Reviewerly/exHarmony huggingface dataset:
Innovative blockchain-based Agent digital passport solution

In this paper, we mean by digital health passport a digital passport or immunity passport that stores information about an individual's personal data, health status, travel history, health conditions, and medical and drug records. The proposed innovative scheme can ease travel, ensure free movement, and minimize economic damages. However, protecting personal and digital health passport information during a crisis is challenging. Therefore, this paper aims to propose an agent-based secure decentralized innovative digital health passport protocol that is interoperable, fault-tolerant, and compatible with privacy standards. The proposed solution is known as Secure Mobile Digital Passport Agent (SMDPA), uses: (1) Private Set Intersection (PSI) to (a) protect Digital Health Passport Information (DHPA) against surveillance, especially during the crisis, (b) generate Bit Passing Coin (BPC); (2) blockchain to store serialized SMDPAs copies (3) mobile agent to disseminate DHPA autonomously; (4) Location and time attributes for data minimization. We prototype the proposed solution using the JADE agent framework. The performance measurements show that the result is acceptable.
Democracy in Your Hands!: Practical Multi-key Homomorphic E-Voting

Digitization of elections demands end-to-end security of the overall process and hence, cyber security for elections is an important issue. Distributed blockchain technology in e-voting can only provide verification advantages. However, voters’ authenticity, data confidentiality, and intermediaries represent other major concerns in this regard. Existing secure voting frameworks either provide final voting results with the help of trusted intermediaries or provide vote verifiability in an unencrypted domain. At present, there is no such realistic mechanism to assure full security of vote casting and result declaration from cyber threats. Even Microsoft’s ElectionGuard is not free from post-quantum attacks due to additive ElGamal cryptosystem. To achieve effective guards against cyberattacks, we propose a voting framework, which reduces vote transmission overhead on a per-voter basis and supports post-quantum secure automated vote counting and winner selection without any manual intervention with the aid of multi-key homomorphic encryption. Along with suitable preventive measures against double voting, vote rigging, and coercion effects, we include a secure result deciphering process evading the possibility of result alteration using multi-key approach. Though our scheme is scalable for smaller as well as large organizations/communities, encrypted processing is inherently performance-costly. Hence, to reduce the overall timing overhead, encrypted hierarchical processing with zonal segregation and parallel computation have been incorporated. Our proposed scheme demonstrates party-specific vote counting without rank generation within 4 days and 22 h and vote counting with winner rank calculation within 5 days (for four candidates and one hundred thousand voters) without any manual intervention with the support of suitable distributed computing.
Additive Poisson regression via forced categorical covariates and generalized fused Lasso

In this study, we use the log-linear link function and propose a generalized fused Lasso (GFL) Poisson regression model in which the nonlinear trend is discretely represented by categorical covariates in the additive model. We use the coordinate descent algorithm for the estimation and show that the optimal solution in a coordinate axis can be found explicitly. To demonstrate the proposed approach, we analyze Japanese crime data. Simulation results showed a fitness ratio for true fusion to be more than 90% in total, demonstrating the reliability of the estimates.
Knowledge Discovery to Improve Students’ Class Achievement Using Questionnaires

This study determines whether students were able to acquire the desired level of knowledge and skills, by using class evaluation questionnaires which are administered after each class. The questionnaire serves many purposes, but the more questions asked, the lower the response rate. Therefore, this study proposes a method to discover areas for improvement of classes by analyzing free descriptions in questionnaires through students' individual evaluations. Further, we propose an algorithm for discovering improvement objects and deciphering their directions. The proposed method was applied to a case study, and the result suggests that the proposed method is effective.
Cryptography at the service of organized crime - a challenge for science and practice

Organized crime represents a serious threat to the security, economy, and sometimes for the legal order of the country. Criminal groups not only use the achievements of information and communication technologies (iCT) for the expansion of the organization, communication and management of activities and resources, but also invest, through secret and illegal channels, to develop their own applications of their own applications that they will use for their activities. Organized criminal groups themselves, the most powerful ones, have their iCT experts whose goal is not only to protect communication channels, but also to devise new ways of ?circumventing? and neutralizing police measures and techniques for detecting and monitoring their activities. Members of organized criminal groups to use those communication applications that enable encrypted transmission to electronic devices using the necessary operating system, enabling the installation of the application and the establishment of a connection to the internet in order to enable the necessary protected communication. There are also applications that allow the entire device to be encrypted, making it difficult to find its contents or discover the necessary information and evidence. This paper presents the characteristics of organized crime and their operating modes, with an emphasis on the use of cryptographic techniques. Case studies are illustrating the size and scope of abuse of encryption by organized criminal groups, by data on cases which were processed, as well as challenges in opposing the abuse of cryptography from a technical-technological and legal aspect, after that we provided guideline for improving the mechanisms for reduction of organized crime.
Fostering Intercultural Understanding and Environmental Consciousness in Maritime Education

Maritime education stands at the crossroads of an international industry, necessitating the preparation of cadets who are not only technically proficient but also culturally competent and environmentally conscious. This research delves into the integration of intercultural understanding, environmental perspectives, and English Language Education (ELE) within the English Environment and Habitual English Communication Program (SCCP) at Sekolah Tinggi Ilmu Pelayaran - Jakarta (STIP Jakarta). The study explores the current state of integration, recognising the successes in fostering intercultural understanding and environmental awareness among maritime cadets. Furthermore, it identifies the challenges that require attention, including the need for continuous curriculum development, practical applications, and ongoing language proficiency development. The implications extend to maritime education institutions worldwide, emphasising the importance of cultivating a more holistic and responsive approach to curriculum development. By addressing these challenges and embracing opportunities for improvement, maritime education can better equip cadets to navigate the complexities of the global maritime industry. This research serves as a guide towards a more sustainable and harmonious future in maritime education and the maritime industry.
Breaking the Waves of Language: Unraveling Students’ Challenges with English Clauses in Coastal Environmental Discourse through a Grammar Teaching Technique

The purpose of this research is to investigate the challenges that students encounter when learning English clauses in Coastal Environmental Discourse through the ‘Stepping the 5 Stairs D’ Technique. This study uses a qualitative descriptive method to complete and in-depth describe the conditions as they are, without giving treatment to or manipulating the variables studied. In collecting the data, researchers used questionnaire. The questionnaire Were 17 multiple choices questions about English clauses. And the subjects of this study were 31 English Language Education students from class K1 and K2 of 2021. From this study it was concluded that most of the 4th semester English education students were able to determine correct and incorrect sentences in sentences and it was also found that students’ ability to use ‘stepping the 5 stairs D’ in identifying main clauses and sub clauses was quite good.
The Importance of Reading Skill for Maritime Literacy: A Literature Review

A language is needed for maritime discipline. This research aims to identify the role of language skills, especially reading skills, on learner’s maritime competence. The research method used is a Systematic Literature Review. The articles used in this research are journal articles determined based on criteria from 2013-2023. Articles were obtained from the Google Scholar search page. Based on the literature review, it was found that reading skill is fundamental to improving learners’ maritime literacy. Mastery of specialized vocabulary is of particular concern in maritime-based reading instruction. In reading instruction, instructors should combine theoretical and practical approaches to help develop effective reading skills for maritime literacy.
Sort11 Algorithm: A New Sorting Algorithm

In the Computer Field, data arranging is one of the important task. This paper describes about a new data arranging algorithm, “Sort11 Algorithm” which is compared with bubble sort, selection sort, and insertion sort algorithms. Here input is divided into two parts. First half part has summation position numbers then combine remaining numbers of first half. The first half numbers are arranged by simple comparison based on First-Last Logic. Then second half numbers are distributed with first half sorted numbers based on First-Last logic. Here final sorted list is derived.
Concentration of randomized functions of uniformly bounded variation

A sharp, distribution free, non-asymptotic result is proved for the concentration of a random functionaround the mean function, when the randomisation is generated by a finite sequence of independent data and the random functions satisfy uniform bounded variation assumptions. The specific motivation for the work comes from the need for inference on the distributional impacts of social policy intervention. However, the family of randomized functions that we study is broad enough to cover wide-ranging applications. For example, we provide a Kolmogorov--Smirnov like test for randomized functions that are almost surely Lipschitz continuous, and novel tools for inference with heterogeneous treatment effects. A Dvoretzky--Kiefer--Wolfowitz like inequality is also provided for the sum of almost surely monotone random functions, extending the famous non-asymptotic work of Massart (1990) for empirical cumulative distribution functions generated by i.i.d. data, to, inter alia, settings without micro-clusters proposed by Canal et al.,(2018). We illustrate the relevance of our theoretical results for applied work via empirical applications.
Forecasting Goal-Oriented Activities: A Deep Learning Approach Leveraging Cross-Activity Relationships

Many individuals have difficulty maintaining consistent efforts toward their goals, such as maintaining a healthy lifestyle or mastering a second language. Online platforms have introduced various goal-oriented programs to help enhance motivation and adherence, such as exercise and study challenges. However, these programs often suffer from high dropout rates, diminishing their effectiveness. To intervene in early dropouts and recommend programs with suitable goals, we propose an approach for forecasting individuals’ program goal completion and their actual performance toward completing the goal. Following the design science paradigm, the proposed approach leverages a combination of convolutional neural networks and gated recurrent neural networks, surpassing traditional methods by capturing the latent interactions among different activity types (e.g., running, cycling) and measures (e.g., distance, duration) recorded in sequential format, and temporal dependencies between daily activities. The applicability of the proposed approach extends beyond goal-oriented programs, as it can be utilized in forecasting contexts involving multiple sequences and benefiting from cross-sequence interrelationships. We test the effectiveness of the proposed approach using simulated and real-world datasets, demonstrating its advantages over canonical machine learning models. This performance enhancement positions the approach as a promising tool for improving adherence to goal-oriented programs and goal attainment. It also provides valuable insights for goal-oriented program intervention and personalization.
Criminal Liability of Robots- A Cri3cal Analysis of the Legal Framework in the US, UK, and Europe

As robots and artificial intelligence systems become integral parts of society, questions about their criminal liability arise. This paper critically analyzes the existing legal frameworks in the United States, United Kingdom, and Europe concerning the criminal liability of robots. The research aims to provide an overview of current legal frameworks, identify gaps and inconsistencies, and propose potential solutions. By comparing the approaches of these jurisdictions, the paper explores similarities, differences, and emerging challenges in addressing the criminal liability of robots. The study delves into the definition of robotic systems, distinguishes criminal from civil liability, and examines the ethical and legal complexities of attributing criminal intent to non-human entities. The legal frameworks in each jurisdiction are dissected, covering federal laws, state legislation, case law, and regulatory measures. The analysis reveals the evolving nature of these legal systems and their efforts to adapt to the rapid advancements in robotics and artificial intelligence. Emerging legal challenges, including autonomy and decision-making, human-machine interaction, product liability, privacy concerns, and ethical considerations, are discussed. The paper concludes with proposed legal solutions, emphasizing the importance of clear definitions, a strict liability framework, risk assessment and safety standards, adaptive governance, ethical frameworks, impact assessments, and international cooperation.
Bitcoin Mining and Energy: A Comparative Analysis of Costs, Drivers, and Policy Implications

This article explores the energy economics of Bitcoin mining, examining the variouscomponents of mining costs and comparing methods for estimating these costs. The driversof energy demand in Bitcoin mining are analyzed, as well as Bitcoin's energy mix and energyefficiency. A case analysis of Bitcoin mining in China and the United States is also provided,including the challenges of crypto damages and issues in energy allocation. Finally, policyimplications are outlined for pricing externalities, incentives for energy-efficient mining,and renewable energy and decentralized energy grids. This review sheds light on thecomplex energy dynamics of Bitcoin mining and provides insights for policymakers,researchers, and industry stakeholders.
Machine Learning Models on Criminal Networks (MLMoCN): Artificial Intelligence to Disentangle Crime

Artificial intelligence (“AI”) is an outstanding technology for analyzing massive volumes of structured and unstructured data. Within the theoretical and methodological framework of Criminal Network Analysis (“CNA”), AI assists in classifying and extracting structured and unstructured data, and generating Criminal Network Graphs (“CNG”). To accomplish these tasks, Machine Learning Models must be trained with real/empirical data that describes the characteristics of the nodes/agents involved in the criminal networks and their interactions. This paper discusses the characteristics of the resulting models, herein defined as Machine Learning Models on Criminal Networks (MLMoCNs), and the prospects and obstacles for applying the most sophisticated AI techniques to CNA.
Expanding functionality of the autonomous voice control system

The article describes the main advantages of voice control systems, notes the shortcomings of the first implemented voice control systems, their functional limitations, and limited use in comparison with modern voice assistants, and also formulates the shortcomings of modern voice control systems. It is noted that with the expansion of functionality, the scope of voice control systems is expanding significantly. The recommendations of leading companies that implement voice technologies, indicating the need to continue research and development in this area. Due to complex calculations in speech recognition, there is a need to use high-speed computers, which makes it difficult to create compact and autonomous voice control systems that work without an Internet connection. At the same time, the growing need for autonomous voice control systems requires a reduction in computational costs, an increase in the speed of calculations by simplifying the methods and algorithms of these systems, implemented in software and hardware in the form of autonomous electronic devices. Methods are distinguished that allow performing recognition with sufficient reliability, such as hidden Markov models and the method for determining mel-frequency cepstral coefficients. In addition, methods are proposed to reduce computational complexity by reducing the number of similar-sounding words included in the command and the commands themselves, and to increase the speed of calculations, the use of electronic components is proposed in the implementation of computational algorithms and their parts. The operability and the possibility of practical application of ways to expand functionality and reduce computational complexity for autonomous voice control systems have been tested and confirmed experimentally, and some of them correspond to the recommendations of leading companies implementing voice technologies.
Ensemble Tweets Emotion Detection Model Using Transformer Based Architecture, Support Vector Machine and Long Short-Term Memory

In this current age of Fourth Industrial Revolution (4IR), there is an exponential growth in public generated data such as mobile data, business data, social media data, Internet of Things (IoT) data, cyber security data which are in form of image, video and text, due to the incessant usage of social media. This available textual data is frequently adopted and significantly important for extracting information such as user’s sentiments, and emotions. Considering the complexity and large amount of textual data, the adoption of various machine learning and deep learning model for the analysis of emotion has not yet attained optimum accuracy. Recently, Bidirectional Encoder Representational from Transformer Based Architecture (BERT) are achieving state of art accuracy. Hence, this study adopts an ensemble-based model using Bidirectional Encoder Representational from Transformer (BERT-Large), Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) for detecting user’s emotion. The three trained model are loaded from the local repository and stack together by comparing their predictions and selecting the majority vote approach. This study performs emotional analysis on imbalanced tweets of six (6) different classes, which includes; sadness, anger, love, surprise, fear, and joy. The experiment shows that the voting of BERT prediction and Ensemble model perform better than the other models with to an optimum accuracy of 93%, 93% respectively. BERT-Large performed well as a standalone model and also the ensemble techniques for prediction of multi-social platforms in real time usage.
A Comprehensive Survey on Distributed Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training that distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this article, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work, are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks (DNNs), emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.
Enabling digital growth through continuous education of project managers: A framework for collaborative, complementary, sustained, and simultaneous learning in software engineering organizations

Introduction/purpose: This paper introduces a novel PMO EDUCT framework that encompasses Collaborative, Complementary, Sustained, and Simultaneous learning explicitly tailored for the dynamic landscape of software engineering. This paper contributes to the ongoing discourse on education and professional development in the digital era by illuminating the challenges faced, the framework developed, and the measurable outcomes achieved. The presented framework underscores the need for agility, adaptability, and a relentless pursuit of knowledge as fundamental tenets for success in an ever-evolving landscape. Methods: Focusing on a large software engineering organization, this study explores the multifaceted aspects of project management education, methodology enhancement, and team optimization. The proposed methodology combines qualitative and quantitative approaches to explore the educational process area in a global organization and generate a flexible and measurable framework to improve corporate knowledge growth. The framework aims to provide learners the most flexibility about learning paths, schedules, collaboration, and best practice exchange while guaranteeing proper alignment with frequent market changes and emerging PM skills. Results: Findings indicate that personalised, continuous learning, facilitated through collaborative environments and custom-tailored content, yields significant benefits. Through analysis of key performance indicators (KPIs), this study showcases improved project onboarding, enhanced team collaboration, and the cultivation of a culture of innovation. Conclusion: This paper presents the PMO EDUCT framework that organizations can use to create a learning culture and empower their employees to succeed in the constantly evolving field of software development. By adopting collaborative, complementary, sustained, and simultaneous learning principles, organizations can develop a workforce with the essential skills of critical thinking, collaboration, and adaptability necessary for success in the digital era.