Reviewerly/exHarmony huggingface dataset:
VLSI Design of Saturation-Based Image Dehazing Algorithm

Hazy weather degrades the vividness of the images captured by real-time systems for applications such as object detection, remote sensing, and surveillance systems. This affects the performance of such real-time systems. The hardware implementation of a real-time haze removal system is imperative to solve these problems. Such a solution is proposed in this article. Here, the saturation-based hardware implementation of an image dehazing system is presented. To estimate atmospheric light more precisely, a 15 × 15 window minimum filter is implemented, which uses the downsampled hazy image to estimate the atmospheric light. Furthermore, we have employed saturation-based transmission map estimation, which makes our approach pixel-based rather than patch-based. Unlike existing patch-based methods, the proposed method requires neither an edge detection unit nor an image filtering unit to suppress halo artifacts around edges. The VLSI architecture of the proposed dehazing system comprises seven pipelined stages. It is implemented on FPGA as well as ASIC (65-nm technology node) platforms. The ASIC implementation of the proposed dehazing system yielded a maximum throughput of 624 Mpixels/s, which is fast enough to process 3840 × 2160 resolution at a rate higher than 70 fps with only 13.2k logic gates count.
TransWeaver: Weave Image Pairs for Class Agnostic Common Object Detection

Measuring the similarity of two images is of crucial importance in computer vision. Class agnostic common object detection is a nascent research topic about mining image similarity, which aims to detect common object pairs from two images without category information. This task is general and less restrictive which explores the similarity between objects and can further describe the commonality of image pairs at the object level. However, previous works suffer from features with low discrimination caused by the lack of category information. Moreover, most existing methods compare objects extracted from two images in a simple and direct way, ignoring the internal relationships between objects in the two images. To overcome these limitations, in this paper, we propose a new framework called TransWeaver, which learns intrinsic relationships between objects. Our TransWeaver takes image pairs as input and flexibly captures the inherent correlation between candidate objects from two images. It consists of two modules ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., the representation-encoder and the weave-decoder) and captures efficient context information by weaving image pairs to make them interact with each other. The representation-encoder is used for representation learning, which can obtain more discriminative representations for candidate proposals. Furthermore, the weave-decoder weaves the objects from two images and is able to explore the inter-image and intra-image context information at the same time, bringing a better object matching ability. We reorganize the PASCAL VOC, COCO, and Visual Genome datasets to obtain training and testing image pairs. Extensive experiments demonstrate the effectiveness of the proposed TransWeaver which achieves state-of-the-art performance on all datasets.
Far-right virtual communities: Exploring users and uses of far-right pages on social media

The role of social media in facilitating far-right networks and propagating far-right narratives is increasingly documented. However, research tends to focus on far-right pages on social media, thus leaving the users of these pages understudied. Relying on interviews with users of the Facebook page of the radical right organization La Meute in Canada, this study investigates individuals’ motivations and practices when using far-right pages on social media, as well as the interplay between their use of these pages and their view of themselves and society. It shows that users of La Meute’s Facebook page perceive (to varying degrees) that mainstream media are biased and do not represent their views nor address their concerns about immigration issues. As such, they seek meaning by using far-right pages on social media, either as their main source of information or to complement mainstream media. On these pages, they encounter a virtual community of like-minded people, which is conceived in opposition to the rest of society who would not be critical enough of mainstream media and immigration. Boundaries delineating the virtual community tend to be particularly pronounced among individuals who use far-right pages on social media as their main source of information.
Crypto Conundrum: Ban or Limited Nod

This article tries to find out the current concerns regarding the crypto currency. India has been engaged in a fierce debate over crypto currency. In India, crypto currency has been treated very conservatively, with the RBI imposing a ban. Although the ban has been reversed, the central bank’s position has not changed. India is on a dual path of trying to regulate or even ban crypto currencies while at the same time encouraging the use of block chain technologies. The Government of India adopted a conservative stance on taxation by announcing a flat 30 per cent tax rate on income from digital virtual assets, or crypto, in the Budget 2022. The Government has also launched e rupee. One of the issues in G20 summit in September,2023 is regulation of crypto currency. But still, it’s impossible to predict when or in what form the crypto law will get through in India’s Parliament. Different countries have their own ways of regulating crypto currency, but it is safe to say that India has so far been among the most closed to crypto currencies. Considering the global nature of trade nowadays even an international crypto currency can be evolved &amp; regulated by international agencies like IMF and World Bank.
LOCAL AREA NETWORK LAN DESIGN FOR VIDEO CONFERENCE SERVICES AT SMK N 6 PALEMBANG

A computer network is a telecommunications network that allows computers to communicate with each other by exchanging data, the purpose of a computer network is to achieve its goals, every part of a computer network can request and provide services. The party that requests the service is called the client and the party that provides the service is called the server.network Service or network service is a service provided by a computer network that serves to provide convenience in using shared resources both software and hardware. To achieve the same goal, each part of the computer network will request and provide services (service). The party that provides the service is called the server and the one who requests the service is called the client. This architecture is referred to as client-server which is used in almost all computer network applications.&#x0D; &#x0D;
Teacher’s Perceives on the Implementation of Genre-Based Approach in Teaching Writing

The present study was focused on the teacher’s perceives on the implementation of genre-based approach (GBA) in teaching writing in one of senior high schools in Surakarta, Central Java, Indonesia. The objectives of this study are to explain; 1) The teacher’s perceptions on the practice of the genre-based approach in teaching writing, 2) the matters faced by English teacher, 3) the solutions regarding the problems faced by the teacher. This research was qualitative. The data were collected by implementing questionnaire and interview. The sample of this study was one teacher in Surakarta by using purposive sampling technique. Writers used a model of triangulation process consisting of data reduction, data display, and conclusion to collect and analyze the data. The results showed that; 1) the teacher had positive perception using GBA in teaching writing, 2) there were problems faced by the teacher such as the ease of internet access so that the students could not show their own ideas, and 3) the teachers’ suggestion was that the teacher should be up to date with the newest topic and give motivation to the students to express their own ideas.
Developing Interactive Instructional Modules for Core Principles of English Language Teaching

This research aims to develop instuctional modul of core principles of EFL Material dealing with English Education Program curriculum at Tarbiyah and Teacher Traning Faculty of STAIN Majene. This research conducted a Research and Development (R&amp;D) by using ADDIE Model. The subject of this research was core principles of EFL Material dealing with English Education Program curriculum of Tadris Bahasa Inggris STAIN Majene. The data of this research were collected and analyzed in proccess of development through Model.The result of the research reveal that the the developing of core principles in ELT class was needed by student. The ADDIE Model can really help the lecturer to develope material for teaching. This research has been trying to develop Core Principle of English Languge Teaching materials in the STAIN Majene which consists of Language Components in English Language Teaching; Methods and Approaches in Language Teaching; and Evaluation and Assessment on Language Teaching which divided into some subtopics which are Language Components covering Elements of Language and Language Skills, then Methods and Approaches in Language Teaching covering The Development of Methods and Approaches of Language Teaching in the world; Kinds of Methods and Approaches in Language Teaching; and Current Methods and Approach in Language Teaching. Evaluation and Assessment on Language Teaching covering The Introduction of Measurement in Language Teaching; and Constructing Evaluation and Assessment Tools in Language Teaching.
Contact-tracing of the COVID-19 spreading using digital technologies with artificial intelligence (literary review)

The emergence of COVID-19 almost coincided with the beginning of an active phase of the digitalization process in all areas, including the healthcare system. Moreover, COVID-19 unwittingly became the impetus that accelerated the adoption of digital technologies, and initiated new, often innovative solutions to combat both the virus and its devastating social and economic consequences.&#x0D; The purpose of the study: the current study conducts a literature review of existing scientific reports in the field of digital contact-tracing COVID-19 using artificial intelligence (AI) technologies, to discuss issues related to the security of personal data when using official mobile applications, to draw conclusions and make recommendations in the field of effective and ethical management of digital contact-tracing as one of the main tools for preventing the spread of the pandemic.&#x0D; Scientific reports contained in the scientific research databases of Ebsco Publishing and SpringerLink for the period March 2020 — April 2021 were analyzed.&#x0D; Digital contact-tracing solutions have found their important place among other anti-epidemic measures in many countries around the world. However, the same solutions, but already using AI, are still gaining popularity. National governments in numerous developed and developing countries understand the importance of national contact-tracing systems, which in turn has introduced such approaches in national pandemic response strategies.&#x0D; Conclusion: Digital contact-tracing technologies using AI can be an effective tool in the fight against COVID-19 and similar pandemics. However, such digital systems are still at a preliminary stage of their development and implementation, and it will take time before the results will be visible. Very few of the considered examples and models of digital tracing solutions using AI technologies have operational maturity at this stage.
Record management with a decision support system for faculty and employees association

This study aimed to develop a Record Management System with Decision Support System for the Faculty and Employees Association in Miagao Campus. The system allows for the creation, maintenance, and disposal of records in an organized manner, creating a transparent record management organization through a certain policy. The Decision Support System enhances the functional aspect of decision-making and data management. The developed system includes a Record Management component for faculty and employee data, used for updating personal records and determining individual benefits. Members can view their monthly contributions and dues, and the system provides a decision support system to evaluate if members can claim their benefits. Additionally, the system generates monthly reports and keeps records in the database system. The prototyping model was used in software development, and the ISO 25010 criteria for software evaluation was adopted to evaluate the system. Five IT experts and ten FEA members were randomly selected to evaluate the system, and based on the given criteria, the overall result of the evaluation was "very effective." The system meets the characteristics of software quality set by ISO 25010 standards, including functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. Overall, the Record Management System with Decision Support System is an effective tool for managing records and supporting decision-making for the Faculty and Employees Association in Miagao Campus.
Image Representation of Numerical Data-points for Classification Using Convolutional Neural Network

Higher dimensional data is a problem for classification. Researchers put forward various approaches like dimension reduction, ranking features for an optimal set, different feature extraction techniques, etc. for classification. In the current context of categorization, CNN (Convolution Neural Network) is a powerful tool, which classifies the data based on images as input. In this work, we have demonstrated a method to represent individual samples in an image, irrespective of dimension, and used CNN for classification. Further, to preserve data privacy, the images are flipped randomly (horizontal and vertical flips) for classification using CNN. The outcomes of both approaches are compared with related techniques and have shown promising results.
EX-LIBRIS AS A TOOL FOR BROADCASTING NATIONAL-CULTURAL CODES IN THE WORKS BY REPRESENTATIVES OF THE KYIV GRAPHIC ARTS SCHOOL ARKADY AND GENNADY PUGACHEVSKY

The article is dedicated to the phenomenon of reflecting the national and cultural code in the work of Kyiv graphic artists Arkady and Gennady Pugachevsky, who were at the origins of the creation of a new page in the history of the Ukrainian bookplate.Artists are a significant segment of the history of modern printmaking, primarily small-scale graphic arts, known outside of Ukraine, but so far not well known in their Motherland.The main vectors of the creative activity of the experts were considered, and printmaking, bookplates, and small plates were selected as the leading areas of application of their creative capabilities.The main role of experts in the process of establishing the national bookplate as an independent phenomenon of the art of printmakig of modern Ukraine, the formation of a new face of the Kyiv bookplate school, is emphasized.The importance of international artistic activity of artists is emphasized, the geography of their exhibition activity, its influence on the formation of the country's image in the foreign art field is analyzed.A comparative analysis of the stylistic features of the sheets of A. and G. Pugachevsky as representatives of two different generations of the creative environment was conducted.The technological complexity of the process of creating of their sheets is emphasized, which determines the high professional level of the sheets and makes it almost impossible to imitate their manner.The dominant motifs of A. and G. Pugachevsky's work are highlighted, and a classification of the main plots of their graphic works is proposed.The work with strongly expressed national motives is analyzed, the dual nature of the national code -the synthesis of Jewish and Ukrainian components -is emphasized.The analysis was carried out on the material of works both with clearly defined national motives (external attributes, symbols) and with deeper manifestations of the cultural code, that is, an appeal to the folk epic, folklore as a basement for self-identification.
Bayesian compositional regression with microbiome features via variational inference

Abstract The microbiome plays a key role in the health of the human body. Interest often lies in finding features of the microbiome, alongside other covariates, which are associated with a phenotype of interest. One important property of microbiome data, which is often overlooked, is its compositionality as it can only provide information about the relative abundance of its constituting components. Typically, these proportions vary by several orders of magnitude in datasets of high dimensions. To address these challenges we develop a Bayesian hierarchical linear log-contrast model which is estimated by mean field Monte-Carlo co-ordinate ascent variational inference (CAVI-MC) and easily scales to high dimensional data. We use novel priors which account for the large differences in scale and constrained parameter space associated with the compositional covariates. A reversible jump Monte Carlo Markov chain guided by the data through univariate approximations of the variational posterior probability of inclusion, with proposal parameters informed by approximating variational densities via auxiliary parameters, is used to estimate intractable marginal expectations. We demonstrate that our proposed Bayesian method performs favourably against existing frequentist state of the art compositional data analysis methods. We then apply the CAVI-MC to the analysis of real data exploring the relationship of the gut microbiome to body mass index.
Malicious language identification based on neural network

Malicious language identification has been a critical issue in current network environment, automatically investigating malicious sentences has been concerned by security protection of neural language processing However, existing research about malicious investigation is almost based on the keywords detection and the system cannot detect the cryptic information in networks, which needs human to sign the malicious keyword and cost lots of computation costs. In this paper, we describe the usage of a neural network for language identification of malicious information. The neural network model was trained on a dataset of malicious and non-malicious information samples and then simulated on a set of unseen malicious text samples. From our extensive experimental results, we can conclude that the proposed model was able to precisely identify malicious language and achieve a high score approximately about 0.874. The results indicate that neural network models can be a useful tool for detecting malicious language.
A blockchain-based carbon transaction model for power industry

This paper seeks to improve the intelligence, real time and security of carbon trading and to reduce the default frequency of carbon trading. For these purposes, a carbon transaction model for the power industry was proposed based on blockchain. Firstly, a blockchain-based carbon trading framework is constructed, based on BIM carbon emission calculation to suit the general transaction scenarios. Then, using credit value evaluation and smart contracts to ensure the transparency, openness, and non-tampering of credit scores. On this basis, the transaction priority reward mechanism promotes the trustworthiness of each transaction node and maintains transaction security. Finally, the direct transaction based on credit value was designed to improve the transaction efficiency and security. Through case analysis, the carbon transaction model for the power industry, which is based on the credit value of the blockchain, supports fast and frequent energy transactions, as it overcomes the confirmation delays of energy blockchain transactions. The proposed model improves the efficiency of carbon transactions, effectively suppresses default frequency, and maintains the order of the carbon trading market.
ADMM-based structure-aligned quantized neural network

Nowadays, deep learning methods such as neural network models are highly effective for various tasks including image classification and natural language processing, but the increasingly high computational cost restricts the deployment of these network models in many kinds of scenarios whose resources are usually limited. Among all kinds of methods to solve these difficulties, quantization is a plausible way to reduce the storage size of these network models and accelerate their inference process by replacing the parameters such as weights with low-bit fixed numbers during the training process. This problem can be viewed as a discrete constrained optimization problem. In this work, we use Alternative Direction Methods of Multipliers (ADMM) to decouple the continuous parameters from the discrete constraints so that the original hard optimization problem is separated into several subproblems. In addition, structure-aligned quantization is also achieved, which is usually more friendly for edge computing devices to execute and accelerate. With extensive experiments on ImageNet and CIFAR10 dataset, models represented by low-bit fixed-point numbers with acceptable accuracy loss compared with original full precision models can be acquired. Compared with some previous quantization works, the quantization models obtained in this work have little classification accuracy drop compared with the original pre-trained full precision model, and a kind of hardware-friendly structure that makes the neural network easier to deploy is achieved.
Certificateless linear homomorphic signature algorithm for cloud computing based on DDH assumption

Aiming at the privacy protection problem in the cloud computing environment, a signature algorithm based on the Decision Diffie-Hellman (DDH) assumption and the discrete logarithm problem is designed when studying the Certificateless Homomorphic Signature (CLHS) technology. Our algorithm can resist pollution attacks from cloud computing users, collusion attacks from semi-honest participants, and malicious attacks from key generation centers. While protecting user privacy, our algorithm also ensures the efficient development of cloud computation server services.