Reviewerly/exHarmony huggingface dataset:
How to Notify Team Members during Asynchronous Remote Collaboration supported by Mixed Reality: Comparing Visual, Audio and Tactile Notifications

Solutions exploring Mixed Reality (MR) have been applied to remote scenarios, allowing to create a common ground. Literature reports that most studies focus on synchronous scenarios (team members interact in real-time). Therefore, an opportunity exists for asynchronous situations, in which collaborative actions occur at different moments and in which content is created/shared for later consumption. Hence, team members require notification methods to call their attention when new content is available. This work explored three notification cues: visual; audio; tactile, to comprehend which one captures the attention of team members easily. A user study was conducted with 26 participants to evaluate these conditions during asynchronous remote maintenance. The study was divided into two parts, focusing on the remote and on-site roles, given that both require methods to enhance their awareness. We report the results obtained, suggesting tactile cues represent the predominant condition for both roles.
Development of Chatbot Learning Media on Earth Rotation and Revolution Materials for Grade 6 Elementary School Students

Difficulties are often found during the learning process. Therefore, learning media is very crucial in learning activities. With the existence of learning media can support teachers when providing learning to students. Chatbot learning media is one of the media that can help improve student learning outcomes. The purpose of this study was to find out and then describe the characteristics and analyze the truth pf increasing learning outcomes of grade VI students through the development of interactive print media in the material rotation and revolutation of earth. Researchers used the research and development (RD) method in this study. The 4D (four-D) development model previously described by Thiagarajan became the procedural model chosen by the researcher. The final score obtained in this study was 4.15 from media experts, 4.56 material experts, 4.8 teacher responses, and 4.58 student trial results. The overall validation score and trial of chatbot learning media is 4.58 which is included in the very good category and is very feasible to use as a learning media tool in grade 6 "earth rotation and revolution" material in elementary schools
Toward Blind-Adaptive Remote Sensing Image Restoration

While deep convolutional neural networks (CNNs) have substantially boosted the performance of low-level vision tasks, they remain largely under-explored in CNN-based remote sensing image restoration. This paper studies the JPEG-LS compressed remote sensing image restoration that faces the following problems. It requires a trade-off in preserving local context information and expanding spatial receptive fields. It needs blind restoration while achieving flexible performance. To this end, we propose a blind-adaptive restoration network, called TBANet, that integrates three modules into an end-to-end network to remedy these problems separately. Specifically, we build a scale-invariant wise-skip ResNet as the baseline to extract more context information. We present a receptive field expansion module by using scale-wise convolution for removing banding artifacts. We design a blind-adaptive controller to provide a deterministic result meanwhile meeting the needs of the user’s preference. In experiments, we compare the restoration accuracy among our model and many different variants of restoration methods on our collected remote sensing image dataset. The proposed network achieves superior performance against state-of-the-art methods in terms of both quantitative metrics and visual quality. Code and models are available at: https://github.com/lmmhh/TBANet.
Blockchain-Based Messaging and Information Sharing Systems for Air Cargo Supply Chains

Air cargo supply chain has become an integral stage in the transportation sector. Transparent information sharing is an important factor to facilitate fast communication and achieve efficient collaboration among stakeholders in handling the air cargo processes. However, the fundamental air cargo communication channel relies on the traditional peer-to-peer messaging approach that cannot ensure efficient communication flow, and this also caused complexity in digital connections due to multiple ownership in an air cargo supply chain. Leveraging the blockchain technology (BCT), which can ensure data transparency and security, our work first explores a variety of real-world applications of BCT in the air cargo activities coordination; we then propose the blockchain-based messaging and information systems and a self-enforceable smart contract framework to streamline the communication among stakeholders. Supported by the proposed BCT-based systems, shipment coordination and collaboration are strengthened through enhanced information transparency, communication efficiency and messages accountability in the end-to-end air cargo supply chain journey.
Leveraging social media in digital scholarship: Perspective from developing country students

Albeit the increasing relevance of digital scholarship in contemporary educational settings, the onset of global pandemics like COVID-19 has necessitated the need for academic institutions to rely on social media for digital scholarship. Digital native students are leveraging on social media for digital scholarship to enhance communication and information dissemination. However, a study from higher institution in a developing country is missing from the global discussion on leveraging social media for digital scholarship. This study seeks to examine students’ knowledge level in the use of social media for digital scholarship and the challenges associated with the use The study adopted stratified and non-probability voluntary response sampling methods because of the flexibility of these techniques. Data was collected from both undergraduate and postgraduate students of University of Cape Coast in Ghana. Students possess more than the average knowledge in social media for digital scholarship activities. However, students use of social media was for video presentations, online class, information sharing, publication of articles, search for academic related information, building proficiency in the search for information and making connections with individuals. Additionally, the conventional notion still holds that social media as a digital scholarship is susceptible to poor internet connection, jamming of digital systems and lack of adequate information on how to use digital scholarly platforms. The information literacy department of higher institutions are recommended to revise the content of their curriculum and incorporate mechanisms to leverage social media for digital scholarship to efficient disseminate scholarly outputs.
TO THE QUESTION OF ANALYSIS OF MACHINE TRANSLATION TECHNOLOGIES

The article deals with the issue of the efficiency of translation by means of machine translation systems. The relevance of the work is related to the importance of a comprehensive analysis of machine translation systems and a detailed description of popular and well-known online translators. Scientific work in the field of machine translation is an important and urgent task, aimed at achieving equivalent translations and contributing to the solution of many applied problems associated with the development and formation of a new communication environment. In this paper, we will consider such concepts as “machine translation”, “translation equivalence”, and study machine translation systems. This article presents translation services such as the domestic service PROMT.One, the American Google Translate, as well as the German service DeepL. These services are considered leading translators with extensive features and options, and they are indispensable tools for language learning and working with foreign languages today. Thanks to machine translation systems, translations become more affordable and easier, since machine translation performs word-for-word translations that require only post-editing. However, incorrectly translated word-for-word translations can be misleading for specialists, especially inexperienced ones. It should be noted that the machine translation systems available perform translations of different quality, as a consequence, evaluation of the quality of the text translation of any orientation allows to identify the most appropriate servicefor use in any field of application.
STRUCTURAL MODEL OF ESTIMATION OF INDEPENDENT WORK OF STUDENTS IN THE EDUCATIONAL PROCESS BASED ON FUNCTIONAL AND STRUCTURAL ANALYSIS

The article provides a structural model for independent work of students in the educational process. This structural model is developed on the basis of functional and structural analysis. It makes it possible to visually represent the learning process as a whole as well as to objectify the process of assessing the independent work of students both for the teacher and the student himself. The developed structural model is general, it is applicable in the process of teaching and assessing independent work both in general education schools, art schools, and in various secondary and higher educational institutions. To concretize the model it is considered in the context of the studied subject supplementing it with specific content. The application of the developed model is shown as an example of music lessons of general and secondary specialized educational institutions with the use of electronic music technologies. Currently there is a contradiction between the large resources of independent work of students and the need to evaluate the process and results of independent work. One of the possible ways to solve this contradiction is to involve the students themselves in the diagnostic process using the methods of functional and structural analysis. The use of the potential of independent work of students contributes to the creation of conditions for self-realization of a person and presupposes the free development of his abilities. This is in line with current trends in education. The article indicates that in the modern education system the role of independent work of students is increasing both in mastering basic knowledge and special knowledge, skills and abilities. In this regard, the problem arises of assessing this work, both by teachers and by the students themselves. It is supposed to involve the students themselves in the process of assessing the independent work on the basis of the methods of functional and structural analysis.
Secure IoT data management and sharing architecture for information security using cryptographic technique

The massive amounts of data produced and gathered by smart devices through the internet support a wide range of applications, considerably improving our daily lives. Data sharing among smart devices must be safeguarded due to the sensitivity of the data involved in the transmission. The Internet of Things (IoT) environment must be protected from unauthorised access due to a variety of variables, including its attractiveness to cybercriminals, previous successful cyber-attacks, and consumers’ perceptions of security and reliability. Blockchain technology appears to be one promising technology that appears to address these security challenges extremely effectively. However, given the volume and rate at which smart devices generate data, Blockchain appears to be inefficient for storing it. The pace of data collection in the IoT context and the speed of transaction confirmation in the Blockchain network are the two key elements behind this. We connect the Blockchain and the Inter-Planetary File System (IPFS) in this study to permit data recording on a distributed storage and a mechanism to restrict access to recorded data to authorised organisations only. Over the Blockchain network, the access policy definition for safe data sharing and cryptographic hash content is stored. The real IoT-generated data, on the other hand, is collected via a distributed storage network, which improves availability and security. The proposed scheme’s analysis and performance evaluation show that it is secure and feasible. Furthermore, simulations are undertaken to assess the operating costs of smart contracts and to test the efficacy and viability of the suggested architecture.
Peer Review Statement

The review process for TechSUD2023 is fulfill to the requirements set by IOP. For this symposium, at least 2 independent reviewers will be assigned to each paper. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. The paper will then be assigned to an Editor for another round of review and finally will reach the Chief Editor for the final decision on acceptance. A single reviewer will be assigned to a maximum of 5 papers. Hence, all papers published in this volume have been reviewed through processes administered by the Editors. 1. Type of peer review: Single anonymous 2. Conference submission management system: Morressier 3. Number of submissions received: 37 4. Number of submissions sent for review: 37 5. Number of submissions accepted: 31 6. Acceptance Rate (Submissions Accepted / Submissions Received × 100): 83.78 % 7. Average number of reviews per paper: 1.84 8. Total number of reviewers involved: 33 9. Contact person for queries: Name: Chun Kiat CHANG Affiliation: REDAC, Universiti Sains Malaysia Email: redac10@usm.my
Digital Humanities Advancement Grants (NEH)

Federal Grants & ContractsVolume 47, Issue 20 p. 1-1 Grant Alerts Digital Humanities Advancement Grants (NEH) First published: 22 September 2023 https://doi.org/10.1002/fgc.33243Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue2021 September 2023Pages 1-1 RelatedInformation
Analysis of the Semantic Shift in Diachronic Word Embeddings for Spanish Before and After COVID-19

Words can shift their meaning across time. This study shows the results obtained by the exploratory analysis of the semantic shifting on Spanish vocabulary using Diachronic Word Embeddings. Diachronic data consists of a 2018 Spanish corpus, before the COVID-19 outbreak, and a second corpus with documents from 2021. This paper addresses the construction of the diachronic Spanish word embeddings model, as well as the results obtained by the analysis using a non-supervised distance vector technique. The results allowed us to identify topics with the most semantic shift between those periods.
Web-Based Expert System For Respiratory Disorders In Human Using Backward Chaining Method

Respiratory disorders are various types of diseases or disorders that inhibit lung function. This disease can affect the ability to breathe. Respiratory disorders can be transmitted anytime and anywhere. Respiratory disorders are more common than other organ system disorders or infections. They start from the common cold with relatively mild to severe pneumonia, cough, fever, sore throat, and shortness of breath. Inadequate medical equipment also triggers the difficulty of treating and diagnosing a disorder. This study aims to help the community diagnose respiratory disorders and determine how to deal with them effectively and efficiently. This website-based expert system for diagnosing human respiratory disorders uses the Backward Chaining inference method. The results of this study will produce a website for diagnosing respiratory disorders in humans.
Comparative Study on Text-to-Image Synthesis Using GANs

Text-to-Image conversion is an active research area where synthetic images are generated based on the text description, semantics, and captions. This application involves both Natural Language Processing (NLP) and Image processing. The text description is processed, and text features are extracted based on the text features an image generates with the corresponding text description. Generative Adversarial Nets have shown significant results in this area. GANs use two models a Generator model uses random Noise to generate an image, and a discriminator model, whose objective is to classify the data points. Both models are combined and compete with each other using an adversarial loss function. Many GAN Models have been proposed for a Text-to-Image conversion application in the past years and address several problems in this domain. In this paper, we reviewed the three latest GAN models, Cycle Consistent Inverse GAN(CI-GAN), Dynamic Aspect-aware GAN(DAE-GAN), and Cycle GAN with BERT, and compared their Inception Scores(IS) of them in the CUB (200) Dataset.
Real-time path planning based on improved artificial potential field method

Nowadays, with the development of robotics-related technology, its applications permeate many aspects of work and life; In product manufacturing and assembly, tech companies switch from manual to robot automation which improves the production volume and reduces the assembly time. In the tertiary sector including health and social work, the robots learn how to interact with people to meet specified requirements. Path planning constitutes a critical module of robotics engineering that aims to provide the optimal solution for the robot to reach its target point. The artificial potential field methods, refers to APF, are widely used to realize path planning due to their simplicity of calculation and effectiveness in obstacle avoidance. However, the traditional artificial potential field method features the local minimum and oscillation, and unreachable target point problems that make it hard for robots to reach the target point. Based on the weaknesses, an improved version of the gravitation and repulsion force function was introduced in this paper. In addition, the concept of safety distance also contributed to the path planning for robots. Through the simulation experiment, it was shown that the improved APF algorithm successfully addressed the local minima and unreachable target point problem, which could navigate robots to arrive at the destination in both 2D and 3D space by avoiding collision with obstacles.
Local minimum enhanced artificial potential field method

Currently, the artificial potential field technique is commonly used in vehicle obstacle avoidance and route planning. However, this method may lead to local minima where the gravitational force and repulsive force are equal during the path planning process, and thus the target location cannot be reached. This paper suggests an approach to enhance the artificial potential field method in light of this. First, the calculation method of the attraction field in the traditional artificial potential field is modified for resolving the issue of unreachable targets. Second, for the local minimum, the path calculation is recovered by modifying the repulsive force range of obstacles and creating a virtual obstacle point, and applying an additional force to get rid of the gravitational force and repulsive force balance. The simulation results check the effectiveness of the designed method, which can solve the cases of unreachable targets and local minima and produce reasonable planned paths.
Dynamic Task Migration for Enhanced Load Balancing in Cloud Computing using K-means Clustering and Ant Colony Optimization

Cloud computing efficiently allocates resources, and timely execution of user tasks is pivotal for ensuring seamless service delivery. Central to this endeavour is the dynamic orchestration of task scheduling and migration, which collectively contribute to load balancing within virtual machines (VMs). Load balancing is a cornerstone, empowering clouds to fulfill user requirements promptly. To facilitate the migration of tasks, we propose a novel method that exploits the synergistic potential of K-means clustering and Ant Colony Optimization (ACO). Our approach aims to maximize the cloud ecosystem by improving several critical factors, such as the system's make time, resource utilization efficiency, and workload imbalance mitigation. The core objective of our work revolves around the reduction of makespan, a metric directly tied to the overall system performance. By strategically employing K-means clustering, we effectively group tasks with similar attributes, enabling the identification of prime candidates for migration. Subsequently, the ACO algorithm takes the reins, orchestrating the migration process with an inherent focus on achieving global optimization. The multifaceted benefits of our approach are quantitatively assessed through comprehensive comparisons with established algorithms, namely Round Robin (RR), First-Come-First-Serve (FCFS), Shortest Job First (SJF), and a genetic load balancing algorithm. To facilitate this evaluation, we harness the capabilities of the CloudSim simulation tool, which provides a platform for realistic and accurate performance analysis. Our research enhances cloud computing paradigms by harmonizing task migration with innovative optimization techniques. The proposed approach demonstrates its prowess in harmonizing diverse goals: reducing makespan, elevating resource utilization efficiency, and attenuating the degree of workload imbalance. These outcomes collectively pave the way for a more responsive and dependable cloud infrastructure primed to cater to user needs with heightened efficacy. Our study delves into the intricate domain of cloud-based task scheduling and migration. By synergizing K-means clustering and ACO algorithms, we introduce a dynamic methodology that refines cloud resource management and bolsters the quintessential facet of load balancing. Through rigorous comparisons and meticulous analysis, we underscore the superior attributes of our approach, showcasing its potential to reshape the landscape of cloud computing optimization.
Understanding First-Person and Third-Person Videos in Computer Vision

Due to advancements in technology and social media, a large amount of visual information is created. There is a lot of interesting research going on in Computer Vision that takes into consideration either visual information generated by first-person (egocentric) or third-person(exocentric) cameras. Video data generated by YouTubers, Surveillance cameras, and Drones which is referred to as third-person or exocentric video data. Whereas first-person or egocentric is the one which is generated by GoPro cameras and Google Glass. Exocentric view capture wide and global views whereas egocentric view capture activities an actor is involved in w.r.t. objects. These two perspectives seem to be independent yet related. In Computer Vision, these two perspectives have been studied by various domains like Activity Recognition, Object Detection, Action Recognition, and Summarization independently. Their relationship and comparison are less discussed in the literature. This paper tries to bridge this gap by presenting a systematic study of first-person and third-person videos. Further, we implemented an algorithm to classify videos as first-person/third-person with the validation accuracy of 88.4% and an F1-score of 86.10% using the Charades dataset..