Reviewerly/exHarmony huggingface dataset:
Policy Forum: Non-Fungible Tokens and Their Income Tax Treatment

This article explores the legal and tax landscape applicable to non-fungible tokens (NFTs)—first, by providing a working definition of an NFT and explaining how NFTs work from a technical perspective, with real-world examples; and second, by exploring the unique income tax issues raised by NFTs as a class of cryptoassets separate from their fungible counterparts. Key features of NFTs are found to be (1) their non-fungible nature; (2) their role as a digital record of authenticity of the ownership of tangible or intangible assets, but not of the existence of the underlying assets; and (3) the ability to integrate further rights for creators, such as commission rights for each sale of the NFT by subsequent owners. The article posits that NFTs may not themselves constitute an actual property right, since the underlying assets are very rarely included in the NFT record. A working definition of an NFT is therefore proposed to be as follows: a blockchain-based record of authenticity of rights to a unique object that contains metadata about the object that it represents and a link to the location where the object or data about the object are stored. The purchase and sale of an NFT is a taxable event, whether made by the original creator (who minted the token) or a subsequent owner. The determination of whether the transaction is on income or capital account, whether the NFT is personal-use property, and whether other rules apply (for example, non-resident withholding tax rules) will depend on the exact nature of what is being sold. The determination of the source of the income and the application of treaties to exempt income is quite difficult. Also raising difficulty is the qualification of the commission that is earned on subsequent sales of the NFT.
CYBERCRIMINALS AND NIGERIAN CYBERCRIMES ACT 2015: CONCEPTUALISING COMPUTERS FOR CYBERCRIME JUSTICE

Cybercrime being an act committed through the use of the computer either as a tool or target makes the computer an indispensable tool for almost all cybercrimes. The increasing number of persons utilizing computers, the different purposes for which they are put, coupled with the intractable problems of defining what is and what is not a computer under the Nigerian Cybercrimes Act 2015 is central to the determination of guilt or otherwise of cybercriminals brought before the court for prosecution. The conceptualization of computers under the Nigerian Cybercrimes Act 2015 provides the trial judge with so much discretion and little or no guidance as to whether a particular device is, or is not a computer. Moreover, considering the development of information and communication technology, the concept is continuously challenged.
DIGITAL LIBRARY AS STRATEGIES TO IMPROVE DIGITAL ISLAMIC LITERACY FOR EDUCATION

National Literacy Movement (GLN) become the main focus of the Government. Digital library is a strategic alternative to improve community literacy skills and become the Government's primary focus. Digital library is a strategic alternative to improve community literacy skills. This research aims to examine the strategy to improve digital Islamic literacy for the benefit of education through a digital library, one of which is to build digital literacy in a mosque. Literacy movement in mosques is a synergistic step as an effort to increase knowledge and insight also deradicalization. Through this research activity, UIN Raden Mas Said Surakarta implements as a digital library for worshipers at Al Fauziyah Mosque, Sukoharjo because it has the advantage of saving space. has multiple access, is not limited by space and time, and costs less. It has multiple access, is not limited by space and time, and costs less. The congregation's response to the ease of learning digital library has the total of 88,42%, the ease of understanding screen display is 87.37%, the ease of finding books 85,26%, the ease to access book is 87,37% and the usefulness of the digital library for the congregation is 95,79%. The accessibility variable with digital library usefulness variable has a very strong correlation
THE FORMATION OF INDEPENDENT THINKING IN TEENAGERS

This article covers the psychological aspects of transforming the independent thinking activity of adolescents into an active process. Also in the article, the means of popularizing the concepts of independence and creativism in adolescent thinking on the basis of what thought is, an adaptable view of the thought development in the life of a teenage personality, mutual strangeness with the image of today are shown.
TO THE ISSUE OF THE CHILDREN’S GENDER SOCIALIZATION

The article considers the main problems of gender socialization in modern soci-ety and also justifies the necessity and main directions of gender-role educa-tion of youth, defines its main directions.
Comparative Study on Temple Structural Element Segmentation using Different Segmentation Techniques

India's Karnataka state is home to a vast treasure trove of artefacts, antiquities, and historic and archaeologically significant monuments.Its culture and tradition are linked.In Karnataka, there are numerous Neolithic and Megalithic structures; these historic buildings from illustrious ruling dynasties have endured for thousands of years.They have miracles of their own in their own style, innate sculpture, architecture, technique, immensity, and enormity.However, modern generation is not ready for mining archaeological knowledge regarding empires or ruling dynasties of these ancient Karnataka temples through the archaeological guidance.Hence, a new approach required to bring this valuable information to the modern generation by a proper platform.In this paper both threshold and regional based segmentation methods are applied in order to segment the structural elements of temple.The analysis of segmented structural elements by applying both methods is done in order to provide comparative study.Comparative study on temple structural element shows that regional segmentation is more accurate than threshold method based on VOE and DSC metrics which are used for evaluating the performance of segmentation methods.Further, more efficient segmentation approaches may be applied to improve the efficiency of segmentation and it may be used for classification of viman styles.
Developing Smart Conversation Agent ECOMBOT for Ecommerce Applications using Deep Learning and Pattern Matching

Chatbots are a technological leap in conversational services, generating messages to users either following a set of rules to respond based on recognized patterns or training themselves from previous data or conversations.The primary goal is to enable a device to communicate with a user upon receiving natural language user requests using artificial intelligence and machine learning to generate automated responses.Technology is progressively catering to the questions, both in academic and business contexts, such as situations that require agents to investigate the cause of customer dissatisfaction or to recommend products and services.Significance of this research is to reduce the human dependency and improving customer support by providing close to human natural responses using pattern matching and deep learning on the custom-made data.The main objective of this work is to (a) study the existing literature on cuttingedge technologies in chatbot development in terms of research trends, legacy components, techniques, datasets, and domains specifically in e-commerce and (b) to develop a product that fill some of the gaps/missing functionality identified in current frameworks.We have achieved the following, (a) generated small yet generic dataset, which can be used for all types of products, (b) the intents are identified accurately by the bot using deep learning, whenever a user query.
A mapping study of language features improving object-oriented design patterns

Object-Oriented Programming design patterns are well-known in the industry and taught in universities as part of software engineering curricula. Many primary studies exist on the impact of design patterns on software, in addition to secondary studies summarizing these publications. Some primary studies have proposed new language features and used them to re-implement design patterns as a way to show improvements. While secondary studies exist, they mainly focus on measuring the impact of design patterns on software. We performed a systematic mapping study to catalogue language features in the literature claiming to improve object-oriented design patterns implementations, as well as how primary studies measure these improvements. We performed a search in three databases, yielding a total of 874 papers, from which we obtained 34 relevant papers. We extracted and studied data about the language features claiming to improve design patterns implementations, the most often cited design patterns, the measures used to assess the improvements, and the case studies and experiments with which these improvements were studied. Using the results, we catalogue 18 language features claimed in the literature to improve design patterns and categorize them into paradigms. We find that some design patterns are more prevalent than others, such as Observer and Visitor. Measures related to code size, code scattering and understandability are preferred. Case studies are done in-vitro, and experiments are rare. This catalogue is useful to identify trends and create a road map for research on language features to improve object-oriented design patterns. Considering the prevalence of design patterns, improving their implementation and adding language features to better solve their underlying concerns is an efficient way to improve object-oriented programming. We intend in the future to use this as a basis to research specific language features that may help in improving object-oriented programming.
Using Social Media Platforms By English For Specific Purposes Students At The National Institute Of Technology

This research analyses how students enrolled in the English for Specific Purposes program at the National Institute of Technology utilize various social media platforms during their English studies. The students are studying English at the National Institute of Technology. The researcher collected the data via a questionnaire filled out by 50 students and through semi-structured interviews with 10 male and female students. The results of the qualitative interviews were studied via the lens of an experienced case study and coded according to themes, while the quantitative questionnaire results were reviewed through the lens of fundamental statistics. According to the study, social platforms are crucial for students learning English as a foreign language. Currently, students use seven different social platforms to learn English, enhance their language skills, and support in developing their language competency. On the other hand, students at the National Institute of Technology taking English lessons for various reasons utilize Instagram more than other social media sites to practice their English at home and in other academic settings.
Iterative histogram equalization using discrete wavelet transform in low-dynamic range

A progressive histogram equalization (HE) and contrast enhancement method is proposed in the frequency domain. This method has an iterative structure based on the 6-sigma rule in the low-dynamic range using discrete wavelet transform. The proposed method determines an automatic threshold for attenuating the high-frequency amplitude of a signal in the discrete wavelet domain, based on the standard deviation of the absolute power of the high-frequency components in the signal band generated from the probability density function (PDF). Then an iterative quantization procedure is used where the PDF values in the frequency domain are randomly quantized. Besides, the maximum Bhattacharyya coefficient, which matches the original input image’s PDF, is used as a cost function to optimize the histogram smoothing output. For the random number generation, a 32-bit pseudorandom generator is employed to produce the same result for the output image at each runtime. The quantization factor parameter enables a controlled contrast enhancement rate to receive balanced equalization results in images. In the experimental studies, the proposed iterative HE method is compared with the well-known global, local, and brightness preserving algorithms. Experimental studies quantitatively and qualitatively display promising and encouraging results in terms of various state-of-the-art quality assessment metrics such as mean squared error, peak signal-to-noise ratio, structural similarity index measurement, contrast improvement index, absolute mean brightness error, and quality-aware relative contrast measure.
End-to-End Entity Detection with Proposer and Regressor

Named entity recognition is a traditional task in natural language processing. In particular, nested entity recognition receives extensive attention for the widespread existence of the nesting scenario. The latest research migrates the well-established paradigm of set prediction in object detection to cope with entity nesting. However, the manual creation of query vectors, which fail to adapt to the rich semantic information in the context, limits these approaches. An end-to-end entity detection approach with proposer and regressor is presented in this paper to tackle the issues. First, the proposer utilizes the feature pyramid network to generate high-quality entity proposals. Then, the regressor refines the proposals for generating the final prediction. The model adopts encoder-only architecture and thus obtains the advantages of the richness of query semantics, high precision of entity localization, and easiness of model training. Moreover, we introduce the novel spatially modulated attention and progressive refinement for further improvement. Extensive experiments demonstrate that our model achieves advanced performance in flat and nested NER, achieving a new state-of-the-art F1 score of 80.74 on the GENIA dataset and 72.38 on the WeiboNER dataset.
A Survey on Sign Language Recognition with Video Chat

The use of sign language is a crucial tool for improving communi- cation between hearing-impaired people and the general public. SLR (Sign Language Recognition) systems in the past have been sophisticated and challenging to train. However, in this research, we provide a novel method that makes use of SSD MobileNet V2 FPNLite 320x320 pre-trained models and object recognition based on TensorFlow's object detection. By enabling the identification and detection of a set of images, this method streamlines the train- ing process. The suggested system will be trained and evaluated using 10 to 15 different American Sign Language symbols. A fun- damental social skill used to exchange information is communica- tion. It is frequently used to express oneself and to fulfill funda- mental human needs including the desire for protection, safety, and connection. Several stages, diverse methods, and distinct conse- quences are used in this procedure. It typically refers to a two-way exchange of information in the local vicinity of touch. Information flows far more easily when people are speaking the same language than when they are speaking languages that are from distinct lan- guage families. In order to facilitate video chat communication be- tween signers and non-signers, our proposed sign language recog- nition system is specifically created. Each peer can see and hear the other during a video conversation thanks to their individual cameras and microphones. Nevertheless, using our method, a spe- cific peer can also view the indicators that the other peer on the other end of the video chat exhibits or copies. Our system employs object detection to recognize and track the signer's hand motions in real-time in order to accomplish this. The technology then over- lays a graphic of the detected sign onto the non-video signer's feed. This overlay is positioned so that it does not obstruct the view of the signer by the non-signer and is not obtrusive. By using contem- porary technology, our suggested solution is made to enable distant and two-way communication between signers and non-signers. With the help of our system, video chat communication is im- proved, allowing signers and non-signers to communicate and in- teract more effectively. Index Terms— SLR(Sign Language Recognition), Video Chat, Object Detection, SSD MobileNet.
Out of distribution detection for medical images

Most previous surveys were conducted under ideal conditions, and the data was mostly in contribution. In reality, however, the condition of out-of-distribution dataset is more common and cannot be avoided. For example, in a dataset, the label only contains cats and dogs, but a new horse image appears in the data image. A model that does not take the OOD problem into account will only be able to classify it under a known label, even if this results in incorrect results; however, the OOD model should alert the researcher to the emergence of a new label class. As a result, this type of partial setting is gaining popularity. Furthermore, the problem of out-of-distribution is exacerbated in the medical environment. The availability of annotated training examples remains a significant barrier to advancement in medical imaging processing. Because experts spend so much time on annotation, the entire process is prohibitively expensive. Furthermore, once a new model has been trained, the acquisition parameters will be altered. As a result, out-of-distribution detection strategy is a critical technique in the field of medical images. Our research focuses on the OOD model in the context of medical images. First, we summarized the OOD models that have been proposed in recent years. Some of them proposed new metrics to set the standard for the model's good or bad performance, while others proposed new methods to continuously increase the model's precision, so that the model could correctly identify the OOD situation. Following that, we will concentrate on using the OOD model in medical images. We are convinced that medical images outside of the original dataset are extremely important for future research into this disease. Finally, we classify the evaluation criteria, methods, and datasets that are commonly used in OOD model training. It is hoped that our research will be beneficial to future researchers.
IMAGE FORGERY DETECTION USING ADAPTIVE OVER-SEGMENTATION AND FEATURE POINT MATCHING USING MATLAB

Digital image forgery has gotten easier to do as computer technology and image processing tools have advanced. The validity of digital images, however, is a significant problem because they are a common source of information. The adaptive over-segmentation and feature point matching approach outlined below is a recommended technique for identifying fake images using a Matlab software method. Block- based and keypoint-based forgery detection techniques are both integrated into the suggested scheme. First, the proposed Adaptive Over-Segmentation algorithm adaptively and non-overlappingly segments the host picture into irregular blocks using image processing methods. Once the feature points have been extracted from each block as block features, the block features are compared to one another to locate the labeled feature points. This process can roughly identify the regions where a forgery is believed to have taken place. To produce the identified forgery regions, it then applies the morphological operation to the merged regions. The experimental findings show that, in comparison to the current state-of-the-art copy-move forgery detection methods, the proposed copy-move forgery detection scheme can produce significantly better detection results even under a variety of difficult conditions. KEYWORDS: Matlab, Adaptive over-segmentation, Colour block feature, Image matching
Chess Engine Based on AI: A Survey

Researchers have been experimenting with several methods to create chess engines that can surpass human players, which has led to a lot of advancement in the field of chess AI in recent years. Neural networks, genetic algorithms, and machine learning are a few of the common methods. Chess engines that use self-play, automatic feature extraction, pattern recognition, and genetic player learning have been created using these methods. Researchers have also looked into various chess AI-related topics, like enhancing the game's visual appeal, creating multi-player engines, and even creating automated chess commentary. Although each strategy has its advantages and disadvantages, the area of chess AI is still developing as researchers work to create ever-more complex engines that can beat this game. Key Words: Chess, Chess AI, Chess Engine, Game ,Chess Algorithm, Negamax Algorithm, Player VS Player Game, Play with Computer,Play against AI, Play against Chess Engine.
Smart Surveillance System using Python and OpenCV

Computer vision expands the paradigm of image processing, including the understanding Security and protection are the most important issues in modern times. This project reviews some of the latest developments in the field of computer vision and image processing to solve safety issues. It also describes the efforts of the development teams to incorporate some of these pioneering ideas into a consistent prototype system of scene content, object tracking and classification. The development of computer vision technology often takes into account very specific applications, and the goal of a more complete understanding of computer vision systems still exists outside the existing technology, at least for now.We are mainly stressing on the features of smart surveillance which are monitoring feature, noise detection, face identification and visitors in room detection. This can be achieved by integrating camera with python computer vision algorithms
DUAL ACCESS CONTROL FOR CLOUD BASED DATA STORAGE AND SHARING

Due to its effective and affordable administration, cloud-based data storage has recently attracted growing interest from academia and business. As services are delivered via an open network, it is critical for service providers to adopt secure data storage and sharing mechanisms to protect user privacy and the confidentiality of data. The most popular technique for preventing the compromise of sensitive data is encryption. The practical necessity for data management, however, cannot be adequately addressed by just encrypting data. Moreover, a strong access control over download requests should be taken into account to prevent Economic Denial of Sustainability attacks from being conducted to prevent users from using the service. Each of the two dual access control systems is intended for a different planned context.We create a technique for controlling download requests without sacrificing security or effectiveness. There is also a presentation of the systems' experimental and security analyses. Keywords: Cloud-based data sharing, Access Control, Cloud Storage Service, Attribute Based Encryption.