Reviewerly/exHarmony huggingface dataset:
Create Engaging Content For Targeted Audiences

Nonprofit Communications ReportVolume 21, Issue 4 p. 1-1 Make the Most of Web 3.0 Create Engaging Content For Targeted Audiences Daniel Lindley, Daniel LindleySearch for more papers by this author Daniel Lindley, Daniel LindleySearch for more papers by this author First published: 12 March 2023 https://doi.org/10.1002/npcr.32145Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume21, Issue4April 2023Pages 1-1 RelatedInformation
Rebana Kercing of Kampung Laut: Sustaining the Communal Activity With Academic Collaboration

The Rebana Kercing of Kampung Laut is a historical religious traditional performing art combining the elements of stylized movements, vocals, and music in a single presentation and is claimed to be a Malay heritage that has been passed down for at least five generations of more than 150 years. The traditional art today is upheld
Depth-First Uncertain Frequent Itemsets Mining based on Ensembled Conditional Item-Wise Supports

Uncertain frequent pattern mining is usually challenged by the single probabilistic frequent threshold or the single expected support as the measurements of frequent itemsets. A promising solution based on multiple expected minimum support has been introduced in more recent studies to distinguish the mining values of each item, but the intrinsic combinatorial explosion still limited this strategy to be further improved for more generic scenarios. In this paper, a novel mining scheme for uncertain frequent itemsets is proposed. By ensembling multiple conditional item-wise supports, the problems of information redundancy as well as loss caused by a single probabilistic frequent threshold can be effectively improved. Furthermore, by using a variety of pruning strategies based on the property of sorted downward closure and the concept of least minimum probabilistic frequent threshold, an UFP-ECIS (Uncertain Frequent Pattern Mining with Ensembled Conditional Item-wise Supports) algorithm is also introduced. Substantial experiments have been proved to demonstrate that the proposed mining scheme and algorithm has enhanced the information precision of the uncertain frequent itemsets mining.
On Atomicity and Confidentiality Across Blockchains Under Failures

Distributed applications that utilize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous</i> blockchain systems have the potential to be widely deployed. In such applications, users from different blockchains can transact with one another through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross-chain transactions</i> . There are two essential features of particular relevance for those applications during cross-chain transactions: the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">atomicity</i> in that either all or none of the blockchains involved confirm a cross-chain transaction, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">confidentiality</i> in that a blockchain involved in a cross-chain transaction is only accessible for designated users. Existing cross-chain proposals have largely relied on permissioned blockchains to ensure confidentiality. However, we found that failures could occur when reading or writing information during transaction confirmations across permissioned blockchains, namely read/write (r/w) failures, which can lead to the violation of atomicity. In this paper, we propose a novel mechanism, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unity</i> , to ensure both atomicity and confidentiality of cross-chain transactions under r/w failures by leveraging permissioned blockchains. When failures occur in reading or writing data, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unity</i> classifies the data into two categories based on its status - whether data is the latest version or not, and presents different solutions for atomicity. Specifically, when data is not the latest, we design a four-phase-commit protocol4pc, in which consensus on confirming or aborting a cross-chain transaction can be achieved. If data is the latest when r/w failures occur, we propose a smart contract based solution SSC. We examine the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unity</i> theoretically and through experiments. With a failure probability of 0.7, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unity</i> achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$98\%$</tex-math></inline-formula> more atomic cross-chain transactions when compared with the state-of-the-art cross-chain platform, Hyperservice.
INSTRUCTION STRATEGY IN RUSSIAN AND ENGLISH ACADEMIC DISCOURSE

The paper addresses the issue of instruction strategy employment in the academic discourse from the viewpoint of cognitive-discursive paradigm. The aim of the paper is to reveal discursive tactics actualizing the instruction strategy in Chemistry textbooks and compare them cross-culturally. Comparison of Russian and English Chemistry textbooks was conducted to draw a sample of discourse moves that aim to develop cognitive skills; they were analysed in order to structure the received sample. The comparative analysis resulted in the proposal of a classification of instruction tactics in accordance with the expected learning outcomes. It comprises two types of instruction tactics: subject-specific and meta-subject. The paper highlights a few differences in the application of instruction tactics in Russian and English Chemistry textbooks. The differences involve variation of verbal expression and position characteristics as well as functional differences. One possible application of research results might be connected with enhancing textbook writer’s awareness of instruction strategy and tactics employment with the view of expected learning outcomes. The proposed classification could be applied in teaching materials design. In summary, cross-cultural comparison of textbooks can be beneficial to our further understanding of academic discourse and cognitive development.
Structure Preserving Semantic Texture Filtering

In structure preserving texture filtering, structural edge based features are more useful than textural features as they are invariant for both regular and varying scale irregular textures. In this work, we proposed a novel structure preserving texture filtering technique by detecting structural edges of the image. The proposed technique first exploits Jensen Shannon Divergence to detect structural edges of the image considering semantic information. Then with help of the detected structural edges, it defines a dynamic window of the median filter for filtering the input image. The experimental results on a variety of images show the potentiality of the proposed technique.
A Path Planning Method Based on Collaborative Learning for Multi-Robot with Connectivity and Obstacle Avoidance Constraints

Cooperative path planning method is one of the research hotspots in the field of multi-agent. In this paper, we study the problem of cooperative path planning, in which the robots under the constraints of communication connectivity and collision avoidance, and choose the destination location independently. It is difficult to find the optimal solution efficiently in sparse solution space by large-scale complex environments. To overcome the problem, in this paper, a novel collaborative learning algorithm, named Park Algorithm (PA) is proposed, which makes full use of exploration experience to improve the exploration efficiency. First, an objective function with three goals including path length, communication and collision is designed. Second, Marker Matrix is designed to evaluate the exploration value of the grids in the map, and each robot uses its own and teammates’ historical experience to update Marker Matrix. Our method can make full use of the exploration experience, realize self-learning and mutual learning between the robots, accelerate the exploration of solution space and avoid falling into local optimal solutions. Finally, the effectiveness and superiority of the proposed method are verified by simulation analysis. The results show that our method has a better performance compared with Genetic Algorithm (GA), Particle Swarm Optimization Algorithm (PSO) and Ant Colony Algorithm (ACO).
Research on PV Power Prediction Model Based on Hybrid Prediction

A hybrid prediction model based on wavelet transform (WT) -sample entropy (SE) -improved particle swarm optimization (IPSO) -weighted least squares support vector machine (WLSSVM) -iterative error correction is proposed to solve the problem of low accuracy and poor stability of photovoltaic output prediction under grid-connected conditions. Firstly, WT is used to reduce the noise in the collected power signal, and SE is used to quantify the weather type. Then IPSO is used to optimize the main parameters of WLSSVM. Finally, power prediction model and error prediction model are established respectively, and the final prediction power is obtained by superposition of power prediction value and error at all levels. Finally, the proposed model is compared with other prediction models, and the results show that the method has high prediction accuracy.
A Neural Dynamic Network Pathfinding Algorithm for Autonomous Underwater Vehicles under the Presence of Obstacles

A neural dynamic network pathfinding algorithm (NDNPA) is proposed to deal with the path-finding for autonomous underwater vehicles (AUVs). The proposed algorithm is intensified in two aspects: the novel neural activity value network and the improved selection strategy. The impact of the covered positions is introduced into the former. The repetitive path points and energy consumption are reduced simultaneously. In the latter part, in addition to the neural activity values, the navigational directions, the proximity to the target and the extended range of selection are applied to reach the target more efficiently. Finally, compared with the results of other algorithms, the high efficiency of the NDNPA algorithm is proven.
Attention-Guided Video Inference for 3D Human posture estimation

Body posture refers to the body shape of the human body during exercise, which reflects the external shape and mental state of the body, and is a description of physical health. If you want to use artificial intelligence to understand human posture. Human pose estimation based on a single image is the foundation. Estimating human pose from monocular videos is still a challenging task. It has three main challenges, including spatial feature representation, temporal information representation, and model computational complexity. To this end, we propose an improved Video Inference for Human Body Pose and Shape Estimation. It can perform feature extraction in the temporal dimension in image sequences. We also define a new temporal network architecture with a self-attention mechanism, and perform feature extraction on the temporal dimension in the original framework. In order to improve, extensive experiments prove that the method has a significant improvement in challenging 3D pose estimation datasets and can more effectively extract the features of human poses from videos.
Comparative Study on Adversarial Captcha with Three Target Detection Models

In order to distinguish whether the user is a human or a robot, prevent decryption of password, accounts and so on, three kinds of target detection model training for Adversarial Captcha are proposed. The process of training and evaluate-on of adversarial samples are described in detail in this paper. Depth target detection model is selected as attack model, and Projected Gradient Descent (PGD) algorithm is applied to generate adversarial samples based on three models: YOLOv4, Single Shot MultiBox Detector (SSD) and Faster Region-CNN (R-CNN). The samples are compared with clean samples under undirected attack. The results show that the captcha samples have successfully misled the neural network with high confidence, making them output incorrect results. The algorithm proposed in this paper can be used in many fields in the Internet to verify whether it is human or not.
Online government information in academic libraries: Accessibility and trust issues

The use of government information in an online environment is vital to citizens and researchers. Trust in the validity of digital government information is a key issue. As agencies make digital government publications available online, preservation of those documents for future generations is an important access component. Online government information does not always guarantee equal access for all. The following articles examine these issues and more concerning government information.
Research on the Education Management of College Students under the Background of Big Data

With the continuous development of big data technology, the education management of college students has become more and more important.Therefore, colleges and universities should use big data technology to conduct a comprehensive research and analysis of the education management of college students, so as to improve the education quality of college students and realize the comprehensive growth of college students.This article discusses how to reform the education management method of college students and combines the latest educational big data technology.It can not only provide practical information for the education work of colleges and universities, but also provide students with personalized teaching programs for students to promote their overall growth.
FORENSIC CREDIT CARD FRAUD DETECTION USING DEEP NEURAL NETWORK

This paper describes the ways in which forensic credit card fraud detection can be achieved using deep neural network (DNN). The problem that this research addresses is to identify a technique that can be used by forensic experts in credit card fraud investigations to detect if a fraudulent transaction has occurred. It is an obligation that institutions providing financial services must implement relevant safeguards against credit card fraud to prevent possible loss of their investments or clients’ funds. This paper presents a forensic detection model of credit card fraud that is based on sequential data modeling using Long Short-Term Memory (LSTM) DNNs. The current study determines whether LSTM-attention algorithm can identify the most important transactions in an input sequence that provides high-accuracy prediction of fraudulent transactions. The effectiveness of the LSTM-attention model is achieved by selecting the most relevant predictive features, uniform manifold approximation, transaction sequences, and attention mechanisms that improve the performance of the model. The results show that LSTM-attention algorithms can be used to conduct forensic credit card fraud detection with high accuracy and precision. The novelty of the research paper is that it successfully uses an LSTM-attention algorithm to detect credit card fraud instances and proves the applicability of the model in mitigating fraudulent transactions in banking institutions. Keywords: Credit Card Fraud, Machine Learning, Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory DOI： https://doi.org/10.35741/issn.0258-2724.58.1.33
MODELLING OF GROUP DECISION SUPPORT SYSTEMS FOR FLOOD ALERT WITH NAKAYASU SYNTHETIC UNIT HYDROGRAPH IN GADANG WATERSHED

Natural disasters still occur regularly and continuously in Indonesia and in the world, such as floods, earthquakes, tsunamis, and landslides. The impact of natural disasters such as floods can be reduced through flood hazard mitigation activities. Flood hazard mitigation can be done by knowing early rainfall in history and today and river water levels river flow to a region upstream of the history and current. This study aims to make a group decision support system modeling for early detection of candidate flooding caused by the condition of the watershed, in this case is Gadang Watershed. The system is designed as a tool to detect candidate field hydrometric flood early. Modelling Group Decision Support System (GDSS) was developed based on the analysis of flood design represented in a Nakayasu Synthetic Unit Hydrograph. A group of Decision Support Systems is a type of computer-based interactive system that is designed for a group of decision-making. The users of the decision support system are a group that consists of some people. Hydrograph is in accordance with the conditions of the watershed, especially in tropical Indonesia. The results of the design flood serve as a reference to the operational unit, the data management unit, and the planning unit of the institution of the watershed maintenance such as Perum Jasa Tirta I. A suitability test is intended to determine whether the distribution of the selected distributions may be used or not, in a series of data available. Analysis of the suitability test using the Chi Square distribution. From the results of the distribution test that the distribution can be accepted and the system can support for analysing flood hazards as preliminary information early detection of flooding. Keywords: Group Decision Support System, Flood Hazard, Nakayasu Synthetic Unit Hydrograph, Water Level DOI： https://doi.org/10.35741/issn.0258-2724.58.1.13
Public Domestic Borrowing and its Effect on Deposit Growth, Private Sector Credit and Interest Rate: Evidence From Uganda

Purpose: This paper investigates the effect of public domestic borrowing (PDB) on deposit growth (DG), private sector credit (PC), and interest rates (IR) among commercial banks in Uganda. Design/methodology/approach: Three equations are formulated to allow the application of the Generalized Methods Moment (GMM) approach to estimate the equations while controlling for bank internal factors, industry level and macro-economic indicators. Data for commercial banks were drawn from annual reports provided by a Bank of Uganda Depository Corporation survey (2008-2019). Findings: The study finds a significant negative relationship between PDB and IR. Although this result is not consistent with the behavioural expectations, it suggests that an increase in PDB would lower IR and increase savings and deposits. The study further finds evidence to support the fact that high PDB facilitates business undertaking and improves deposit growth in the banking sector. Lastly, the study result reveals that banks create more loans to the private sector and households when PDB reduces. Practical implications: Our results contribute to academia and policy on the effective use of public domestic borrowing to harness deposit growth, private sector credit and interest rates. The results suggest that efficient and effective utilisation of public borrowed funds would ease the cost of doing business, boost labour productivity, output, employment, savings and taxes; all these are associated with improved GDP and deposit growth that causes a reduction in the lending rate charged by banks. Originality/value: Existing literature has not paid enough attention to the effect of public domestic borrowing on the three specific outcomes, deposit growth, private sector credit and interest rates, mostly in the African context.
The use of smart technologies to implement the professional activities of scientific and pedagogical workers

Digital competence is an indispensable skill today due to the large-scale digitization processes in the world. Enrichment of pedagogical tools in the educational process through the use of smart technologies fully meets the problems and requirements of the pedagogical school and supports ambitious large-scale educational reforms in Ukraine. The use of smart technologies in the educational process will significantly affect the quality of pedagogical education and improve the digital and didactic competencies of teachers, school students, lecturers and students of higher education institutions of Ukraine. Smart technologies are information technologies aimedat developing the competencies of participants in the educational process, which are necessary for successful activities in the conditions of a digital society. New intelligent smart technologies require a change in the platforms used for knowledge transfer and the widespread use of smart devices. The need for the development of educational space is explained by the rather high level of development of smart technologies and their rapid penetration into everyday life. In turn, Smart Education is a concept that involves the comprehensive modernization of all educational processes, as well as the methods and technologies used in these processes, which allows for a new construction of the process of developing content, its delivery and updating. Smart education involves flexibility and the availability of a large number of sources, the maximum variety of different multimedia and quick adjustment to the level and needs of the listener. the use of smart technologies in education is determined by the development of students' critical thinking, the ability to independently plan and organize all stages of their own educational activities, in particular, from the formulation of the goal to the implementation of various project operations, from the implementation of the project to self-control and self-evaluation and reflection. This process, covering several stages, involves familiarization with a new type of activity, awareness of its meaning, and initial mastery of it, as well as the development of the skills of independent development of various educational projects.
Factors of the formation of competitiveness of future specialists in the information and documentation industry

The article focuses on the need to implement changes in the higher education system, the result of which is the training of highly qualified specialists who are competitive in the modern labor market. Scientific approaches to improving the system of professional training of higher education applicants in specialty 029 «Information, library and archival affairs» have been substantiated. The solution to this problem is carried out by applying an integrated pedagogical approach, which is based on the use of traditional and innovative learning technologies in a higher education institution. The factors of competitiveness of future specialists in the information and documentary field are characterized, in particular, the development of analytical abilities, critical thinking, and creativity. It was determined that due to the variety of functions performed in the field of documentation and non-documentation support of management (depending on the field of professional activity), in modern conditions, only such a specialist can be competitive, who shows initiative, the ability to generate new ideas, readiness to make decisions in non-standard production situations.