Reviewerly/exHarmony huggingface dataset:
Blockchain Scenes: A Research Agenda

Over the last fifteen years, the development of blockchain technologies has attracted a large volume of professional expertise, capital investment and media attention. This burgeoning sector of technology practices has coalesced around a few major initiatives (Bitcoin, Ethereum), but it is still moving at a fast pace and its configuration is evolving. If this sector is marked by a variety of technological protocols, financial arrangements and organizational forms, it is also, we would argue, a site of social effervescence. Parties, meet-ups, and the sorts of informal socializing which gather around events and networks of all kinds function to endow the blockchain sector with the characteristics of what, in cultural analysis, are often called “scenes”. The aim of this special issue is to examine the interest of the notion of scene for the analysis of blockchain practices. We argue that the notion of scene may be mobilized as a useful analytical framework not only for the study of blockchain practices, but for that of technology practices more generally. In this introductory article, we ask the following questions: how can the notion of scene contribute to the understanding of blockchain practices? And what sort of research agenda does the notion point to? In the following sections we first identify some “scenic” components in blockchain phenomena. Then we review how media discourses and academic scholarship have framed these phenomena to show that the scene perspective is undertheorized in the context of technology-related social groupings. Finally, we propose a framework to analyse the main dimensions of blockchain scenes, before presenting the contributions to the special issue. With this special issue, we aim to establish a research agenda around technology scenes at the junction of STS and cultural analysis.
Exploring Students’ Experiences of Virtual Learning Environment for Art History Classroom

During the Covid 19, students and instructors experienced double-way synchronization distance classes. Recently, the metaverse was drawn much attention to the online virtual world as a channel for social interaction. It has received renewed interest as a new method of surmounting the limitations of external activities after the pandemic. A few studies have focused on the usability of metaverse in a classroom, but no attention was paid to its applicability to Art History as well as to the higher level of education. In order to compensate for the limitations of two-way synchronized remote learning in university art history classes, Gather.Town was adopted as a metaverse platform in both Western modern art history classes and art history methodology classes. In this study, we surveyed and interviewed students who used both WebEx and Gather.Town. This study examines students’ experiences in accessibility, social presence, convenience of interaction, interest and immersion, and satisfaction when the students use the metaverse platform, Gather.Town. The examination of this study confirms that the virtual space of metaverse was more helpful in making students feel a sense of presence, interaction, and engagement than WebEx and demonstrates the possibility of this platform as a sustainable and valid tool in Art History Education.
Financial Innovation and Demand for Money in Nigeria: Further Empirical Insights

The quest to achieve an efficient payment system that reduces the demand for money for cash transactions and other motives, and makes the economy more competitive in the global market, necessitated the introduction of financial innovation in the financial system.This paper examined the impact of financial innovation on the demand for money, searching into the nature of their relationship and evaluating the stability of the money demand function in Nigeria, to provide further empirical insights into the unresolved debate of demand for money in the Nigerian economy.The paper adopted the ARDL model to determine whether there is a long run relationship between financial innovation and demand for money; the Granger causality test, to investigate the nature of the relationship; and CUSUM and CUSUMSq to explore the stability of money demand function in Nigeria.Quarterly data from 2009q1-2020q4 were used in the analysis.A disaggregated measure of financial innovation using the value of e-payment transactions of ATM, POS, MOB, and WEB to ascertain the relative impact of each financial innovation channel on the demand for money was employed.The disaggregated approach to measuring financial innovation and examining the causality issue, constitutes the novelty and point of departure of this paper from previous studies in Nigeria.The findings showed a long-run relationship between financial innovation and demand for money existed.Also, there is the existence of a unidirectional causal relationship with causality running from broad monetary aggregate to financial innovation.The findings further revealed that financial innovation impacted demand for money positively, implying that the Nigerian economy is predominantly cash-based; and the money demand function was found to be stable in the face of financial innovation.The findings also showed that income (GDP), interest rate, and inflation are significant variables influencing the demand for money in Nigeria.
Multi-Modality Deep Network for Extreme Learned Image Compression

Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur and severe semantics loss at extremely low bitrates. To address this issue, we propose a multimodal machine learning method for text-guided image compression, in which the semantic information of text is used as prior information to guide image compression for better compression performance. We fully study the role of text description in different components of the codec, and demonstrate its effectiveness. In addition, we adopt the image-text attention module and image-request complement module to better fuse image and text features, and propose an improved multimodal semantic-consistent loss to produce semantically complete reconstructions. Extensive experiments, including a user study, prove that our method can obtain visually pleasing results at extremely low bitrates, and achieves a comparable or even better performance than state-of-the-art methods, even though these methods are at 2x to 4x bitrates of ours.
Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression

Automatic image cropping algorithms aim to recompose images like human-being photographers by generating the cropping boxes with improved composition quality. Cropping box regression approaches learn the beauty of composition from annotated cropping boxes. However, the bias of annotations leads to quasi-trivial recomposing results, which has an obvious tendency to the average location of training samples. The crux of this predicament is that the task is naively treated as a box regression problem, where rare samples might be dominated by normal samples, and the composition patterns of rare samples are not well exploited. Observing that similar composition patterns tend to be shared by the cropping boundaries annotated nearly, we argue to find the beauty of composition from the rare samples by clustering the samples with similar cropping boundary annotations, i.e., similar composition patterns. We propose a novel Contrastive Composition Clustering (C2C) to regularize the composition features by contrasting dynamically established similar and dissimilar pairs. In this way, common composition patterns of multiple images can be better summarized, which especially benefits the rare samples and endows our model with better generalizability to render nontrivial results. Extensive experimental results show the superiority of our model compared with prior arts. We also illustrate the philosophy of our design with an interesting analytical visualization.
Security Attacks on E-Voting System Using Blockchain

Electronic voting has become popular in democratic countries, and thus the cyber security of this system is demanded. In this paper, some attacks were made on a proposed electronic election model based on blockchain technology, where the impact of each attack (Sybil, DDoS, Eclipse, Selfish mining, 51% attack) was calculated, and the time in which it achieved 51% of the attack was calculated. In this study, we investigate of Blockchain technology’s attack surface, focusing on general blockchains. The following factors show how these attacks have an impact on the proposed model: 1) The cryptographic architecture of the Blockchain. 2) The distributed architecture of systems using Blockchain. 3) The Blockchain application context. For each of these factors, we identify several attacks, including selfish mining, 51% attack, sybil attacks, eclipse attacks, distributed denial-of-service (DDos) attacks, consensus delay (due to selfish behavior or distributed denial-of-service attacks), blockchain forks, orphan blocks, block swallowing, wallet theft, smart contract attacks, and privacy attacks.
TEACHERS’ PROFESSIONAL KNOWLEDGE APPLICATION PRACTICES AND STUDENTS’ ACADEMIC PERFORMANCE IN PUBLIC SECONDARY SCHOOLS IN KAKAMEGA NORTH SUB-COUNTY, KENYA

The purpose of this study was to assess the effect of teachers' professional knowledge application practices on students' academic performance in public secondary schools in Kakamega North sub-county, Kenya. The study was guided by the Goal setting theory. A descriptive survey research design was utilized. The target population was 772 persons comprising of 723 teachers, 49 principals, and 49 deputy principals from Kakamega North's public secondary schools. A sample size of 232 was obtained using Krejcie and Morgan's Table for determining sample sizes. The researcher then applied proportionate sampling approach to determine the number of respondents in each group. Thus, 217 teachers, 15 principals, and 15 deputy principals were used as respondents. Questionnaires, interview schedules, and document analysis were utilized in data collection. The reliability coefficient of the instruments was determined using the test-retest technique with Cronbach's alpha statistic being used to calculate its reliability. A reliability value of 0.842 was established which was considered sufficient to get consistent outcomes. The validity of the instruments was determined by the experts in the area of specialization. Data was analyzed using descriptive statistics and Chi-Square analysis. The qualitative data was processed and presented using quotations according to the study's topics and sub themes. The study established that there is no statistically significant relationship between teacher professional knowledge application appraisal practices and students’ academic performance in public secondary schools in Kakamega North Sub-county, Kenya. The study recommends that a policy is needed to strengthen these variables in order to realize better academic results from students in public secondary schools. The study is hoped to benefit the education stake holders, policy makers and community members in improving the students’ academic performance.
ANALISIS IMPLEMENTASI KEBIJAKAN GERAKAN LITERASI SEKOLAH DI SD NEGERI TOMBOLO PAO

Analysis of the Implementation of School Literacy Movement Policies at the Buttono Pao Public Elementary School. Thesis. Department of Postgraduate Basic Education, University of Muhammadiyah Makassar. Advisor I Sulfasyah, S.Pd., M.A., Ph. D, Supervisor II Dr. Agustan S, M.Pd. This study aims to identify and describe the implementation, supporting factors, and inhibiting factors for the implementation of the Literacy Movement at SD Buttono Pao Public School for the 2022/2023 academic year. This research is a qualitative research with a case study approach regarding the School Literacy Movement. This study involved four people as research subjects and focused on the implementation of educational policies in developing literacy at SDN Buttono Pao. Data collection techniques in this study are interviews, observation, and documentation. Data analysis techniques used in this study are: (1) data collection; (2) data reduction; (3) Data presentation; (4) Drawing conclusions. The programs implemented by SDN Kuncio Pao, Gowa Regency in order to realize the policy of the school literacy movement are as follows: 1. Routine Program; 2. Incidental Program. Factors supporting the implementation of the school literacy movement are 1) Support from the school principal for carrying out literacy activities; 2) Social media (whatsapp groups); 3) There is financial support from students' parents (literacy infaq); 4) There is support from alumni and relations (book donations); 5) There is good commitment from the school principal and the school literacy team. The inhibiting factors for implementing the school literacy movement are 1) Some parents don't care about literacy activities; 2) Some of the students' parents do not have WhatsApp accounts, making it difficult for schools to provide information quickly; 3) Since 2022, the number of students at SDN Kuncio Pao, Gowa Regency has decreased; 4) Library room facilities are inadequate; 5) Policy implementers have other responsibilities (teaching and administrative obligations) so they cannot focus only on literacy activities.
Special Issue: Epigraphy and Paleography: Bringing Records from the Distant Past to the Present

Abstract This special issue brings together three areas of research and scholarly work areas that would have demonstrated few obvious relationships three decades ago. Digital libraries research, practices and infrastructures have transformed the study of ancient inscriptions by providing organizing principles for collections building, defining interoperability requirements and developing innovative user tools and services. Yet linking collections and their contents to support advanced scholarly work in epigraphy and paleography tests the limits of current digital libraries applications. This is due, in part, to the magnitude and heterogeneity of works created over a time period of more than five millennia. The remarkable diversity ranges from the number of types of artifacts to the methods used in their production to the singularity of individual marks contained within them. Conversion of analog collections to digital repositories is well underway—but most often not in a way that meets the basic requirements needed to support scholarly workflows. This is beginning to change. In addition to efforts to develop complex data objects, linking strategies and repositories aggregation, there is a new use of imaging technologies and computational approaches to recognize, enhance, recover and restore writings. Most recently, leading-edge artificial intelligence methods are being applied for the automated transcription of handwritten text into machine readable forms. The articles in this special issue will give examples of each.
Exact Anytime Multi-Agent Path Finding Using Branch-and-Cut-and-Price and Large Neighborhood Search

Given a set of agents on a grid, the multi-agent path finding problem aims to find a path that moves each agent from its given start location to its target location such that they do not collide and that the sum of arrival times is minimized. LNS2 is a state-of-the-art algorithm for anytime, suboptimal solving. It is an upper-bounding algorithm that repeatedly adjusts an existing solution and, being a local search, is oblivious to optimality. BCP is a state-of-the-art algorithm for exact solving. It is a lower-bounding tree search that attempts to tighten the lower bound until a solution appears. As BCP operates on the lower bound, the first solution it finds is optimal or nearly optimal, and therefore has poor anytime behavior. This paper proposes to tightly couple LNS2 and BCP to achieve better anytime, suboptimal solving while retaining the optimality guarantee of BCP. Experiments indicate that the combination achieves better anytime behavior than BCP in general and better suboptimal performance than LNS2 on congested maps.
Greedy Priority-Based Search for Suboptimal Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths, one for each agent, in a shared environment, while minimizing their sum of travel times. Since solving MAPF optimally is NP-hard, researchers have explored algorithms that solve MAPF suboptimally but efficiently. Priority-Based Search (PBS) is the leading algorithm for this purpose. It finds paths for individual agents, one at a time, and resolves collisions by assigning priorities to the colliding agents and replanning their paths during its search. However, PBS becomes ineffective for MAPF instances with high densities of agents and obstacles. Therefore, we introduce Greedy PBS (GPBS), which uses greedy strategies to speed up PBS by minimizing the number of collisions between agents. We then propose techniques that speed up GPBS further, namely partial expansions, target reasoning, induced constraints, and soft restarts. We show that GPBS with all these improvements has a higher success rate than the state-of-the-art suboptimal algorithm for a 1-minute runtime limit, especially for MAPF instances with small maps and dense obstacles.
Online and offline determinants of drug trafficking across countries via cryptomarkets

Abstract Drug cryptomarkets are a significant development in the recent history of illicit drug markets. Dealers and buyers can now finalize transactions with people they have never met, who could be located anywhere across the globe. What factors shape the geography of international drug trafficking via these cryptomarkets? In our current study, we test the determinants of drug trafficking through cryptomarkets by using a mix of social network analysis and a new dataset composed of self-reported transactions. Our findings contribute to existing research by demonstrating that a country’s level of technological advancement increases the probability of forming trafficking connections on cryptomarkets. Additionally, we found that a country’s capacity to police cryptomarkets reduces the number of trafficking connections with other countries. We also observed that trafficking on cryptomarkets is more likely to occur between countries that are geographically close. In summary, our study highlights the need to consider both online and offline factors in research on cryptomarkets.
Sample Complexity of Decentralized Tabular Q-Learning for Stochastic Games

In this paper, we carry out finite-sample analysis of decentralized Q-learning algorithms in the tabular setting for a significant subclass of general-sum stochastic games (SGs) – weakly acyclic SGs, which includes potential games and Markov team problems as special cases. In the practical while challenging decentralized setting, neither the rewards nor the actions of other agents can be observed by each agent. In fact, each agent can be completely oblivious to the presence of other decision makers. In this work, the sample complexity of the decentralized tabular Q-learning algorithm in [1] to converge to a Markov perfect equilibrium is developed.
Factors affecting implementation of competence based curriculum in selected Secondary Schools of Kabale Municipality- Kabale District

This study aimed to explore factors affecting the implementation of the competence-based curriculum in selected secondary schools in Kabale municipality, Kabale district.The research objectives were to identify the teaching approaches used, determine the impact of instructional materials, and assess the role of education agencies in the implementation of the curriculum.The study used Social constructivism theory by Lev Vygotsky, a Russian psychologist who believes that individuals play an active role in creating their own knowledge.The study used a case study design alongside qualitative approach, and data was collected through interviews guides in one on one and focus group discussion with teachers, learners, and head teachers, from the selected secondary schools and Municipal education officer and analysed thematically.Purposive sampling was used to get the study population of 126.Findings revealed that project-based learning, experiential learning, inquirybased learning, and collaborative learning were the common teaching approaches used in the implementation of the competence-based curriculum.Instructional materials were found available though inadequate to have a significant impact on the implementation of the curriculum.Furthermore, education agencies such as the Ministry of Education and Sports, the NCDC and UNEB are the responsible bodies and play crucial role in providing guidelines, training, and support to teachers and head teachers to ensure the effective implementation of the curriculum.The study recommends that teachers should be trained on various teaching approaches to effectively implement the curriculum through training programs and workshops organized by Education agencies to equip teachers with necessary skills and knowledge to provide quality education and ensure that students receive the best possible education and also should provide adequate support and resources to ensure the effective implementation of the competence-based curriculum.
API Entity and Relation Joint Extraction from Text via Dynamic Prompt-tuned Language Model

Extraction of Application Programming Interfaces (APIs) and their semantic relations from unstructured text (e.g., Stack Overflow) is a fundamental work for software engineering tasks (e.g., API recommendation). However, existing approaches are rule based and sequence labeling based. They must manually enumerate the rules or label data for a wide range of sentence patterns, which involves a significant amount of labor overhead and is exacerbated by morphological and common-word ambiguity. In contrast to matching or labeling API entities and relations, this article formulates heterogeneous API extraction and API relation extraction task as a sequence-to-sequence generation task and proposes the API Entity-Relation Joint Extraction framework (AERJE), an API entity-relation joint extraction model based on the large pre-trained language model. After training on a small number of ambiguous but correctly labeled data, AERJE builds a multi-task architecture that extracts API entities and relations from unstructured text using dynamic prompts. We systematically evaluate AERJE on a set of long and ambiguous sentences from Stack Overflow. The experimental results show that AERJE achieves high accuracy and discrimination ability in API entity-relation joint extraction, even with zero or few-shot fine-tuning.
FUTURE TEACHER’S FORMATION OF PROFESSIONAL COMPETENCIES IN THE PROCESS OF MULTILINGUAL EDUCATION IN MODERN CONDITIONS

Currently, higher education institutions are facing new challenges, the purpose of which is to provide highly qualified specialists who have mastered not only professional knowledge, but also ready for intercultural communication in a multilingual world, independent problem solving, teaching subjects in two or more languages. For teacher training, we need a multilingual multicultural environment, design and implementation of new technologies. This article discusses the interpretation of the concepts of "competency " and "competence ", as well as the diversity of the proposed structures. The practical significance of the work is that the results obtained allow us to offer teachers of pedagogical universities scientifically -based recommendations for the formation of professional competencies of future teachers in the process of multilingual training.