Reviewerly/exHarmony huggingface dataset:
Bridging the Digital Divide

Abstract This chapter discusses the disparity in access to digital services, which exists between countries, regions, communities, and people in the world. This disparity is referred to as the digital divide. Digital information and communication are obviously of key importance for the development of countries and regions. However, different approaches exist to address this problem. In this chapter, from a digital humanist perspective, we explore practice-oriented digital design approaches to serve people and communities in currently unconnected regions of the world. We discuss how this might best be done, upholding ethical standards, inclusivity, and human-centered principles. To illustrate this in action, we present a case study from rural Ghana.
PGDS-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images

Recently, object detection has become a research hotspot in computer vision, which often detects regular images with small viewing angles. In order to obtain a field of view without blind spots, fisheye cameras, which have distortions and discontinuities, have come into use. The fisheye camera, which has a wide viewing angle, and an unmanned aerial vehicle equipped with a fisheye camera are used to obtain a field of view without blind spots. However, distorted and discontinuous objects appear in the captured fisheye images due to the unique viewing angle of fisheye cameras. It poses a significant challenge to some existing object detectors. To solve this problem, this paper proposes a PGDS-YOLOv8s model to solve the issue of detecting distorted and discontinuous objects in fisheye images. First, two novel downsampling modules are proposed. Among them, the Max Pooling and Ghost’s Downsampling (MPGD) module effectively extracts the essential feature information of distorted and discontinuous objects. The Average Pooling and Ghost’s Downsampling (APGD) module acquires rich global features and reduces the feature loss of distorted and discontinuous objects. In addition, the proposed C2fs module uses Squeeze-and-Excitation (SE) blocks to model the interdependence of the channels to acquire richer gradient flow information about the features. The C2fs module provides a better understanding of the contextual information in fisheye images. Subsequently, an SE block is added after the Spatial Pyramid Pooling Fast (SPPF), thus improving the model’s ability to capture features of distorted, discontinuous objects. Moreover, the UAV-360 dataset is created for object detection in fisheye images. Finally, experiments show that the proposed PGDS-YOLOv8s model on the VOC-360 dataset improves mAP@0.5 by 19.8% and mAP@0.5:0.95 by 27.5% compared to the original YOLOv8s model. In addition, the improved model on the UAV-360 dataset achieves 89.0% for mAP@0.5 and 60.5% for mAP@0.5:0.95. Furthermore, on the MS-COCO 2017 dataset, the PGDS-YOLOv8s model improved AP by 1.4%, AP50 by 1.7%, and AP75 by 1.2% compared with the original YOLOv8s model.
ChatGPT and L2 Written Communication: A Game-Changer or Just Another Tool?

The development of English language written communication skills across many contexts has been hindered by factors such as examination-oriented cultures, anxiety associated with both oral and written communication, limited opportunities for engaging in authentic communication, and a lack of individualized, personalized feedback. Multiple studies throughout the last decade have explored chatbot integration as a means to address these issues and enhance students’ language learning toolkits. This paper delves into the potential benefits and roles of advanced Generative AI (GenAI) chatbots (e.g., ChatGPT) in enhancing second language (L2) communicative practices. We evaluate findings associated with various types of chatbots and present pedagogical strategies for their application of GenAI in both in-class and out-of-class spaces to support students’ language learning experiences. We also propose future research directions, emphasizing the necessity to explore the ethical use of AI tools, their impact on L2 communication, and the comparative effectiveness of retrieval-based and GenAI-powered chatbots in language education.
Multi-Level Pixel-Aligned Implicit Function for High- Resolution 3D Human Digitization

Current strides of image dependent 3 dimension human outline estimation have progressed due to remarkable strides in depiction capabilities facilitated by deep NN. Despite the strides made in real-world applications, existing methods still fall short in generating reconstructions that match the intricate details often found in the original images. We posit that this deficiency primarily arises from the clash between two competing demands: accurate predictions necessitate extensive contextual information, while precise predictions hinge on higher resolutions. Owing to the limitations in current hardware memory, prior techniques have leaned towards utilizing low-resolution images to encompass broader spatial context, resulting in less precise or lower-resolution 3D estimations. To overcome this hurdle, we have devised multi layered algorithm that undergoes endwise training. At rough level, model comprehensively processes the entire image at a reduced resolution, emphasizing holistic reasoning. This coarse level furnishes essential context to a finer level, which focuses on estimating highly intricate geometries by scrutinizing higher-resolution images. Our research demonstrates a substantial enhancement in performance compared to prior methodologies, showcasing the superior capabilities of our approach.
Mean-field coherent Ising machines with artificial Zeeman terms

Coherent Ising Machine (CIM) is a network of optical parametric oscillators that solve combinatorial optimization problems by finding the ground state of an Ising Hamiltonian. In CIMs, a problem arises when attempting to realize the Zeeman term because of the mismatch in size between interaction and Zeeman terms due to the variable amplitude of the optical parametric oscillator pulses corresponding to spins. There have been three approaches proposed so far to address this problem for CIM, including the absolute mean amplitude method, the auxiliary spin method, and the chaotic amplitude control (CAC) method. This paper focuses on the efficient implementation of Zeeman terms within the mean-field CIM model, which is a physics-inspired heuristic solver without quantum noise. With the mean-field model, computation is easier than with more physically accurate models, which makes it suitable for implementation in field programmable gate arrays and large-scale simulations. First, we examined the performance of the mean-field CIM model for realizing the Zeeman term with the CAC method, as well as their performance when compared to a more physically accurate model. Next, we compared the CAC method to other Zeeman term realization techniques on the mean-field model and a more physically accurate model. In both models, the CAC method outperformed the other methods while retaining similar performance.
Why do consumers buy NFTs?: Multi-path of attitude and the bandwagon effect

Purpose The non-fungible token (NFT) market has been multiplying in recent years. NFTs are tokens stored on a blockchain network based on smart contract technology that can be used to represent ownership of digital assets and cannot be changed like-for-like. With NFTs, all recorded digital properties can be freely traded and stored with values, making them possible to increase content transactions' privacy and security. In addition, NFTs engender new ways to organize, consume, share and store digital content. Despite the rapid growth of the NFT market, related consumer behaviors have yet to be well-known and relevant academic research results are very scarce. This study aims to explain how NFT fits with blockchain and cryptocurrency and how consumers accept it. This paper also develops a structured causal model with multiple paths to explain the antecedents and attitude variables for NFT acceptance. Design/methodology/approach The data collection was conducted from 542 young consumers in Korea via an online survey. The structural equation modeling method was used to analyze the hypotheses. Findings Attitudes toward technology and assets positively affect NFT purchase behavioral intentions. Additionally, symbolic driver affects behavioral intention directly. Originality/value The results expanded the understanding of the NFT market and consumers, which are still in their early stages. They also provide valuable insights for establishing future market strategies for NFT.
Characterization and Classification of Some Soils Formed on Ophiolitc Rocks in Al-Bassit Region /Northwestern Syria

Abstract The aim of this study is to characterize and classify soils formed from ophiolitic rocks (mafic and ultramafic) by determining their morphological, physical and chemical properties, petrographic and elemental composition studies. The profiles were prepared under forest vegetation (pines) and described based on the referenced methods in Al-Bassit region (NW Syria) between the coordinates: N: 35° 41` - N: 35° 57`, E: 35° 42` - E: 36° 05`, which belongs to Mediterranean climate regime. Samples were collected from horizons of all profiles and some physical and chemical analyses were performed in addition to petrographic and Chemical composition studies. The horizons were of the form A (Mollic)-Cr-R in profile P1, and A (Mollic)-BW-C- in the profile P2, and of the form A ((Mollic)-C1-Cr2- in the profile P3. They were ordered as: A(Ochric)-C1-C2 in the profile P4. The fires that took place in the region affected the characteristics of surface horizons of some soils, such as the percentage of organic matter, structure, and the degree of soil interaction .The ultramafic derived soils in the P1 profile contained a high level of clay, while the percentage of sand increased in mafic derived soil P2, P4. The soil contained high levels of exchangeable Ca +2 and Mg +2 , likely because they are in the structure of the rock. Carbonates are in low levels and are absent in some horizons, this is due to the composition of the parent material. The soil classification for studied profiles show that the profiles (P1, P2, P3) classified as Mollisols and the Profile P4 as Entisols.
Developing a Cross-Platform Application for Integrating Real-Time Time-Series Data from Multiple Wearable Sensors

This research presents a cross-platform application, developed with Flutter, for the efficient integration and management of real-time data from wearable sensors including Apple Watch, Android Wear, and Empatica E4. Compatible with iOS and Android, the app collects various physiological signals for easy analysis by health professionals. Utilizing InfluxDB, a time-series database, our development ensures efficient data handling, even from multiple sources, and enables real-time analytics. This robust, scalable tool signifies a notable advancement in mHealth, offering seamless data integration and management for those utilizing wearable sensor technology in healthcare research and practice.
Intelligent Cloudlet Scheduling for Optimized Execution Time in Cloud Computing Environments

Cloud computing has become a cornerstone of modern IT infrastructure, offering scalable and flexible resources. However, efficient resource management, particularly cloudlet scheduling, presents a significant challenge due to its NP-hard nature. This paper introduces a novel heuristic-based cloudlet scheduling algorithm aimed at minimizing execution time and improving load balancing in cloud computing environments. We detail the development and implementation of the algorithm, along with a simulation setup using the CloudSim toolkit to evaluate its performance against existing methods. Results from extensive simulations demonstrate that the proposed algorithm consistently reduces turnaround times, thus optimizing resource allocation. The findings suggest that our approach can significantly impact cloud computing efficiency, paving the way for improved service provider offerings and user satisfaction. The implications of these advancements are discussed, alongside potential directions for future research in dynamic cloud environments.
Optimizing Task Scheduling in Cloud Computing Environments using Hybrid Swarm Optimization

Cloud computing has revolutionized the Information Technology (IT) landscape by offering on-demand access to a shared pool of computing resources over the internet. Effective task scheduling is pivotal in optimizing resource utilization and enhancing the overall performance of cloud systems. Tasks are allocated to virtual machines (VMs) based on a server's workload capacity, aiming to minimize traffic congestion and waiting times. Although Particle Swarm Optimization (PSO) is currently the most effective algorithm for task scheduling in cloud environments, this study introduces a Hybrid Swarm Optimization (HSO) algorithm that combines the strengths of PSO and Salp Swarm Optimization (SSO). The proposed hybrid algorithm addresses the challenges associated with task scheduling in cloud computing. The performance of the HSO algorithm is evaluated using the CloudSim simulator and compared against traditional scheduling algorithms. Simulation results indicate that the hybrid PSO-SSO algorithm outperforms existing methods regarding makespan time, cloud throughput, and task execution efficiency. Consequently, the hybrid approach significantly improves resource utilization and overall system performance in cloud computing environments.
Risk-Based UAV Flight Path Optimization in Accordance with SORA

With the EU regulation of drone operations varying based on the specific type of drone, path planning can be done to consider risk mitigation. This paper proposes a transition-based rapidly exploring random tree star (T-RRT*) path planning algorithm for fixed-wing drone operations over rural areas. Risk is decoded in the cost function, which mainly considers population density and special infrastructure types. It was found that the algorithm is capable of finding paths that minimize exposure of the general population and infrastructure. However, path and computational inefficiencies were found. Usage of another data structure or algorithm might improve performance.
Embracing Digital Transformation in Financial Services: From Past to Future

Financial services are significantly impacted by digital transformation, which has been believed as one of the primary trends reshaping society and industry. A comprehensive understanding of digital transformation from an academic and management perspective is required, and it is crucial to close the knowledge gap using bibliometric and qualitative research techniques. Hence, the purpose of this paper is to identify the extensive research themes in digital transformation in financial services from 2000 to 2021, their significance and interdependencies, to determine which trending topics seem to be the most influential, to trace the evolution of digital transformation in the financial service, and to address new and under-researched field of research that is intriguing for future research. This study used a bibliometric analysis of 288 peer-reviewed research articles published over the last 22 years in digital transformation in financial services. The data were extracted from the Scopus database and used various bibliometric analyses such as conceptual structure analysis, co-citation network analysis, social structure, and keyword analysis using R software. The findings show an overall increasing trend in journal publications, author productivity, collaborative research by institutions, and countries’ collaboration from the year 2019, as well as major insights from co-citation analysis. According to Lotka’s law, most authors (769, or 95.77%) contributed only one article. In addition, the study assesses multiple research areas, such as the effects of digital transformation in financial services, applied technology and insights, and digitalization processes, comprising the latest trending topics in this research stream that extensively cover up in financial services. One of the limitations of this study is that it used only a single database. The author recommends that the findings of this research be reinforced with more targeted and extensive qualitative literature in this field of study. This study establishes a framework for policymakers, academics, and researchers to investigate the areas where digital technologies influence financial services. This study also highlighted several cutting-edge digital technologies used for different functions of the financial services of an organization. This study illustrates the bibliometric structure of the digital transformation literature in financial services and provides insights into the literature’s growth year over year. It provides a comprehensive understanding of this evolving field, guiding future research and facilitating informed decision-making in the financial industry.
Promoting Innovative Work Behavior Through Innovative Work Environment

The study examines the effect of an innovative work environment on the innovative work behaviour of employees. To broaden the concept of the study, related literature was reviewed. The study used a descriptive assessment and correlational research design and the population of the study was all employees from the two colleges (DWCL and DWCV). The data was gathered through research questionnaires and used inferential statistics to analyze the data. The study found that the innovative work environment and innovative work behaviour are high but not very high. The ANOVA result suggests that there is a significant correlation between an innovative work environment and the innovative work behaviour of employees. It recommends that nurturing innovative work behaviour is to nurture an innovative workplace. The study recognizes its limitation and recommends further investigation concerning different dimensions of innovative work environments and their effect on work performance.
The decisive learning skill for the scientific knowledge acquisition developed by artificial intelligence

Several researchers in the field of education have taken an interest in the development of scientific skills. Their objective is to enable learners to have an attitude of reflection and behavior in the face of the different pedagogical situations that may arise. We are also interested in these scientific skills, namely C 1 : Appropriate, C 2 : Analyze and reason, C 3 : Achieve, C 4 : Validate and C 5 : Communicate, we have characterized them by capacities whose objective is to evaluate them with interesting precision. We are interested in all these scientific skills but with significant weightings in order to realize a balanced, complete and effective overall assessment of scientific learning. In this work, we will also discover the most important scientific skill for the construction of Knowledge in a given scientific session, using artificial intelligence based on the decision tree by implementing a program in Python. Therefore, taking into consideration the one that leads to the birth of ideas and that allows to converge towards the objective, the teacher gives more importance to this skill and develops it by proposing different specific pedagogical situations. This learning approach allows the teacher to make the right decisions at the right time and it allows equity between the learners to acquire the different scientific skills according to their learning ability. Therefore it allows the learner an intrinsic motivation for the acquisition of scientific knowledge.
Investigating Geochemical Patterns of Major and Trace El-ements in Granitic Rocks of the Oulmes Region, Central Morocco: Instrumental Neutron Activation Analysis (INAA)

In this study, we used the instrumental neutron activation analysis technique (INAA) for analysis of granite samples from four locations in Oulmes region of central Morocco. In order to investigate the geochemical behavior of both major and trace elements in granite rocks from study area. The four samples almost accurately reflect the geochemical variety and heterogeneity of the formations were taken from distinct outcrops. The aim of study was to characterize the mineralization patterns of granitic rocks, their sources and basic composition. The results highlight the importance of nuclear techniques in geochemical investigations and provide some insights into the geochemical processes that shaped these rocks.
Usability analysis of environmental disaster mitigation efforts in Lampung Province: case study of <i>Jaring Bencana</i> application

Disaster management requires a variety of innovations to intervene or prevent the adverse effects of the disaster, including through various applications made for various purposes and uses. One of the applications is the Jaring Bencana , to strengthen the mitigation aspects of natural and non-natural disasters, including environmental disasters. This study aims to examine the level of usability in disaster applications by measuring the variables of effectiveness, efficiency, and user satisfaction. The method used in this study is usability testing with technique performance measurements and retrospective think-aloud . Respondents in this study 30 individuals as test participants, were selected purposively based on criteria of interest and responsibility in disaster management. Results from testing usability show that mark component effectiveness is significant at 89%, mark efficiency component of 0.24 goals/second , and satisfaction score of 68. It can be concluded that the Jaring Bencana application has been functionally effective and efficient, although it must be optimized to achieve maximum user satisfaction. The results of this research have implications for the development of disaster applications that are in accordance with disaster needs.
A novel method for mobile application recognition in encrypted channels

&lt;abstract&gt;&lt;p&gt;In the field of mobile application traffic analysis, existing methods for accurately identifying encrypted traffic often encounter challenges due to the widespread adoption of encryption channels and the presence of background traffic. Consequently, this study presents a novel mobile application traffic identification model that is in encrypted channels. The proposed model utilizes an adaptive feature extraction technique that combines Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to effectively extract abstract features from encrypted mobile application traffic. Additionally, by employing a probability-based comprehensive analysis to filter out low-confidence background traffic interference, the reliability of recognition is further enhanced. Experimental comparisons are conducted to validate the efficacy of the proposed approach. The results demonstrate that the proposed method achieves a remarkable classification accuracy of 95.4% when confronted with background traffic interference, surpassing existing techniques by over 15% in terms of anti-interference performance.&lt;/p&gt;&lt;/abstract&gt;