Reviewerly/exHarmony huggingface dataset:
Using Multi-dimensional Quorums for Optimal Resilience in Multi-resource Blockchains

Permissionless blockchains commonly use resource challenges to defend against sybil attacks. For example, popular resource challenge designs include Proof-of-Work and Proof-of-Stake. It is well-known that simultaneously exploiting multiple resources can help make a permissionless blockchain more robust. For example, combining PoW and PoS can help to keep a blockchain secure, even when the attacker controls more than 50% of the computational power in the system.While there have been existing efforts for combining multiple resources in blockchains, they only provide partial solutions. Specifically, it is currently still unclear how to combine PoW and PoS, or multiple resources in general, to achieve optimal resilience. Here by optimal resilience, we mean that the blockchain can tolerate every security region, unless that security region is proven to be impossible to tolerate. Existing designs are not able to achieve such optimal resilience.As our central contribution, this work proposes the novel design and formal security analysis of a blockchain protocol that combines PoS and PoW, which can be further generalized to multiple resources. Our blockchain is the very first blockchain that can achieve optimal resilience. Our design also overcomes a common tricky issue of PoW difficulty adjustment in previous designs. We have further implemented a research prototype of our blockchain design, and experimentally demonstrate its good end-to-end performance.
The Machine Learning Techniques for Enhancing Software Requirement Specification: Literature Review

This study explores the potential of machine learning (ML) and deep learning (DL) techniques for enhancing Software Requirement Specifications (SRS). The research questions focus on identifying the issues related to SRS that can be addressed using ML/DL and understanding the commonly used ML/DL techniques in SRS analysis. A systematic review methodology was followed, involving defining research questions, a comprehensive search process, and selecting relevant studies. The identified patterns can assist business analysts and software engineers in effectively utilizing ML/DL techniques during the requirement analysis stage.
Research of Facial Recognition Systems and Criteria for Identification

Biometric security systems are rapidly developing and becoming widespread. The purpose of the research is to develop a system for recognizing a person's face, since these are contactless identification systems and this topic is particularly relevant. Video surveillance systems have become widespread, where it is often necessary to identify a person. A face identification method that works in video surveillance systems is proposed. Experiments were conducted with selfies, portraits and documentary photography. The developed method captures a person's face, saves it, and then goes into recognition. A method for recognizing human eyes based on the method of Sherlock and Monroe has been developed.
Matrix Hyper-Basis Function Neural Network and Its Online Learning

A matrix hyper-basis function neural network and a recurrent online algorithm for its training are proposed. The system aims to process not traditional signals in vector form but matrix ones describing the images of arbitrary nature. It simplifies the architecture of the neural network by eliminating the autoencoders, which are always a part of deep convolutional neural networks. Moreover, a distinctive feature of the proposed approach is the ability to adjust not only the synaptic weights but the parameters of hyper-basis activation functions also, reducing their number compared to traditional RBFN. The proposed system is intended to solve Data Stream Mining issues with a small training set.
Classification of Tourist Photos with Gorillas and Other Animals to Detect Distancing Violations

Many people insist on getting close to wildlife without realizing how harmful the consequences of their actions can be such as the death of the animals or even themselves. To decrease the number of such events, we aim to classify tourist photos of mountain gorillas, a critically endangered species, and other animals to detect violations derived from the distance maintained between the animals and the humans. We plan to implement object detection technology to locate the human and animal targets in the image and generate features. Then, image processing techniques are applied. We propose innovative models following multi-input logic which takes in image data and non-image features with ensemble model logic to perform the classification. We show that this model performs more accurately and has a relatively shorter run-time compared to other models when detecting violations in tourist photos with animals.
Prediction of Higher Education Student’s Programming Skills Using Item Response Theory

The current review plans to dissect Object-oriented programming multiple-choice questions got from a preliminary testing led in different degree colleges. Earlier in classical test theory, responses were modeled based on the total number of correct answers. If candidate A provides more correct answers than candidate B, candidate A is thought to have an ability level than B. Item Response Theory is a theory, that describes how to model a candidate’s response in such a way that we can examine each response separately rather than as a group. In this proposed system, three different models are used to design the IRT-1PL,2PL, and 3PL model which uses certain parameters to evaluate the test than just the number of correct answers. Initially, the proposed system calculates the discrimination index and difficulty index of the questions answered by the students and then it uses the log-likelihood function and Newton Raphson method to get the IRT scores and also uses Bayesian methods to provide the feedback to each student. The accuracy of the current research is 92.4%. IRT can be used to determine scaled scores on a new type of test without collecting data from those who take it. If the test is given by computer, IRT can send a scaled score to each test taker immediately after the test. Along with the score the system also provides feedback to the student and the student will get to know about his skill set.
The Importance of Using Computer Assisted Language Learning (CALL) in Learning and Teaching English Language Skills

Using computers in learning and teaching environment has been widely presented to find a better way to implement computer-based activities and tasks in classrooms. Lately the significance of using computers appeared directly in learning and teaching English language and in this paper the researcher tries to investigate and understand usage of CALL (Computer Assisted Language Learning ) in language learning in general and in learning and teaching language skills in particular providing which type of activities and tasks that CALL may offer in each skill and how the language teacher can integrate these activities in his/her lesson(s) with finding out any obstructions that can oppose the flexibility of the learning process with using CALL.The survey method used for the collection of data to investigate the effectiveness of CALL in learning and teaching English at the graduate level. This questionnaire is designed to students who were studying in English department in faculty of Arts &amp; Science in Ghemines. The results that found will be showed at the end of this work. It concludes that English language teaching through the CALL technique assist English teachers to provide quality-based language education along with the improvement of English language teaching skills. It also empowers the students to learn and appropriate knowledge of the English language.
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

State of the art astronomical simulations have provided datasets which enabled the training of novel deep learning techniques for constraining cosmological parameters. However, differences in subgrid physics implementation and numerical approximations among simulation suites lead to differences in simulated datasets, which pose a hard challenge when trying to generalize across diverse data domains and ultimately when applying models to observational data. Recent work reveals deep learning algorithms are able to extract more information from complex cosmological simulations than summary statistics like power spectra. We introduce Domain Adaptive Graph Neural Networks (DA-GNNs), trained on CAMELS data, inspired by CosmoGraphNet (Villanueva-Domingo et al 2023). By utilizing GNNs, we can capitalize on their capacity to capture both astrophysical and topological features of galaxy distributions. Mixing these capabilities with domain adaptation techniques such as Maximum Mean Discrepancy (MMD), which enable extraction of domain-invariant features, our framework demonstrates enhanced accuracy and robustness. We present experimental results, including the alignment of distributions across domains through data visualization. These findings suggest that DA-GNNs are an efficient way of extracting domain independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
Information Crimes in the Kingdom of Saudi Arabia

In light of the Kingdom’s Vision 2030, we believe that it is necessary to analyze the information crime system and the electronic transactions system to determine the correct legal adaptation of digital crime and the applicable law. The spread of technology and the expansion of the circle of digital crimes and their exit from the scope of the threat to the stage of reality, which has become a concern for many, and threatens countries and societies from the economic and social aspects, as well as the emergence of frequent and increasing technological development in all the circles that surround us, have made it a tool for committing many crimes with technology. High rates that violate freedoms, steal a lot of money, and destabilize the stability and security that prevailed in previous eras, before the spread and development of technology and its increasing impact on all areas of our Arab society in particular, and in the world as a whole. The Saudi regulator has addressed the criminalization and punishment of all criminal forms of digital crime, explaining its dimensions and consequences under a special law on information crimes. It has also allocated penalties for contributors, and even considered the contributor to the digital crime to be an original perpetrator. It also concluded many rules, in addition to the penal rules in Saudi law, which must be strictly affected by the legal person in the event that he commits digital crimes, in addition to punishing the ordinary person representing the legal person and not being satisfied with imposing penalties on the legal person only. It is also necessary to take into account the seriousness of the person instigating the digital crime, as he is an original perpetrator who deserves the full original punishment. Moreover, the instigator may be from outside the country that is the subject of the digital crime.
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks. Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
HOW NOT TO DATA ARCHIVE IN 10 WAYS

Abstract Data sharing offers opportunities to replicate published findings, improves transparency in science, and increases the impact of the research by allowing other researchers to conduct new analyses. Effective January 25, 2023, the National Institute on Aging has new mandates for data sharing. However, many researchers are not experienced in data sharing and curation. Researchers will need to become more familiar with data sharing, specifically archiving data in data repositories. With these new requirements, many teams are attempting data sharing or archiving for the first time. We present 10 lessons learned the hard way, from real experience, prohibiting successful data archiving. Many of the challenges encountered could be avoided by effective planning, such as securing funding for curation and/or de-identification, adding data archiving details into the grant, and finding multiple repositories to potentially use. Other problems can be avoided by writing a consent form that allows for data archiving, such as by not limiting access to data, or limiting the type or format of data to be shared. Consulting with a legal team with experience in data archiving, such as at a data repository, is also recommended. By avoiding these pitfalls, researchers can successfully archive their data, ensuring their data are maintained and usable for other researchers in the future.
HUMAN FACTORS ISSUES IN THE DESIGN OF VIRTUAL REALITY TO SUPPORT OLDER ADULTS’ SOCIAL AND COGNITIVE ENGAGEMENT

Abstract Emerging technologies such as virtual reality (VR) hold tremendous potential to improve the lives of older adults, including the opportunity to support social and cognitive engagement. However, this potential will only be realized if VR technologies and applications (apps) are well-designed, considering the needs, preferences, and abilities of older people. To explore and understand design barriers ahead of a randomized controlled trial testing the efficacy of a psychosocial VR intervention for older adults, a team of eight evaluators who underwent standardized and comprehensive training on how to perform a heuristic analysis assessed the design of 28 commercial VR apps. Apps were chosen for their potential to support social interactions, enhance cultural opportunities, facilitate new learning, or support leisure activities. Each app was evaluated based on the Nielsen Norman Group’s ten usability heuristics. For different VR components (hardware, app menus, VR environment), this analysis highlighted specific heuristic violations, examined the general human factors of these apps, and identified positive design elements while suggesting solutions for observed heuristic violations. The heuristic analysis revealed significant usability challenges, especially in areas like “System Status Visibility,” “User Control and Freedom,” and “Help and Documentation.” The primary system components exhibiting these heuristic violations were the “User Interface” and “Virtual Environment.” It was concluded that current virtual reality systems do not adequately address human factors issues, particularly considering the needs and abilities of older adult users. Based on this extensive evaluation, training and design solutions will be presented and VR guidelines for older adults will be introduced.
ENGLISH FOR BUSINESS MANAGEMENT’ STUDENTS: NEED ANALYSIS IN ENGLISH FOR SPECIFIC PURPOSES (ESP)

The purpose of this research is to investigate the students need in learning English for specific purposes (ESP) of the students in Business Management Study Program. This research employs descriptive and qualitative approach. The questionnaire and interview are the only instrument used in this research. The questionnaire distributed to the student that has an answer which can be directly selected and the interview aims to collect more information related to the questionnaire. The participants of this study are the fourth semester of business management students in the Politeknik Negeri Manado in the academic year 2022-2023. The total of the business management students are 60 students who were or had completed general Basic English. The result indicate that students’ main purpose in learning English are for study/education and students’ needs of language proficiency skill are speaking. The students’ needs of the English sub-skills materials are related to the job vacancy, listening to the movies/songs/drama/role play, present a business plan for the speaking material and chose business letter/ job application letter for writing material. In the other hand, grammar knowledge and vocabulary mastery also needed by the student.
CRIMINOLOGICAL FEATURES ORGANIZED COUNTERFEITING IN THE DIGITAL AGE

Relevance. In recent years, in the field of counterfeiting, organized groups have been using the possibilities of digital technologies, which contributes to changing the structure of crime in this category, associated with erasing the criminal relationship and personal interaction of accomplices.&#x0D; In the digital age, as at any historical stage, legal means fulfill the most important task of social regulation – the protection of the monetary sphere from criminal encroachments. In the context of the development of new technologies, legal means are doubling their importance in the system of countering organized counterfeiting.&#x0D; Purpose and methods. The purpose of the study is to analyze the legal foundations of state policy in the field of countering organized counterfeiting in the digital age. To achieve this goal, dialectical and historical methods, sociological methods, and the method of expert assessments were used in the framework of the study.&#x0D; Conclusion. The study of organizational and criminological aspects of organized counterfeiting in the digital age has shown that a significant part of the acts of this category is committed in a virtual environment. Block-network transnational organized crime in the field of counterfeiting is spreading in the digital space.&#x0D; Digital systems and computer programs have become a tool and a means of making counterfeit money and counterfeit securities. In the digital world, non-cash money has become the subject of organized counterfeiting. Significant changes in the structure of organized counterfeiting dictate certain requirements for its prevention system. The paper proposes some organizational and criminological measures to counter organized digital counterfeiting.
Efficient Information Dissemination in Blockchain-Enabled Federated Learning for IoV

With the rise of smart vehicles, an intelligent transportation system (ITS) learning from the tremendous volume of the data generated by the distributed vehicles is becoming a reality. Blockchain (BC) based federated learning (FL) facilities a highly secure and trustless collaborative learning framework; however, it could be inefficient for the time-sensitive services of the Internet of vehicles (IoV). In this regard, this study investigates information dissemination delay in BC-based FL for IoV using a small world network based peer selection strategy. Additionally, we also incorporate IoV specific informed decision for peer selection to facilitate even faster dissemination of information in the network and make blockchain based system efficient. Network topology of vehicles is represented using a graph approach and based on that the information dissemination delay is formulated. The delay is compared between scenarios when peer selection strategy uses informed decision and that does not use informed decision. The performance of the proposed approach is evaluated through the graph analysis which demonstrates that the inclusion of informed decision reduces the information dissemination delay remarkably.
ISCom: Interest-aware Semantic Communication Scheme for Point Cloud Video Streaming on Metaverse XR Devices

In the metaverse era, point cloud video (PCV) streaming on mobile XR devices is pivotal. While most current methods focus on PCV compression from traditional 3-DoF video services, emerging AI techniques extract vital semantic information, producing content resembling the original. However, these are early-stage and computationally intensive. To enhance the inference efficacy of AI-based approaches, accommodate dynamic environments, and facilitate applicability to metaverse XR devices, we present ISCom, an interest-aware semantic communication scheme for lightweight PCV streaming. ISCom is featured with a region-of-interest (ROI) selection module, a lightweight encoder-decoder training module, and a learning-based scheduler to achieve real-time PCV decoding and rendering on resource-constrained devices. ISCom’s dual-stage ROI selection provides significantly reduces data volume according to real-time interest. The lightweight PCV encoder-decoder training is tailored to resource-constrained devices and adapts to the heterogeneous computing capabilities of devices. Furthermore, We provide a deep reinforcement learning (DRL)-based scheduler to select optimal encoder-decoder model for various devices adaptivelly, considering the dynamic network environments and device computing capabilities. Our extensive experiments demonstrate that ISCom outperforms baselines on mobile devices, achieving a minimum rendering frame rate improvement of 10 FPS and up to 22 FPS. Furthermore, our method significantly reduces memory usage by 41.7% compared to the state-of-the-art AITransfer method. These results highlight the effectiveness of ISCom in enabling lightweight PCV streaming and its potential to improve immersive experiences for emerging metaverse application.