Reviewerly/exHarmony huggingface dataset:
The patterns of users’ activity in the media ecosystem: the results of sociological analyses

The article concentrates on the analyses of the content characteristics of the users’ activity patterns functioning in the media ecosystem. Media ecosystem is viewed by the author as information environment of a modern individual which dialectically connects the users’ media activity practices as well as the institutional conditions of their realization. The author refers to such key practices of users’ activity in the media ecosystem as media consumption and media production, notably an important factor of such practices realization are definite patterns and normals, which are the behavior models articulates in the media ecosystem environment. It is shown that these patterns and normals can be localized in such media sources as the news in mass media, fiction texts and program manifestations, technological regulations (exploitation rules, access procedures etc.), society knowledge (etiquette, etc.) of the network community. On the basis of the author’s sociological research, carried out using the content analyses (N=300) method (different types of media sources in the modern media ecosystem were studied), peculiarities of actual patterns and normals of users’ activity in media ecosystem are analyzed. The author shows how the imperative character of the patterns and normals declared reveals in a number of characteristics that are the criteria of the analyses. As a result of the research it was found out that the level of imperativeness of articulated patterns and normals is defined not only by their immediate localization (the type of media source where they were placed) but also by the referentiality of these normals to the unique experience of the users and the prevailing types of media activity.
CBA-GAN: Cartoonization style transformation based on the convolutional attention module

Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video.
Polar sampler: A novel Bernoulli sampler using polar codes with application to integer Gaussian sampling

Abstract Cryptographic constructions based on hard lattice problems have emerged as a front runner for the standardization of post-quantum public-key cryptography. As the standardization process takes place, optimizing specific parts of proposed schemes, e.g., Bernoulli sampling and integer Gaussian sampling, becomes a worthwhile endeavor. In this work, we propose a novel Bernoulli sampler based on polar codes, dubbed “polar sampler”. The polar sampler is information theoretically optimum in the sense that the number of uniformly random bits it consumes approaches the entropy bound asymptotically. It also features quasi-linear complexity and constant-time implementation. An integer Gaussian sampler is developed using multilevel polar samplers. Our algorithm becomes effective when sufficiently many samples are required at each query to the sampler. Security analysis is given based on Kullback–Leibler divergence and Rényi divergence. Experimental and asymptotic comparisons between our integer Gaussian sampler and state-of-the-art samplers verify its efficiency in terms of entropy consumption, running time and memory cost. We envisage that the proposed Bernoulli sampler can find other applications in cryptography in addition to Gaussian sampling.
ELBOW JOINT TRAINING IN TABLE TENNIS TEACHING

ABSTRACT Introduction Table tennis is widely acclaimed by the public and is also one of the most popular activities chosen by Chinese athletes. However, many athletes only pay attention to learning sports skills in sports training and neglect muscle strength training. Objective Study the training strategy of the muscles that make up the elbow joint in table tennis players and its influence on sports. Methods This paper selected 5 high-level high school table tennis athletes from a university as research volunteers. The training content included strength training, flexibility training, and other physical tests. The duration of the training was one hour, four times a week. The total training lasted six weeks. Results For the movement at a speed of 180°/s, the peak torque, the relative peak torque, and the percentage of peak flexor and extensor torque of the elbow joint of the athletes showed significant gains. However, the elbow joint change in flexion range was much shorter than in extension. Conclusion Coaches and athletes should choose a mode of elbow muscle training according to their actual needs, promoting the development of elbow muscle strength in athletes to raise their competitive level. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
Historical, Intra-scientific and Social Factors of the Scientific Leadership in Islamic Psychology

The relevance of the article is determined by the scientist’s need to reflect on science, its history and factors that change its appearance. Science and society today are so closely connected that the existence of one dialectically determines the existence of the other. On the one hand, the society creates a request, to which the science gives its answer, and the science sensitively captures any changes in the society, sometimes even anticipates them. The purpose of this article is to analyze the phenomenon of scientific leadership in Islamic Psychology as a branch of modern Psychological Science, which is the result of the relationship and mutual influence of historical, intra-scientific and social factors The achievement of this goal is determined by the objectives where the first is to observe the main stages of psychological problems evolution in the context of the history of Western and Islamic intellectual traditions, and the second is to review and analyze of intra-scientific and social factors of leadership in Islamic Psychology as a branch of modern Psychological Science. The material for writing the article was the theoretical primary sources and analytical scientific publications on the history, philosophy and methodology of science in general, as well as in the field of Psychology and Islamic Psychology as its branch. The general dialectical approach made it possible to consider the problem of the scientific leadership of Islamic Psychology in both historical and logical angles, and to present the history of the development of psychological problems in the mutual influence of intra-scientific (the change of scientific criteria, differentiation and integration of sciences, methodological pluralism, externalism, etc.) and social factors (dissemination of popular culture, virtualization of public relations, globalization, etc.) as a system. Comparative analysis made it possible to collate the historical and modern interaction of Islamic and Western intellectual traditions in the field of psychological problems. This study disclosed not only the reasons for scientific leadership and the growing popularity of Islamic Psychology, but also the influence of these trends on changing the image of modern science, its paradigm, which actualizes psychological research and practice in the field of religion and spirituality based on the methodological principles of multiculturalism and post-secularism.
CLG-Trans: Contrastive learning for code summarization via graph attention-based transformer

Automated code summarization is the task of automatically generating natural language descriptions of source code, which is an important research topic in the software engineering field. Many methods in recent studies were based on deep learning techniques, which effectively improve the performance of code summarization. Most of the existing code summarization methods use different kinds of neural networks to learn source code information. Some methods use graph neural network (GNN) to represent abstract syntax tree (AST) and fuse the structural information of source code. However, these methods still have two important issues: 1) they cannot solve the Out-Of-Vocabulary (OOV) problem effectively; 2) the structural information of source code they can capture is limited. In order to address the above-mentioned challenges, we propose a novel automated code summarization model named CLG-Trans in this work. This model uses the Byte Pair Encoding (BPE) algorithm and pointer-generator network to tackle the OOV problem. Then it utilizes the fusion of contrastive learning strategy and dynamic graph attention mechanism to effectively capture rich structure information of source code sequences. Experimental results on Funcom dataset show that CLG-Trans outperforms seven state-of-the-art models (i.e., Hybrid-DRL, Ast-Attendgru, Transformer, codeGnn, Rencos, CodeBERT and SIT) by averagely increasing 19.48% and 13.17% on BLEU scores and ROUGUE-L score, respectively. In addition, CLG-Trans achieves an improvement of 16.14% and 4.70% in BLEU scores and ROUGE-L score compared with our previously proposed model DG-Trans.
Blockchain Technology in Pharmaceutical Supply Chain Management

The coronavirus pandemic caused global health and economic disruption of an unknown scale. Several issues with the pharmaceutical supply chain such as the counterfeiting of drugs came to light in these dire times. While continual efforts are being applied in order to effectively treat and annihilate the coronavirus, so are the efforts of supply chains to prepare for patient care in case of a recurrence. The requirement of superior quality medicines has sky rocketed, subsequently, so has the influx of counterfeited drugs. The security of the pharmaceutical supply chain is unquestionably necessary due to the large scale increase in demand for drugs. Tampering of the supply chain is not easily detectable when unethical practices are suspected currently. A radical and innovative method that contains the potential to overcome the challenges of securing the pharmaceutical supply chain is the blockchain technology. So, our proposed solution amalgamates blockchain technology into the drug supply chain to make it tamper-proof. This study proposes a system that harnesses blockchain's properties to ensure supply of safe and traceable pharmaceuticals from end to end. The smart contracts designed have been deployed on a local blockchain using Ganache. Results of our experimentation indicate with certainty that not only is this solution feasible but it is more secure than the present day scenario of the pharmaceutical supply chain.
Detecting Open Banking API Security Threats Using Bayesian Attack Graphs

Particularly amid Covid-19, enterprises' digital transformation has rapidly accelerated, making cybersecurity an even bigger challenge. Financial institutions adopt FinTech technologies to advance their service and achieve an enhanced customer experience that creates a competitive edge in the market. FinTech products utilise open banking API services to allow communication between a financial institution and a FinTech provider. However, such an integration introduces significant security concerns. Therefore, financial firms must ensure that a robust API service to protect the bank's infrastructure and its customers' information. To address this concern, we propose a Framework for Open Banking API security that utilises STRIDE model to identify security threats in FinTech integration via Open Banking API and Bayesian Attack Graphs to automate predictions of the most exploitable attack paths.
Rat Swarm Optimizer based Transform for Performance Improvement of Machine Learning Classifiers in Diagnosis of Lung Cancer

Usage of Machine Learning algorithms for assisting healthcare providers is increasing day by day. But the performance and robustness of the machine learning algorithms are the main concerns while implementing them for critical healthcare applications such as detection of cancer. This work concentrates on the performance improvement of supervised classifiers through the feature transform based on Rat Swarm Optimizer in diagnosing lung cancer using histopathological images. Rat Swarm Optimizer used for the transformation of features. These transformed features are more capable of providing better classification accuracy when compared to normal features. The dataset is downloaded from the publicly available website and three classes are present: normal, lung squamous cell carcinomas, and lung adenocarcinomas. In each class, 1000 histopathological images are considered. Four supervised classifiers namely Histogram-Gradient boosting classifier, Random forest classifier, K-Nearest Neighbor classifier, and Linear Discriminant Analysis classifiers are tested. The highest accuracy of 90.66% is offered by Histogram-Gradient boosting classifier and this is increased to 95.82% when Rat Swarm Optimizer is used as transform before classification.
Performance Comparison of Software-Efficient Implementations of the PRESENT Block Cypher

PRESENT is one of the first standard hardware efficient, ultra-lightweight block cyphers designed to secure highly resource-constrained devices. The 4x4 s-boxes and 64-bit permutations provide an optimum level of confusion and diffusion in the cypher. The bitwise permutation involving the rerouting of wires provides faster diffusion in hardware, however, it is far from being an optimal choice to achieve good performance in software. Even though the 4-bit PRESENT s-box requires a small amount of memory to store the pre-defined s-box table, the overall efficiency of a block cypher is determined by both the linear and non-linear strategies used in the design. In this paper, three software techniques for implementing the PRESENT block cypher, namely direct, wide-table and combined wide-table, are being analysed and compared in KEIL MDK IDE and ARM Cortex-M3-based LPC1768 IoT development platform. The techniques have been evaluated for execution time, code footprint, and power and energy consumption. Therefore, this paper helps the designers to select the best software efficient technique as per their application use case.
Performance Investigation of Handwritten Equation Solver using CNN for Betterment

Identifying strong handwritten characters is a difficult task in the field of medical field and it is tedious process on decoding handwritten medicines. Recognition of handwritten mathematical expressions is a complex issue. The distribution and classification of specific characters makes the task more difficult. In our project, handwritten numbers and symbols are read and further addition, subtraction and multiplication operations are performed. The project includes a study about model deployment using convoluted neural networks and flasks. We use the CNN to classify specific characters. Tracking of character string operations are used to solve equations. The maximum accuracy of the proposed model is 99.12% recall is 95%, sensitivity is 89% and specificity is 68%. Effectiveness of our proposed system is helpful for students who want to get handwritten answers. The equation can be extended to more complex equations and more user data can be trained to improve correction and accuracy.
File Security using Image-based Encryption (FSUIE)

The process of exchanging information via electronic communications is a sensitive matter in our time, and with the development of devices, technologies, and methods of analysis, the usual methods may be unsafe, so we always resort to developing them or perhaps new ideas. In this research, we will talk about a new method of encryption called (FSUIE) that depends on the image, where the way to work will be by comparing the bytes of characters with the bytes of the image and using the locations of these bytes as an encrypted text sent to the future, regardless of the length of this encrypted text. In addition to mentioning some algorithms other to compare them to how each works.
An Implementation of a New Proposed Round-Robin Algorithm with Smart Time Quantum in Cloud Computing Environment

The popularity of cloud computing platforms has risen dramatically in recent years. As cloud computing serves millions of users at the same time, it must be able to handle all those users' demands efficiently. Thus, choosing a suitable scheduling algorithm is crucial in the cloud computing environment in order to ensure efficient performance with a reasonable degree of quality of service (QoS). The primary goal of this research is to empirically implement and evaluate a recently proposed Round-Robin algorithm with smart time quantum (RR-STQ) in a cloud computing environment, as well as, to enhance the RR-STQ with a dynamic smart time quantum. The CloudSim tool was used to simulate the cloud computing platform to implement RR-STQ and evaluate it with several algorithms using different scenarios. In addition, three scheduling performance metrics were used in the evaluation process. In all comparison scenarios, the (RR-STQ) achieved a significant improvement rate in terms of average response time (RT). Moreover, (RR-STQ) has a better performance in the average turnaround time (TAT), waiting time (WT), and response time (RT) than the traditional RR algorithm. Also, the implemented algorithm (RR-STQ) with dynamic time quantum has a better performance than static time quantum. Based on the evaluation results, it is beneficial to integrate the RR algorithm with other scheduling models such as shortest job first (SJF) to enhance the WT and TAT. Furthermore, the investigations revealed that the dynamic time quantum improves the performance of the RR algorithm.
Automated Essay Scoring System with Grammar Score Analysis

An Automated Essay Scoring System is a system that deals with grading Hand written essays without any human intervention. Most of the research done in this field involves direct mapping of an essay’s numerical representation, using word embedding, to its golden score, without any specific trait scoring. So, this research aims to use latest contextual Text Embedding i.e., BERT Embedding for numerical representation of Essay and granulate the scoring of essay into two modules: structure scoring module which deals with scoring essay on the basis of its structure and grammar scoring module, which deals with scoring essay on the basis of its grammatical correctness. To evaluate the proposed model, Quadratic Weighted Kappa Score is used. In this implementation, a QWK score of 0.75 for Structure score and 0.70 for Grammar Score has been obtained. This research and its specific trait scoring can be used as base for future implementation with more detailed feature specific scoring tasks and improve the scope of grammar scoring by considering more grammatical cases.
Implementation Techniques for GIFT Block Cypher: A Real-Time Performance Comparison

Lightweight cryptography is gaining popularity for securing private and sensitive data collected by smart IoT devices. It provides security solutions tailored for constrained devices with the low area, low power and low latency requirements. The PRESENT is one of the most popular block cyphers that are efficient in hardware and offer an optimum level of security. However, the PRESENT cypher does not provide much security against the linear cryptanalytic attack. These security concerns have been addressed through the design of the GIFT block cypher that makes an appropriate choice and efficient use of lighter s-box and bit-permutations, thereby making the overall design more secure and hardware efficient than the PRESENT block cypher. However, the realisation of the linear layer by the bit-permutation method makes the GIFT cypher inefficient in software. This paper describes the software-efficient lookup table, bit-slicing and fix-slicing implementation techniques for the GIFT block cypher. These techniques have been simulated in KEIL MDK IDE and implemented on the ARM Cortex-M3-based LPC1768 hardware platform. Performance comparison of these techniques has been carried out using ULINKpro and ULINKplus debug adapters in terms of various metrics such as power, energy, execution time and memory code size.
E-Archive Document Clustering Information System Using K-Means Algorithm

Archiving is an important activity in a company, because the archive is a form of decision making as physical evidence owned by the company. Records management system in a company or organization must be managed properly. Currently, the archive management system is still manual which has the risk of documents being easily damaged and lost either due to natural disasters or unwanted accidents. Therefore, it is necessary to manage archives that can use a computerized system to facilitate users and avoid data loss with various factors. The research method uses the K-Means Clustering algorithm to facilitate the process of grouping archives, and the prototype is the method that will be used for software development. The function of this application is to simplify the process of archiving and searching for archived data. Management carried out by the system includes archive storage, archive destruction, and archive file reports.
Comparison of the K-Nearest Neighbor and Decision Tree algorithm to the Sentiment Analysis of Investment Applications Users in Indonesia

Investment is a familiar thing, especially for millennials, now investment can be done easily only with a smartphone, through applications on the Google Play Store, you can invest. But these apps have the highest downloads and high ratings on the Google Play Store. Many stock investing apps have nearly the same downloads and ratings, so the title of best app is a problem. Based on this, this sentiment analysis research aims to analyze user feedback of stock investment applications as a variable to determine which stock investment application is the best. This research was conducted by using the comparison of the K-Nearest Neighbor and Decision Tree algorithms. To see the level of accuracy between the 2 algorithms. The research uses google colab tools as data retrieval tools and rapidminer as data preprocessing tools. From this research, resulting in sentiment data, the highest positive sentiment results are the Ajaib Application at 74% and Negative 26%. So it can be concluded that the Ajaib application as the best stock investment application based on user comments reviews where this application has the most positive sentiment reviews with a high accuracy value.