Reviewerly/exHarmony huggingface dataset:
A Multi-stage Optimization Strategy Based on Fuzzy QFD and Fuzzy Swara to Adopt Blockchain Technology in the Drug Supply Chain

Introduction: Utilizing blockchain technology helps to address issues that the supply chain faces, including the intricate connections between chain network members, regulation of the distribution network, and inventory management.&#x0D; Objective: To adopt blockchain technology in the drug supply chain, this study aims to offer a multi-stage optimization strategy based on Swara and QFD approaches and a zero-one nonlinear optimization problem employing a fuzzy approach.&#x0D; Methods: First, field and library research was conducted to identify the barriers to the adoption of blockchain technology in the drug supply chain and the ways to overcome these barriers. The relevance of the barriers is ascertained using the fuzzy Swara approach following their refinement employing the Friedman test. The output is entered as an input in the house of quality (HOQ) rows, while the barrier-reducing techniques are presented in the columns. These approaches to reducing barriers are arranged in rows in the second phase of the house of quality.&#x0D; Results: Among the criteria of the barrier-reduction approach, the Performance Expectancy criterion with a score of 0.0521 has the greatest score, and the criterion of Focusing on the Major Points has the minimum score, as determined by the findings of the fuzzy Swara model. The criterion of Enhancing Transparency with a score of 0.5374 and Reducing Risk with a score of 0.4045 have the maximum, and Sustainability Performance with a score of 0.07 has the minimum score, according to the findings concerning the objectives of blockchain technology adoption in the drug supply chain. The criterion of lack of customers' awareness and attitude about sustainability and blockchain technology has the greatest score among the barriers to adoption, scoring 0.521, and the criterion of difficulty in altering organizational culture, scoring 0.033, has the minimum value.&#x0D; Conclusion: Based on the results of the fuzzy house of quality, we concluded that, for the adoption of blockchain technology in the drug supply chain, the eighth (long-term perspective), first (performance expectation), and tenth (focused on the primary strengths) strategies are more beneficial. In addition, it was demonstrated that the second (Effort Expectation), ninth (information sharing), and sixth (Trust) strategies have poor rankings.
The extent to which principals’ use of transformational leadership style influences students’ academic performance in public secondary schools in Kenya

The purpose of this study was to determine the extent to which principals’ use of transformational leadership style influences students’ academic performance in public secondary schools in Mwala sub-county, Machakos County. This study used descriptive survey research design. It targeted 72 schools, 72 principals and 505 teachers in Mwala Sub-county. The study employed stratified random sampling technique to select 36 principals and simple random sampling technique to include 216 teachers. The instruments were validated through piloting and by research experts to ascertain content validity while reliability was achieved through piloting using test-retest method. Data was coded and entered into Statistical Package for Social Sciences (SPSS) version 23.0 for analysis. Descriptive statistics which included frequencies, percentages, means and standard deviations were employed to analyse data. Inferential statistics which basically involved running correlation analysis at 0.05 level of significance were employed to test the nature and strength of relationships between variables. The study employed tables to present the analysed results. The results revealed that there was weak positive but statistically significant correlation between transformational leadership style and students’ academic performance (R=.374, p=.038). The study recommends to: Kenya Institute of Educational Management (KEMI) and the Teachers’ Service Commission (TSC) to regularly in-service teachers on leadership styles; principals to enhance practices associated with transformational leadership practices since they improve academic performance.
Comparative Analysis for Open-Source Large Language Models

Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP), demonstrating exceptional performance across diverse language tasks such as content summarization, sentiment analysis, and conversational AI. The advent of these models has profoundly impacted human-computer interaction research, marking the onset of a new era in the field. The pioneering model, ChatGPT, was introduced by OpenAI in November 2022, catalyzing the development of other commercial tools like Bing Chat and Google Bard. Subsequently, the emergence of open-source LLMs has democratized access to these powerful tools, enabling end-users to deploy them internally with relative ease. As the landscape of open-source LLMs continues to expand and evolve, researchers and practitioners are presented with a plethora of choices for NLP applications. This paper presents a comparative analysis for various open-source LLMs, assessing their unique features, strengths, and limitations. Our focus of comparisons will include licensing, training methods, computing resources needed, available application programming interfaces (APIs), robustness, and bias.
DEVELOPMENT OF QUANTUM COMMUNICATIONS TECHNOLOGY (QCT) IN MALAYSIA

The transfer of information from one single device to another has been more secure in recent years through to the development of quantum communication technology (QCT) in a variety of technological uses. Preparing for this new, emerging sector is important since information communication technology (ICT) in the future will certainly depend on QCT, which is built on quantum physics laws to secure the transfer of information. Future quantum technology will not only optimize computers and the internet but also change the way we communicate. This study discusses the importance and impact of QCT in the context of national security, framework, and policy. Furthermore, this paper also discusses the latest research trends of QCT in several countries including the United States, Canada, and China that Malaysia can learn from. In addition to that, this paper traces the development of QCT policy in other countries along with the existing development of security policy and framework in Malaysia. Finally, this paper discusses the importance of QCT with recommendations.
Assisted Normative Reasoning with Aristotelian Diagrams

We design a framework for assisted normative reasoning based on Aristotelian diagrams and algorithmic graph theory which can be employed to address heterogeneous tasks of deductive reasoning. Here we focus on two problems of normative determination: we show that the algorithms used to address these problems are computationally efficient and their operations are traceable by humans. Finally, we discuss an application of our framework to a scenario regulated by the GDPR.
Permission in a Kelsenian Perspective

Although permissions are of crucial importance in several settings, they have garnered less attention within the deontic logic community than obligations. In previous work we showed how to reconstruct deontic logic using Kelsen’s quasi-causal conception of norms, restricting ourselves to the notion of obligation. Here we extend the account to permission, and show how to analyse the notion of strong permission through a Kelsenian lens. In our framework various forms of conflicts between obligation and permission are disentangled.
Information Extraction from Lengthy Legal Contracts: Leveraging Query-Based Summarization and GPT-3.5

In the legal domain, extracting information from contracts poses significant challenges, primarily due to the scarcity of annotated data. In such situations, leveraging large language models (LLMs), such as the Generative Pretrained Transformer (GPT) models, offers a promising solution. However, the inherent token limitations of these models can be a bottleneck for processing lengthy legal contracts. This paper presents an unsupervised two-step approach to address these challenges. First, we propose a query-based summarization model that extracts sentences pertinent to predefined queries, concisely representing lengthy contracts. This summarization ensures that the core information remains intact while simultaneously addressing the token limitation issue. Subsequently, the generated summary is fed to GPT-3.5 for precise information extraction. Our approach effectively overcomes the challenges of token limitations and zero resources, enabling efficient and scalable information extraction from legal contracts. We compare our results with those obtained from supervised models that have been fine-tuned on domain-specific annotated data. Experimental results demonstrate the remarkable effectiveness of our approach, as it achieves state-of-the-art performance without the need for domain-specific training data.
A Legal System to Modify Autonomous Vehicle Designs in Transnational Contexts

Autonomous vehicles, one of the signature technologies of the rapid development of artificial intelligence, have brought about a rapid change in the relevant legal norms and legal mandates. This change makes it more challenging for manufacturers and designers of autonomous vehicles to ensure the legal compliance of their product designs in a more dynamic way. Therefore, rather than approaching the issue from the perspective of judges or the cars themselves, we propose a legal reasoning system applicable to the adjustment of autonomous vehicle design options from the designer’s perspective, building on a series of previous studies. Focusing on the circulation of autonomous vehicles between different countries, the system attempts to help designers accomplish the adjustment of design solutions between different legal systems instead of designing new prototypes.
REMOAC: A Retroactive Explainable Method for OCR Anomalies Correction in Legal Domain

Efficient OCR Anomaly Detection and Correction is essential in the legal domain, as it significantly enhances the ability of legal professionals to extract accurate information from documents. This paper presents a novel approach called REMOAC, that improves the performance of a legal text classifier through OCR anomaly detection and correction in legal documents, by actively using state-of-the-art models and explainability techniques. Explainability is a key aspect of our approach, both because we provide transparency and comprehensibility in the OCR anomaly correction process and, more importantly, because we actively use it to improve classification performance.
Giving Examples Instead of Answering Questions: Introducing Legal Concept-Example Systems

Question-Answering Systems (QASs) have seen a big development in recent years and various attempts have been made to extend them to the legal domain. Nevertheless, the needs and methodology of legal research relies often less on getting answers and more on finding positive and negative examples for certain legal concepts. In this paper, we introduce a sub-category within QASs that focuses on such legal tasks and design a methodology for the automated production of such systems.
Nearest Neighbor Search for Summarization of Japanese Judgment Documents

With the increasing demand for summarizing Japanese judgment documents, the automatic generation of high-quality summaries by large language models (LLMs) is expected. We propose a method to select exemplars using the nearest neighbor search for the one-shot learning method. The experiments showed our method outperforms baseline methods.
Web-Based Crime Management System for Samara City Main Police Station

Crime is a human experience, and it must be controlled. The Samara town police station plays a significant role in controlling crime. However, the management of crime activities is done manually, which is due to the lack of an automated system that supports the station workers in communicating with citizens to share information and store, retrieve, and manage crime activities. To control crime efficiently, we need to develop online crime management systems. This project, entitled "Web-Based Crime Management System," is designed to develop an online application in which any citizen can report crimes; if anybody wants to file a complaint against crimes, they must enjoy online communication with the police. This project provides records of crimes that have led to disciplinary cases in addition to being used to simply retrieve information from the database. The system implemented is a typical web-based crime record management system based on client-server architecture, allowing data storage and crime record interchange with police stations.
Dialect Variations the Enrichment of Standard Igbo Language: A Linguistic Study

Historically, many languages of the world evolved their standard forms through centralized control or political unity. Considering the decentralized natured of the Igbo unity, the dream evolving a standard form through much means, remains a mirage and unrealistic. Therefore, Igbo scholars are forced with the task of achieved this goal, we must used what we have to get what we want. In other words, we cannot enrich the evolving standard Igbo by using the dialectal variants in the language. Igbo dialectal variation and the dialect variants as synonyms, with comparative study of five dialects: Owerri, oguuta, Afikpo, Nnewi and Ngwa (ibeme) at the phonological, morphological and syntactic levels. The paper discuss the standard Igbo vis-a- vis dialects and meta-language as the tool for its enrichment which consist of the effect of dialectal variation, including the conclusion and recommendations made that rather than being a conclusive linguistic study on dialectal variation for the enrichment of standard Igbo, would serve as a stimulus for further intensive researches.
Criminological Review of Mugging Crime Cases

Crime is a complex phenomenon that can be understood from various different angles. That's why in everyday life we can catch comments about crime incidents that are different from each other. In our experience it turns out that it is not easy to understand crime itself. Efforts to understand this crime have actually been thought up centuries ago by famous scientists. Plato, for example, stated that gold was the source of human evil. Aristotle said that poverty gives rise to crime and rebellion. Great crimes are not committed to obtain what is necessary for life, but for luxury. Criminology is a branch of science that emerged in the 19th century, which is essentially a science that studies the causes and causes of crime. In another sense, seen from a criminological perspective, crime is any specific act or deed whose action is approved by society to be interpreted as a crime. This means that every crime does not have to be formulated first in a criminal law regulation. So every act that is anti-social, detrimental and annoying to society, can be said to be a crime in criminological terms. In this modern era, human growth and development seems to know no boundaries of space and time because it is supported by the rapid flow of information and knowledge of technology. New discoveries in the field of science and technology have a direct influence on human outlook on life which can ultimately change the human outlook on life.
Algorithms for Constructing Optimal Covering of Planar Figures with Disks Sets of Linearly Different Radii

The problem of optimal covering of plane figures with sets of a fixed number of different circles is considered. We suppose that each circle has a radius equal to the sum of the parameter common to all and its individual number. The main aim of the paper is to develop algorithms that allow the construction of a covering with a minimum common parameter. It is proved that the problem can be reduced to minimizing a function of several variables depending on the coordinates of the centers of the circles. The zones of influence of points serving as the centers of circles for a fixed set of individual numbers have been studied. Iterative algorithm for solving the problem is proposed using the concepts of the Chebyshev center and a generalization of the Dirichlet zone. The possibilities of applying the results of the article to the construction of sensor networks are shown.
Microsoft building footprint application To detect human exposure due to tsunami

Tsunami events at night are more prone to causing fatalities because humans are resting in residential buildings (houses). In this study, residential buildings were extracted using the Microsoft Building Footprint (MBF), which resulted from applying artificial intelligence technology. This study aims to analyze the number of people exposed to tsunamis at night using MBF. The tsunami modeling was carried out using the Berryman method. Sentinel 2-A Image extracted from Google Earth Engine. The results of the inundation modeling analysis show that the total inundated area is 717 Ha or 17.34% of the total area. The results of the MBF accuracy analysis on the entire data are a Precision of 99.02%, Recall of 98.40%, and F1 score of 98.71%. The results of the MBF error analysis are False Positive 0.97%, False Negative 1.60%, and Intersection of Union 0.12%. The number of people exposed is 2,749, or 6.32% of the total population.
MOTIVASI BELAJAR SISWA PANTI ASUHAN AISYIYAH SIDOARJO : STUDI KASUS PADA PANTI ASUHAN AISYIYAH SIDOARJO

This study aims to determine the types of student motivation at the Aisyiyah Sidoarjo Orphanage, the factors that influence student learning motivation at the Aisyiyah Sidoarjo Orphanage, and the efforts made to increase student motivation. This research was conducted with a qualitative descriptive approach. The subjects in this study were 2 teachers, 3 students, and 1 caregiver at the Aisyiyah Sidoarjo Orphanage. The research results show that there is intrinsic and extrinsic motivation, the factors that influence student learning motivation are interest and lack of support facilities. Efforts are given to increase student motivation is to provide objective value, fulfillment of infrastructure facilities, and intensive assistance to students.
Deriving models of change with interpretable parameters: linear estimation with nonlinear inference

In the modeling of change over time, there is often a disconnect between developmental theories advanced in substantive research and statistical models specified in longitudinal analysis. That is, theory is understood and advanced in terms of meaningful developmental quantities (e.g., peaks, inflections, timing, and tempo) while common polynomial models estimate the effect of powered terms of time in a linear, additive form. This linear parameterization approach has many advantages, especially its computation efficiency in obtaining stable results, but the quantities estimated in these models are often difficult to directly connect to theoretical ideas of change over time. To bridge the gap between estimation and theory development, I propose a series of approaches for linear estimation with nonlinear inference (LENI), where the results of the stable, easily-estimated linear model are converted through a set of principled transformation functions into nonlinear estimates which align more closely with theoretical quantities of interest.I first lay out how to derive these interpretable nonlinear parameters, then show how to transform the results of the linear model – including fixed and random effects and the conditional effects of covariates – into the effects we would have obtained by fitting a nonlinear version of the model. I conclude by summarizing a linearized structural equation model approach which can be flexibly applied to model any known nonlinear target function into a linearly-estimable model. I conclude with recommendations for applied researchers and directions for fruitful future work in this area.
MEANS OF INFORMATIONAL AND TECNHOLOGICAL SERVICE FOR INSTRUCTORS' PROFESSIONAL REPUTATION METRICS

The article presents a content analysis of modern sciences and fields of knowledge such as scientometrics, pedagogical qualimetry, sociometry, bibliometrics, and cybermetrics. It suggests a set of means of informational and tecnhological services – software to organize preparations, tools for communication with consumers of educational services, and tools for educational and scientific project development. The structure of instructors' professional reputation metrics is revealed. Key words: professional reputation metrics, informational and technological service, means of informational and technological service.
Evaluation of Drainage Network System (Case Study of Pangkajene Kepulauan State Agricultural Polytechnic Campus Area)

The purpose of this research is to identify the condition of highway drainage channels and evaluate the existing highway drainage network system in accommodating runoff discharge, channel dimensions and flow direction in channels that occur inundation in the Pangkajene Islands State Agricultural Polytechnic Campus area which is expected to help solve flooding problems in the area. The research method used is descriptive quantitative, which is a method of calculation and description of the results of field data processing. Secondary data collection includes; rainfall data for the last 10 years, obtained from the Department of Resources; topographic maps, drainage network maps and situation maps, obtained from the Politani Pangkep Campus planning section. Primary data collection is obtained from direct measurements in the field using a Waterpass measuring instrument to measure elevation and channel length. The results of this study found that the drainage capacity &lt; 2-year and 5-year return period Plan Flood Discharge, so planning is needed to increase drainage capacity
Holographic Visual Cues for Mission Task Elements

Abstract Mission Task Elements (MTEs) are commonly used to evaluate the Handling Qualities (HQs) of rotorcraft. However, the traditional approach of physical ground courses faces challenges due to evolving rotorcraft designs and flight profiles. Infrastructure limitations and lack of flexibility for research pose significant obstacles, particularly for high-speed tasks. The emergence of new air vehicles, such as eVTOLs in the civil sector and Future Vertical Lift configurations in the military domain, requires an adapted visual cueing process for previously unaddressed mission profiles. This paper proposes an innovative solution: an Augmented Reality (AR) system utilizing a Head-Mounted Display to create virtual MTE courses called Holographic Visual Cues (HVCs). A piloted simulation campaign performed at DLR's AVES simulator compares the effectiveness of HVCs with dome projection-based visual cues, evaluating the performance of the AR system. The objective is to validate the efficacy of this technology for HQ evaluations. The initial results suggest that test pilots responded positively to this approach, indicating its potential as a more adaptable and efficient method for MTE visual cueing.
Analysis of the Inactive Archive Storage System at PT Supra Nusa Kencana

Archive storage systems are divided into two, namely active and inactive archive storage systems. This research was conducted in order to find out how good archive management is assessed from the archive storage system, especially in the inactive archive storage system. This research was conducted using a descriptive qualitative approach, with data collection techniques in the form of observation, interviews, documentation and article searches, as well as using purposive sampling. The research location is Jalan Baung Raya, Kebagusan, Pasar Minggu, South Jakarta. The archive management system at PT Supra Nusa Kencana is running well, focusing management on four stages, namely structuring archives, borrowing archives, maintaining archives, and destroying archives. The inactive archive storage system is also working well.