Reviewerly/exHarmony huggingface dataset:
Blockchain Based Service Restoration in a Power Distribution System During Crisis

Natural hazards disrupt power networks hampering societal services and individual lives. Microgrids donate or trade their surplus capacity that the grid can use to power up critical load and communities that do not have alternative power arrangements. This article proposes a blockchain approach for service restoration in a power distribution network using energy donation in the aftermath of a disaster. The proposed approach's key element is a novel consensus mechanism called Proof of Welfare (PoWel). The consensus protocol enables prioritized restoration of critical load, minimizes losses, enhances power network stability, and achieves welfare for the affectees. It employs an evolutionary approach to select the winner block based on the multi-objective criterion. Simulation results prove the effectiveness of the proposed approach.
Explainable spiking neural network for real time feature classification

The work presents a concept of an implementation of an explainable artificial intelligence (XAI) using effective models of third-generation neurons. The article discusses a concept of building a neural network based on spiking neurons modelled on ladder nervous systems. A distinction is made between voltage signals encoding information in a network and current signals which contain the correlation between information in the network and pattern features. Analyzes feature a neuron model based on the cusp catastrophe theory eliminating network sensitivity to problems of synapse plasticity, weight mismatch and coupling of neurons based on electric models. The paper presents applications of a spiking neural network for reporting the state of water quality while generating justifications. The article contains results of an analysis of confusion of justifications with ACC = 1 for a set of 10,000 patterns. It also discusses the speed of pattern analysis in the simulated network.
How to Improve Indonesian Language Learning Outcomes at Madrasah Tsanawiyah Through the Talking Stick Learning Model

This research is classroom action research because it relates to efforts to improve learning practices in the classroom. The data collection technique used in this research is multi-technique or multi-instrument, namely: observation, measurement, and documentation. The data analysis technique used is descriptive quantitative analysis. The data obtained in the form of learning outcomes and activity observations were processed using descriptive quantitative analysis but first scoring and tabulated, then the frequency and percentage were calculated to be a reference in conducting descriptions. The results showed that; (1) The value of student learning outcomes class VII MTs Negeri Palopo in the first cycle of 20 students, there are ten students or 50% who meet the minimum completeness criteria, whereas in cycle II it increased from 20 students there were 16 students or 80% who met the minimum completeness criteria; (2) Thus, the action hypothesis proposed in this study is proof that if the talking stick model of learning is applied, the learning outcomes of the Indonesian language studies for grade VII students of MTs Negeri Palopo will increase.
Research on the Influencing Factors of the Willingness of New Professional Farmers to Participate in Skills Training and Its Promotion during the Development of Urban ModernAgriculture

Objective: Talent is the key factor in the implementation of the Rural Revitalization Strategy. Based on the willingness and influencing factors of new professional farmers to participate in skill training in the development of modern urban agriculture, this study seeks to study the education of professional farmers from the perspective of demand. Methods: Based on the questionnaire survey data of new vocational farmers in Wenzhou, this study systematically analyzed the current situation, training willingness, training methods, and training effect of new vocational training, and made quantitative statistical analysis of the original basic data. Combined with the characteristics of agricultural industry and post, this study empirically analyzed their perception of participating in skill training and the influencing factors. Results: The number of new vocational farmers willing to participate in training was significantly higher than that of farmers unwilling to participate in training. The frequency of technical problems encountered in agricultural production, the times of training, the evaluation of training effect, effect, cost, teacher level, hardware level, certificate, and other factors have a significant impact on the willingness of new vocational farmers to participate in skills training. Conclusion: This study proposes to build a vocational occupation education system to enhance the training intention of new occupation farmers. During the COVID-19, the innovative form of webcast sales realized the unification of technology and service.
Sistem Pendukung Keputusan Pemilihan Tanaman Hias Hoya Carnosa Berbasis Android Menggunakan Metode TOPSIS

Along with the rapid development of the times, the number of consumers who need ornamental plants provides good prospects for the future of the ornamental plant business, especially for ornamental plant farmers. Ornamental plants are not limited to ornamental plants that live in pots, but also include cut flowers, cacti, bonsai, hoya carnosa hydroponic plants and tabor flowers. This study aims to develop a Decision Support System (DSS) for the selection of hoya carnosa ornamental plants. The criteria for selecting hoya carnosa ornamental plants are to have a beautiful basic shape, come from long-lived plants, easy-to-form stems and branches, attractive and curvy skin surface, small leaves and quite dense, the condition of the plant is old enough and the plant is strong to form. The system was built using the Android programming language and applied the TOPSIS method to determine the hoya carnosa ornamental plants.
Increase the Diversity of Your Dataset with Data Augmentation

We guide you in the creation of augmented data experiments to increase the diversity of a training set by applying random (but realistic) transformations. Data augmentation is very useful for small datasets because deep learning models crave a lot of data to perform well.
Mining Property-driven Graphical Explanations for Data-centric AI from Argumentation Frameworks

Adoption of AI-equipped systems and their societal benefits are heavily dependent on human understanding of the rationale behind the systems’ outputs. Such systems’ widespread inability to explain their outputs causes human mistrust and doubts regarding their regulatory compliance. Research in psychology points to the amenability of argumentation as a paradigm for human reasoning, advocating that humans developed reasoning in order to argue. We here overview a number of approaches using computational argumentation frameworks as the scaffolding for explanations for human consumption. Our argumentation frameworks are automatically mined from data and data-centric methods. We define explanations as graphs obtained from these argumentation frameworks, which are customisable by means of properties. We illustrate our methods with various consumer-oriented tasks in the media and entertainment industry, providing reasoning outputs that can be explained to consumers, and that consumers can directly interact with to give rise to improved recommendations.
Q&amp;A MAESTRO: Q&amp;A Post Recommendation for Fixing Java Runtime Exceptions

Programmers often use Q&A sites (e.g., Stack Overflow) to understand a root cause of program bugs. Runtime exceptions is one of such important class of bugs that is actively discussed on Stack Overflow. However, it may be difficult for beginner programmers to come up with appropriate keywords for search. Moreover, they need to switch their attentions between IDE and browser, and it is time-consuming. To overcome these difficulties, we proposed a method, "Q&A MAESTRO", to find suitable Q&A posts automatically for Java runtime exception by utilizing structure information of codes described in programming Q&A website. In this paper, we describe a usage scenario of IDE-plugin, the architecture and user interface of the implementation, and results of user studies. A video is available at https://youtu.be/4X24jJrMUVw. A demo software is available at https://github.com/FujitsuLaboratories/Q-A-MAESTRO.
Digitized Paintings For Crack Detection And Restoration Using Median Filter And Threshold Algorithm

Due to a variety of factors such as long time storage, dry environment, and volatilization of painting,some cracks and stains may appear on the surface of the oil paintings. These tend to seriously affect the value of the paintings. Image restoration techniques are proving to be of great help in the analysis and documentation of our cultural heritage. Digital image restoration techniques provide a multitude of choices for improving the visual quality of images. In this work, we propose a multi-dimensional median filter and threshold algorithm for detection and removal of cracks in digital images. The work conducts an analysis of multi-dimensional median filter and threshold algorithm for effective restoration of cracks in digitized painting using the Java programming language version 8.0. To demonstrate the usefulness of this technique, cracked images of different resolution are collected for use in testing the efficiency of these models. The results show a remarkable difference between the original and enhanced images. This work is implemented using the Java programming language on Netbeans IDE.n
An Improved Method for Detecting Phishing Websites Using Data Mining on Web Pages

Phishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is regarded as one of the important prerequisites in designing an accurate detection system. Therefore, in order to detect phishing features, a list of 30 features suggested by phishing websites was first prepared. Then, a two-stage feature reduction method based on feature selection and extraction were proposed to enhance the efficiency of phishing detection systems, which was able to reduce the number of features significantly. Finally, the performance of decision tree J48, random forest, naive Bayes methods were evaluated{cke_protected_1}{cke_protected_2}{cke_protected_3}{cke_protected_4} on the reduced features. The results indicated that accuracy of the model created to determine the phishing websites by using the two-stage feature reduction based Wrapper and Principal Component Analysis (PCA) algorithm in the random forest method of 96.58%, which is a desirable outcome compared to other methods.
The Random Forest

In this chapter, you will discover the Random Forest model. It is an easy-to-use model, and it is known to be very performant. The Random Forest and the XGBoost model, which you will discover in the next chapter, are two of the most used machine learning algorithms in modern applications.
Supporting Interaction in a Virtual Chorus: Results from a Focus Group

To get effective participation in a virtual chorus is particularly difficult because there is the need of artificially creating that common sense of union among all the group members. This paper aims at understanding how technology may enhance virtual chorus members’ interaction through a focus group involving three chorus masters with experience in conducting virtual chorus. We used the Thematic Analysis Template (TAT) for planning and analyzing the results. Results revealed that three types of interaction modalities have been employed: asynchronous audio/Video multi tracking recording grouping the participant single tracks, synchronous teleconferencing with one participant speaking, and social gaming. These activities are surrogates of F2F choir rehearsals because present technology is not adequate for real synchronous interaction. While waiting for connection empowerment, participants expressed their needs for enhancing present videoconferencing tools, including simultaneous recording of participants in an individual way and integration of the various environments.
Do Organisational Ethical Culture, Audit Experience and Ethics Awareness Influence Internal Auditors’ Ethical Judgment?

This study examines the factors influencing internal auditors' ethical judgement.Specifically, this study examines the effect of organisational ethical culture, audit experience and ethics training on internal auditor's ethical judgement in a bank in Malaysia.Using questionnaire survey on 162 internal auditors, this study shows that organisational ethical culture, audit experience and ethics training can influence the internal auditors' ethical judgement.The findings in this study can assist the management team in executing effective control measures and procedures in achieving effective ethical judgment.This study can also assist in strengthening the governance structure to promote the importance of ethical judgement at all levels.In addition, this study adds values to the existing literature on the factors that can influence the internal auditors' ethical judgement in the context of a financial institution.
Research on English Teaching Ability Evaluation Algorithm Based on Computer Corpus

Corpus refers to a large-scale electronic library with a certain capacity, which is built by collecting naturally occurring continuous language application texts or discourse fragments according to certain linguistic principles and using random sampling methods. The application of corpus in English teaching can provide abundant context for learners, which is beneficial to the construction of learners' language knowledge. In view of the low accuracy of English teaching ability evaluation at present, a teaching quality evaluation method based on computer corpus is proposed. Combining K-means clustering algorithm, clustering and integrating the index parameters of English teaching ability, the teaching resource allocation plan is completed, thus realizing the scientific evaluation of English teaching ability. The research shows that the algorithm can combine the characteristics of schools, compare the overall teaching quality of universities of different grades or the same grade, and objectively reflect the overall teaching level of schools.
A fault propagation model based on Bayesian network

Any minor fault may spread, accumulate and enlarge through the causal link of fault in a closed-loop complex system of civil aircraft, and eventually result in unplanned downtime. In this paper, the fault propagation path model (FPPM) is proposed for system-level decomposition and simplifying the process of fault propagation analysis by combining the dual strategy ant colony optimization algorithm (DS-ACO) with the Bayesian network (BN). In FPPM, the modeling of the fault propagation path consists of three models, namely shrinking model (SM), ant colony optimization model (ACOM) and assessment model (AM). Firstly, the state space is shrunk by the most weight supported tree algorithm (MWST) at the initial establishment stage of BN. Next, DS-ACO is designed to improve the structure of BN in order to study the fault propagation path accurately. Finally, the experiment is conducted from two different perspectives for the rationality of the well-trained BN’s structure. Experimental results illustrate the effectiveness of the proposed model.
Challenges of Online Learning Systems during COVID-19 in the UAE universities and its Effect on Business Students’ Academic Performance

Purpose: All educational institutions in the United Arab Emirates (UAE) were obliged to remain closed during the COVID-19 pandemic and use online learning instead. This transformation, accelerated the usage of online learning and has placed more challenges to the higher education institutions in terms of students’ academic performance. This paper empirically investigates the factors that were imposed during the COVID-19 pandemic to these online learning systems. E-assessment and social challenges are factors that need to be addressed as they had not been extensively studied before the pandemic.Methodology: This study was designed as a quantitative research study using the convenience sampling technique to select students. The data was collected using online surveys distributed amongst undergraduate students in different business colleges around the UAE. The research model was assessed based on an analysis of 202 undergraduates’ students. The data collected was analyzed using IBM SPSS statistics 26, and Smart PLS Structural Equation Modeling.Findings: The results indicate that teaching challenges negatively affect students' performance, while the factors influencing the greatest impact are the assessment, technical, social, and communication challenges on UAE Business students’ online learning and academic performance during COVID-19 pandemic.Unique contribution to theory, practice and policy: this study provides a valuable perspective for understanding and explaining the challenges of student online learning and academic performance, as it emphasizes and recommends a reviewing of the teaching methodologies and tools to align with present-day online teaching and learning. Training is essential in order to enhance the academic staff’s qualifications and technical skills during the COVID-19 ongoing situation.
Explanation in AI systems

As AI systems come to permeate human society, there is an increasing need for such systems to explain their actions, conclusions, or decisions. This is presently fuelling a surge in interest in machine-generated explanation in the field of explainable AI. In this chapter, we examine work on explanations in areas ranging from AI to philosophy, psychology, and cognitive science. We point to different notions of explanation that are at play in these areas. We further discuss the theoretical work in philosophy and psychology on (good) explanation and its implications for the research on machine-generated explanations. Lastly, we consider the pragmatic nature of explanations and showcase its importance in the context of trust and fidelity. Throughout the chapter we suggest paths for further research on explanation in AI, psychology, philosophy, and cognitive science.
LEARNING SKILLS FORMATION AMONG PRIMARY SCHOOL STUDENTS IN THE PROCESS OF NETWORK INTERACTION AT COMPUTER SCIENCE LESSONS

Statement of the problem. This article discusses the problem of universal learning skills (ULS) assessment among primary schoolchildren in the process of network interaction during computer science lesson activities. The research problem is determined by the requirements of the modern information society and becomes especially relevant in the context of the transition to distance learning in connection with the Covid-19 pandemic in 2020. The purpose of the article is to give scientific grounds and description of the assessment means for identifying the ULS formation level among primary schoolchildren at computer science lessons in the process of network interaction. The research methodology is based on the analysis and synthesis of regulatory documents on primary school education, research works of Russian and foreign scientists on the problem of digital socialization of generation Z and the organization of distance learning in the context of the Covid-19 pandemic; analysis of research in the field of networking; analysis of research in the field of ULS diagnostics among students. Research results. We described a method for communicative and regulatory ULS assessment formed among primary schoolchildren of the fourth grade at computer science lessons during the stage of network interaction using Google cloud services. The proposed method based on the level model of network interaction among primary school students was tested in Gymnasium No. 9 in Krasnoyarsk and School No. 11 in Abakan from 2016 to 2019. In total, the study covered 120 students over three years. Conclusion. As a result of our study, we have come to the conclusion that organization of network interaction based on distributed groups in space according to the level model contributes to the ULS formation of the basic and advanced levels that is confirmed by the results of their assessment. The materials presented in the article can be used by both elementary school teachers and subject teachers at the level of primary school education, as well as by methodologists and school administration to assess the ULS among students.
Formalizing Moessner's theorem and generalizations in Nuprl

Moessner's theorem describes a procedure for generating a sequence of n integer sequences that lead unexpectedly to the sequence of nth powers 1n, 2n, 3n, … . Several generalizations of Moessner's theorem exist. Recently, Kozen and Silva gave an algebraic proof of a general theorem that subsumes Moessner's original theorem and its known generalizations. In this note, we describe the formalization of this theorem that the first author did in Nuprl. On the one hand, the formalization remains remarkably close to the original proof. On the other hand, it leads to new insights in the proof, pointing to small gaps and ambiguities that would never raise any objections in pen and pencil proofs, but which must be resolved in machine formalization.
Multi-Armed Bandits and Reinforcement Learning: Advancing Decision Making in E-Commerce and Beyond

The areas of reinforcement learning and multi-armed bandits have recently seen significant innovation, while many application domains, such as e-commerce, are full of problems and challenges to which vanilla RL or MAB methods cannot directly apply. This workshop aims at filling this communication gap by creating a platform for researchers and practitioners from both the method/theory side and application side of the community. Having this platform now instead of at a later time is beneficial to all sides of the community: practitioners and frontline scientists are able to avoid re-inventing existing techniques; theory-oriented researchers can find motivation in industry problems, working within more realistic settings, and making real-world impact. The 1st Multi-armed Bandits and Reinforcement Learning Workshop was a full day workshop co-located with the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2021) in Singapore.
Coherent Control and Distinguishability of Quantum Channels via PBS-Diagrams.

Even though coherent control of quantum operations appears to be achievable in practice, it is still not yet well understood. Among theoretical challenges, standard completely positive trace preserving (CPTP) maps are known not to be appropriate to represent coherently controlled quantum channels. We introduce here a graphical language for coherent control of general quantum channels inspired by practical quantum optical setups involving polarising beam splitters (PBS). We consider different situations of coherent control and disambiguate CPTP maps by considering purified channels, an extension of Stinespring’s dilation.First, we show that in classical control settings, the observational equivalence classes of purified channels correspond to the standard definition of quantum channels (CPTP maps). Then, we propose a refinement of this equivalence class generalising the half quantum switch situation, where one is allowed to coherently control which quantum channel is applied; in this case, quantum channel implementations can be distinguished using a so-called transformation matrix. A further refinement characterising observational equivalence with general extended PBS-diagrams as contexts is also obtained. Finally, we propose a refinement that could be used for more general coherent control settings.