Reviewerly/exHarmony huggingface dataset:
Enumerating class relations weights to assess their importance in a graph representation model for detecting key classes using PageRank

The most crucial classes in an object-oriented software system are known as key classes. They serve as the backbone of the software system and are typically documented in project documentation or executive summaries. Key classes play a vital role in software reengineering tasks, particularly when there is a lack of or inadequate documentation for the project. Therefore, the detection of key classes becomes a significant concern. Various methods have been reported for detecting key classes, including static code analysis, dynamic code analysis, and machine learning. In a static code analysis approach, a weighted graph model is employed to represent the relationships between classes, such as inheritance, interface realization, field usage, and return type usage. By applying a link analysis algorithm on the weighted graph model, the classes are ranked based on their importance, enabling the identification of the top 20 most critical classes. To evaluate the significance of class relations, we generate all possible combinations of weights for three pairs (0,1), (1,2) and (1,5) for each class relation during the construction of the weighted graph. Our approach is tested on 14 analyzed systems of varying sizes, encompassing different domains like games, web servers, utilities, and more. The results indicate an improvement of 1% of the Recall values over the best known result from the state of the art.
Vehicle routing problem optimization with machine learning in imbalanced classification vehicle route data

The object of this research is a combinatorial optimization problem arising in the problem of the route of goods delivery vehicles. In this study, the proposed method for solving combinatorial optimization problems consists of several stages: Data Cleaning, Data Preprocessing, K-NN and Cavacity Vehicle Routing Problem model. The results show that the machine learning approach can optimise combinatorial optimization problems, especially in generating vehicle route points and delivery capacity. The characteristics in determining vehicle routes by considering latitude and longitude points. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification from the influence of the number of 'nodes' on vehicle routes with machine learning. The purpose of the model in general is to gain an understanding of the mechanism in the problem so that it can classify unbalanced vehicle route data based on Jalur Nugraha Ekakurir delivery routes. So that with the availability of the model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods. The results of research with machine learning models and vehicle routing problems with testing K values 11, 13, 15. Where it has a percentage of K=11 accuracy 57.3265 % and K=13 accuracy 57.3265 % and K=15 accuracy 81.8645 %. From the test results with odd K values have better accuracy and the K 15 K=15 value is better with a percentage of 81.8645 % compared to K 11 K=11, and 13 K=13. As a result, the developed model in terms of accuracy of the cavacity vehicle routing problem model has an accuracy of 93.80 % and the time series achieves an average precision of 93.31 % and with a recall value of 93.80 %. The results obtained can be useful in developing a more modern model, Cavacity Vehicle Routing Problem with Machine Learning
DEVELOPMENT OF RESEARCH COMPETENCE OF FUTURE TEACHERS IN HIGHER SCHOOL: FOREIGN EXPERIENCE

Teacher research constitutes an integral component of a pedagogue's professional activity, serving the purpose of comprehending and transforming the pedagogical reality through the application of scientific insights and methods. One of the benefits of such endeavors is enhanced research competence. The present paper delves into the approaches to developing research competence of students within the higher education institutions of European countries. The research methods include literature analysis, systematization (to elucidate key research concepts), comparative analysis (to discern the peculiarities of research competence development among students in European higher education), and synthesis (to formulate original conclusions). Based on the synthesis of scientific approaches (including the personal, activity-based, competence-based, systemic, knowledge-based, procedural-technological), research competence is construed as an integral quality of an individual, encompassing skills for conducting research to address educational issues and instructional management. A systematic examination of the approaches to research competence development among students in Germany, the United Kingdom, and France reveals a consistent and methodical progression. In the early stages of formal education, students are exposed to research-oriented learning, while in subsequent phases, they actively participate in undergraduate and graduate research. A recurring theme is the early engagement of students in research activities, bolstered by specific pedagogical conditions and teaching methodologies. These conditions include the encouragement of students' involvement in research, the reinforcement of interdisciplinary dimensions, and the pivotal role played by research advisors in coordinating student research endeavors. Additionally, active teaching methods, such as discussions, workshops, seminars, simulations, situational analyses, practical experiences, internships, and independent research, significantly contribute to the effective engagement in research activities. Project-based and problem-oriented learning methods emerge as predominant didactic approaches, seamlessly blending students' autonomous work with collaborative endeavors within small research groups. Key forms of research competence development encompass lectures, seminars, practical sessions, and consultations. These educational formats not only motivate students to apply their acquired knowledge in practical contexts but also ignite their enthusiasm for independent exploration, fostering creativity.
Cybersecurity in AI-Driven Casual Network Formation

The paper describes a methodology for forming thematic causal networks using artificial intelligence and automating the processes of their visualization. The presented methodology is considered on the example of ChatGPT, as an artificial intelligence for analyzing the space of texts and building concepts of causal relationships, and their further visualization is demonstrated on the example of Gephi and CSV2Graph programs. The effectiveness of the disaggregated method in relation to traditional methods for solving such problems is shown by integrating the means of intelligent text analytics and graphical network analysis on the example of the problem of data leakage in information systems and a selection of news clippings on the selected topic.
On Inverse Protocols of Post Quantum Cryptography Based on Pairs of Noncommutative Multivariate Platforms Used in Tandem

&#x0D; &#x0D; &#x0D; Non-commutative cryptography studies cryptographic primitives and systems which are based on algebraic structures like groups, semigroups and noncommutative rings. We continue to investigate inverse protocols of Non-commutative cryptography defined in terms of subsemigroups of Affine Cremona Semigroups over finite fields or arithmetic rings Zm and homomorphic images of these semigroups as possible instruments of Post Quantum Cryptography. This approach allows to construct cryptosystem which are not public keys, when protocol finish correspondents have mutually inverse transformations on affine space Kn or variety (K*)n where K is the field or arithmetic ring.&#x0D; The security of such inverse protocol rests on the complexity of word problem to decompose element of Affine Cremona Semigroup given in its standard form into composition of given generators. We discuss the idea of usage combinations of two cryptosystems with cipherspaces(K*)n and Kn to form a new cryptosystem with the plainspace(K*)n, ciphertextKn and nonbijective highly nonlinear encryption map.&#x0D; &#x0D; &#x0D;
Workshop Pengelolaan Referensi Ilmiah Dengan Mendeley Untuk Penyelesaian Skripsi Mahasiswa Pendidikan Ekonomi

Scientific work is one of the important elements for higher education in maintaining its reputation and increasing the achievements. It is very important to produce good and quality scientific work. One of the indicators is freedom from plagiarism. This can be achieved by providing knowledge to students in quoting and managing student scientific references. One application to facilitate the management of scientific references is Mendeley. The purpose of holding community service is to increase student digital literacy regarding reference management and to help them implement it in making their student theses, which they will need to complete their final assignments. The training was held at the Merah Putih Building, University of Palangka Raya, with the target participants being undergraduate students of the Economics Education Study Program, Faculty of Teaching and Education, University of Palangka Raya. The results of the participants' activities were full of enthusiasm and high motivation to take part in scientific reference management workshops with Mendeley for the completion of their college assignments and theses.
Penerapan Metode Fuzzy Mamdani Dalam Menentukan Persediaan Beras Tahun 2023

Indonesia is an agricultural country that relies heavily on agriculture, especially the production of rice as a staple food. The government has an important role in maintaining food security through the Logistics Affairs Agency (BULOG). Perum BULOG must anticipate uncertainty in rice demand and ensure sufficient stock. Fuzzy logic methods, especially the Mamdani Method, are used to optimize rice supplies. This research is a quantitative approach with descriptive methods, describing the characteristics of the research object without relationships or comparisons with other variables. Fuzzy logic methods, especially the Mamdani Method, are used to optimize rice supplies. This research aims to apply the Fuzzy Mamdani Method in determining the amount of rice inventory at Perum BULOG on Jl. Asahan.The centroid method is used to calculate z values ​​based on the results of rule aggregation. So, the z value (inventory) based on the Mamdani method with input x 10044820 and y 936000 is 805086. The results of this defuzzification indicate that inventory is "High" at a level of around 805086. The Mamdani method produces rice supplies of around 805,086 kg based on income and distribution. Uncertainty related to fluctuations in income and distribution.
GenAI tools proliferate as risks rise and policy lags

Headline INT: GenAI proliferates as risks rise and policy lags
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. 1. Type of peer review: Single anonymous 2. Conference submission management system: Morressier 3. Number of submissions received: 109 4. Number of submissions sent for review: 98 5. Number of submissions accepted: 59 6. Acceptance Rate (Submissions Accepted / Submissions Received × 100): 54.1 % 7. Average number of reviews per paper: 1.23 8. Total number of reviewers involved: 63 9. Contact person for queries: Name: Dr Netram Kaurav Affiliation: Department of Physics, Govt. Holkar (Model Autonomous) Science College, A. B. Road, Indore (MP)-452001 India Email: netramkaurav@collegeholkar.org
Market Basket Analysis Using Apriori Algorithm to Find Effective Fiscal Policy Mix with R Programming

Fiscal policy drives a country's economy and is the most effectivepolicy to restore a country's economy. When a recession occurs, the fiscal policy helps a country increase aggregate demand in the market for goods and services. This study proposes a fiscal policy mix that can be implemented based on historical data. So this research focuses on using association rules to assist decision-makers (regulators in adopting appropriate fiscal policies in the global VUCA (Volatility, Uncertainty, Complexity, Ambiguity) era. Therefore, an experimental research approach was used in this study to produce the best association rules. The research was carried out in six stages to obtain conclusions, namely problem identification in the research sample and literature review related to apriori algorithms, data collection, data pre-processing, parameter determination, research findings building a priori algorithms, and knowledge extraction formed from a priori algorithms. Based on the experimental results using the a priori algorithm, 657 rules were obtained with a minimum variation of two to six itemsets. Rule 6 produces an average value of the budget performance of 90.65. Rule 6 also says that the performance value of the budget can be increased by spreading out funding sources and ensuring that operational spending is as efficient as possible.
The Positive Transfer Effect of Chinese Tone on English Stress Learning

In the large-scale investigation and research of Chinese primary and secondary school students' oral English mastery in various aspects, it is found that at present, primary and secondary school students have little learning and practice of oral English, and English stress is the pain point, which directly causes the distinctive oral pronunciation of primary and secondary school students, and indirectly causes dumb English and other problems. Therefore, this study tries to analyze the similarities between Chinese tone and English stress based on the Chinese tone familiar to Chinese learners from three reasons: society, school education and students, for which English stress learning is neglected. Starting from the similarities of language system, this study makes use of the positive transfer effect of Chinese tone on learning English stress. Aiming at the common English stress problem of domestic learners, we strive to form specific and efficient training methods to improve learners' stress level.
Learning-Based Dynamic Memory Allocation Schemes for Apache Spark Data Processing

Apache Spark is an in-memory analytic framework that has been adopted in the industry and research fields. Two memory managers, Static and Unified, are available in Spark to allocate memory for caching Resilient Distributed Datasets (RDDs) and executing tasks. However, we find that the static memory manager (SMM) lacks flexibility, while the unified memory manager (UMM) puts heavy pressure on the garbage collection of the JVM on which Spark resides. To address these issues, we design a learning-based bidirectional usage-bounded memory allocation scheme to support dynamic memory allocation with the consideration of both memory demands and latency introduced by garbage collection. We first develop an auto-tuning memory manager (ATuMm) that adopts an intuitive feedback-based learning solution. However, ATuMm is a slow learner that can only alter the states of Java Virtual Memory (JVM) Heap in a limited range. That is, ATuMm decides to increase or decrease the boundary between the execution and storage memory pools by a fixed portion of JVM Heap size. To overcome this shortcoming, we further develop a new reinforcement learning-based memory manager (Q-ATuMm) that uses a Q-learning intelligent agent to dynamically learn and tune the partition of JVM Heap. We implement our new memory managers in Spark 2.4.0 and evaluate them by conducting experiments in a real Spark cluster. Our experimental results show that our memory manager can reduce the total garbage collection time and thus further improve Spark applications' performance (i.e., reduced latency) compared to the existing Spark memory management solutions. By integrating our machine learning-driven memory manager into Spark, we can further obtain around 1.3x times reduction in the latency.
Open Archive of Hate: Terrorism and Violent Extremism on Internet Archive

The recognized correlation between the growing spread of violent extremist and terrorist content on the Internet and online platforms and the significant increase of attacks inspired by these postings caused governments and security agencies to launch various countermeasures. These measures included removal of terrorist and violent extremist online content (or “deplatforming”), suspension of their social media accounts, hacking websites and pressuring social media companies to remove terrorist propaganda. Struggling to adjust to these countermeasures and sustain their online presence, terrorist and violent extremists moved from mainstream online platforms to alternative online channels including anonymous sharing portals and cloud services. The most popular archiving service used by various extremist and terrorist group is the Internet Archive. While the few studies on hate speech and violent contents on Internet Archive focused only on a certain type of content or a specific group, this study attempts to reveal a wider range of contents, groups and organizations who use this archive for sharing and promoting such contents. To examine the use of Internet Archive by various groups and organizations involved in violence, terrorism, hate speech, racism and neo-Nazism we applied several stages of data collection and analysis. The findings highlight an alarming volume of terrorist, extremist, and racist material on the Internet Archive. These findings are discussed in terms of ethical and practical implications.
Enabling Scalable VQE Simulation on Leading HPC Systems

Large-scale simulations of quantum circuits pose significant challenges, especially in quantum chemistry, due to the number of qubits, circuit depth, and the number of circuits needed per problem. High-performance computing (HPC) systems offer massive computational capabilities that could help overcome these obstacles. We developed a high-performance quantum circuit simulator called NWQ-Sim, and demonstrated its capability to simulate large quantum chemistry problems on NERSC’s Perlmutter supercomputer. Integrating NWQ-Sim with XACC, an open-source programming framework for quantum-classical applications, we have executed quantum phase estimation (QPE) and variational quantum eigensolver (VQE) algorithms for downfolded quantum chemistry systems at unprecedented scales. Our work demonstrates the potential of leveraging HPC resources and optimized simulators to advance quantum chemistry and other applications of near-term quantum devices. By scaling to larger qubit counts and circuit depths, high-performance simulators like NWQ-Sim will be critical for characterizing and validating quantum algorithms before their deployment on actual quantum hardware.
Sound of the summer

What triggers cicadas to make noises in unison, then go silent periodically? T. Bard, Tascott, New South Wales, Australia
Topics of Thought: The Logic of Knowledge, Belief, Imagination, by Francesco Berto

Journal Article Topics of Thought: The Logic of Knowledge, Belief, Imagination, by Francesco Berto Get access Topics of Thought: The Logic of Knowledge, Belief, Imagination, by Francesco Berto. Oxford, UK: Oxford University Press, 2022. Pp. xi + 229. Igor Douven Igor Douven IHPST / CNRS / Panthéon–Sorbonne University, France igor.douven@univ-paris1.fr Search for other works by this author on: Oxford Academic Google Scholar Mind, fzad051, https://doi.org/10.1093/mind/fzad051 Published: 09 November 2023
Signal Pre-processing Method for Condition Monitoring based on Vibration Signals from On-Site Manipulators

Handling irregular and noisy field data is challenging in condition monitoring. In contrast to refined lab data, where external influences are kept to a minimum, acquired signal from accelerometer attached to mechanical devices involves a great deal of uncontrollable variables. Especially, irregular operation cycles of the process make difficult to specify significant vibration signals for monitoring without mechanical expertise and information of the manipulators' motion. In this study, we distinguish motion signals from noisy raw signal using Shannon Energy Envelope (SEE). The extracted individual motion signals are algorithmically clustered through the signal graph characteristics for each robot motion. Clusters are evaluated for the effectiveness of monitoring, and it enables users to obtain a reference whose signal can perform the same accuracy for condition monitoring with expert knowledge.
Systemic symptom detection in telemetry of ISS with explainability using FRAM and SpecTRM

Explainability is important for machine learning-based anomaly detection of safety critical systems. In this respect, we propose a new systemic symptom detection method by combining two methodologies: the Functional Resonance Analysis Method (FRAM) and the Specification Tools and Requirement Methodology-Requirement Language (SpecTRM-RL) with machine learning-based normal behavior prediction model. The method was verified with data of thermal control system of Japanese Experimental Module of the International Space Station, and the result found that the proposed method enables flight controllers and specialists to obtain additional information for identifying causes of anomaly with the method.
Fuzzy-membership-based labeling: a new labeling method for both classification task and regression task

In the machine learning and deep learning field, there are two main kinds of tasks: classification and regression. The label for the former is discrete, while for the latter is continuous. Due to the big gaps in labels, these two tasks are generally re solved separately, bringing low training efficiency and waste of computing resources. To this end, this paper proposes a new labeling method based on fuzzy membership. The main idea is to build an intermediate variable, which behaves between continuous and discrete variables. Then, the relation between the intermediate variable and the discrete label can be identified with fuzzy membership. Finally, the fuzzy membership is adopted for building labels to train the source model. After training, the source model can be transferred to achieve both classification and regression tasks. To validate the new labeling method, two typical tasks in the PHM field, aging stage classification and RUL prediction, are selected as the representative for classification and regression tasks, respectively. Furthermore, LSTM with two dense layers is chosen as the benchmark source model. With the C-MAPSS dataset, the superiority of the proposed fuzzy-membership based labeling to improve the network’s task transfer learning performance has been verified.
Analysis of Statistical Data Heterogeneity in Federated Fault Identification

Federated Learning (FL) is a setting where different clients collaboratively train a Machine Learning model in a privacy-preserving manner, i.e., without the requirement to share data. Given the importance of security and privacy in real-world applications, FL is gaining popularity in many areas, including predictive maintenance. For example, it allows independent companies to construct a model collaboratively. However, since different companies operate in different environments, their working conditions may differ, resulting in heterogeneity among their data distributions. This paper considers the fault identification problem and simulates different scenarios of data heterogeneity. Such a setting remains challenging for popular FL algorithms, and thus we demonstrate the considerations to be taken into account when designing federated predictive maintenance solutions.