Reviewerly/exHarmony huggingface dataset:
eLeap and End-Semester Report System for Teaching, Learning, and Assessing Program Education Outcome in Faculty of Engineering Unimas

This paper deals with sharing experience in teaching and learning for engineering courses in the Faculty of Engineering, UNIMAS, particularly during the COVID-19 pandemic. This paper consists of 2 (two) sections, firstly conducting a teaching and learning platform using the e- Learning Enrichment and Advancement Platform (eLEAP) and secondly regarding the implementation of an end-semester report (ESR) system for assessing the achievement of program education outcomes for engineering courses in the Faculty of Engineering UNIMAS. By implementing these two systems, it was found that the eLEAP system provided many benefits during the COVID-19 pandemic in Malaysia. Therefore, the teaching and learning process including evaluation was implemented during the tough period of the pandemic (Movement Control Order; MCO) where students and lecturers were not allowed to come physically to the campus.
Low-scale leptogenesis with Dirac phase CP-violation

The freeze-in scenario of leptogenesis via oscillations within the type-I seesaw model with two quasi-degenerate heavy Majorana neutrinos $N_{1,2}$ can be viable in the mass range $M\sim (0.1-100)\,\text{GeV}$ even when the Dirac phase $\delta$ of the Pontecorvo-Maki-Nakagawa-Sakata neutrino mixing matrix is the only source of CP-violation in the leptonic sector and in leptogenesis. In particular, viable leptogenesis with Dirac phase CP-violation is compatible with $N_{1,\,2}$ total squared coupling to the Standard Model leptons within the reach of future planned and proposed experiments. Moreover, the parameter space differs from the one associated with the Casas-Ibarra sources of CP-violation. Future precise determination of $\delta$ and/or the $N_{1,2}$ couplings to the Standard Model leptons could establish whether, within the context of low-scale leptogenesis, the Dirac phase alone can be responsible for the present baryon asymmetry of the Universe.
Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts：a diagnostic study

We aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts.A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only.The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% confidence interval [95%CI], 0.874~1.000) in the test cohort, and an AUC of 0.906 (95%CI, 0.817~0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone (P<0.05).The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis.
A 2+1 line-scan camera measurement method for efficient and accurate narrow-space 3-D scanning with motion compensation

Vision-based three-dimensional (3-D) shape measurement plays a crucial role in the inspection of vehicles and trains in the field of transportation. Consequently, the demand for more comprehensive narrow-space inspection has become an inevitable necessity and presents a great challenge to conventional vision methods. We propose the following efficient and accurate narrow-space 3-D scanning method based on triple line-scan cameras. First, the structure of coplanar dual line-scan cameras with a narrow reflector is proposed, making it easy for the optical path to enter a narrow space and obtain 3-D shape information. Efficient in-motion measurement with a large field of view is thereby achieved without the worries that often accompany narrow-space constraints. Secondly, an additional line-scan camera is attached to the coplanar dual cameras and creates a time-space constraint in point cloud stitching direction through the triple line-scan camera structure. With an efficient algorithm template including matching and motion estimation, accurate point cloud stitching is ensured. Lastly, point cloud texture mapping and light source optimization are realized. Our experimental results prove that the method realizes low-distortion in-motion reconstruction in narrow space despite motion variation.
The Microverse: A Task-Oriented Edge-Scale Metaverse

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge–fog–cloud computing, and machine learning (ML)/artificial intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), blockchain, and digital twins (DTs). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces the microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike all-encompassing metaverses, each microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge–fog–cloud computing framework. The microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the metaverse.
Federated Learning Backdoor Attack Based on Frequency Domain Injection

Federated learning (FL) is a distributed machine learning framework that enables scattered participants to collaboratively train machine learning models without revealing information to other participants. Due to its distributed nature, FL is susceptible to being manipulated by malicious clients. These malicious clients can launch backdoor attacks by contaminating local data or tampering with local model gradients, thereby damaging the global model. However, existing backdoor attacks in distributed scenarios have several vulnerabilities. For example, (1) the triggers in distributed backdoor attacks are mostly visible and easily perceivable by humans; (2) these triggers are mostly applied in the spatial domain, inevitably corrupting the semantic information of the contaminated pixels. To address these issues, this paper introduces a frequency-domain injection-based backdoor attack in FL. Specifically, by performing a Fourier transform, the trigger and the clean image are linearly mixed in the frequency domain, injecting the low-frequency information of the trigger into the clean image while preserving its semantic information. Experiments on multiple image classification datasets demonstrate that the attack method proposed in this paper is stealthier and more effective in FL scenarios compared to existing attack methods.
Enhancing institutional policies and frameworks for E-learning: A case study of the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

The onset of Covid-19 has made learning online an important component of tertiary-level education. However, for the Kwame Nkrumah University of Science and Technology (KNUST), the challenge has been the lack of a clear policy guiding teaching and learning within online environments. Instructors seem to be unclear about which standards/policies are expected of them when delivering instructions in online settings, and learners are equally unsure of the ethics and responsibilities that come with online learning. This research thus sets out to address this problem by developing frameworks for the institutionalization of a codified E-learning policy for KNUST to guide/regulate online teaching and learning. The research adopted a convergent parallel design in which qualitative and quantitative approaches were used. Three key institutional E-Learning innovators and 73 lecturers from different Departments and Colleges of KNUST were conveniently and purposefully sampled to interview schedules and survey questionnaires for data collection. Audio-recorded interviews were transcribed and coded. Furthermore, a focused group discussion was conducted with seven level 400 students. The quantitative data was analysed using descriptive statistical methods (including standard deviations, percentages, means, and graphs where appropriate). From our preliminary qualitative results, participants agreed that existing policies and infrastructure in KNUST are inadequate and do not fully address online teaching and learning needs. In terms of policy recommendations, while teaching staff are concerned about and advocate for policies that regulate learners’ ethical behavior within online learning spaces; learners are interested in policies that regulate the ethical behavior of learners and those that specify standards of teaching for facilitators. Some learners emphasized the need for policies to include adequate support staff for each online learning activity to ensure that both learners and facilitators uphold the standards. Seventy percent of the respondents indicated the need for designed institutional sanctions for student misconduct during online teaching and learning engagements. Again, more than 50% of respondents indicated the need for specific and clearer ethical and copyright guidelines for online teaching and learning as well as clearer instructions regarding student behavior within online teaching environments. The findings that emerge from this research seek to make specific recommendations to address the barriers that hinder effective teaching and learning in online environments. The authors hereby recommend that government institutions such as the Ghana Tertiary Education Commission should strongly encourage stakeholder discussions for a nationwide E-learning policy from which tertiary institutions could use as a springboard in drafting policies/guidelines, policy audit, and conducting a needs assessment.
HistoriView: Implementation and Evaluation of a Novel Approach to Review a Patient Using a Scalable Space-Efficient Timeline without Zoom Interactions

Abstract Background Timelines have been used for patient review. While maintaining a compact overview is important, merged event representations caused by the intricate and voluminous patient data bring event recognition, access ambiguity, and inefficient interaction problems. Handling large patient data efficiently is another challenge. Objective This study aims to develop a scalable, efficient timeline to enhance patient review for research purposes. The focus is on addressing the challenges presented by the intricate and voluminous patient data. Methods We propose a high-throughput, space-efficient HistoriView timeline for an individual patient. For a compact overview, it uses nonstacking event representation. An overlay detection algorithm, y-shift visualization, and popup-based interaction facilitate comprehensive analysis of overlapping datasets. An i2b2 HistoriView plugin was deployed, using split query and event reduction approaches, delivering the entire history efficiently without losing information. For evaluation, 11 participants completed a usability survey and a preference survey, followed by qualitative feedback. To evaluate scalability, 100 randomly selected patients over 60 years old were tested on the plugin and were compared with a baseline visualization. Results Most participants found that HistoriView was easy to use and learn and delivered information clearly without zooming. All preferred HistoriView over a stacked timeline. They expressed satisfaction on display, ease of learning and use, and efficiency. However, challenges and suggestions for improvement were also identified. In the performance test, the largest patient had 32,630 records, which exceeds the baseline limit. HistoriView reduced it to 2,019 visual artifacts. All patients were pulled and visualized within 45.40 seconds. Visualization took less than 3 seconds for all. Discussion and Conclusion HistoriView allows complete data exploration without exhaustive interactions in a compact overview. It is useful for dense data or iterative comparisons. However, issues in exploring subconcept records were reported. HistoriView handles large patient data preserving original information in a reasonable time.
Detection of Coal and Gangue Based on Improved YOLOv8

To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications.
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition

Human activity recognition (HAR) in wearable and ubiquitous computing typically involves translating sensor readings into feature representations, either derived through dedicated pre-processing procedures or integrated into end-to-end learning approaches. Independent of their origin, for the vast majority of contemporary HAR methods and applications, those feature representations are typically continuous in nature. That has not always been the case. In the early days of HAR, discretization approaches had been explored—primarily motivated by the desire to minimize computational requirements on HAR, but also with a view on applications beyond mere activity classification, such as, for example, activity discovery, fingerprinting, or large-scale search. Those traditional discretization approaches, however, suffer from substantial loss in precision and resolution in the resulting data representations with detrimental effects on downstream analysis tasks. Times have changed, and in this paper, we propose a return to discretized representations. We adopt and apply recent advancements in vector quantization (VQ) to wearables applications, which enables us to directly learn a mapping between short spans of sensor data and a codebook of vectors, where the index comprises the discrete representation, resulting in recognition performance that is at least on par with their contemporary, continuous counterparts—often surpassing them. Therefore, this work presents a proof of concept for demonstrating how effective discrete representations can be derived, enabling applications beyond mere activity classification but also opening up the field to advanced tools for the analysis of symbolic sequences, as they are known, for example, from domains such as natural language processing. Based on an extensive experimental evaluation of a suite of wearable-based benchmark HAR tasks, we demonstrate the potential of our learned discretization scheme and discuss how discretized sensor data analysis can lead to substantial changes in HAR.
Constrained Cubature Particle Filter for Vehicle Navigation

In vehicle navigation, it is quite common that the dynamic system is subject to various constraints, which increases the difficulty in nonlinear filtering. To address this issue, this paper presents a new constrained cubature particle filter (CCPF) for vehicle navigation. Firstly, state constraints are incorporated in the importance sampling process of the traditional cubature particle filter to enhance the accuracy of the importance density function. Subsequently, the Euclidean distance is employed to optimize the resampling process by adjusting particle weights to avoid particle degradation. Further, the convergence of the proposed CCPF is also rigorously proved, showing that the posterior probability function is converged when the particle number N → ∞. Our experimental results and the results of a comparative analysis regarding GNSS/DR (Global Navigation Satellite System/Dead Reckoning)-integrated vehicle navigation demonstrate that the proposed CCPF can effectively estimate system state under constrained conditions, leading to higher estimation accuracy than the traditional particle filter and cubature particle filter.
Point Cloud Painting for 3D Object Detection with Camera and Automotive 3+1D RADAR Fusion

RADARs and cameras have been present in automotives since the advent of ADAS, as they possess complementary strengths and weaknesses but have been underlooked in the context of learning-based methods. In this work, we propose a method to perform object detection in autonomous driving based on a geometrical and sequential sensor fusion of 3+1D RADAR and semantics extracted from camera data through point cloud painting from the perspective view. To achieve this objective, we adapt PointPainting from the LiDAR and camera domains to the sensors mentioned above. We first apply YOLOv8-seg to obtain instance segmentation masks and project their results to the point cloud. As a refinement stage, we design a set of heuristic rules to minimize the propagation of errors from the segmentation to the detection stage. Our pipeline concludes by applying PointPillars as an object detection network to the painted RADAR point cloud. We validate our approach in the novel View of Delft dataset, which includes 3+1D RADAR data sequences in urban environments. Experimental results show that this fusion is also suitable for RADAR and cameras as we obtain a significant improvement over the RADAR-only baseline, increasing mAP from 41.18 to 52.67 (+27.9%).
Matrix Diffractive Deep Neural Networks Merging Polarization into Meta‐Devices (Laser Photonics Rev. 18(2)/2024)

Laser & Photonics ReviewsVolume 18, Issue 2 2470013 Front CoverFree Access Matrix Diffractive Deep Neural Networks Merging Polarization into Meta-Devices (Laser Photonics Rev. 18(2)/2024) Yuzhong Wang, Yuzhong WangSearch for more papers by this authorAxiang Yu, Axiang YuSearch for more papers by this authorYayun Cheng, Yayun ChengSearch for more papers by this authorJiaran Qi, Jiaran QiSearch for more papers by this author Yuzhong Wang, Yuzhong WangSearch for more papers by this authorAxiang Yu, Axiang YuSearch for more papers by this authorYayun Cheng, Yayun ChengSearch for more papers by this authorJiaran Qi, Jiaran QiSearch for more papers by this author First published: 14 February 2024 https://doi.org/10.1002/lpor.202470013AboutPDF 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 Graphical Abstract Matrix Diffractive Deep Neural Networks In article number 2300903, Yuzhong Wang, Axiang Yu, Yayun Cheng, and Jiaran Qi propose the Matrix Diffractive Deep Neural Networks, a general framework effectively merging polarization into meta-devices. Advanced polarization-loaded meta-devices are demonstrated with exceptional functionality and high capacity, including optoelectronic classifier, Jones Matrix full parameter modulator, and custom-polarization encryptors. This work provides a new paradigm enabling advanced cascaded spatial light/wave modulators, which further drives the evolution of meta-devices toward superior functionality, multi-task integration, and design scalability. Volume18, Issue2February 20242470013 RelatedInformation
Development of 3D Visualization Training System for Switch Room Safety Procedures

Safety procedures are the fundamental criteria to ensure the safety of personnel and equipment, and their proficiency and the rigor of the application are crucial. According to the actual existing training technology digital means is relatively backward, intuitive, affect the lack of training depth and effect, this paper to the switch room equipment as the object, based on 3Dmodeling and virtual simulation technology design and developed a can realize safety procedures learning, training of 3D visualization software tools, to solve the difficulty of maintenance training, safety procedures training tools missing problems.
Green Training, Green Recruitment, and Green Transformational Leadership on Employee Performance in Retail Store

Purpose: This study aims to investigate how green training, green recruitment, and green transformational leadership affect employee performance at self-service retail in Yogyakarta, Indonesia.&#x0D; Study design: The study was carried out with a quantitative methodology, application of a quantitative research strategy, specifically by giving respondents questionnaires. There are 214.000 responders in this research population, while with the slovin formulation a sample of 178 respondents was obtained. The Cluster Sampling technique is applied in Probability Sampling to take the sample. Utilizing the Variance Based Structural Equation Modeling (SEM) methodology, the SmartPLS program version 4.0 was tested for validity and reliability using an analytical tool.&#x0D; Findings: The research results show that Green Training has a significant positive effect on Employee Performance, Green Recruitment has a significant positive effect on Employee Performance and Green Transformational Leadership has no significant effect on Employee Performance.&#x0D; Originality/value: This research contributes to research on retail companies where research is very rarely conducted, especially on green management research, from an employee perspective.