Reviewerly/exHarmony huggingface dataset:
Multi-objective genetic programming strategies for topic-based search with a focus on diversity and global recall

Topic-based search systems retrieve items by contextualizing the information seeking process on a topic of interest to the user. A key issue in topic-based search of text resources is how to automatically generate multiple queries that reflect the topic of interest in such a way that precision, recall, and diversity are achieved. The problem of generating topic-based queries can be effectively addressed by Multi-Objective Evolutionary Algorithms, which have shown promising results. However, two common problems with such an approach are loss of diversity and low global recall when combining results from multiple queries. This work proposes a family of Multi-Objective Genetic Programming strategies based on objective functions that attempt to maximize precision and recall while minimizing the similarity among the retrieved results. To this end, we define three novel objective functions based on result set similarity and on the information theoretic notion of entropy. Extensive experiments allow us to conclude that while the proposed strategies significantly improve precision after a few generations, only some of them are able to maintain or improve global recall. A comparative analysis against previous strategies based on Multi-Objective Evolutionary Algorithms, indicates that the proposed approach is superior in terms of precision and global recall. Furthermore, when compared to query-term-selection methods based on existing state-of-the-art term-weighting schemes, the presented Multi-Objective Genetic Programming strategies demonstrate significantly higher levels of precision, recall, and F1-score, while maintaining competitive global recall. Finally, we identify the strengths and limitations of the strategies and conclude that the choice of objectives to be maximized or minimized should be guided by the application at hand.
Suitability- and utilization-based cost–benefit analysis: a techno-economic feasibility study of virtual reality for workplace and process design

Abstract Virtual reality (VR) is increasingly being used in the corporate environment. Benefits of using VR have also already been identified in the area of combined workplace and process design. However, whether organizations should invest in VR for this use case is only feasible with knowledge of all operational and strategic costs and benefits. Since previous methods for simulating the costs and benefits of information systems rely strongly on prior knowledge and experience, these approaches are not effective for novel technologies such as VR for less tested use cases due to low empirical databases. In order to provide a more accurate cost–benefit analysis (CBA) of the use of VR for strategical planning like workplace and process design, design science research is applied. Subsequently, by including task technology fit theory, a suitability- and utilization-based CBA method emerged. The contribution thus provides, first, a systematically derived method for quantification and simulation of costs and benefits of strategic VR use in organizations. Second, it provides concrete insights into factors influencing profitability of an investment in a specific VR system for strategic planning projects for workplace and process design based on case study insights.
Research on the Application of Blockchain Technology in Credit Risk Control of Chinese Commercial Banks

China is actively promoting the development of blockchain-related technologies, and hopes to apply the technology to commercial banks. Nowadays, blockchain technology has become one of the most important research directions of financial technology for Chinese commercial banks. Under the traditional model, it is difficult for banks to reasonably evaluate customers before lending, update customer information during lending, and make timely adjustments to unfavorable conditions after lending. These are the difficulties faced by banks in the traditional mode. Blockchain technology can screen customers for banks before lending, monitor customers status in real time during lending, and deal with problems in time after lending. However, if this technology is to be introduced perfectly, China needs to solve the problems of imperfect laws and mechanisms, low maturity of technology, insufficient performance and scalability, unprotected security, and excessive cost and mismatch with revenue.
Violations of Grice’s Maxims in Social Media Influencers’ Advertisements

This research investigates the violations of Grice’s maxims (1975) in the discourse of Social Media Influencers’ (SMIs’) advertisements. Eight different advertisement videos from Snapchat of eight different SMIs are examined. A program named QDA Mine Lite is used to code the violations and to extract the frequencies of each violation in order to know the most violated maxim. As a result, the most violated maxim is the maxim of quantity, in which it is violated in three instances. The least violated maxims are the maxims of quality and manner in which they are only violated once.
"Map" or "territory": knowledge in the digitalisation of primary education

The digitalisation of primary education is generating ethical paradoxes related to the clash between the values inherent in the digital world and the ethical and humanistic principles underlying any educational process. This clash manifests itself in the form of axiological risks, shedding light on the complex ethical landscape of digital education. This article focuses on one such risk - the risk of losing the value of knowledge. It describes the process of mutation of the concept of &amp;quot;knowledge&amp;quot; in modern conditions with reference to the concept of simulacra proposed by the French philosopher Jean Baudrillard.
The Use of Digital Literacy in Teaching English at Junior High Schools in Maros Regency

This research analyses how teachers use digital literacy and students' perception, especially using digital applications. The subjects were two English teachers who teach Junior High Schools in Maros Regency. They were taken by using deliberate or purposive sampling. Each teacher was from UPTD SMPN 28 Satu Atap Salenrang Maros and UPTD SMP 14 Tanralili Maros. The research instruments were an observation checklist and an interview. Based on data analysis, it gathered several results; first, the use of digital literacy, especially using digital applications in teaching English, is actualized in the main activity before and after the presentation of the lesson target and is also used as a final assessment of the lesson. Furthermore, the teachers have to make comprehension preparation for digital literacy, which leads to long time preparation. Second, the students in Maros Regency perceive that using digital applications is very important because it can help them learn and understand the material provided easily. They also perceive that using digital applications has positive and negative impacts. However, the students enjoy every learning process using digital applications. They involve themselves during the learning process and complete assignments on time. The findings also show some barriers perceived by the students. They indicate that barriers come from internal and external factors while using digital applications, including mobile phones' RAM capacity, inadequate internet networks, and a gradual understanding of digital applications.
The everyday enactment of interfaces: a study of crises and conflicts in the more-than-human home

By 2027 more than 530 M homes will likely adopt at least one type of automated system. This means that a growing number of residents will be living with automated technology in the home, everyday. But living with smart homes is full of conflicts between what residents find appropriate and what technology does instead. Previous research, centering end-user needs, has often focused on smooth living experiences through graphical user interfaces and improved predictions. In this research, we take the more-than-human lens of co-performance to put crises in everyday practices in view, and to conceptualize a new notion of interface. Based on ethnographic data from 11 households, our findings illustrate how crises reveal conflicting ideas of appropriateness, how residents reconfigure their co-performances with technology in response to everyday crises, and how new interfaces are enacted as a result. We conclude by illuminating how researchers and designers should not look at the conflicts and crises emerging in the more-than-human home as something of which to get rid. Instead, they are opportunities for residents and buildings to respond to one another in the context of everyday life and to enact interfaces that were not pre-designed into the building.
Enhancing Path Planning Efficiency for Underwater Gravity Matching Navigation with a Novel Three-Dimensional Along-Path Obstacle Profiling Algorithm

This paper presents a study on enhancing the efficiency of underwater gravity matching navigation path planning in a three-dimensional environment. Firstly, to address the challenges of the computational complexity and prolonged calculation times associated with the existing three-dimensional path planning algorithms, a novel Three-Dimensional Along-Path Obstacle Profiling (TAOP) algorithm is introduced. The principles of the TAOP algorithm are as follows: (1) unfolding obstacles along the path using the path obtained from two-dimensional planning as an axis, interpolating water depth values based on downloaded terrain data, and subjecting obstacles to dilation treatment to construct a dilated obstacle profile for path segments; (2) conducting height direction course planning and a secondary optimization of the path based on the profile contours of the dilated obstacles; and (3) integrating height planning with the path points from two-dimensional planar planning to obtain a complete path containing all turning points in the three-dimensional space. Secondly, gravity anomaly data are utilized to delineate gravity suitability areas, and a three-dimensional planning environment that is suitable for underwater gravity matching navigation is established by integrating seafloor terrain data. Under identical planning environments and parameter conditions, the performance of the TAOP algorithm is compared to that of the RRT* algorithm, Q-RRT* algorithm, and Depth Sorting Fast Search (DSFS) algorithm. The results show that, compared to the RRT* algorithm, Q-RRT* algorithm, and DSFS algorithm, the TAOP algorithm achieves efficiency improvements of 15.6 times, 5.98 times, and 4.04 times, respectively.
Introduction

This Edition of the Journal comprise eleven papers, of which six are related to the COVID- 19 pandemic. The papers are balanced between those mainly concerned with media or online pedagogies (four), staff development and experiences (three) and strategic university wide policy or institutional change (four). The papers continue to reflect a varied range of participant countries both in terms of the authors, but perhaps more importantly the study sites, (Colombia, Italy, Indonesia, Iran, Lebanon, South Africa, Spain, and Turkey). Universities from both the state and private sector are represented, either through individual case studies or via multi-site studies in subject areas, departments, or institutions. What is fascinating, and perhaps unsurprising, is that common themes emerge from different countries. From the papers in this edition, several issues emerge. First, we can conclude that with distance learning there was/is a particular challenge of dealing with cheating and plagiarism- and exhibited during the pandemic. Second, responses, or readiness to change, with respect to new initiatives and pedagogies can vary by discipline within the same institution, between institutions and within a country. Third, it was noted that when change is made, either planned or in an emergency, the extent of the impact is related to the prior experience and expertise of both teachers and students and the available infrastructure with respect to the new situation. Several papers raised concerns about the pedagogical competences of the teaching staff and how, when this is limited in relation to a required change in pedagogy, stress and workload problems are encountered by both teachers and students. Furthermore, the learning experience is hindered and, where technology changes are involved, students can become isolated and demotivated. Accessibility and equality e.g. (gender, age, income, access to resources) issues were also raised, most evident during the pandemic and emergency remote teaching. The conclusion is perhaps that universities need to plan more carefully for emergency situations taking account of their demographic and socio-economic community – whether teachers, students or other stakeholders.
Discrete-Time Quantum Walk on Multilayer Networks

A Multilayer network is a potent platform that paves the way for the study of the interactions among entities in various networks with multiple types of relationships. This study explores the dynamics of discrete-time quantum walks on a multilayer network. We derive a recurrence formula for the coefficients of the wave function of a quantum walker on an undirected graph with a finite number of nodes. By extending this formula to include extra layers, we develop a simulation model to describe the time evolution of the quantum walker on a multilayer network. The time-averaged probability and the return probability of the quantum walker are studied with Fourier, and Grover walks on multilayer networks. Furthermore, we analyze the impact of decoherence on quantum transport, shedding light on how environmental interactions may impact the behavior of quantum walkers on multilayer network structures.
Spontaneous muscle activity classification with delay-based reservoir computing

Neuromuscular disorders (NMDs) affect various parts of a motor unit, such as the motor neuron, neuromuscular junction, and muscle fibers. Abnormal spontaneous activity (SA) is detected with electromyography (EMG) as an essential hallmark in diagnosing NMD, which causes fatigue, pain, and muscle weakness. Monitoring the effects of NMD calls for new smart devices to collect and classify EMG. Delay-based Reservoir Computing (DRC) is a neuromorphic algorithm with high efficiency in classifying sequential data. This work proposes a new DRC-based algorithm that provides a reference for medical education and training and a second opinion to clinicians to verify NMD diagnoses by detecting SA in muscles. With a sampling frequency of Fs = 64 kHz, we have classified SA with EMG signals of 1 s of muscle recordings. Furthermore, the DRC model of size N = 600 nodes has successfully detected SA signals against normal muscle activity with an accuracy of up to 90.7%. The potential of using neuromorphic processing approaches in point-of-care diagnostics, alongside the supervision of a clinician, provides a more comprehensive and reliable clinical profile. Our developed model benefits from the potential to be implemented in physical hardware to provide near-sensor edge computing.
Service Reliability Based on Fault Prediction and Container Migration in Edge Computing

With improvements in the computing capability of edge devices and the emergence of edge computing, an increasing number of services are being deployed on the edge side, and container-based virtualization is used to deploy services to improve resource utilization. This has led to challenges in reliability because services deployed on edge nodes are pruned owing to hardware failures and a lack of technical support. To solve this reliability problem, we propose a solution based on fault prediction combined with container migration to address the service failure problem caused by node failure. This approach comprises two major steps: fault prediction and container migration. Fault prediction collects the log of services on edge nodes and uses these data to conduct time-sequence modeling. Machine-learning algorithms are chosen to predict faults on the edge. Container migration is modeled as an optimization problem. A migration node selection approach based on a genetic algorithm is proposed to determine the most suitable migration target to migrate container services on the device and ensure the reliability of the services. Simulation results show that the proposed approach can effectively predict device faults and migrate services based on the optimal container migration strategy to avoid service failures deployed on edge devices and ensure service reliability.
METHODS FOR ADAPTING LEGAL TEXT LEARNING SUPPORT SYSTEMS TO A TWO-DIMENSIONAL LEARNING STYLE MODEL

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 1920-1927ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.0558Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
Pair programming with ChatGPT for sampling and estimation of copulas

Abstract Without writing a single line of code by a human, an example Monte Carlo simulation-based application for stochastic dependence modeling with copulas is developed through pair programming involving a human partner and a large language model (LLM) fine-tuned for conversations. This process encompasses interacting with ChatGPT using both natural language and mathematical formalism. Under the careful supervision of a human expert, this interaction facilitated the creation of functioning code in MATLAB, Python, and . The code performs a variety of tasks including sampling from a given copula model, evaluating the model’s density, conducting maximum likelihood estimation, optimizing for parallel computing on CPUs and GPUs, and visualizing the computed results. In contrast to other emerging studies that assess the accuracy of LLMs like ChatGPT on tasks from a selected area, this work rather investigates ways how to achieve a successful solution of a standard statistical task in a collaboration of a human expert and artificial intelligence (AI). Particularly, through careful prompt engineering, we separate successful solutions generated by ChatGPT from unsuccessful ones, resulting in a comprehensive list of related pros and cons. It is demonstrated that if the typical pitfalls are avoided, we can substantially benefit from collaborating with an AI partner. For example, we show that if ChatGPT is not able to provide a correct solution due to a lack of or incorrect knowledge, the human-expert can feed it with the correct knowledge, e.g., in the form of mathematical theorems and formulas, and make it to apply the gained knowledge in order to provide a correct solution. Such ability presents an attractive opportunity to achieve a programmed solution even for users with rather limited knowledge of programming techniques.
Research on privacy information retrieval model based on hybrid homomorphic encryption

Abstract The computational complexity of privacy information retrieval protocols is often linearly related to database size. When the database size is large, the efficiency of privacy information retrieval protocols is relatively low. This paper designs an effective privacy information retrieval model based on hybrid fully homomorphic encryption. The assignment method is cleverly used to replace a large number of homomorphic encryption operations. At the same time, the multiplicative homomorphic encryption scheme is first used to deal with the large-scale serialization in the search, and then the fully homomorphic encryption scheme is used to deal with the remaining simple operations. The depth of operations supported by the fully homomorphic scheme no longer depends on the size of the database, but only needs to support the single homomorphic encryption scheme to decrypt the circuit depth. Based on this hybrid homomorphic encryption retrieval model, the efficiency of homomorphic privacy information retrieval model can be greatly improved.
Saudi undergraduate EFL learners’ attitudes toward using textism in their academic writing

Abstract Textism, which refers to the special language used in text messaging, is widely used in several forms of communication, such as texting, online chatting and emails. This increased use of textism has alarmed the media and the public because this special language can impact the academic writing of the younger generation. This paper aimed at exploring the attitudes of undergraduate Saudi learners of English as a foreign language (EFL) toward using textism in their academic writing. (115) students of different levels of language proficiency completed a survey about the use of textism across several modalities and recipients. Additionally, (240) student essays were analyzed to identify the potential use of textism in student writing. The results showed that students are generally conscious of the appropriate contexts for the use of textism. However, when it comes to the actual use of textism in writing, low-proficient students tend to produce more textism than those who are at medium and high levels of writing competence.
Applications of Quasigroups in Cryptography and Coding Theory

This survey article discusses some applications of quasigroups in cryptography and coding theory. Here mainly results obtained by the authors of this article are considered and obtained in the last quarter of the century. Not all of their results are presented; emphasis is given to those that were interested for the wider community. Security of the modern world is dependent on the many cryptographic products like block ciphers, stream ciphers, digital signatures and encryption schemes, hash functions, pseudo-random number generators, ... These products are mainly produced by using associative structures (number theory, group and finite field theory, Boolean algebras, etc.) The development of quantum computers questioned security based on associative structures. So, nowadays, the use of quasigroups for building cryptographic products is becoming more important. This short survey presents how quasigroups can be exploited for building suitable cryptographic primitives. For that aim, we define some types of quasigroups that are suitable for that purpose, we give the definitions of several kinds of quasigroup transformations, and we explain the constructions of some types of cryptographic primitives obtained by quasigroup transformations. (We notice that cryptographic properties are not discussed in this survey. The efficiency and security of the crypto products based on quasigroups is an open research problem for cryptographers and cryptanalysts.) The quasigroups are also suitable algebraic structures for building error detecting and error correcting code. We give one type of error detecting code based on quasigroups. Error correcting codes resistant to an intruder attack, so called RCBQ (Random Codes Based on Quasigroups) are given in details, as well as some of their applications in processing images and audio signals.
A Data Set of Extracted Rationale from Linux Kernel Commit Messages

Developer’s commit messages contain information about decisions taken and their rationale. Extracting this information is challenging since we lack a detailed understanding of how developers express these concepts. Our work-in-progress targets this challenge by producing a labelled data set of commit messages for a Linux Kernel component. We report preliminary analyses which suggest that larger commit messages and more experienced developers commits tend towards having 40% of sentences containing rationale. This may indicate a guideline for developers to target.