output over any Boolean logic functions, especially those like factorization through a multiplier Hamiltonian. A proof-of-concept demonstration of a hybrid computing architecture for domain-specific quantum computing is described.
A Simulation-Based Study of Maze-Solving-Robot Navigation for Educational Purposes

The point of education in the early stage of studying robotics is understanding its basic principles joyfully. Therefore, this paper creates a simulation program of indoor navigations using an open-source code in Python to make navigation and control algorithms easier and more attractive to understand and develop. We propose the maze-solving-robot simulation as a teaching medium in class to help students imagine and connect the robot theory to its actual movement. The simulation code is built for free to learn, improve, and extend in robotics courses or assignments. A maze-solving robot study case is then done as an example of implementing navigation algorithms. Five algorithms are compared, such as Random Mouse, Wall Follower, Pledge, Tremaux, and Dead-End Filling. Each algorithm is simulated a hundred times in every type of the proposed mazes, namely mazes with dead ends, loops only, and both dead ends and loops. The observed indicators of the algorithms are the success rate of the robots reaching the finish lines and the number of steps taken. The simulation results show that each algorithm has different characteristics that should be considered before being chosen. The recommendation of when-to-use the algorithms is discussed in this paper as an example of the output simulation analysis for studying robotics.
Hardware Countermeasures against Power Analysis Attacks: a Survey from Past to Present

Modern cryptographic circuits are increasingly demanding security requirements. Since its invention, power analysis attacks are a threat to the security of such circuits. In order to contribute to the design of secure circuits, designers may employ countermeasures in different abstraction levels. This work presents a brief survey of countermeasures to help designers to find good solutions for the design of secure cryptographic systems. A summary is highlighted to compare the pros and cons of the approaches to help designers choose a better solution, or even provide subsidies so that new solutions can be proposed.
A Blockchain-Based Distributed Authentication System for Healthcare

The use of digital health records, stricter health laws and the growing need for health records exchange points towards the need for an efficient security and privacy preserving mechanism. For Health Insurance management systems, multiple entities exchange health information which is used for decision making. Since multiple authoritative entities are involved, a secure and efficient information sharing protocol is required as extremely sensitive health information is exchanged among the entities. Hence this paper aims to put forward a novel a decentralized authentication system based on Blockchain known as Insurance Claim Blockchain (ICBChain) system. The proposed system ensures privacy of patients, provides secure information exchange and authentication of entities. An implementation of the proposed system is provided using Ethereum Blockchain. The security and performance analysis of the system shows its potential to satisfy Healthcare security requirements and its efficiency respectively
Anomaly detection in the CERN cloud infrastructure

Anomaly detection in the CERN OpenStack cloud is a challenging task due to the large scale of the computing infrastructure and, consequently, the large volume of monitoring data to analyse. The current solution to spot anomalous servers in the cloud infrastructure relies on a threshold-based alarming system carefully set by the system managers on the performance metrics of each infrastructure’s component. This contribution explores fully automated, unsupervised machine learning solutions in the anomaly detection field for time series metrics, by adapting both traditional and deep learning approaches. The paper describes a novel end-to-end data analytics pipeline implemented to digest the large amount of monitoring data and to expose anomalies to the system managers. The pipeline relies solely on open-source tools and frameworks, such as Spark, Apache Airflow, Kubernetes, Grafana, Elasticsearch . In addition, an approach to build annotated datasets from the CERN cloud monitoring data is reported. Finally, a preliminary performance of a number of anomaly detection algorithms is evaluated by using the aforementioned annotated datasets.
Classification of Image using Convolutional Neural Networks

Abstract: Now a day, with the rapid advancement in the digital contents identification, auto classification of the images is most challenging job in the computer field. Programmed comprehension and breaking down of pictures by framework is troublesome when contrasted with human visions. A Several research have been done to defeat issue in existing classification system,, yet the yield was limited distinctly to low even out picture natives. Nonetheless, those approach need with exact order of pictures. This system uses deep learning algorithm concept to achieve the desired results in this area like computer. Our framework presents Convolutional Neural Network (CNN), a machine learning algorithm is used for automatic classification the images. This system uses the Digit of MNIST data set as a bench mark for classification of gray-scale images. The gray-scale images are used for training which requires more computational power for classification of those images. Using CNN network the result is near about 98% accuracy. Our model accomplishes the high precision in grouping of images. Keywords: Convolutional Neural Network (CNN), deep learning, MINIST, Machine Learning.
Usernames on a Finnish Online Marketplace for Illegal Drugs

Usernames play a major role in online communication by providing a vital first impression and including clues about the identities and personal characteristics of users. Usernames are extremely important in the online trade of illegal drugs, which carries several risks and therefore requires a high degree of trust between the transaction parties. This study examines how sellers and buyers of illegal drugs represent themselves in their usernames. Data on 1,654 usernames collected from a Finnish darknet marketplace, Torilauta (2017–2020) are analyzed both qualitatively and quantitatively. The usernames are mostly in Finnish and often feature slang and spoken language expressions. Many names include clues about a user’s age, gender, and location. References to various illegal drugs in usernames were found to express a close relationship with the substances. In contrast, they only rarely utilize typical real-life marketing strategies, which suggests that the users do not seek publicity and do not wish to enhance their online reputation. Differences between drug sellers’ and buyers’ usernames are minimal, possibly because of group overlap. Overall, it appears that the users aim to blend in with the drug user community rather than stand out from it by building distinctive images of themselves.
Training of Future Geography Teachers to Local Lore and Tourist Work on the Basis of Competence Approach

Against the background of the renewal of approaches to the development of geography education, the formation of national-patriotic consciousness among students, the introduction of pedagogical technologies into the educational process in order to study the characteristics of their native land, the question arises of the need to modernize the training of future geography teachers in local lore and tourism work. The purpose - to theoretically substantiate and experimentally check pedagogical conditions of preparation of future teachers of geography for local lore and tourist work on the basis of the competence approach. 236 students majoring in "Geography and Biology" with a bachelor's degree formed a control group and 232 - an experimental group. The pedagogical conditions of training of future teachers of geography for local lore and tourist work on the basis of the competence approach are defined and realized: stimulating positive motivation to study local lore material in the disciplines of the cycle of general (fundamental) and professional (scientific-subject) training and disciplines of additional specialization "Local lore and tourism work"; acquisition of knowledge, skills (competencies) for the implementation of local lore and tourism work through the optimization of student groups; intensification of the experience of local lore and tourism work during educational and pedagogical practices. The effectiveness of pedagogical conditions is proved by the results of the formative experiment: the number of students with a high level of local lore competence in the experimental group increased, while in the control group the same indicator did not increase significantly. The results of the study indicate a dynamic positive change in the formation of local lore competence of experimental groups under the influence of the proposed innovations.
Classification of Microblog Users’ Sentiments Based on BERT-BiLSTM-CBAM

This study proposes a method for more accurate classification of microblog users' sentiments based on the BERT-BiLSTM-CBAM hybrid model. First, the text information is pre-trained by the bidirectional encoder representation from transformers (BERT) model to get feature vectors. Then, the feature vectors are spliced and recombined using bidirectional long-short-term memory network (BiLSTM) and CBAM mechanism to obtain new feature vectors. Finally, these new feature vectors are input to the full connection layer and then processed by the softmax function to obtain the sentiment category of the text. The experiment conducted on the sample dataset demonstrates that the model proposed in this study yielded accurate and dependable result in classifying microblog texts in the sample dataset. The model based on BERT-BiLSTM-CBAM algorithm is more efficient than the traditional depth model in processing microblog contents
Burst the Bubble: How to defend freedom of expression from algorithmic personalization

44% of the general populationor 62% of US adultsget news from social media1 as it has become the most important forum where we share, consume and discuss news.Social media platforms, however, use some kind of artificial intelligence or algorithm to show people the stories that are most relevant to them.Social media websites, however, optimize content not (or at least not only) because they wish to provide a better service for their users but because they are financially interested in targeting consumers as precisely as possible on the basis of individualized preferences.On one hand, platforms display personalized advertisement to each and every user in order to increase the number of clicks the ad receives and therefore to charge the advertisers with a higher advertising fee.On the other hand, platforms' algorithms optimize the content in order to generate a higher traffic, so that the platform can sell the place of the advertisement for a higher price at ad auctions.Content personalization eventually and inevitably force the user into information bubbles.Moreover, since humans (as irrational creatures) are not incented to seek complete information especially data that contradict their beliefs, platforms' personalizing algorithms create echo chambers, parallel but separate universes that coexist without even being aware of the other's presence.Algorithmic personalization, therefore, is a direct threat to the freedom of expression, especially to its aspect to freely seek, receive and impart information and ideas of all kinds, regardless of frontiers, either orally, in writing or in print, in the form of art, or through any other media of one's choice.
Measure of Singular Value Decomposition (M-SVD) based Quality Assessment for Medical Images with Degradation

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.
A Feature Extraction Approach for Multi-Object Detection Using HoG and LTP

In the field of computer vision, object detection is getting more attention due to its huge applications in visual monitoring.Multiple object detection identifies the position of objects or regions of objects in the image or videos.Many methods were developed for detecting multiple objects, but the overall detection accuracy of those methods was limited due to the congested environment, complex background, and similarities between the objects.To solve such an issue, this research study proposed the feature extraction method for multiple object detection using Histogram of Oriented Gradient (HOG) with Local Ternary Pattern (LTP).The Caltech 101 dataset is used in the proposed method where the images are converted to LAB.The process of feature extraction takes place by using the proposed HoG and LTP to detect prominent regions from the image.Further, the obtained features are fused by using Deep Convolutional Neural Network (D-CNN) and then forwarded to Region-based Convolutional Neural Network (R-CNN) to detect the multiple objects.The proposed HoG and LTP feature extraction method has the advantages of improving the classification accuracy by effectively extracting the oriented features and texture features.The proposed method achieved better accuracy of 92.48%, whereas the existing Multi-Object Detection and Tracking (MODT) method achieved an accuracy of 76.23% for the detection of multiple objects.
PROFESSIONAL DEFICITS AMONG MATHEMATICS TEACHERS AND THEIR CAUSES

Statement of the problem. The reform of Russian education, which is in demand by the rapid technical development of all branches of production, is associated with the search for opportunities for appropriate changes in its content and methodological aspects. The general education school requires more and more from a teacher, thus creating a certain tension in terms of the growth of professional deficits among teachers. The purpose of the article is to determine the main approaches to identifying professional deficiencies among mathematics teachers, their structure and causes. Methodology (materials and methods). The research methodology consisted of a competence-based approach as the basis for structuring the professional competencies of a mathematics teacher; a systematic approach as a basis for diagnosing professional deficiencies of a mathematics teacher; Federal State Educational Standard of Higher Education in the direction of training “Pedagogical education”, professional standard “Teacher (pedagogical activities in the field of preschool, primary general, basic general, secondary general education)”; module “Discipline training. Mathematics”, the requirements of the Federal State Educational Standard for the mathematical training of secondary school students. Research results. The composition of professional deficits among mathematics teachers was revealed and substantiated: discipline-wise, methodological and general pedagogical competencies. The main causes for the emergence of professional deficits among mathematics teachers are formulated: shortcomings of professional training at the university; inconsistency of terms in the requirements for professional competence in the Federal State Educational Standards and the Professional Teacher Standard; overestimated requirements of the employer in terms of the workload; insufficient dissemination of experience obtained from individual advanced training of a mathematics teacher. Conclusion. The approach proposed in the article to structuring the professional deficits among mathematics teachers and describing the complex of causes for their occurrence can be extended to the study of professional teacher deficits in other subject areas.
Compensation of degradation, security, and capacity of LSB substitution methods by a new proposed hybrid n-LSB approach

This study proposes a new hybrid n-LSB (Least Significant Bit) substitution-based image steganography method in the spatial plane. The previously proposed n-LSB substitution method by authors of this paper is combined with the Rivest-Shamir-Adleman (RSA), RC5, and Data Encryption Standard (DES) encryption algorithms to improve the security of the steganography, which is one of the requirements of steganography, and the Lempel-Ziv-Welch (LZW), Arithmetic and Deflate lossless compression algorithms to increase the secret message capacity. Also, embedding was done randomly using a logistic map-based chaos generator to increase the security more. The classical n-LSB substitution method and the proposed hybrid approaches based on the previously proposed n-LSB were implemented using different secret messages and cover images. When the results were examined, it has been seen that the proposed hybrid n-LSB approach showed improvement in all three criteria of steganography. The proposed hybrid approach that consists of previously proposed n-LSB, RSA, Deflate, and the logistic map had the best results regarding capacity, security, and imperceptibility.
WASTOPHOBIA: A PATH TOWARDS SUSTAINABILITY IN RESPONSIBLE BEHAVIOR-A CASE OF DOMESTIC SECTOR ELECTRICITY WASTE MANAGEMENT

Electricity is indispensable for socio-economic developments. Its demand is dramatically increasing in the domestic sector at an incredible pace despite distressing electricity deficiency in most of the world's developing economies like Pakistan. The synthesis of the literature portrays that domestic consumers irresponsibly (consciously or unconsciously) waste a major portion of their electricity consumption that is detrimental to electricity security, climate, and sustainable developments. To effectively deal with the issue of electricity waste, this study is presenting a theoretical research framework containing a fear based promotional marketing strategy. The novelty of the model is to present an intriguing behavioral strategy for developing consumer sustainable responsible behavior. Confronting consumers with the awareness of wasteful consumption and its detrimental conditional impacts (financial, environmental, personal future &amp; electricity security threats and religious punishments) can significantly create threats of electricity wastage (wastophobia) in the electricity consumers' mind. Wastophobia strategy will set ground foundations for the researchers and policymakers to manage electricity waste as well as wastage of other goods &amp; services. This strategy will significantly contribute to the theoretical knowledge of various disciplines, such as marketing, psychology, waste management and so on. Sustainable responsible behavior is categorically supporting consumers as well as holds huge potential in present policy guidelines under the arena of electrical purification and waste management. Moreover, such policy implications are viable, sensible, and supportive for national electrical transmission and production regulatory authorities at both public and public counterparts.Keywords: Energy Efficiency, Wasteful Consumption Awareness, Religious Sermons, Wastophobia, Sustainable Responsible BehaviorJEL Classifications: Q4, Q40, D1DOI: https://doi.org/10.32479/ijeep.11450
Virtualization of Teaching and Learning of Engineering Students and its Impact on Self-Perception of Attitude Acquisition, in the Context of COVID-19

This present describes the results of the evaluation regarding the self-perception of personal and social attitudes acquired by university students from an engineer-ing faculty at a state university in Peru, in the context of virtual teaching and learning, declared by the health emergency by COVID-19; For which the follow-ing objectives were proposed, to determine the variation or impact that the self-perception of personal and social attitudes experienced, having as reference sce-narios, the academic semester with face-to-face teaching (academic semester 2019B) and then the academic semester developed totally virtual (2020A). An exploratory-descriptive research level was used, with a longitudinal non-experimental design, whose population and sample is made up of 674 and 761 students, in the 2019B and 2020A semesters respectively. The data collection in-struments were validated through Cronbach's Alpha, whose average results per academic semester were 0.960 and 0.958. After the investigation, it was found that there is no negative impact, due to virtual teaching; On the contrary, on aver-age, there was an increase in all levels of satisfaction, increasing the level very satisfied by 52.8% and the level satisfied by 3.25%.