Reviewerly/exHarmony huggingface dataset:
Implementation of Crowd Sale using ERC-20 Tokens

The paper aims at creating ERC-20 Token and crowd sale step-by-step with Ethereum smart contracts. In the process, it focuses on testing the smart contracts, deploying the smart contracts to the Ethereum blockchain, and building an ICO website deployed to the web for the buying and selling of the tokens. The Ethereum blockchain provides a platform for creating our cryptocurrency, or tokens that can be purchased via Ether which is the native cryptocurrency of the Ethereum blockchain. ERC-20 is a standard document that specifies the behavior of the tokens so that they are compatible with other platforms like cryptocurrency exchanges. We used Ethereum which is a blockchain like Bitcoin. We created a token called ;Token ; and there are 100,000,000 such tokens. Firstly, the token smart contract keeps track of some token attributes which are basic. It also keeps track of who owns ;My Token ; and how much. ERC-20 tokens can be used as a payment just, just like any other cryptocurrency, from one account to another. They can also be purchased in a crowd sale, like an ICO.
WAM 3D Discrete Chaotic Map for Secure Communication Applications

Chaotic systems have become widely adopted as an effective way for secure data communications, because of its simple mathematical complexity and good security. The relationship between encryption algorithms and chaos systems has gained a lot of attention in the past few years, since it avoids the data spreading as well as lower the transmission delay and costs. In this paper a novel 3D discrete chaotic map is proposed for data encryption and secure communication and named as WAM. For secure communication, the Pecora and Carroll (P-C) method was utilized to achieve synchronization between the master system and the slave system. The simulation results of WAM 3D discrete chaotic map showed that the system has a chaotic behavior and a characteristic randomness and can pass 0-1, Lyapunov exponent (LE) and NIST tests which are usually used to check chaotic behavior. The statistical outcomes of the LE test were 0.0193, the frequency test (FT) was 0.4237, and the run test (RT) yielded a value of 0.0607. As a result, it enrich the theoretical basis of the equations and implementation of chaos, and it is superior for encryption algorithms and communication security applications.
Application of knowledge graph in software engineering field: A systematic literature review

Knowledge graphs describe knowledge resources and their carriers through visualization. Moreover, they mine, analyze, construct, draw, and display knowledge and their interrelationships to reveal the dynamic development law of the knowledge field. Furthermore, knowledge graphs provide practical and valuable references for subject research. With the development of software engineering, powerful semantic processing and organizational interconnection capabilities of knowledge graphs are gradually required. Current research suggests using knowledge graphs for code or API recommendation, vulnerability mining, and positioning to improve the efficiency and accuracy of development and design. However, software engineering lacks a systematic analysis of the knowledge graphs application. This paper explores the construction techniques and application status of knowledge graphs in the field of software engineering, broadens the application prospects of knowledge graphs in this field, and facilitates the subsequent research of researchers. We collected over 100 documents from 2017 to date and selected 55 directly related documents for systematic analysis. Then, we analyzed the organized knowledge mainly stored in software engineering knowledge graphs, including software architecture, code details, and security reports. We studied the emerging research methods in ontology modeling, named entity recognition, and knowledge fusion in graph construction and found that current knowledge graphs are mainly used in intelligent software development, software vulnerability mining, security testing, and API recommendation. Our research on the innovation of knowledge graph in software engineering and the future construction of integrating open-source community software and developer recommendations with knowledge-driven microservice O&M aspects can inspire more scholars and knowledge workers to use knowledge graph technology, which is important to solve software engineering problems and promote the development of both fields.
A Bibliometric Analysis: Metaverse in Education Concept

During the last decade technological transformation prospered, and digital environments such as Metaverse started to come to life. Metaverse is a hypothesis of the next generation of the Internet, which consists of an online 3D virtual environment and limited physical interaction. Due to the growth of Metaverse technology in various fields and the importance of using it in education, it is being observed that published research related to this topic is also growing. Therefore, the purpose is this study is to review and identify the application areas of this emerging technology for the education field by providing a bibliometric analysis of the literature related to Metaverse. This research contributes to literature as it addresses the status, gaps, and the direction for future research. In the bibliometric analysis, an electronic search was done via a scientific database named Dimensions by combining topic-related keywords for ‘metaverse’ and ‘education’ within the time frame of 2004 and 2022. The data gathered by a data extraction table from 5,048 articles retrieved and analysis run by VOSviewer data visualization tool. Of 5,048 articles found through the initial search, most of the studies (48.02%) were published in 2022, and Singapore, Japan, China, and UK are the main countries for the studies and citations appearing dominantly. The main three keywords for ‘metaverse’ and ‘education’ articles include: virtual worlds, video and metaverse platforms. The use of metaverse in education has been expanding rapidly in literature during recent years. Yet this study reveals that research is still limited to the main four countries, and studied subtopics are very primitive and vague. Available citations show weak link strength meaning the depth of the studies in the literature is not satisfactory, yet, which is because the metaverse itself is not enough without the supporting technologies. Educators and scientific researchers could rethink what types of technologies belong to the metaverse and how it has the potential to influence the education sector. Policymakers and educators could refer to this study for metaverse learning environment expansion of their future policy and executions.
FORMATION OF THE PERSONALITY ORIENTATION OF STUDENTS TO THE CHOICE OF PEDAGOGICAL PROFESSIONS

The authors analyze the role of the personality orientation. The article highlights an issue of forming the orientation of the schoolchildren personality orientation to the choice of teaching professions. Professional orientation has a certain impact on resolving the contradictions between social requirements and individual needs and serves as a driving force for the development of the creative activity of educational subjects. According to A. Rostunov, professional orientation is based on a wide range of needs, interests, ideals, motives and attitudes of a person. It characterizes the unity of interests and personality traits in the system of professional self-determination, which is the most important in adolescence. The article highlights an issue of the orientation as a component for creation in the transformative activity of students of psychological faculties and practical psychologists in the development of the orientation of their personality, self-development, self-improvement and self-realization of potential opportunities by means of innovative teaching methods and educational technologies. It also presents the specific innovative teaching methods, educational technologies that contribute to developing the personality orientation of future psychologists, their self-development, self-improvement and self-realization of potential opportunities. Particular importance is attached to what the scientist-psychologist, doctor in Psychology professor Rostunov left in his legacy in terms of the research, methods and innovative methods in shaping the personality orientation.
ESSENTIAL GENERALIZATION OF THE RESULTS OF THE EMPIRICAL RESEARCH ON THE FORMATION OF THE READINESS OF FUTURE LAWYERS FOR LAW-MAKING ACTIVITIES

Relevance. The article interprets the results of an empirical study on the formation of the readiness of future lawyers for law-making activities based on the use of pedagogical stimulation methods and the introduction of developed pedagogical support, means, conditions and methods into educational activities. The training of future qualified lawyers is carried out in educational institutions of higher education, where a psychological and pedagogical process is built to form their readiness for law-making activities. The criteria for assessing the level of formation of the readiness of future lawyers for law-making activities in the course of the empirical study were: cognitive-legal; orientation-value; activity-creative. The criteria levels were familiarization-informational, professionally necessary, optimally sufficient. The use of these criteria for diagnostic purposes allows for a comprehensive assessment of the readiness of future lawyers for law-making activities.&#x0D; Materials and methods. As a methodological and practical tool for assessing the readiness of future lawyers for law-making activities, author’s questionnaires, testing, analysis of the results of extracurricular activities, academic performance and involvement in scientific research by students were used.&#x0D; The purpose of this study is to present and analyze the empirical data obtained during the pedagogical experiment on pedagogical stimulation of the formation of the readiness of future lawyers for law-making activities.&#x0D; The hypothesis of the study was the assumption that the consistent development and implementation of psychological and pedagogical support, conditions and means, as well as diagnostic criteria in educational activities will successfully stimulate the readiness of future lawyers for law-making activities.&#x0D; Research results. The data obtained as a result of the introduction of the developed conditions and means of pedagogical stimulation, as well as those obtained in the course of diagnostics during an empirical study, after appropriate adjustments to the content of education, confirm with a high degree of certainty the hypothesis of the study that the introduction of pedagogical support, conditions and means will be effective, and statistical processing of the obtained results testifies to their significance.
Employing Online Paraphrasing Tools to Overcome Students’ Difficulties in Paraphrasing

This study aimed to investigate the mechanical engineering vocational education students’ difficulties in making paraphrasing in academic writing course and to investigate the use of the online paraphrasing tools in overcoming those difficulties. The data were taken from two questionnaires distributed to the students regarding the two problems mentioned previously. The items were adapted from Paraphrasing Strategy theory by Schuemann (2006), and Kirszner (2008) which consists of four strategies: 1) changing its synonym; 2) changing part of speech; 3) changing the sentence pattern from active to passive; 4) keep using keywords from the original text. The study revealed that the most commonly used strategy was to change the synonyms with a percentage of 69%. Then, the most difficult problems in terms of content, structure, language, and paraphrase strategy are paraphrasing itself, using punctuation, identifying the part of speech, and inability to state the same information as the source text. Further research is needed in which a more significant number of participants and various academic writing difficulties should take part in, and a variety of research instruments should be employed.&#x0D; &#x0D;
Third Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023)

Evaluation is important when developing and deploying recommender systems. The PERSPECTIVES workshop sheds light on the different, potentially diverging or contradictory perspectives on the evaluation of recommender systems. Building on the discussions and outcomes of the PERSPECTIVES workshops held at RecSys 2021 and 2022, the third edition of the PERSPECTIVES workshop held at RecSys 2023 brought together researchers and practitioners from academia and industry to reflect on the evaluation of recommender systems critically. The workshop featured a keynote and focused on the interactive part with discussions in small groups and the plenum. We discussed problems and lessons learned, encouraged the exchange of the many perspectives on evaluation, and aimed to move the discourse forward within the community.
Benchmarking of modeled solar irradiation data in Uruguay at a daily time scale

Accurate solar radiation data are required for solar energy development. In absence of long-term ground measurements, practitioners rely on modeled data, which typically is of unknown uncertainty. This article benchmarks solar irradiation estimation models over Uruguay, analyzing uncertainty behavior and providing recommendations. The performance of six models is evaluated using controlled-quality ground measurements for an extended period, being one of the few benchmark studies in Latin America. The LCIM, a model specially adapted for the region and based on GOES-East satellite images, exhibits the highest accuracy and spatial consistency with a remarkably low root mean squared deviation of 6% and mean bias of less than 1%. The NSRDB and GL1.2 estimates have also a competitive performance and are well-suited alternatives. The MERRA2 database presents high deviations and should not be an option for solar resource assessment in the region without post-processing. This research is a first step towards a South American benchmark and provides information on which estimation models are suitable for large-scale solar energy projects in the region.
Reinforcement Learning Control for Moving Target Landing of VTOL UAVs With Motion Constraints

This article investigates the motion planning and reinforcement learning control method for autonomous landing issue of a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) on a moving target. A novel funnel shaped surface is first proposed to maintain the relative position within a preassigned set for precise and safe landing. An orientation constraint planning is designed to avoid the flipping over. Moreover, a data-based reinforcement learning strategy is proposed to update the learning weight online, which not only effectively relieves the typical persistent excitation condition to the finite excitation but also allows the inadmissible initial control condition. A filter-based disturbance estimator is employed to compensate the unknown terms. The stability of the overall closed-loop system is analyzed theoretically and concluded to be uniformly ultimately bounded. Simulation and flight experiment are performed to validate that UAV can land on the moving target along the preassigned trajectory.
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of computation, especially for minor editing operations. In this work, we present Spatially Sparse Inference (SSI), a general-purpose technique that selectively performs computation for edited regions and accelerates various generative models, including both conditional GANs and diffusion models. Our key observation is that users prone to gradually edit the input image. This motivates us to cache and reuse the feature maps of the original image. Given an edited image, we sparsely apply the convolutional filters to the edited regions while reusing the cached features for the unedited areas. Based on our algorithm, we further propose Sparse Incremental Generative Engine (SIGE) to convert the computation reduction to latency reduction on off-the-shelf hardware. With about 1%-area edits, SIGE accelerates DDPM by <inline-formula><tex-math notation="LaTeX">$3.0\times$</tex-math></inline-formula> on NVIDIA RTX 3090 and <inline-formula><tex-math notation="LaTeX">$4.6\times$</tex-math></inline-formula> on Apple M1 Pro GPU, Stable Diffusion by <inline-formula><tex-math notation="LaTeX">$7.2\times$</tex-math></inline-formula> on 3090, and GauGAN by <inline-formula><tex-math notation="LaTeX">$5.6\times$</tex-math></inline-formula> on 3090 and <inline-formula><tex-math notation="LaTeX">$5.2\times$</tex-math></inline-formula> on M1 Pro GPU. Compared to our conference paper, we enhance SIGE to accommodate attention layers and apply it to Stable Diffusion. Additionally, we offer support for Apple M1 Pro GPU and include more results to substantiate the efficacy of our method.
Blockchain-Assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning

Multi-Agent Deep Reinforcement Learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes with several challenges that hinder its usability, including sample efficiency, curse of dimensionality, and environment exploration. Recent works proposing Federated Reinforcement Learning (FRL) to tackle these issues suffer from problems related to model restrictions and maliciousness. Other proposals using reward shaping require considerable engineering and could lead to local optima. In this paper, we propose a novel Blockchain-assisted Multi-Expert Demonstration Cloning (MEDC) framework for MDRL. The proposed method utilizes expert demonstrations in guiding the learning of new MDRL agents, by suggesting exploration actions in the environment. A model sharing framework on Blockchain is designed to allow users to share their trained models, which can be allocated as expert models to requesting users to aid in training MDRL systems. A Consortium Blockchain is adopted to enable traceable and autonomous execution without the need for a single trusted entity. Smart Contracts are designed to manage users and models allocation, which are shared using IPFS. The proposed framework is tested on several applications, and is benchmarked against existing methods in FRL, Reward Shaping, and Imitation Learning-assisted RL. The results show the outperformance of the proposed framework in terms of learning speed and resiliency to faulty and malicious models.
Deep guided transformer dehazing network

Abstract Single image dehazing has received a lot of concern and achieved great success with the help of deep-learning models. Yet, the performance is limited by the local limitation of convolution. To address such a limitation, we design a novel deep learning dehazing model by combining the transformer and guided filter, which is called as Deep Guided Transformer Dehazing Network. Specially, we address the limitation of convolution via a transformer-based subnetwork, which can capture long dependency. Haze is dependent on the depth, which needs global information to compute the density of haze, and removes haze from the input images correctly. To restore the details of dehazed result, we proposed a CNN sub-network to capture the local information. To overcome the slow speed of the transformer-based subnetwork, we improve the dehazing speed via a guided filter. Extensive experimental results show consistent improvement over the state-of-the-art dehazing on natural haze and simulated haze images.
Bayesian statistics

This chapter focuses on Bayesian statistics and frequentist statistics. It starts with the philosophy of frequentist statistics that highlighted the significance of using a <italic>p-value</italic>. Bayes' theorem is an equation that governs the combination of conditional probabilities, so the Bayesian methods involve different assumptions and different procedures in an effort to achieve a sensible choice in data analysis. Moreover, Bayesian techniques try to estimate the probabilities of both the experimental and the null hypotheses being true. The chapter then looks at Bayes' theorem and its approach to statistical inference. It explains how to calculate Bayes factors in <italic>R</italic> using the <italic>BayesFactor</italic> package.
The Effect, Dilemma and Countermeasures of Cultivating Students' Craftsman Spirit in Higher Vocational Colleges in the New Era

The cultivation of artisan spirit in higher vocational colleges is an urgent demand for high-quality economic and social development and people's better life, as well as the internal pursuit of building a high-quality higher vocational education system in the new era. However, the cultivation of artisan spirit in higher vocational colleges has some practical difficulties, such as imperfect cultivation system, weak cultural environment of colleges, poor integration of quality and skills, weak learning initiative of students, weak willpower, lack of home-school joint education platform, and comprehensive quality of parents to be improved. The cultivation of craftsman spirit requires system guarantee, school effort, students' cooperation and parents' responsibility to form a joint force and jointly cultivate the craftsman spirit of higher vocational students.
Zero-RADCE: Zero-Reference Residual Attention Deep Curve Estimation for Low-Light Historical Tibetan Document Image Enhancement

To improve the reading experience and the performance of subsequent document analysis and recognition algorithms, the background of low-light document image is expected to be smoothed and its foreground text information needs to be highlighted. The enhanced document images can improve the reading experience and the subsequent document analysis and recognition algorithms. In this paper, first construct a low-light historical Tibetan document image dataset. then improve the deep curve estimation network of Zero-DCE by using encoder-decoder architecture, residual network, and spatial attention mechanism; finally extract the text information features in low-light historical Tibetan document images by specially designed Gaussian and Laplace filters for improving the spatial consistency loss, which not only achieves low-light image enhancement but also improves the spatial consistency loss. Experiments show that the proposed method achieves better results in both quality and quantitative evaluations for low-light historical Tibetan document image enhancement. Meanwhile, the training parameters are reduced by 39.52% and the Flops are reduced by 70.63% compared to the original network.
Inclusive bank based financial development in countries with special needs: A semiparametric analysis

What is the role of bank based financial development in inclusive sustainable development? Our paper tries to answer this question with a novel methodology that provides an estimate of the inequality-finance relationship for every country-year observation, allowing us to examine the distribution of the relationship of interest for various country groups. Unlike other papers in the literature, our estimates are free of both functional form mis-specification bias as well as bias from endogenous regressors. Our results indicate that financial development lowers income inequality in low income countries and countries that invest in primary education. This inequality-finance relationship is also sensitive to a country's size of financial sector and quality of institutions. Our findings should be of particular interest to scholars and domestic policy makers of Countries with Special Needs, who face unique challenges meeting the goals set by United Nation's 2030 Agenda for Sustainable Development.