Reviewerly/exHarmony huggingface dataset:
The Impact of Leadership on the Growth of ICT Companies

Nowadays, one of the major purposes of any company is to survive in the competitive business markets. This purpose would be achievable through improving and developing growth in most cases. Prior studies suggest that leadership is a vitally important factor in achieving growth in any company. Some researchers studied leadership topics as one of the significant motivations for developing and improving the growth of a company and agreed that understanding the role of leadership and its effect on the growth of the company is influential. In all countries, Information Communication Technology (ICT) companies are recognized as significant growth drivers by leaving considerable impacts on that. According to the literature, growth is a determining factor for the productivity of any company. Based on the literature of the ICTs, weak and insufficient leadership skills are considered as the main factors that cause failure in achieving the planned growth and development of ICT. Consequently, the goal of this study is to clarify how leadership affects the development of ICT enterprises.
Comparative Analysis ofK-Nn, Naïve Bayes, and logistic regression for credit card fraud detection

Introduction:This paper highlights the outcome of the comparative study of “Various Machine learning algo-rithms namely K-NN, Naive Bayes, and Logistic Regression for Credit Card Fraud Detection” carried out based on a dataset taken from UCI.com in 2022-23 at Manav Rachna International Institute of Research and Studies.Problem: Credit card fraud is still rife today and the modes are increasingly varied. Quite often we hear of fraud cases that cause irreplaceable injury to banks and financial institutions which cannot be compensated in terms of costs. To avoid scams with various modes of credit cards, we must be able to identify and find out the modes often used by fraudsters. This scheme liberates such financial institutions and banks with complete and appropriate information using Machine Learning Techniques, not only about the modes that scammers or fraudsters often use but also ways to protect against such frauds.Objective: The present paper discusses the various machine learning models based on classification and re-gression, namely K-Nearest Neighbors, Naïve Bayes, and Logistic Regression, which are successfully able to achieve the classification accuracy of 80% using Logistic Regression with a Precision of 78%, Recall of 100%, and F1-Score of 88% for fraudulent credit card transactions. Methodology: The comparative analysis demonstrates that for Precision, Recall, and Accuracy parameters, the K-Nearest Neighbor is a better approach for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes. Results:The accuracy is marginal high in Logistic Regression but the False Positive parameters are not able to identify the imbalanced data; therefore, they disguise the results and accuracy of Logistic Regression and K-Nearest Neighbor deems fit for such cases.Conclusion: This scheme depicts the automated fraud classification systems using machine learning techni-ques, namely K-Nearest Neighbor, Logistic Regression, and Naive Bayes, to produce a model that can distin-guish valid and invalid credit card transactions.Originality:Through this research, the most relevant features are used to go through the visualization of accu-racy with the confusion matrix, and accuracy calculations are obtained from the dataset used.Limitations:Deep learning techniques could have been used to fetch even better results.
Using Scrabble as a Gamification to Enhance Students’ Motivation and Vocabulary Acquisition: Teachers’ Perspective

Technology has aided in the development of learning media with a variety of media creations as media for students to be more engaged in learning to use a laptop or smartphone with Android capability by installing the application in question. In this study, the use of games or gamification applications in learning English, specifically digital scrabble games, is discussed. This study attempts to investigate the use of scrabble to support students' motivation and vocabulary acquisition based on teachers' perspectives. The subjects for this research were 12 (twelve) English teachers of North Sumatera. In conducting this research, the researcher used the case study methodology. The data were collected through an open questionnaire requiring a lengthy response from the respondent and a closed questionnaire with an alternative answer decided by the questionnaire's creator, namely yes or no. The results showed that, in accordance with the teachers' perceptions, utilizing scrabble can motivate students to study because they become interested, feel challenged while using it, and want to keep playing. Due to the students' ability to learn and assimilate new words through scrabble, their vocabulary acquisition also enhanced.
Is ChatGPT Humanly Irrational?

Abstract We delve into the fascinating crossroads of artificial intelligence (AI) and cognitive science, spotlighting the OpenAI advanced language model, ChatGPT. Renowned for generating human-like text, ChatGPT has been widely used in various applications. However, its ability to replicate human cognitive processes, particularly decision-making behavior, remains largely unexplored and untapped. We evaluate ChatGPT's decision-making patterns and show that they strikingly mirror those of human subjects, even patterns traditionally termed ''irrational'' under standard economic theory. This finding challenges the prevailing assumption that AI systems operate solely on rational computations. It suggests that, despite its algorithmic nature, ChatGPT can reflect human cognitive biases when simulating human roles, thus adding a new dimension to our understanding of AI behaviour. Our result places AI models like ChatGPT in a broader context of cognitive science, indicating their potential to mimic not just human language but also human cognitive processes. From a broader perspective, our findings underscore the capacity of AI in behavioral research and stimulate a necessary dialogue on AI design, transparency, and ethical implications. Our study bridges human and machine intelligence, highlighting the potential of AI to enhance our understanding of decision-making processes in artificial agents.
Generation of Medical Reports from Chest X-Ray Images using Multi Modal Learning Approach

Objectives: The purpose of this research is to use a multimodal learning approach to perform suggestive diagnosis and generate reports based on chest X-rays and associated data. This research falls under the Vision Language Generation, or VLG, which in this case produces reports given a chest X-ray. Methods: We use a Transformer model with CNN and RNN as part of a multimodal architecture in addition to greedy beam search to generate report impressions in order to construct a proper transformer model capable of producing precise report impressions. We will also collect reports and chest X-rays from the dataset in order to evaluate the adaptability of the model: Indiana University’s Open-I CXR(1). This will be done so that the results can be evaluated and the model’s ability to produce accurate and grammatically correct impressions of reports can be improved. Findings: We achieved better BLEU-1 and BLEU-2 scores compared to the research selected for this research. We have been able to achieve following BLEU scores through our proposed model: BLEU-1 = 0.592, BLEU-2 = 0.422, BLEU-3 = 0.298, BLEU-4 = 0.205. Novelty: We propose a Transformer model to generate report impressions. This transformer model has CNN as an encoder and RNN as a decoder with attention mechanism on top of it. Additionally, greedy beam search has been used to get grammatically correct sentences. Keywords: Chest XRay; Transformers; OpenI CXR; CNN; RNN
ORGANIZATION ОF EXPERT ECOSYSTEM ОN DIGlTAL PLATFORM USING SYMBIOTIC INTELLIGENCE

The formation of expert ecosystems is the next, natural stage in the formation of expert and analytical activities. The possibility of such implementation is opened uр thanks to research in the field of ecosystem design based оп digital platforms, а platform state, reflection of the process of transforming artificial intelligence systems into technical objects and symbiotic intelligence systems, revision of organizational and technological aspects of expert activities. According to the authors, the network expert and analytical platform and subject (profile) digital platforms, whose асtivities сап Ьe optimized thanks to symbiotic intelligcnce, should become integra1 elements of the expert ecosystem.
Expansion and Case Study of Topic Modeling Using Semantic Network

In this paper, we proposed an extended topic modeling technique that enables clearer topic interpretation by converging the semantic network analysis technique and topic modeling technique, which are techniques that can analyze text documents.BR The extended topic modeling enables to clearly set the semantic interpretation of the topic by extracting the primary and secondary keywords for each topic derived by Topic modeling and Semantic network analysis. The primary keywords are extracted by the existing topic modeling method, and the secondary keywords are extracted by applying semantic network analysis to a set of partial text documents corresponding to each topic.BR As a result of applying the extended topic modeling technique to text documents searched with
Business Administration Research Trends in Korea Using Social Network Analysis

As the business environment becomes ambiguous in the boundaries between industries, many changes and attention are focused on business administration studies. In the Big Blur environment, a new organization is managed and operated to achieve the purpose of the company, and many changes are expected in management research issues. This study aims to analyze research trends by major functions of Korean business administration through trend analysis of business administration research. Six elements of Accounting, Finance, Human Resources, Management Information Systems, Marketing, Production and Operation, and KCI
The Blackbox of Social Media Content Moderation A first look into a novel Twitter dataset

Out of all social media platforms, Twitter has been the most generous in sharing its data with academia and industry to date. However, like other platforms, Twitter has been increasingly criticized for its lack of transparency regarding its content moderation practices and the reasons behind their decisions to restrict or delete certain content. Addressing these concerns, in September 2022, Twitter launched the `Twitter Moderation Research Consortium' (TMRC), which grants approved researchers access to data on numerous accounts that have been deleted due to violations of Twitter’s Platform Manipulation and Spam Policy. In this paper, we conduct an in-depth investigation of Twitter's moderation decisions in line with their community guidelines using the TMRC14- and TMRC15-datasets, which contain Tweets and user information belonging to accounts deleted from 2016 to October 2022. By employing descriptive analytics, network analysis, and topic modeling, we uncover alignment between Twitter's suspension decisions and their reported moderation practices while also noting instances of opacity and geographical disparities in moderation reasons. Despite the noisiness of these datasets, our findings predominantly confirm the concurrence of account suspensions with community guidelines, shedding some first light on the black box of social media content moderation. However, the results also underscore the need for greater transparency, suggesting avenues for understanding the complexities surrounding content moderation practices and emphasizing the need for continued investments in transparency initiatives like TMRC.
flempar: An R-package for analyzing data from the Flemish Parliament

This note introduces flempar, an R-package designed to streamline data acquisition from the Flemish Parliament’s open data API. flempar provides an interface that allows for easy extraction and linking of data, ranging from details of parliamentary proceedings to legislators’ bibliographies. The package aims to reduce the complexity of navigating the numerous API endpoints and the unnesting of the data objects, allowing users to focus on actually working with the data. Implemented in R, the package offers broad applicability for scholars, journalists, and data scientists interested in Flemish parliamentary activities. We provide a comprehensive guide to the package’s installation, scope, and data extraction workflow.
A Multi-Encoder Model for Automatic Code Comment Generation

Abstract Automatic code comment generation is an important research topic in software engineering, which aims to help developers understand the source code. However, this task is challenging due to the issues of long dependencies, source code structure information, and out-of-vocabulary (OOV) words. In this paper, we propose HCCM, a novel neural network model that uses three encoders to generate natural language comments for Java methods. The proposed model incorporates three novel techniques: (1) the S-SBT method to encode the abstract syntax tree of the source code; (2) a pointer generation network to copy OOV words from the source code; and (3) a convolutional neural network to capture local features of the source code tokens. We evaluate our model on a state-of-the-art large-scale Java dataset and show that it outperforms the existing methods on several metrics such as BLEU, METEOR, and ROUGE.
Pelatihan Pengisian Beban Kerja Dosen (BKD) Berbasis Sistem Informasi Sumber Daya Terintegrasi (SISTER)

Lecturer Workload (BKD) is a detailed report on Tri Dharma Higher Education activities carried out by lecturers which includes education and teaching activities, research, community service, and supporting activities which are reported every semester. The purpose of the training activities is to increase lecturers' knowledge, understanding and skills in filling out BKD through SISTER, increase lecturers' understanding of BKD provisions in accordance with applicable operational guidelines, and increase lecturers' awareness of the obligation to complete portfolio evidence for filling in BKD independently through SISTER. The methods used to achieve the training objectives are lecture, practice and discussion methods. The results show that there is an increase in lecturers' understanding of training materials and awareness for filling out the BKD portfolio through SISTER. Recommendations for further activities in order to improve the quality and career development of lecturers, namely training in the publication of scientific articles, training and writing community service activities in community service journals.
Detecting Sybil Attack in Blockchain and Preventing through Universal Unique Identifier in Health Care Sector for privacy preservation

Health care data requires data secrecy, confidentiality, and distribution through public networks. Blockchain is the latest and most secure framework through which health care data can be transferred on the public network. Blockchain has gained attention in recent year’s due to its decentralized, distributed, and immutable ledger framework. However, Blockchain is also susceptible to many attacks in the permission less network, one such attack is known as Sybil attack, where several malicious nodes are created by the single node and gain multiple undue advantages over the network. In this research work, the Blockchain network is created using the smart contract method which gets hampered due to Sybil attack. Thus, a novel method is proposed to prevent Sybil attack in the network for privacy preservation. Universal Unique Identifier code is used for identification and prevention of the Sybil attack in the self-created networks. Results depict that proposed method correctly identifies the chances of attack and the prevention from the attack. The approach has been evaluated on performance metrics namely, true positive rate and accuracy which were attained as 87.5 % and 91% respectively, in the small network. This demonstrates that the proposed work attains improved results as compared to other latest available methods.
Simplices in thin subsets of Euclidean spaces

Let $\De$ be a non-degenerate simplex on $k$ vertices. We prove that there exists a threshold $s_k<k$ such that any set $A\subs \R^k$ of Hausdorff dimension $dim\,A\geq s_k$ necessarily contains a similar copy of the simplex $\De$.
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition

Traffic signs play a critical role in road safety and traffic management for autonomous driving systems. Accurate traffic sign classification is essential but challenging due to real-world complexities like adversarial examples and occlusions. To address these issues, binary neural networks offer promise in constructing classifiers suitable for resource-constrained devices. In our previous work, we proposed high-accuracy BNN models for traffic sign recognition, focusing on compact size for limited computation and energy resources. To evaluate their local robustness, this paper introduces a set of benchmark problems featuring layers that challenge state-of-the-art verification tools. These layers include binarized convolutions, max pooling, batch normalization, fully connected. The difficulty of the verification problem is given by the high number of network parameters (905k - 1.7 M), of the input dimension (2.7k-12k), and of the number of regions (43) as well by the fact that the neural networks are not sparse. The proposed BNN models and local robustness properties can be checked at https://github.com/ChristopherBrix/vnncomp2023_benchmarks/tree/main/benchmarks/traffic_signs_recognition. The results of the 4th International Verification of Neural Networks Competition (VNN-COMP'23) revealed the fact that 4, out of 7, solvers can handle many of our benchmarks randomly selected (minimum is 6, maximum is 36, out of 45). Surprisingly, tools output also wrong results or missing counterexample (ranging from 1 to 4). Currently, our focus lies in exploring the possibility of achieving a greater count of solved instances by extending the allotted time (previously set at 8 minutes). Furthermore, we are intrigued by the reasons behind the erroneous outcomes provided by the tools for certain benchmarks.
Identifying Vulnerabilities in Smart Contracts using Interval Analysis

This paper serves as a progress report on our research, specifically focusing on utilizing interval analysis, an existing static analysis method, for detecting vulnerabilities in smart contracts. We present a selection of motivating examples featuring vulnerable smart contracts and share the results from our experiments conducted with various existing detection tools. Our findings reveal that these tools were unable to detect the vulnerabilities in our examples. To enhance detection capabilities, we implement interval analysis on top of Slither [3], an existing detection tool, and demonstrate its effectiveness in identifying certain vulnerabilities that other tools fail to detect.
Linguistic Analysis of Dove Indonesia’s Website and Its Localization

This paper analyzes the website localization of Dove Indonesia’s website. The study, then, analyzes the adaptation and the translation technique of the translation part that includes the home page and its first next link of the website. In analyzing website localization, the theories that are used are the theory of website localization by Pym (2012) and the translation technique by Molina and Albir (2002). This study found that the website is highly localized with both adaptation and translation. Adaptation includes changes in data and time format, legal inventions, and content. The translation itself uses single and duplet translation techniques. Single technique includes amplification, borrowing, compensation, discursive creation, generalization, literal translation, modulation, particularization, reduction, and transposition. Duplet technique includes compensation and borrowing and reduction and amplification. However, the mostly used translation technique is a single technique, literal translation.
Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

The purpose of this study is to show how ensemble learning-driven machine learning algorithms outperform individual machine learning algorithms at predicting ovarian cancer on a biomarker dataset. Additionally, this study provides model explanations using explainable Artificial Intelligence methods, The method involved gathering and combining 49 risk factors from 349 patients. We hypothesize that ensemble machine learning systems are superior to individual Machine Learning systems in predicting ovarian cancer. The Machine Learning system consists of five individual Machine Learning and five ensemble Machine Learning systems were trained using K-10 cross validation protocols. These training models were then used to predict the development of benign ovarian tumors and ovarian cancer tumors patients. The AUC and Accuracy metrics for ensemble machine learning increased by 19% and 16%. The MCC and Kappa scores for ensemble Machine Learning also increased over individual machine learning by 29% and 33%, respectively. As a result, we draw the conclusion that ensembled-based algorithms outperform individual machine learning in terms of ovarian carcinoma prediction.
A Hybrid Optimization Approach for Neural Machine Translation Using LSTM+RNN with MFO for Under Resource Language (Telugu)

NMT (Neural Machine Translation) is an innovative approach in the field of machine translation, in contrast to SMT (statistical machine translation) and Rule-based techniques which has resulted annotable improvements. This is because NMT is able to overcome many of the shortcomings that are inherent in the traditional approaches. The Development of NMT has grown tremendously in the recent years but NMT performance remain under optimal when applied to low resource language pairs like Telugu, Tamil and Hindi. In this work a proposedmethod fortranslating pairs (Telugu to English) is attempted, an optimal approach which enhancesthe accuracy and execution time period.A hybrid method approach utilizing Long short-term memory (LSTM) and traditional Recurrent Neural Network (RNN) are used for testing and training of the dataset. In the event of long-range dependencies, LSTM will generate more accurate results than a standard RNN would endure and the hybrid technique enhances the performance of LSTM. LSTM is used during the encoding and RNN is used in decoding phases of NMT. Moth Flame Optimization (MFO) is utilized in the proposed system for the purpose of providing the encoder and decoder model with the best ideal points for training the data.
DEVELOPMENT OF SMARTPHONE APPLICATION TO SUPPORT TSUNAMI EVACUATION

Kamakura is a historical city situated along the Japanese coastline, which is at risk of being devastated by a tsunami due to its relatively flat topography and proximity to the Sagami and Nankai Troughs. Evacuation would play an important role to reduce the potential loss of lives resulting from a tsunami event. While carrying out evacuation drills is helpful to enable participants to take appropriate responses during an actual tsunami event, it is not realistic for all the residents to participate in such drills. Furthermore, as some coastal areas are renowned sightseeing spots, there might be many visitors who do not know the area well and struggle to evacuate in the event of a tsunami (Takabatake et al., 2017; 2020). Thus, the authors developed a smartphone application (App) that supports swift tsunami evacuation, which can be especially helpful for those who do not have sufficient knowledge about evacuation routes and refuge places. The present study aimed to investigate the effectiveness of the developed app, and to clarify its impact on people’s decision making with regards to evacuation behavior.