Reviewerly/exHarmony huggingface dataset:
Students’ perception of taking career-related subjects in the high school credit system and the standard-based assessment

Objectives The purpose of this study is to examine students’ perception of taking career-related subjects in the high school credit system and the standard-based assessment to understand how career-related subjects are operated in high schools, what factors are related with students’ selections, how career-related subjects are related with the university admission system.&#x0D; Methods A questionnaire was administered to the 3rd grade students in general academic high schools located in capital areas, who entered in high schools in 2019 when the standard-based assessment began to get implemented to career-related subjects. It consisted of 30 items for student background and five domains related with the operation of career-related subjects and the factors influencing students’ selections of subjects, and the association between career-related subjects and university admission. For the purpose of the study, descriptive statistical analyses, regression analysis, correlation analysis, and ANOVA were made.&#x0D; Results The results showed that the major factor for students’ selecting subjects was their career and aptitude, but students were found to require more professional counseling. Also ‘common curriculum’ and ‘online curriculum’ need to be developed to provide the variety of subjects for students. To help students select subjects for their own career and aptitude, the reliability of standard-based assessment should also be ensured. For students’ satisfaction of taking subjects, the quality of teaching should, first of all, be improved. Finally, it was found that students considered the career-related subjects they had taken should be reflected for university admission.&#x0D; Conclusions Career-related subjects tend to be operated in high schools appropriately for the purpose of the high school credit system, but more efforts should be made with the consideration of students’ selective curriculum-related conditions, standard-based assessment, and university admission system.
An improved Marine biometric detection algorithm based on YoloX

The identification of marine biological organisms is essential for the exploitation of underwater resources. In an underwater setting, light is easily refracted, making the captured image subject to distortion. The image produced by underwater light imaging technology is crisper and the imaging speed is faster when the distance is close, so underwater collection images are mainly big objectives. One must first increase the detection accuracy of large objects if one is to enhance the underwater target identification model's overall performance. One of important methods is to improve the receptive field of the detection model. This paper implements the conversion of the model's neck from PA-FPN to NAS-FPN, reduces the number of input feature maps, and keeps the same number of output characteristic maps in order to increase the down sampling multiple of the YOLOX model. It increases the down sampling of the original map several times while only adding a small number of anchor frames, making it more sensitive to huge targets. After numerous tests, the accuracy has increased by 1.5% on average, and a more accurate underwater target detection model has been created.
Students' Perception towards the Use of ICT in EFL Learning at Eleventh Grade SMA Negeri 1 Dolok Batu Nanggar

This research aims at exploring high school students’ perception towards the use of ICT in EFL learning. To achieve the objective, 44 students at eleventh grade majoring in Language and Culture Science (IBB) SMA Negeri 1 Dolok Batu Nanggar located in Serbelawan were surveyed using questionnaire. The data obtained was analyzed by employing the descriptive analysis technique using questionnaire distributed to students in the form of Google Form. The results showed that the students’ perception was positive and high in each perception aspects in the questionnaire given. They are attractiveness, perceived effectiveness, and relevancy aspects that obtained index score of 86%, 84%, and 85% or categorized as strongly agree response, and 77% for perceived motivation aspects or categorized as agree response. Based on the result obtained, this study can be concluded that the use of ICT in EFL learning is attractive, effective, and relevant to the English material students need to learn. It also motivates students to learn English more.
An integrated Linkage Analysis Study between current Student Education Satisfaction Survey, Core Competency Diagnosis, and College Life Adaptation Diagnosis to improve the Quality and Result of University Education: Focusing on the Case of University D

An integrated Linkage Analysis Study between current Student Education Satisfaction Survey, Core Competency Diagnosis, and College Life Adaptation Diagnosis to improve the Quality and Result of University Education: Focusing on the Case of University D - current student education satisfaction;core competency diagnosis;university life adaptation diagnosis;undergraduate education survey;quality and performance of university education
Futuristic applications of voice user interference on child language development

Voice User Interface (VUI) is an Artificial Intelligence tool that enables children to access a computing device and complete tasks through speech instead of using learning methods. VUI, a form of AI (Artificial Intelligence), takes a sound that children articulate in a spoken statement and use intent recognition to understand the action required to fulfill the child’s spoken request. The design and features of VUI have been developed to increase the interpersonal level of communication with users and, to some degree, make voice assistants behave like humans. The features that have been created, have been shaped in such a way as to improve learning efficacy and ease of use for early childhood learning development. The current available VUIs in the market have been geared to provide children with a simpler way to interact with access to educational technology learning tools. The research posits that there are two primary uses of VUI in childhood learning development exploration, whereby children use VUI as a form of entertainment and information seeking, and children use VUI to develop various knowledge facets. For children in the early language stages currently using language to communicate, VUI language stimulation can help children to engage in continuous communication processes, use and understand various words, and successfully complete more complex sentences. The research seeks to state the problems associated with VUI and the standard opinions based on research associated with the problem. Moreover, the study seeks to articulate the hypothesis that VUI is an effective tool for early childhood language learning through the use of peer-reviewed evidence and examples, to the hypothesis, to generate new and innovative perspectives.
LiDAR-assisted accuracy improvement strategy for binocular visual measurement

The measurement model of binocular vision is inaccurate when the measurement distance is much different from the calibration distance, which affects its practicality. To tackle this challenge, we proposed what we believe to be a novel LiDAR-assisted accuracy improvement strategy for binocular visual measurement. First, the 3D points cloud and 2D images were aligned by the Perspective-n-Point (PNP) algorithm to realize calibration between LiDAR and binocular camera. Then, we established a nonlinear optimization function and proposed a depth-optimization strategy to lessen the error of binocular depth. Finally, the size measurement model of binocular vision based on the optimized depth is built to verify the effectiveness of our strategy. The experimental results show that our strategy can improve the depth accuracy compared to three stereo matching methods. The mean error of binocular visual measurement decreased from 33.46% to 1.70% at different distances. This paper provides an effective strategy for improving the measurement accuracy of binocular vision at different distances.
Digitalization of Enterprises from a New Normal Sustainable Development Perspective: Opportunities and Challenges

After the pandemic, leaders look for new ways to transform their organizations. One of these is finding a competitive workforce that can support the digital transformation of their businesses. Leaders will be expected to change the culture of their organizations, and they will also be responsible for setting safety guidelines. New goals and innovative methods will also be needed to support the digital transformation. This chapter will discuss the various aspects of implementing new policies and procedures to support digital transformation. They will also discuss how these can help organizations bring a sense of security to their customers. The goal of technology leaders is to restore the trust of their customers and employees following a pandemic. It is done through the removal of the anxiety and reactions that were caused by the outbreak.
MWPoW+: A Strong Consensus Protocol for Intra-Shard Consensus in Blockchain Sharding

Blockchain sharding splits a blockchain into several shards where consensus is reached at the shard level rather than over the entire blockchain. It improves transaction throughput and reduces the computational resources required of individual nodes. But a derivation of trustworthy consensus within a shard becomes an issue as the longest chain based mechanisms used in conventional blockchains can no longer be used. Instead, a vote-based consensus mechanism must be employed. However, existing vote-based Byzantine fault tolerance consensus protocols do not offer sufficient security guarantees for sharded blockchains. First, when used to support consensus where only one block is allowed at a time (binary consensus), these protocols are susceptible to progress-hindering attacks (i.e., unable to reach a consensus). Second, when used to support a stronger type of consensus where multiple concurrent blocks are allowed (strong consensus), their tolerance of adversary nodes is low. This article proposes a new consensus protocol to address all these issues. We call the new protocol MWPoW +, as its basic framework is based on the existing Multiple Winners Proof of Work (MWPoW) protocol but includes new mechanisms to address the issues mentioned previously. MWPoW+ is a vote-based protocol for strong consensus, asynchronous in consensus derivation but synchronous in communication. We prove that it can tolerate up to f &lt; n /2 adversary nodes in a n-node system as if using a binary consensus protocol and does not suffer from progress-hindering attacks.
A Comparative Study on Method Comment and Inline Comment

Code comments are one of the important documents to help developers review and comprehend source code. In recent studies, researchers have proposed many deep learning models to generate the method header comments (i.e., method comment), which have achieved encouraging results. The comments in the method, which is called inline comment, are also important for program comprehension. Unfortunately, they have not received enough attention in automatic generation when comparing with the method comments. In this paper, we compare and analyze the similarities and differences between the method comments and the inline comments. By applying the existing models of generating method comments to the inline comment generation, we find that these existing models perform worse on the task of inline comment generation. We then further explore the possible reasons and obtain a number of new observations. For example, we find that there are a lot of templates (i.e., comments with the same or similar structures) in the method comment dataset, which makes the models perform better. Some terms were thought to be important (e.g., API calls) in the comment generation by previous study does not significantly affect the quality of the generated comments, which seems counter-intuitive. Our findings may give some implications for building the approaches of method comment or inline comment generation in the future.
Unassisted Clinicians Versus Deep Learning–Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis

A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification.We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification.PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality.In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups.The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required.PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.
Bringing AR/VR to Everyday Life - a Wireless Localization Perspective

Augmented reality (AR) and virtual reality (VR) have the potential to revolutionize the way we interact with the environment around us. These technologies allow us to experience and collaborate with people in an immersive and intuitive way. Today, AR/VR is no longer limited to gaming and entertainment, rather it's blending into our everyday lives with applications in medical fields, education, grocery shopping, virtual try-ons etc. While a lot of progress has been made in visual rendering and scene understanding, little work is done on multi-user localization. To fully realize the potential of collaboration and multi-user applications, it is important to have an accurate and real-time 3D localization. Current 3D localization frameworks use cameras to create a relative coordinate system but these visual technologies struggle in low light conditions, and also require all devices to share an overlap of visual features which limit the applications to line-of-sight and room-level.
Autonomous landing scene recognition based on transfer learning for drones

In this paper, we study autonomous landing scene recognition with knowledge transfer for drones. Considering the difficulties in aerial remote sensing, especially that some scenes are extremely similar, or the same scene has different representations in different altitudes, we employ a deep convolutional neural network (CNN) based on knowledge transfer and fine-tuning to solve the problem. Then, LandingScenes-7 dataset is established and divided into seven classes. Moreover, there is still a novelty detection problem in the classifier, and we address this by excluding other landing scenes using the approach of thresholding in the prediction stage. We employ the transfer learning method based on ResNeXt-50 backbone with the adaptive momentum (ADAM) optimization algorithm. We also compare ResNet-50 backbone and the momentum stochastic gradient descent (SGD) optimizer. Experiment results show that ResNeXt-50 based on the ADAM optimization algorithm has better performance. With a pre-trained model and fine-tuning, it can achieve 97.845 0% top-1 accuracy on the LandingScenes-7 dataset, paving the way for drones to autonomously learn landing scenes.
Blockchain and IFPS based Secure System for Managing e-FIR

In recent times, various forms of crime have been happening worldwide.The law-and-order department of any country officially records a crime in electronic forms or on paper when the crime is reported by a victim or someone on behalf of the victim.The document that is prepared to file any perceptible committed crimes including dowry, kidnap, murder, rape, theft, and others is called First Information Report(FIR).Nowadays, online FIR also known as e-FIR has been used worldwide.Every day a number of e-FIR are filed, and they are maintained in a centralized database with the aid of third-party trust.Consequently, malicious entities including insiders and outsiders' dishonest personnel, and third-party authorities may tamper with e-FIR that questions the transparency and integrity of FIR reports.To address this exposure, in this paper, we propose a blockchain based FIR system to store all kinds of offense-related records to assure security, fidelity and privacy of FIR records.In this proposed system, the blockchain technology that refers to a decentralized and distributed ledger across peer-to-peer networks continually updates the shared ledger and strictly maintains synchronization among all network nodes.Though blockchain technology guarantees tamper-proof of the data, it cannot store a large amount of data due to the replication of ledger among all network nodes.To solve this issue, we adopt the Inter-Planetary File system (IPFS) protocol to store data in the blockchain.IPFS is a distributed file-sharing system that can be leveraged to store and share large files.The blockchain based FIR system has been tested on an Ethereum environment using blockchain and IPFS technology.
Development of a Dual-CPU Equipment Controller Special for Seismic Stations

This paper introduces the principle and design of a Dual-CPU Equipment Controller, which base on SMS of GSM. The terminal consists of a wireless GSM module (Q24plus+ module produced by WAVECOM company) and a monitor and control unit taking dual- microcomputer (P89s52) as the cores. This control terminal is a terminal device that can be controlled remotely through mobile phone short messages, or network control commands from Ethernet.
Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image

Abstract: Machine Learning (ML) plays an essential part in the research area of medical image processing. The advantages of ML techniques lead to more intelligent, accurate, and automatic computeraided detection (CAD) systems with improved learning capability. In recent years, deep learning-based ML approaches developed to improve the diagnostic capabilities of CAD systems. This study reviews image enhancement, ML and DL methods for breast cancer detection and diagnosis using mammogram images and provides an overview of these methods. The analysis of different ways of ML and DL shows that the usages of traditional ML approaches are limited. However, DL techniques have an excellent future for implementing medical image analysis and improving the ability to exist CAD systems. Despite the significant advancements in deep learning methods for analyzing medical images to detect breast cancer, challenges still exist regarding data quality, computational cost, and prediction accuracy.
The role of teacher support in the acquisition of digital skills associated with technology-based learning activities: the moderation of the educational level

The presence of the technology in the lives of young students does not guarantee that they know how to use it as a learning resource. Likewise, doubts remain about the role of teacher support in the digital literacy of their students. Assuming the moderating capacity of educational level, the aim of this study was to understand to what extent teacher support can determine students’ ability to use technology as a learning resource. In order to respond to this objective, the Model for Developing Effective e-Learners (MDEeL) was used. A multigroup analysis with structural equations and a simple quota sample of secondary education (N = 300) and higher education (N = 300) students in Spain were used. The results showed that the influence of teaching support on basic digital skills associated with the use of the Internet as a learning resource was moderated by educational level. The study provides an approach that allows teacher support for digital literacy to be evaluated in the context of student learning practices.
MILO: Multi-Bounce Inverse Rendering for Indoor Scene With Light-Emitting Objects

Recently, many advances in inverse rendering are achieved by high-dimensional lighting representations and differentiable rendering. However, multi-bounce lighting effects can hardly be handled correctly in scene editing using high-dimensional lighting representations, and light source model deviation and ambiguities exist in differentiable rendering methods. These problems limit the applications of inverse rendering. In this paper, we present a multi-bounce inverse rendering method based on Monte Carlo path tracing, to enable correct complex multi-bounce lighting effects rendering in scene editing. We propose a novel light source model that is more suitable for light source editing in indoor scenes, and design a specific neural network with corresponding disambiguation constraints to alleviate ambiguities during the inverse rendering. We evaluate our method on both synthetic and real indoor scenes through virtual object insertion, material editing, relighting tasks, and so on. The results demonstrate that our method achieves better photo-realistic quality.
Alpha Phi-shing Fraternity: Phishing Assessment in a Higher Education Institution

Phishing is a common social engineering attack aimed to steal personal information. Universities attract phishing attacks because: 1) they store employees and students sensitive data, 2) they save confidential documents, 3) their infrastructures often lack security. In this paper, we showcase a phishing assessment at the University of Redacted aimed to identify the people, and the features of such people, that are more susceptible to phishing attacks. We delivered phishing emails to 1.508 subjects in three separate batches, collecting a clickrate equal to 30%, 11% and 13%, respectively. We considered several features (i.e., age, gender, role, working/studying field, email template) in univariate and multivariate analyses and found that students are more susceptible to phishing attacks than professors or technical/administrative staff, and that emails designed through a spearphishing approach receive a highest clickrate. We believe this work provides the foundations for setting up an effective educational campaign to prevent phishing attacks not only at the University of Redacted, but in any other university.