Reviewerly/exHarmony huggingface dataset:
SUM CODE BASED ON THE PRINCIPLE OF WEIGHTING TRANSITIONS BETWEEN DIGIT GROUPS OF THE INFORMATION VECTOR

The problem of code modification with summation of weighted transitions is considered. A method for designing summation codes is proposed, which is based on summing the weight coefficients attributed to groups of several digits in the information vector. It is proposed to choose weight coefficients from a sequence of natural numbers. The modified code with the summation of weighted transitions can be used in the construction of devices with the detection of malfunctions and errors in calculations at the outputs of blocks and nodes.
Computational Utilization of Numerical Equivalences: Fuzzy systems and Neural Networks – A comparative Survey

The various numerical equivalence’s which is observed to be most common in two important mathematical and computational concepts namely fuzzy systems and neural networks are compared and their pros and cons are discussed in this survey paper. In this study each method had analyzed with their numerical convergence in different applications and the common factor which interconnect their performance are analyzed using various existing examples. The survey provides useful information for computational utilization of these important numerical concepts.
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments With Support Samples

This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures that different views of the same unlabeled instance are assigned similar pseudo-labels. The pseudo-labels are generated non-parametrically, by comparing the representations of the image views to those of a set of randomly sampled labeled images. The distance between the view representations and labeled representations is used to provide a weighting over class labels, which we interpret as a soft pseudo-label. By non-parametrically incorporating labeled samples in this way, PAWS extends the distance-metric loss used in self-supervised methods such as BYOL and SwAV to the semi-supervised setting. Despite the simplicity of the approach, PAWS outperforms other semi-supervised methods across architectures, setting a new state-of-the-art for a ResNet-50 on ImageNet trained with either 10% or 1% of the labels, reaching 75.5% and 66.5% top-1 respectively. PAWS requires 4x to 12x less training than the previous best methods.
Clockwork Finance: Automated Analysis of Economic Security in Smart Contracts.

We introduce the Clockwork Finance Framework (CFF), a general purpose, formal verification framework for mechanized reasoning about the economic security properties of composed decentralized-finance (DeFi) smart contracts. CFF features three key properties. It is contract complete, meaning that it can model any smart contract platform and all its contracts -- Turing complete or otherwise. It does so with asymptotically optimal model size. It is also attack-exhaustive by construction, meaning that it can automatically and mechanically extract all possible economic attacks on users' cryptocurrency across modeled contracts. Thanks to these properties, CFF can support multiple goals: economic security analysis of contracts by developers, analysis of DeFi trading risks by users, and optimization of arbitrage opportunities by bots or miners. Because CFF offers composability, it can support these goals with reasoning over any desired set of potentially interacting smart contract models. We instantiate CFF as an executable model for Ethereum contracts that incorporates a state-of-the-art deductive verifier. Building on previous work, we introduce extractable value (EV), a new formal notion of economic security in composed DeFi contracts that is both a basis for CFF analyses and of general interest. We construct modular, human-readable, composable CFF models of four popular, deployed DeFi protocols in Ethereum: Uniswap, Uniswap V2, Sushiswap, and MakerDAO, representing a combined 17 billion USD in value as of August 2021. We uses these models to show experimentally that CFF is practical and can drive useful, data-based EV-based insights from real world transaction activity. Without any explicitly programmed attack strategies, CFF uncovers on average an expected \$56 million of EV per month in the recent past.
Evolution of mathematical modelling as a method of scientific cognition and its didactic functions in educational process

We investigate historical aspects of developing mathematical modelling method and analyze the functions of mathematical modelling in the process of cognition. It is justified that modelling in the educational process acts simultaneously as a method of scientific knowledge, a part of the content of educational material and an effective mean of its study.We show that the development of students' ideas about the role of mathematical modelling in scientific knowledge and practice, the development of their ability to build mathematical models of life phenomena, are an important task of modern school. A possibility of improvement pupils’ literacy in mathematics is highlighted by developing their correct conceptions about the method of mathematical modelling.
Text complexity of reading comprehension passages in the National Matriculation English Test in China: The development from 1996 to 2020

This study examined the development of text complexity for the past 25 years of reading comprehension passages in the National Matriculation English Test (NMET) in China. Text complexity of 206 reading passages at lexical, syntactic, and discourse levels has been measured longitudinally and compared across the years. The natural language processing tools used in the study included TAALES, TAALED, TAASSC, and TAACO. To compare the differences across the years at various levels of text complexity, ANOVA and MANOVA tests were conducted. The results suggested that lexical level text complexity revealed the most evident changes throughout the years, lexical sophistication, density, and diversity levels of the most recent years of reading passages have increased remarkably compared to the early years. The syntactic level text complexity indicated a moderate elevation toward the recent years of reading passages. For the discourse level text complexity, regarding cohesion, insignificant fluctuation occurred throughout the years and the general trend was not necessarily increasing. Combined, the results indicated that text complexity of the reading comprehension passages in the NMET over the past 25 years had been steadily increasing by including more low frequency and academic vocabulary, diversifying vocabulary in the passages, and complicating sentence and grammatical structures. The results were further examined against the general curriculum standards and guidelines to analyze whether the changes were reflected in the policies. It showed that the exams required a much larger vocabulary size than the number indicated in the guidelines, suggestions for test designers and pedagogical practices were provided accordingly.
ADAPTIVE LEARNING EXPERT SYSTEM FOR DIAGNOSIS AND MANAGEMENT OF VIRAL HEPATITIS

Viral hepatitis is the regularly found health problem throughout the world among other easily transmitted diseases, such as tuberculosis, human immune virus, malaria and so on. Among all hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using both structured and semi-structured interviews from internists of St.Paul Hospital. Once the knowledge is acquired, it is modeled and represented using rule based reasoning techniques. Both forward and backward chaining is used to infer the rules and provide appropriate advices in the developed expert system. For the purpose of developing the prototype expert system SWI-prolog editor also used. The proposed system has the ability to adapt with dynamic knowledge by generalizing rules and discover new rules through learning the newly arrived knowledge from domain experts adaptively without any help from the knowledge engineer
Universal deterministic quantum operations in microwave quantum links

We propose a realistic setup, inspired by already existing experiments, within which we develop a general formalism for the implementation of distributed quantum gates. Mediated by a quantum link that establishes a bidirectional quantum channel between distant nodes, our proposal works both for inter- and intra node communication and handles scenarios ranging from the few to the many modes limit of the quantum link. We are able to design fast and reliable state transfer protocols in every regime of operation, which, together with a detailed description of the scattering process, allows us to engineer two sets of deterministic universal distributed quantum gates. Gates whose implementation in quantum networks does not need entanglement distribution nor measurements. By employing a realistic description of the physical setup we identify the most relevant imperfections in the quantum links as well as optimal points of operation with resulting infidelities of $1-F \approx 10^{-2}-10^{-3}$.
Reflections on the Extent of Egypt's Revolution: Socioeconomic Indicators

Observers of modern Egypt clearly share some skepticism as to whether Egypt actually did experience a revolution in 1952. Indeed, that Egypt today is still grappling with most of the socioeconomic problems that the Free Officers had pledged to overcome brings to mind the old Russian adage that a pair of pants turned inside out still has the same holes. Between 1960 and 1976, Egypt reduced its illiteracy rate from 71 percent to 57 percent, and female illiteracy from 84 percent to 71 percent. In the early 1970s, the rate of illiteracy was 76 percent in Morocco, 62 percent in Turkey, and 64 percent in Iran. Likewise, Egypt did not do well in promoting female employment outside the rural family. Egypt's public enterprise sector, even after more than a decade of infitah, was one of the largest in the developing world, its magnitude approaching those of some Eastern European economies.
Pembuatan Motion Graphic Iklan Aplikasi FlickApp dan Silvi+ di PT Kreigan Sentral Teknologi:

The rapid technology development has a major impact on the economic activities that develop in the community. The number of products and services that have sprung up today makes entrepreneurs have to compete to get the attention of consumers so that they are not less competitive. PT. Kreigan Sentral Teknologi, which is engaged in digital finance, currently has 2 mobile applications, namely FlickApp and Silvi +, but the company does not have advertisements regarding related applications so that these applications are not widely known by the public. Therefore the company needs an  interesting and informative promotional media. The purpose of this project is to produce a promotional media which including the information about FlickApp and Silvi+ in the form of motion graphics using Adobe XD, Adobe Illustrator, and Adobe After Effect.
Smooth Visual-Coverage Path Planning for Escort Missions using UAVs

This paper investigates the trajectory generation problem for a fleet of Unmanned Aerial Vehicles (UAVs) performing escort missions with respect to a convoy with a time-varying velocity. Using camera-equipped UAVs, we propose a framework allowing to adapt the UAVs altitudes to achieve a trade-off between the size of the covered surface and the image resolution by solving an optimization problem. Furthermore, we perform polynomial refinement based on the generated planar way-points to yield a minimum-jerk time-parameterized trajectory with dynamical constraints on the virtual leader to follow. The numerical simulations presented in this paper show the effectiveness of the optimization algorithm in adapting the UAVs altitudes to satisfy a compromise between the size of the covered area and its corresponding image resolution.
Premises for a Multicultural Approach to Education

The goal of education at the present stage is development of a person able to participate in intercultural communication in modern multicultural conditions. Based on this goal, modern education should be based on the principles of a multicultural approach to education that can serve as a response to the challenges of ethnic diversity. In order to more fully disclose the value of a multicultural approach to education, we need to examine its premises. In the article, the premises for a multicultural approach to education are analyzed. It is revealed that there are some pedagogical, philosophical, and psychological premises for it.
Secure Wire Shuffling in the Probing Model

In this paper we describe the first improvement of the wire shuffling countermeasure against side-channel attacks described by Ishai, Sahai and Wagner at Crypto 2003. More precisely, we show how to get worst case statistical security against t probes with running time \({\mathcal O}(t)\) instead of \({\mathcal O}(t \log t)\); our construction is also much simpler. Recall that the classical masking countermeasure achieves perfect security but with running time \({\mathcal O}(t^2)\). We also describe a practical implementation for AES that outperforms the masking countermeasure for \(t \ge 6\,000\).
Review on Distributed Database Systems

Distributed database system refers to a pool of databases incorporated and integrated physically across various sites in a computer network. The topic of the research paper will be anchored on A New Scalable and Expandable Access Control Model for Distributed Database Systems in Data Security. On top of this, the study will establish how social media, the Internet of Things (IoT), and self-driving automobiles integrate distributed database systems in their models. The chosen research topic would provide a unique opportunity to explore and analyze the study's most critical issues. In addition, the topic will provide room whereby areas in distributed database systems will be identified for future research. It would be crucial that I spend time reading about the identified topic for this study. Consequently, I would develop accurate, credible, and well-researched findings that would widen my scope and understanding. In addition, my research on distributed database systems would be used in future studies as a reference.
Impact of Channel Variation on One-Class Learning for Spoof Detection

The value of Spoofing detection in increasing the reliability of the ASV system is unparalleled. In reality, however, the performance of countermeasure systems (CMs) degrades significantly due to channel variation. Multi-conditional training(MCT) is a well-established technique to handle such scenarios. However, which data-feeding strategy is optimal for MCT? is not known in the case of spoof detection. In this paper, various codec simulations were used to modify ASVspoof 2019 dataset, and assessments were done using data-feeding and mini-batching strategies to help address this question. Our experiments aim to test the efficacy of the various margin-based losses for training Resnet based models with LFCC front-end feature extractor to correctly classify the spoofed and bonafide samples degraded using codec simulations. Contrastingly to most of the works that focus mainly on architectures, this study highlights the relevance of the deemed-of-low-importance process of data-feeding and mini-batching to raise awareness of the need to refine it for better performance.
Klasifikasi citra makanan menggunakan metode transfer learning model residual network

The activity of people who often upload photos of food before eating is common on popular social media. Food image data circulating on social media can be used for business purposes, such as analyzing customer behavior patterns. However, not all uploaded images are food images, so prior to analysis it is necessary to do a classification process between food images and non-food images. Therefore, the researcher will classify food images automatically using the transfer learning method using the Residual Network model version 18 (ResNet-18). The Residual Network model is used. This model is used because it has a residual connection mechanism to solve the vanishing gradient problem. In addition, transfer learning itself was chosen because this method is able to take advantage of the features and weights that have been obtained in the previous training process so that it can reduce computation time and increase accuracy. In this study, we tested and compared the capabilities of the ResNet18 and AlexNet models. In addition, we also added a fine tuning and freeze layer method to increase the various quality of the resulting model. In this study, we used 3000 images for training data and 1000 images for testing data and in the evaluation process we used accuracy calculations. The results obtained, namely the ResNet18 model with the fine tuning method produces an accuracy value of 0.981000 and the freeze layer 1-3 method produces the best accuracy value of 0.988000. The AlexNet model with the fine tuning method produces an accuracy value of 0.970000 and the freeze layer method produces the best accuracy value of 0.978000. From all these mechanisms, it can be concluded that the mechanism with the best accuracy is found in the RestNet18 architecture, freeze layer 1-3 method.
Efficient Video Compression via Content-Adaptive Super-Resolution

Video compression is a critical component of Internet video delivery. Recent work has shown that deep learning techniques can rival or outperform human-designed algorithms, but these methods are significantly less compute and power-efficient than existing codecs. This paper presents a new approach that augments existing codecs with a small, content-adaptive super-resolution model that significantly boosts video quality. Our method, SRVC, encodes video into two bitstreams: (i) a content stream, produced by compressing downsampled low-resolution video with the existing codec, (ii) a model stream, which encodes periodic updates to a lightweight super-resolution neural network customized for short segments of the video. SRVC decodes the video by passing the decompressed low-resolution video frames through the (time-varying) super-resolution model to reconstruct high-resolution video frames. Our results show that to achieve the same PSNR, SRVC requires 16% of the bits-per-pixel of H.265 in slow mode, and 2% of the bits-per-pixel of DVC, a recent deep learning-based video compression scheme. SRVC runs at 90 frames per second on a NVIDIA V100 GPU.
Enhance Web-Based Job Search Recommendation System of Hybrid-Based Recommendation

The main activity carried out daily for an individual to earn money is by working. Finding a job that matches our educational background is not easy. Many individuals do not know their abilities and the limited information on job vacancies is also an obstacle for applicants who want to find work. Therefore, a 'toolkit is needed that can provide recommendations on what fields of work are by the educational background in question. The hybrid approach method is to combine collaborative-filtering techniques (decision tree algorithm) and content-based (nearest neighbor algorithm). The decision tree algorithm is used to classify the fields of work while for job recommendations, the nearest neighbor algorithm is used. In the nearest neighbor, the similarity formula is used to calculate the proximity between applicants and job vacancies based on matching the existing weights and attributes. The output generated from this system is a list of job recommendations that match the applicant's educational background.
Artificial Intelligence For Breast Cancer Detection: Trends & Directions.

In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths, limitations and enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer using various imaging modalities. Generally, in this article we have focused on to review frameworks that have reported results using mammograms as it is most widely used breast imaging modality that serves as first test that medical practitioners usually prescribe for the detection of breast cancer. Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets. Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.
Theoretical aspects of improving the implementation of the updated content of education on the subject of «Physical Culture»

The article defines the need for the introduction of an updated content of education. This is the improvement of the information education of students. Social reasons. The transition from giving ready-made solutions to finding approaches for independent summing up to the formulation of ways to implement them. Correction in the readiness of the younger individual to realize himself in society. The key aspects of the implementation of the updated content of education on the subject in the Republic of Kazakhstan are identified. They are: mobility in training, possession of a set of competencies, the need to achieve the adequacy of the essence of university and school programs, the adequacy of setting achievable benchmarks and the corresponding content of children's motor activity is not realized. The features of updating the content of education on the subject of  Physical Education  in Russia are determined. These are difficulties of a motivational, programmatic, methodological, and personnel nature. Updating creative teaching methods. The development of students ' experience on the impact of the studied material on their physical condition. Optimal coordination in the construction of mobile plans. The ways of further development of the introduction of updating the content of education on the subject of  Physical are developed. This is the development of tools for directing students to independently search for information to solve the task. Preparation of content-compatible university and academic programs. Setting achievable benchmarks and selecting adequate implementation tools.