Reviewerly/exHarmony huggingface dataset:
Smart Contracts for Efficient Supplier Relationship Management in the Blockchain

Smart contracts have become a game-changing tool for improving supplier relationship management (SRM) efficiency and transparency within the blockchain ecosystem. This study aims to understand better how intelligent contracts facilitate supplier-buyer communication, increase accountability, and streamline procurement procedures. The study's primary goals are to examine the advantages and difficulties of smart contracts in supply chain management (SRM), explore how they affect supply chain operations, and determine how these findings may affect industry standards and regulatory frameworks. A review methodology based on secondary data amalgamates extant literature and empirical research on smart contracts for supply chain management. Important discoveries show that smart contracts promote efficiency and cooperation in SRM by automating contract execution, improving transparency, and enabling trustless transactions. To fully utilize intelligent contracts in SRM, however, obstacles, including regulatory ambiguity, scalability constraints, and interoperability concerns, must be resolved. Implementing smart contracts in SRM will require clear regulatory frameworks, the development of standards and interoperability, and funding for research and teaching.
A Case Study on School Dropout in Secondary Education: Causes and Solutions According to the Views of Administrators and Teachers

In this study, according to the views of teachers and administrators, the reasons why students drop out of school and the measures to be taken for these reasons were revealed. Based on the data obtained, the reasons for students' dropout are divided into three groups as follows: individual reasons, family-related reasons, and reasons arising from the education system and Ministry of National Education policies. This study was conducted as a qualitative research and semi-structured interview method was used by the researcher to collect the data. In addition, "maximum diversity" sampling, one of the purposeful sampling methods, was preferred in this study. This research was conducted with the participation of 20 administrators and teachers in Nilüfer district of Bursa province. The research was conducted in the 2022-2023 academic year and with this study, solutions were developed for the causes of the problem according to the opinions of administrators and teachers working in secondary education institutions on school dropout.Every step to be taken to find a solution to the secondary school dropout problem in secondary education institutions will be beneficial to solve the problem and that the decrease in dropout rates will contribute to a sustainable economy for our country and a generation that grows up physically and mentally healthy.
Assessing Google Translate ASR for feedback on L2 pronunciation errors in unpredictable sentence contexts

Following previous research into predictable sentence contexts, this study assesses the pronunciation feedback provided by Google Translate’s (GT) Automatic Speech Recognition (ASR) in unpredictable contexts. We examined the accuracy of GT transcriptions for target items recorded by male and female Quebec Francophones (QFs). The items occurred in neutral carrier sentences such that no contextual cues help ASR identify the targets. Th-initial vs t-initial (thank-tank) and h-initial vs vowel-initial (heat-eat) items were used to investigate the potential for feedback on the QF errors of th-substitution, h-deletion, and h-epenthesis, comparing real-word (thank→tank) vs nonword output (thief→tief). As with predictable contexts in our previous research, we observed high transcription accuracy for real words only. Without contextual cues, accuracy rates were lower than in predictable contexts for correctly pronounced items but higher than for incorrect pronunciations constituting real words. Unpredictable contexts are thus inferior at confirming correct pronunciation (confirmative feedback) but superior at flagging real-word errors (corrective feedback). Contrary to the anticipated ASR gender bias, female recordings showed higher transcription accuracy than male recordings. Our findings both confirm the usefulness of GT’s ASR for generating pronunciation feedback and highlight the importance of context (predictable vs unpredictable) and lexical status (real vs nonword).
A Rasch analysis validation of a survey on the use and beliefs of machine translation

Many English as a Foreign Language (EFL) students use Machine Translation (MT) to a varying degree and studies on the students’ use and beliefs of MT are primarily conducted through surveys and interviews. It is important for educators to understand students’ use and beliefs related to MT, however, there are few studies in the Japanese setting, and none validated the surveys prior to implementation. The goal of this study was to validate a survey on MT to be used in the Japanese context. Considerations for validity are crucial when designing surveys for quantitative analysis. In this study, a 41-item Likert scale survey designed to understand students’ opinions of MT use and beliefs in the Japanese tertiary education system was piloted with 93 first-year EFL learners in an academic writing class. Subsets of items targeting use, volume of text, and degree of acceptability were validated and optimized using the Rasch analysis. Results showed that further work is required to increase items for subsets and to reoptimize the instrument.
A machine learning approach to Czech readability

We present a new corpus of Czech texts labeled for second-language readability, and show results of experiments to train machine-learning classifiers to automatically label new texts according to reading level. We report results comparing the performance of traditional machine-learning models (including Random Forest, XGBoost, Linear Discriminant Analysis, and XGBoost Random Forest) and a neural network (XLM-RoBERTa). The results of our research can be implemented in tools to support learning Czech, a less commonly taught language. We extract 46 linguistic features in various categories for use with traditional machine-learning algorithms. We train models on these features and evaluate their performance with recursive feature elimination to determine how informative each feature is for each model. We then compare those results to those of a transformer trained for the same task on the same corpus. XGBoost achieves the highest accuracy at 0.81, suggesting that these traditional models can still perform as well as, or better, than newer models on this task. Notably, the transformer has the lowest mean F1 at 0.74.¹ ¹ Code available at https://github.com/peterjwms/czech-readability.
Virtual reality: “Awesome”, “OK”, or “Not so good” for language learning?

High-immersion Virtual Reality (VR) has rapidly gained popularity as an innovative tool that provides users with immersive and engaging learning experiences. Meanwhile, large-scale studies that provide empirical evidence regarding its effectiveness are scarce. This pilot study is part of a Meta and Immerse co-funded large-scale project investigating cognitive and affective aspects of language learning in VR. This short paper reports on one aspect of this pilot: language learners' (n=123) impressions of French as a foreign language (L2) classes conducted in Immerse, a VR language learning platform. Despite its acclaimed potential, VR may not be immediately perceived as an “awesome” tool for language learning. Our findings indicate that most learners viewed VR as "OK" or “awesome” for learning. However, only a few participants expressed VR to be “not so good.” Stemming from positive and negative feedback from the learners, we identify which aspects of VR are most appealing, and which should be carefully considered. Based on lessons learned, we share recommendations of how to avoid typical mistakes when implementing VR in a classroom.
Japanese readability assessment using machine learning

We present a new corpus of Japanese texts, labeled according to six second-language readability levels. We also show the results of experiments training machine-learning classifiers to automatically label new texts according to reading level. The resulting models can be used in language-learning websites and applications to enhance Japanese language learning. The best-performing model, Random Forest, achieved an F1 score of 0.86, with an adjacent accuracy of 0.97. Of the 114 features used, we identify a small subset of five features that are sufficient to achieve an F1 score of 0.74. The corpus, code, and resulting models are free and open-source.¹ ¹ https://github.com/reynoldsnlp/japanese_readability_corpus
Artistic license with the dark matter tree

We are visual animals. What we see informs our perception of the world, so it often helps to make a sketch to help conceptualize difficult material. When first confronted with MOND phenomenology in galaxies that I had been sure were dark matter dominated, I made a sketch to help organize my thoughts.
JWST Twitter Bender

I went on a bit of a twitter bender yesterday about the early claims about high mass galaxies at high redshift, which went on long enough I thought I should share it here. For those watching the astro community freak out about bright, high redshift galaxies being detected by JWST, some historical context in an amusing anecdote… The 1998 October conference was titled “After the dark ages, when galaxies were young (the universe at 2 &amp;lt; z &amp;lt;
Some Outsider Perspective from Insiders

Avi Loeb has a nice recent post &lt;em&gt; Recalculating Academia &lt;/em&gt; , in which he discusses some of the issues confronting modern academia. One of the reasons I haven’t written here for a couple of months is despondency over the same problems. If you’re here reading this, you’ll likely be interested in what he has to say. I am not eager to write at length today, but I do want to amplify some of the examples he gives with my own experience.
A Profitable Portfolio Allocation Strategy Based on Money Net-Flow Adjusted Deep Reinforcement Learning

Portfolio allocation with Deep Reinforcement Learning (DRL) has been the focus of many researchers. In investing, a portfolio optimization strategy is selecting assets that maximize return on investment while minimizing the risk. Asset optimization involves balancing risk and return, where stock returns are profits over time, and risk is the standard deviation value of the asset's return. Many of the existing methods for portfolio optimization are essentially the expansion of diversification methods for assets in the investment. Signiant drawdowns and early entry into the share are still challenging in portfolio construction. The idea is that having a portfolio based on net money flow is less risky than allocating a portfolio based on historical data only and turbulence as risk aversion. This paper proposes a profitable stock recommendation framework for portfolio construction using the DRL model based on the net money flow (MNF) indicator. We develop a new risk indicator based on the intelligent net-flow behavior of smart money to help determine the optimal market timing for buying and selling. The experimental results of real-world trading scenario validation show that the model outperforms all the considered baselines and even the conventional Buy-and-Hold strategy. Moreover, in this paper, the effect of defining different environments made of various information with hyper parameter optimization on the performance of models has been investigated, and the performance of DRL-driven models in different markets and asset positions has been investigated. The empirical results show the dominance of DRL models based on MNF indicators.
The Role of Small Projects in the Economic Empowerment of Libyan Woman

We know that the trend of women towards establishing small projects is increasing day after day to find a decent life or to achieve a certain level of well-being therefore this study aimed to indentify the role of small projects in the economic empowerment of Libyan woman. This study used the Descriptive method, and used SPSS for analysis data. The most important findings of the study centered on the effectiveness of small projects in the economic empowerment of Libyan women, as the small projects achieved for women in the study community a greater degree of freedom, self-fulfillment and self-confidence after their financial independence, also the small projects achieved for women on the study community a good standard of living for their selves and their families, those projects contributed to the employment of some women, and encouraging some women to set up their economic projects.
Effects and Repercussions of Sexual Harassment against Women in the Work Places

Through this article, we aim to address the repercussions resulting from sexual harassment on female employees who are exposed to this phenomenon, which takes a different path from one woman to another. Because of its repetitive nature, it is confronted in different ways according to the nature of the affected woman (her personality, the way she confronts it), and the different ways of confronting it by the organization And by fellow employees, and this is based on analyzing the results of interviews directed at women exposed to the phenomenon under study, The study was conducted on a sample of 30 women who were subjected to sexual harassment behaviors in the professional field. In order to analyze and extract the most important effects left by sexual harassment behaviors, we tried at first to refer to the most frequent or most obvious repercussions of the respondents, and then we moved on to the results that were less visible to them, in the end we reached the division of these residuals into three aspects that were affected, which is the health aspect The fact that health is the most important indicator of physical and mental safety, which is taken into account in many organizations for employment, and then we dealt with the social aspect, then the economic aspect
Interactive Media Application for Banana Cultivation Among Farming Groups at Rajabasa Lama 2 Village

This research project aims to develop an interactive media application to enhance the knowledge and skills of banana cultivation among farming groups in Rajabasa Lama 2 Village. The development approach utilized the MDLC (Multimedia Development Life Cycle) method, with a specific focus on the implementation phase. The application has been meticulously designed to facilitate effective learning and tailored to the specific needs of the local farming community. The results of the implementation phase have demonstrated a positive impact on both productivity and the understanding of banana cultivation within the farming groups. Consequently, this application makes a valuable contribution to the advancement of sustainable agriculture at the village level.
Development of Front-End Web Applications Utilizing Single Page Application Framework and React.js Library

In an era marked by rapid technological advancements, the impact of these developments is profoundly significant, particularly in the context of the ubiquity and indispensability of the internet. Motivated by this modern landscape, this study focuses on employing the Single Page Application (SPA) technique for the development of the Mbantu website. Central to this research is the utilization of the React.js library. The methodology encompasses several key phases: a comprehensive literature review, thorough analysis of the problem domain, design and prototyping, and the implementation of React.js for front-end development. The application of the SPA technique is observed to offer notable benefits for developers, including enhanced efficiency and time savings in the web development process. The findings presented in the Results and Discussion section demonstrate that the incorporation of React.js substantially contributes to the development of Mbantu's front-end architecture. Rigorous unit testing is performed on each component to ensure robustness and error minimization. The study concludes with suggestions for future research aimed at expanding the Mbantu web application's feature set to further improve user experience.
Design and Development of An Information System for Indemnity Claim Box Recapitulation Using SDLC Method at Mandiri Inhealth Insurance

The recapitulation of indemnity claim box system is an ongoing procedural development activity aimed at producing a new system. This activity is undertaken once the system analysis phase has been completed. Based on the results of the current system analysis discussed in the previous section, this paper presents the outcomes of the new system. The performance of the new system is expected to address several issues related to claim data recapitulation. The design of the Indemnity Claim Box Recapitulation system can be applied to reduce the potential for missing documents, simplify reporting, and ensure easy, fast, and accurate access. Implementation testing can assist users and leaders in the claim data recapitulation process.
PELESTARIAN KOLEKSI BUKU LANGKA DI PERPUSTAKAAN UNIVERSITAS GADJAH MADA

Rare books are collections that have high value and must be preserved, the potential for damage to rare collections is quite high due to the age factor, most of which are very old, therefore it is necessary to carry out good maintenance and preservation. The purpose of this study was to determine damage prevention and preservation of rare book collections at the Gadjah Mada University Library. This research is a qualitative research. The samples taken in this study were UGM Library Librarians. As for data collection techniques through observation, interviews, and documentation. Then finally, in the data analysis, the writer uses three stages, namely data collection, data reduction, and finally data presentation. The results of this study note that the factors causing damage to rare book collections are from biological factors such as fungi and insects, then from physical factors consisting of lighting, dust, temperature and humidity of the room, from human factors such as book storage techniques, book transfers, negligence readers when using rare books that are hundreds or even tens of years old, and most recently due to natural disasters such as fires and floods. Then regarding the conservation actions carried out by the UGM Library for rare book collections including: 1) making a special box to store ancient manuscripts that cannot be repaired. 2) lamination of ancient book collections. 3) digitizing ancient manuscripts. 4) carry out transliteration or language translation on ancient books with Javanese writing.
Ambient intelligence: Technologies and applications

Ambient intelligence (AmI) is an emerging discipline that brings intelligence to our everyday environments and makes those environments sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence (AI). It is because these contributing fields have experienced tremendous growth in the last few years, ambient intelligence (AmI) research has strengthened and expanded. It is because ambient intelligence (AmI) research is maturing, the resulting technologies promise to revolutionize daily human life by making people’s surroundings flexible and adaptive. However, Ambient Intelligent (AmI) services may assist us both reactively (through user-initiated requests) and proactively (with AI-initiated help). In other words, Ambient Intelligence (AmI) does not ask the user what they require, but rather has the power to make judgments and take action based on user context. Smart Homes is one example of such systems but the idea can also be used in relation to hospitals, public transport, factories and other environments. The achievement of Ambient Intelligence (AmI) largely depends on the technology deployed (sensors and devices interconnected through networks) as well as on the intelligence of the software used for decision-making. The aim of this research work is to discuss the concept of ambient intelligent (AmI), applications, its architectural components, and benefits. This research may also highlight challenges and opportunities that Ambient Intelligence (AmI) researchers will face in the coming years.
Autonomic computing: Prospect and challenges

The rapid growth of technology and increasing demand for computing services have created a complex and challenging environment for information and technology (IT) professionals. The complexity of modern computer systems has made it difficult for IT specialists to manage and maintain these systems effectively. As a result, there is a need for a new approach that can help IT professionals manage their complex systems better. Autonomic computing is a new approach that aims to develop and manage complex computer systems that can manage itself, autonomously. Autonomic computing is an advanced approach to computing that allows computer systems to learn, adapt, and manage itself without human intervention. It is based on the principles of self-management, self-optimization, self-configuration, self-protection and self-healing. With autonomic computing, computer systems can detect problems and take corrective actions automatically without requiring human intervention. This technology offers many advantages for organizations, including reduced downtime, improved system performance, increased reliability and better resource utilization. The aim of this research is to discuss the concept of autonomic computing, its architectural components, benefits, and limitations.
Development of English-to-Igala machine translation system using neural machine translation with attention mechanism

Translation is a matter of absolute necessity in the globally united and yet; linguistically and culturally separated world in which we live. Translation allows individuals who speak different languages to communicate and understand one another. The obvious limitations in human translation in terms of speed, scalability and high cost gave birth to the quest for alternative means of translation which is machine translation. Machine translation involves the use of computers to translate from one natural language (source) to another natural language (target). Machine translation applications can be developed using rule-based, example- based and statistical-based technologies. Machine translation applications have been developed to translate English language text to Igala language text using rule-based and example-based approaches. Recently, Neural Machine Translation (NMT) emerged as a new paradigm that swiftly superseded other methods of machine translation evolved with the development of deep learning. This study is aimed at developing a system that can translate contents rendered in English language to Igala language using the Neural Machine Translation with attention mechanism approach. The Neural Machine Translation model for English-to-Igala translation was built with encoder-decoder architecture with an attention layer. The model was trained with a dataset using English-Igala parallel corpus that contains 50,000 parallel sentences. The dataset was partitioned into training, validation and test set in the ratio of 80/20 percent. The model was implemented using python programming language. After training, validation and testing, the output of the model was tested on a corpus of 276 selected English texts using the Bilingual Evaluation Understudy (BLEU) method for evaluating Machine Translation system. An accuracy of 71.0% was obtained. The adoption of this model will play crucial role in facilitating everyday interaction, information exchange and active participation in society among Igala people. It will give Igala people access to information in Igala language that will enhance their standard of living as it will enable them to participate in abundant business opportunities available in the online community, and ensure proper integration in the emerging information society.