Reviewerly/exHarmony huggingface dataset:
Statistical Investigation of the Relationship between Gold and Associate Minerals: A case Study of Kagara Area of Niger State Nigeria Soil

In the Kagara region of Niger State, north-central Nigeria, an investigation was conducted into the gold occurrence and availability of other economic-benefit associate minerals. 39 samples from the study area were subjected to fire assay analysis and multi-element analysis to determine the gold and other mineral recovery in the case study formation. Statistical examination Methods of Pearson correlation and R-mode varimax rotated factor analysis were used to interpret the results. The analysis revealed that the recovered gold (Au) had a grade between 0.01g per tonne and 0.19g per tonne. Li was also identified as the associate mineral with the lowest quantity, with a range of 1-20% and a mean value of 8.49%, whereas Manganes displayed some skewness with a minimum value of 156 and a maximum value of 3080 ppm. According to the Pearson correlation analysis, Lithium and Magnesium have a moderately positive correlation, indicating that they come from the same source. In addition, Mo and Ni have a strong positive correlation whereas Au and Na have a weak positive correlation. The factor analysis performed on the gold and associated mineral occurrences revealed that the deposit had been significantly altered by both environmental and mineralization factors in the study area's soil. Importantly, the study demonstrates that an associated mineral with gold has substantial economic value. Considering the capital and operating costs required for the exploration and exploitation of gold-bearing soil and rock, it has been determined that the refining of other associate minerals to improve the cost-benefit ratio is highly advantageous.
Patentability Analysis of Artificial Intelligence and Big Data Patent Applications

With the flourishing development of artificial intelligence and big data, the number of patent applications has also increased significantly. Since most improvements in the development of artificial intelligence and big data involve mathematical algorithms improvements, the patentability of such developments have been controversial in many countries. The purpose of the patent law is to protect technological innovation, but it does not clearly define what constitutes technology. Whether a patent application constitutes a technical solution is a major reason for the dispute over whether artificial intelligence and big data patent applications can be granted patent rights. This article discusses whether this type of applications can be granted patent rights from the standpoint of patent legislation and the three levels and three dimensions involved in the artificial intelligence patent applications, and gives the relevant analysis of the patentability.
Analysis of Evaluation in Artificial Intelligence Music

The widespread application of artificial intelligence (AI) technology has profoundly changed various industries, including the music field. The application of AI technology in music creation and evaluation has already begun to show its impact, and this trend is expected to drive the development of the music industry. Specifically, AI technology can generate new music compositions by learning and simulating the styles and techniques of musicians in music creation. In terms of music evaluation, AI technology can objectively evaluate the quality and style of music compositions by analyzing elements such as pitch, rhythm, and chords, and provide objective evaluations of music compositions.
Brief reflections on the role of forensic evidence in the italian criminal justice system.

The paper expands on the role of scientific evidences within criminal justice. After a brief analysis on Deoxyribonucleic acid (DNA) and ballistics evidence, it focuses on the issues connected to the use of digital forensics evidence.
Sentimen Analis Thread Investasi Pada Twitter Menggunakan NaïveBayes

Social media is one of the market shares that allows people to consume news or promotions from an investment service because the source data is abundant and is updated every second on the internet. Social media is vulnerable to containing news or information on an investment platform and other forms of investment that are illegal or called fraudulent investments, so to overcome this, an Investment Thread Analyst Sentiment on Twitter is needed. The method used to evaluate investment threads to obtain positive and negative feedback is nave Bayes. The research results received a precision score of 0.69, recall of 0.70, f1-score (f-measure) of 0.74, and accuracy of 0.7.
Comparison of Performance Analysis of Comodo SSL and Let's Encypt SSL (Case Study: Bukalapak and Tokopedia)

The world is becoming connected with the internet, the arrival of the internet has greatly influenced new network technology. The number of internet users is increasing day by day, there is an increase in online transactions. As a growing number of websites have sprung up with easy information transmission facilities, it has been seen that it has caused a huge increase in fraud. Broad e-commerce spectrum (B2B / B2C / C2C), banking, finicial trading and others. Business applications require very high data exchange for security. Secure Sockets Layer (SSL), and its influence on web server performance. Secure SocketLayer (SSL) is the most popular protocol used on the internet to facilitate communication security through authentication, encryption, and decryption. Although the use of SSL provides adequate security, this can cause a decrease in performance compared to insecure protocols as well as a decrease in the percentage of downloads compared to insecure protocols. In this paper, we analyze the performance and impact of SSL on web performance and the type of SSL used by Bukalapak e-commerce and Tokopedia
Using the Discovery Learning Model to Improve Student Learning Outcomes in Indonesian Language Instruction

Teachers utilize the learning implementation plan as a guide to help them concentrate the classroom learning process. The primary content is used to build the learning implementation strategy in detail. The content presented in the learning implementation plan is organized into a learning model. In order to inspire students throughout learning, learning models can provide a more engaging and dynamic learning environment. In this study, the researcher examines how the learning implementation plan for the class XII SMK 01 Bengkulu City observation report uses the Discovery Learning learning paradigm. Two of the meetings included in the learning implementation plan were used for research. Triangulation (mixed) procedures are used in qualitative research methodologies to gather data. Reading the Discovery Learning learning model's steps was how the research's data was collected. Using a triangulator, the validity of the findings data was analyzed. The goal of this study is to enable researchers to explain the phases in the learning model for Discovery Learning that are included in the suitable learning implementation plan. 42 pieces of data were found. You will be able to comprehend the observation report's structure at the first meeting, and at the second, you will give an overview of the report's content.
Floyd-Warshall Algorithm for Web-Based Route Optimization for a Road Network Study in Medan City

The socio-demographic conditions of the region have an influence on the transportation performance in the region. This study aims to determine the shortest path between two specified points, namely the starting and the end point of several sub-districts in Medan city, especially the sub-district of Medan Amplas. To get the output, several steps are needed, namely collecting the coordinates of street names and road lengths, which is obtained from Google maps. Graphs are used to present the data. Data analysis is used by Floyd–Warshall Algorithm with HTML Web Application supported by PHP programming language, JavaScript and MySQL database. The test results using the Black Box method show that the system functionality is 100% running well. For testing, the results of the final W_ij and Z^* final matrix calculations generated by the system is same with the manual calculations. Finally, to test the reliability of the system made, the results of the reliability test show the truth of working well and it can be accepted. There are no path deviations, so that the results are as expected
The Role of Wishtleblowing as Mediation on the Effect of Organizational Justice on Employee Fraud

This study aims to analyze the effect of organizational justice on employee fraud mediated by whistleblowing intention in the context of banking in Jakarta. The research sample consisted of 78 bank employees working in various branches in Jakarta. Data were collected through questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through Smart PLS software. The results showed that organizational justice has a significant influence on employee cheating. This finding indicates that employees' perceptions of fairness in their workplace are directly correlated with the likelihood of fraud. In addition, the study also found that whistleblowing intentions serve as a mediator in this relationship, suggesting that the propensity to report unethical or suspicious activities is a key factor in reducing the incidence of fraud. These results have important implications for bank management in developing fraud prevention strategies, particularly through enhancing organizational justice and promoting a whistleblowing culture. This study provides new insights into the dynamics of employee fraud and its prevention mechanisms, highlighting the important role of organizational justice and whistleblowing intentions in the context of banking in Jakarta
A proposed Hyper-Heuristic optimizer Nesting Grey Wolf Optimizer and COOT Algorithm for Multilevel Task

It can be extremely difficult to find the optimal solution in many complex optimization problems. The goal of optimization algorithms in such cases is to locate a feasible solution that is as close as possible to the optimal one. These algorithms are called metaheuristic optimization algorithms and the majority of them take their inspiration from nature and work to solve challenging problems in a variety of fields. In this paper, a combination between GWO and Coot algorithm was proposed. The effectiveness of the GWO algorithm has been demonstrated in many fields, including engineering and medicine. However, GWO has a disadvantage: the potential to enter the local minima due to a lack of diversity. GWO and the Coot algorithm were merged to fix this flaw. Ten benchmark functions were used to evaluate the performance of this hybrid technique, and its results were compared to those of other common optimization algorithms, including GWO, Cuckoo Search (CS), and the Shuffled Frog Leaping algorithm (SFLA). The results show that the suggested algorithm can provide results that are both competitive and more consistent than the other algorithms in most test functions.
The Influence of the Jigsaw Learning Model on the Speaking Skills of Class V Students Theme 3 Maintaining Human Respiratory Organs at SDN 122390 Pematang Siantar

The aim of this research is to see the effect of Jigsaw learning on the speaking skills of class V students on theme 3 Maintaining Human Respiratory Organs at SDN 122390 Pematang Siantar. This type of research uses experimental research methods, which is a quantitative research method. This research design uses Pre-Experimental Design, where the research design is not yet a true experiment, because there are external variables that influence the shape of the dependent variable. The results of the research showed that students' speaking skills obtained an average pretest score of 58.91, whereas after being given treatment using the Inquiry model, an average posttest score of 81.30 was obtained and there was a significant influence of using the Jigsaw Type Cooperative learning model on student learning outcomes. It is known that the significance value (2-tailed) is 0.000. Then 0.000 &lt; 0.05. This can be concluded to mean that Ho is rejected and Ha is accepted, which means that there is an influence of the Jigsaw cooperative learning model on the speaking skills of class V students, Sub Theme 3 Maintaining the Health of Human Respiratory Organs at SD N 122390 Pematangsiantar.
Demystifying the Morphosyntactic Features of the Tagalog and English Languages: A Contrastive Analysis

This study was conducted to analyze the morphological, syntactical, and morpho syntactical features of the Tagalog and English languages. This study employed a qualitative contrastive analysis research design. According to the findings, the two languages are made up of distinct morphological components: grammatical inflection is comprised of inherent inflections, pronoun and adjective inflections, and declensions; derivational affixation is comprised of verb conjugations; and grammatical markers are comprised of bound morphemes. When it comes to the syntactic patterns, Tagalog adheres to the predicate-initial pattern, also known as the V-S-O syntactic pattern, whereas English adheres to the S-V-O pattern. In contrast, in terms of morphosyntactic feature, the subject does not have a direct effect on the verb in Tagalog, whereas in English, the subject has a significant impact on the verb. Constructions using the active and passive voices are also different between the two.
Studi Kasus Implementasi Teknologi Chatbot sebagai Asisten Virtual dalam Menjawab Pertanyaan Mahasiswa di Lingkungan Kampus

ABSTRACT.&#x0D; This research investigates the implementation of chatbot technology as a virtual assistant in the campus environment to answer students' questions. A case study was conducted at a large university with a significant number of students. The research method used was field research with a qualitative approach. Data was collected through interviews with campus information system administrators and several students who used the chatbot. Data was also collected from chatbot conversation logs. The results of the research show that the implementation of chatbot technology has a positive impact on students. The chatbot is able to provide quick and accurate answers to routine questions such as class schedules, classroom locations, and other academic information. The availability of the chatbot service 24/7 is well-received by students as it allows them to obtain information without waiting for office hours. However, there are some challenges in implementing this chatbot. The main issue is related to natural language understanding, especially for complex or ambiguous questions. Students prefer to talk to administrative staff in certain situations that require more detailed explanations. This research provides a significant contribution to understanding the implementation of chatbots as virtual assistants in higher education. Further efforts are needed to improve the artificial intelligence of the chatbot to respond to questions more effectively. In facing challenges and potentials, universities and other educational institutions can use these findings as a guide to optimize the use of chatbots and enhance students' services and experiences in the future. By continuously improving and developing chatbot technology, it is hoped to further enhance the efficiency and accessibility of information for students in the campus environment.&#x0D; Keywords: Chatbot, Virtual Assistant, Students
To What Extent is Virtual Reality Therapy Efficient as A Treatment of Social Anxiety Disorder?

The effectiveness of virtual reality therapy as a therapeutic intervention for individuals with social anxiety disorder is investigated in this study. This research was created using a meta-analytical method and a review of several studies and conclusions by prior experts. It seeks to obtain a better knowledge of how virtual reality treatment impacts key symptoms and indications of social anxiety disorder, such as self-esteem and social interaction abilities. It also contributes to the current body of research and advances understanding of virtual reality therapy's potential as a cutting-edge, effective treatment for psychiatric issues. The study also incorporates culturally sensitive adaptations to VR therapies that take into consideration individuals' diverse cultural backgrounds. Additionally, the study looks at VR therapy's cost, flexibility, and economic factors. In conclusion, this research paper offers a thorough grasp of the possible uses and restrictions of virtual reality treatment.
Netizen’s Language Behavior in Response about Government

This research is concerned with netizens’ comments to respond to government policies. Based on the discussion, the results were obtained by Indonesian netizens commenting on government policy posts using the words cursing, satirising, cursing, demeaning, and saying politely. Factors causing the netizen's language behaviour: 1) The leader is not consistent with the statement given. 2) Freedom of opinion on social media because they don't know each other. 3) Most netizen accounts are fake. 4) For real accounts, still keep their word. 5) Disappointment of netizens with laws that are not in favour of the common people.
Automated De-Identification of Arabic Medical Records

As Electronic Health Records (EHR) become ubiquitous in healthcare systems worldwide, including in Arabic-speaking countries, the dual imperative of safeguarding patient privacy and leveraging data for research and quality improvement grows. This paper presents a first-of-its-kind automated de-identification pipeline for medical text specifically tailored for the Arabic language. This includes accurate medical Named Entity Recognition (NER) for identifying personal information; data obfuscation models to replace sensitive entities with fake entities; and an implementation that natively scales to large datasets on commodity clusters. This research makes two contributions. First, we adapt two existing NER architectures— BERT For Token Classification (BFTC) and BiLSTM-CNN-Char – to accommodate the unique syntactic and morphological characteristics of the Arabic language. Comparative analysis suggests that BFTC models outperform Bi-LSTM models, achieving higher F1 scores for both identifying and redacting personally identifiable information (PII) from Arabic medical texts. Second, we augment the deep learning models with a contextual parser engine to handle commonly missed entities. Experiments show that the combined pipeline demonstrates superior performance with micro F1 scores ranging from 0.94 to 0.98 on the test dataset, which is a translated version of the i2b2 2014 de-identification challenge, across 17 sensitive entities. This level of accuracy is in line with that achieved with manual de-identification by domain experts, suggesting that a fully automated and scalable process is now viable.
Performance Implications of Using Unrepresentative Corpora in Arabic Natural Language Processing

Wikipedia articles are a widely used source of training data for Natural Language Processing (NLP) research, particularly as corpora for low-resource languages like Arabic. However, it is essential to understand the extent to which these corpora reflect the representative contributions of native speakers, especially when many entries in a given language are directly translated from other languages or automatically generated through automated mechanisms. In this paper, we study the performance implications of using inorganic corpora that are not representative of native speakers and are generated through automated techniques such as bot generation or automated template-based translation. The case of the Arabic Wikipedia editions gives a unique case study of this since the Moroccan Arabic Wikipedia edition (ARY) is small but representative, the Egyptian Arabic Wikipedia edition (ARZ) is large but unrepresentative, and the Modern Standard Arabic Wikipedia edition (AR) is both large and more representative. We intrinsically evaluate the performance of two main NLP upstream tasks, namely word representation and language modeling, using word analogy evaluations and fill-mask evaluations using our two newly created datasets: Arab States Analogy Dataset (ASAD) and Masked Arab States Dataset (MASD). We demonstrate that for good NLP performance, we need both large and organic corpora; neither alone is sufficient. We show that producing large corpora through automated means can be a counter-productive, producing models that both perform worse and lack cultural richness and meaningful representation of the Arabic language and its native speakers.
Machine Translation of Omani Arabic Dialect from Social Media

Research studies on Machine Translation (MT) between Modern Standard Arabic (MSA) and English are abundant. However, studies on MT between Omani Arabic (OA) dialects and English are very scarce. This research study focuses on the lack of availability of an Omani dialect parallel dataset, as well as MT of OA to English. The study uses social media data from X (formerly Twitter) to build an authentic parallel text of the Omani dialects. The research presents baseline results on this dataset using Google Translate, Microsoft Translation, and Marian NMT. A taxonomy of the most common linguistic errors is used to analyze the translations made by the NMT systems to provide insights on future improvements. Finally, transfer learning is used to adapt Marian NMT to the Omani dialect, which significantly improved by 9.88 points in the BLEU score.
UFAL-ULD at BLP-2023 Task 1: Violence Detection in Bangla Text

In this paper, we present UFAL-ULD team’s system, desinged as a part of the BLP Shared Task 1: Violence Inciting Text Detection (VITD). This task aims to classify text, with a particular challenge of identifying incitement to violence into Direct, Indirect or Non-violence levels. We experimented with several pre-trained sequence classification models, including XLM-RoBERTa, BanglaBERT, Bangla BERT Base, and Multilingual BERT. Our best-performing model was based on the XLM-RoBERTa-base architecture, which outperformed the baseline models. Our system was ranked 20th among the 27 teams that participated in the task.
Assessing Political Inclination of Bangla Language Models

Natural language processing has advanced with AI-driven language models (LMs), that are applied widely from text generation to question answering. These models are pre-trained on a wide spectrum of data sources, enhancing accuracy and responsiveness. However, this process inadvertently entails the absorption of a diverse spectrum of viewpoints inherent within the training data. Exploring political leaning within LMs due to such viewpoints remains a less-explored domain. In the context of a low-resource language like Bangla, this area of research is nearly non-existent. To bridge this gap, we comprehensively analyze biases present in Bangla language models, specifically focusing on social and economic dimensions. Our findings reveal the inclinations of various LMs, which will provide insights into ethical considerations and limitations associated with deploying Bangla LMs.
Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through Dialect Identification using Transformer-based Approach

In this paper, we present our approach for the “Nuanced Arabic Dialect Identification (NADI) Shared Task 2023”. We highlight our methodology for subtask 1 which deals with country-level dialect identification. Recognizing dialects plays an instrumental role in enhancing the performance of various downstream NLP tasks such as speech recognition and translation. The task uses the Twitter dataset (TWT-2023) that encompasses 18 dialects for the multi-class classification problem. Numerous transformer-based models, pre-trained on Arabic language, are employed for identifying country-level dialects. We fine-tune these state-of-the-art models on the provided dataset. Ensembling method is leveraged to yield improved performance of the system. We achieved an F1-score of 76.65 (11th rank on leaderboard) on the test dataset.