Reviewerly/exHarmony huggingface dataset:
Comparing Industry Training Using Virtual Reality Against Conventional Training: A Case Study

Virtual reality (VR) for industry training has recently received much attention. In VR, a simulation environment of the actual process and situation is provided. In this study, the effectiveness of the VR-based industry training for operators to acquire a process skill was examined. Selected operators from a medical manufacturing plant were divided into two groups and separately undertook the training of the same process either via VR or real-life demonstration (conventional training). Their related skill levels were measured and compared in terms of knowledge retention and knowledge interpretation. Results showed that no significant difference exists between the two groups. Theoretical knowledge should be enhanced in VR demonstration. The survey and observations also showed several advantages of VR over real-life demonstrations.
Sound and Video Examples – Issue 27(1)

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Reflections of a Saudi Female Practitioner

This chapter is an auto-ethnographic narrative provides an insight into my experience as a mother, instructor, researcher, and instructional technology specialist regarding distance education during the COVID-19 pandemic. I aim to shed light on my experience of performing those roles during the period of school suspension due to the pandemic. Reflecting on my experience in the first person and quoting several reflections and comments from others in the same field will describe the experiences of various people in the context being studied. These reflections and comments revealed different perspectives on distance education, including the positives and negatives, advantages and disadvantages, facilities, and difficulties. According to these results, I developed several implications for practice.
Construction of an Online Shopping Platform Based on DApp

Shopping platform increases service speed and reduces transaction cost. However, it also leads to issues of information asymmetry. Some sellers fabricate the price of promotional goods prior to promotion, not only affecting the reputation of sellers, but also viciously influencing the goodwill of the shopping websites. On this basis, a shopping decentralized Application (DApp) based on blockchain technology was designed; this research integrates the blockchain and shopping App. The App used the decentralized technology of distributed blockchain tamper resistance and its advantages of distributed ledger, decentralization, traceability, smart contract, and information tamper-proof to reduce the adverse impact of information asymmetry on consumers.
Artificial Intelligence and Machine Learning in Biomedical Applications

Nowadays artificial intelligence (AI) and machine learning (ML) play a very significant role in biomedical science that is undergoing a revolution. Currently, there are many challenges in the biomedical field like the rarity of image data sets, analysis of this heterogeneous appearance of images, extracting important features, etc. to find out disorders in human beings. Basically, AI uses computer techniques to identify the smallest disorder in human beings. The main purpose of the chapter is to provide an outline of ML and deep learning (DL) technique for some of the futuristic application in biomedical field. ML improved many characteristics of human visual perception to discover noteworthy patterns from image data sets, and with the assistance of neural network, a variety of tasks like image segmentation, feature extraction, and classification have been done to find the smallest disorder in humans. There are certain medical problems related to thyroid disease, breast cancer, and skin disease, where MI plays a vital role to find out the exact problem in a particular disease. The AI and ML field explores many biomedical applications like disease identification, discovery of drugs, radiotherapy, healthcare system, epidemic outbreak prediction, etc. The chapter also explains how artificial neural network and DL will help to solve biomedical imaging problems. Moreover, the chapter illuminates different popular ML and DL technique that aimed at image segmentation, feature extraction, and classification for finding human health and disease.
Multi-authority threshold attribute-based encryption from R-LWE in fog and cloud computing environments

Fog computing is an evolving paradigm which extends cloud computing to the edge of the network. Recently, how to combine the fog computing with cloud computing will play a significant role in Internet of Things (IoT), smart grid, health care, etc. However, fine-grained data access control and quantum computing attack resistance are two main secure problems in fog and cloud computing environments. To handle these problems, in this paper, we propose a multi-authority threshold attribute-based encryption scheme from R-LWE in fog and cloud computing environments (MAT-ABE-FC). Our MAT-ABE-FC scheme innovatively integrates the traditional cloud computing with the promising fog computing environment, and can not only provide fine-grained data access control, but also withstand quantum computing attacks. Moreover, our MAT-ABE-FC scheme can support multi-authority, and realize secure attribute revocation. Both security and performance analysis indicate that our scheme is secure and efficient.
Webpage text extraction algorithm based on text block density and tag path features

In addition to the body text, web pages also contain a lot of noise information such as advertisements and navigation bars. Accurately extracting text content from web pages is a key technology to improve the quality of web page analysis. The web page itself is a highly heterogeneous special text, and different types of web pages have different web page structures, which increases the difficulty of web page text extraction. After a lot of analysis, we found that there is a potential correlation between the body text and the tag path and text block density, so we propose a webpage text extraction method based on the tag path feature and the text block density feature. Combining the advantages and disadvantages of tag path features and text block density features, we design a fusion strategy to solve the problem of low accuracy of web page text extraction. The method does not require training, and improves the efficiency of webpage text extraction. The experimental results on the dataset constructed in this paper show that the classification accuracy of this method reaches 81.11%, the recall rate reaches 83.15%, and the average accuracy on all datasets is 17.7% higher than that of the BDF algorithm and 6.21% higher than that of the CEPF algorithm, the experiments show that the method has strong generalization ability.
DC-GNN: Decoupled Graph Neural Networks for Improving and Accelerating Large-Scale E-commerce Retrieval

In large-scale E-commerce retrieval, the Graph Neural Networks (GNNs) has become one of the stage-of-the-arts due to its powerful capability on topological feature extraction and relational reasoning. However, the conventional GNNs-based large-scale E-commerce retrieval suffers from low training efficiency, as such scenario normally has billions of entities and tens of billions of relations. Under the limitation on efficiency, only shallow graph algorithms can be employed, which severely hinders the GNNs representation capability and consequently weakens the retrieval quality. In order to deal with the trade-off between training efficiency and representation capability, we propose the Decoupled Graph Neural Networks (DC-GNN) to improve and accelerate the GNNs-based large-scale E-commerce retrieval. Specifically, DC-GNN decouples the conventional framework into three stages: pre-train, deep aggregation, and CTR prediction. By decoupling the graph operations and the CTR prediction, DC-GNN can effectively improve the training efficiency. More importantly, it can enable deeper graph operations to adequately mine higher-order proximity to boost model performance. Extensive experiments on large-scale industrial datasets demonstrate that DC-GNN gains significant improvements in both model performance and training efficiency.
Research on transmission line lightning arrester online monitoring system based on wireless communication technology

The traditional mechanical lightning arrester monitoring device needs to climb the tower manually. The number of records is limited, with the safety risks of lightning data false alarms and inspection personnel falling. The online monitoring scheme of transmission line arresters can automatically measure leakage current and record lightning time and cumulative lightning times. The arrester monitor with microprocessor (MCU) as the core, composed of solar cell and wake up, leakage current collection, lightning strike count pulse, wireless receiving/transmission control, LCD and other circuits. The arrester online monitoring system consists of an arrester monitor, concentrator and network monitoring system. The lightning arrester online monitoring system can be applied to different tower types and voltage levels. The system has been implemented in Fushun, Liaoning Province, with a good operation state and total application value.
Multi Resources and Constraints Based Consolidation Model to Mitigate the Energy Extravagance

To design green clouds with optimal energy and resource consumption, the host consolidation process became the most prominent cloud management policy today.In case of a consolidation, the underloaded host's VMs are shifted to the other hosts to control the energy extravagance at a data center.The consolidation process is considered an NP-hard problem, as it depends on several computational resources (i.e., CPU, RAM, Bandwidth) and migration constraints.Although many former cloud researchers were focused on designing the consolidation process, the total consolidation process is based on single computational resource (i.e., CPU) utilization only.In a single resourcebased consolidation model, only the selected resource utilization value is considered in decision-making and the other resource utilization values are simply discarded.The problems identified in single resource-based consolidation process are: (i) Ignoring the other prominent resources in consolidation (ii) unnecessary consolidation of hosts (iii) aggressive migrations and (iv) energy extravagance.To overcome the limitations involved with the single resourcebased consolidation process, this study proposed the "Multi Resources and Constraints-based Consolidation Model (MRCCM)".The MRCCM is designed to solve the energy extravagance issues and aggressive migrations in host consolidation process.In MRCCM, multiple resources and resource constraints are considered while host consolidation for best target host selection and efficient resource management.Cloudsim toolkit with 800 HP-ProLiant host models and EC2 VM models are configured for experimental analysis.Proposed MRCCM model with MMT host selection method recorded the total 11570 migrations, 1853 shut_downs and 111.84 Kwh of energy consumption.Experimental results are proven that the MRCCM controlled the energy extravagance, unnecessary consolidations, and aggressive migrations at a considerable rate when compared to the other prominent consolidation models.
Pilot Study of a Tutorial Dialogue System That Emulates the Contingent Scaffolding of Human Tutors

This paper describes an initial classroom evaluation of Rimac, a natural-language tutoring system for physics.Rimac uses a student model to guide decisions about what content to discuss next during reflective dialogues that the student and automated tutor engage in after students solve quantitative physics problems, and how much support to provide during these discussions-that is, domain contingent scaffolding and instructional contingent scaffolding, respectively (e.g., Wood, 2001).The pilot study compared learning gains of high school students who were randomly assigned to use an experimental version of Rimac, which uses students' responses to pretest items to initialize the student model and dynamically updates the model based on students' responses to the automated tutor's questions during reflective dialogues (Chounta et al., 2017), with a control version of Rimac, which initializes its student model based on students' pretest performance but does not update the model further.Preliminary results do not reveal a significant difference in learning between conditions.Analyses of student learning gains of selected knowledge components (KCs) to which students were frequently exposed showed that the experimental condition outperformed the control.This highlights the importance of frequent exposure to KCs that students have trouble with, during the dialogues.These analyses also indicate the need for improvements to student model accuracy in the experimental version of the tutor, so that the model more reliably predicts which KCs need to be discussed and the level of granularity at which to address these KCs.
The Impact of Virtual Professional Development on Teachers' ESL Pedagogical and Content Knowledge

Teachers and school districts are increasingly turning to teacher virtual professional development (VPD; Author, 2015) to stay current and learn new pedagogical knowledge and skills.This study examined the improvement of in-service teachers' English as a second language (ESL) pedagogical and content knowledge after participating in a VPD program designed for ESL and bilingual education teachers.An analysis of covariance (ANCOVA) and t-tests were conducted with participant pre-and post-survey data (n=61).Results indicated a significant improvement in teachers' ESL pedagogical and content knowledge after completing the program.There were also differences in teachers' knowledge gains based on their location (i.e., rural, suburban, and urban).
Seeing Science: Using Mobile Technology to Make Learning Transformative

Using transformative experience theory as a framework, we investigate the potential of utilizing mobile technology to foster science engagement by connecting in-school and out-of-school experience.A ninth-grade science teacher instructed students to take pictures with their mobile devices when they noticed a connection to unit content and upload these with a caption to a private social media group.Analysis of digital artifacts and focus group interviews found that students were inclined to post content from the internet more than pictures from their everyday experience.Nevertheless, participation in the project was associated with transformative engagement (i.e., engagement with content in everyday life and expansion of perception and value related to the content).However, participation varied and barriers to engagement were identified.
Web-Based E-Learning Application to Support the Teaching and Learning Process at Genta Syaputra Senior High School

Along with the rapid development of technology today, we need a tool or method that can take advantage of existing technology in order to support the teaching and learning process at an educational institution, making it easier for students to understand the teaching and learning process using online methods in this COVID-19 era. The purpose of this study was to find out how the use of e-learning as a learning medium and the factors that influence it in SMA Genta Syaputra Regency were studied based on the characteristics of e-learning consisting of Non-linearity, Self-managing, Feedback-interactivity, Multimedia-Learners style, Just in Time, Dynamic Updating, Easy Accessibility and Collaborative Learning. This study uses a descriptive design using observation, interviews, and literature study. System analysis method using UML,analysis database using ERD and prototype design using Mockup. The results of this study concluded that the condition of the use of e-learning as a learning medium in SMA Genta Syaputra Regency is in a fairly good condition with a percentage level of 78%.
On-road Trajectory Planning with Spatio-temporal Informed RRT

In this study, a 3D sample trajectory planning approach for on-road scenarios is provided, which can directly supply the coordinate and velocity of each planning point. Simultaneously, increase sample pertinence and improve sampling efficiency. To generate a coarse trajectory in the Cartesian-time coordinate system, we utilize a method based on Informed RRT*. However, owing to the random nature of the sample-based method, the quality of the solution is dependent on the density of the sample. When the sampling density setting is low, the solution is not practicable or has no solution, and when the setting is high, dimension curse may occur. A function to identify barriers ahead of the vehicle is implemented before sampling to mitigate the impact of this flaw on online planning in extreme situations such as cornering and U-turn. We also sample the endpoint and circle the whole sampling region before planning for this task. As a result, the algorithm becomes more targeted. We used B-spline to refine the initial trajectory after acquiring it. The reliability of this method is demonstrated by a real-world vehicle experiment.
Lightning Arrester Target Segmentation Algorithm Based on Improved DeepLabv3+ and GrabCut

Since the camera is severely disturbed by sunlight during the surge arrester replacement task of the distribution network robot, and the operation background is complex, it is important to achieve accurate lightning arrester target segmentation. Aiming at the problem that the GrabCut algorithm requires user initialization in lightning arrester image segmentation and has a poor effect on segmentation of images with similar foreground and background colors, a lightning arrester segmentation algorithm based on improved DeepLabv3+ and GrabCut is proposed in this paper. Firstly, the acquisition images disturbed by sunlight are processed by the EnlightenGAN network for low light enhancement; then the lightning arrester target is segmented for the first time with the improved DeepLabv3+ network, then the segmented regions are made into dilation and erosion operations respectively, and the obtained results are used as the input images and initialization regions of GrabCut separately, and through two segmentations, the lightning arrester target and The separation of the operation background. The experimental results show that the algorithm proposed in this paper can not only realize the automatic segmentation of GrabCut but also can segment the target lightning arrester very well.
Exploiting data locality in memory for ORAM to reduce memory access overheads

This paper proposes a locality-aware Oblivious RAM (ORAM) primitive, named Green ORAM, which exploits spatial locality of data in the physical memory for reducing ORAM overheads. The Green ORAM is novel consisting of three policies. The first is row-guided label allocation used for mapping spatial locality onto ORAM tree to reduce the number of memory commands. The second is segment-based path replacement able to improve the data locality within the path in the ORAM tree in order to remove the redundant memory accesses. The third is multi-path write-back able to improve the data locality between different paths in order to obtain theoretical best stash hit rate. Notably, the Green ORAM still maintains the security as we analyzed. Experimental results show that Green ORAM achieves a 28.72% access latency reduction, and a 19.06% memory energy consumption reduction on average, compared with the state-of-the-art String ORAM.
Multi-Task Video Captioning with a Stepwise Multimodal Encoder

Video captioning aims to generate a grammatical and accurate sentence to describe a video. Recent methods have mainly tackled this problem by considering multiple modalities, yet they have neglected the difference in modalities and the importance of shrinking the gap between video and text. This paper proposes a multi-task video-captioning method with a Stepwise Multimodal Encoder. The encoder can flexibly digest multiple modalities by assigning a proper encoding depth for each modality. We also exploit both video-to-text (V2T) and text-to-video (T2V) flows by adding an auxiliary task of video–text semantic matching. We successfully achieve state-of-the-art performance on two widely known datasets: MSVD and MSR-VTT: (1) with the MSVD dataset, our method achieves an 18% improvement in CIDEr; (2) with the MSR-VTT dataset, our method achieves a 6% improvement in CIDEr.
Exploring Aspects of Agile Software Development Risk – Results from a MLR

Agile software development methods are widely used by software organisations, focusing on short developmental life cycles and customer satisfaction through the iterative and incremental development of software products. Despite their popularity, these methods present risks that may be underappreciated. This paper examines certain risks attributed to agile software development, with a focus on the lack of documentation, scope creep, technical debt and job satisfaction. Through the application of a multivocal literature review, we find that agile software development can greatly benefit projects. However, when agile methods are implemented inappropriately or sub-optimally, projects risk over-spending, delayed or defective software, employee turnover, and overall decreased productivity. Understanding the risks associated with agile software development can help practitioners to achieve higher efficiency and success in their software development projects.
A Neural Blockchain for Requirements Traceability: BC4RT Prototype

The ever-increasing globalization of the software industry presents challenges related to requirements engineering activities. Managing requirements’ changes and tracing software artifacts is not trivial in a multi-site environment composed of a variety of stakeholders that do not trust each other. In this study, we propose a neural blockchain prototype for the traceability of requirements (BC4RT) throughout the software development lifecycle in interorganizational software projects. The prototype is implemented using a neural blockchain platform, namely NDL ArcaNet, due to its inherent properties: performance efficiency, sustainability, and scalability. Besides these features, the proposed prototype provides a holistic and reliable view of software artifacts, requirements’ changes, and trace links. The increased visibility enhances collaboration, communication, and trust among stakeholders, and can potentially improve software development efficiency and quality.
Hybrid AI Talent Acquisition Model: An Opinion Mining and Topic based approach

Artificial Intelligence models have found their usage in the human resource domain. In this paper, job reviewers' opinions on online discussion boards have been captured. The relative importance of factors has been established through an extensive literature review. First, LDA Topic modelling by adopting PCA is performed on unstructured text data has been analyzed. Second, sentiment analysis using the Li-Hu method has been employed to understand job seekers' satisfaction with job portals. The proposed model, “Hybrid AI Talent Acquisition Model,” follows a novel approach to streamlining the jobseeker opinion related to online outlets.