Reviewerly/exHarmony huggingface dataset:
MOTC: Abdominal Multi-objective Segmentation Model with Parallel Fusion of Global and Local Information

Authors:
raw_author_name: GuoDong Zhang
raw_affiliation_string: School of Computer, Shenyang Aerospace University, Daoyi South Street, Shenyang, 110135, Liaoning Province, China
raw_author_name: YanLin Li
raw_affiliation_string: School of Computer, Shenyang Aerospace University, Daoyi South Street, Shenyang, 110135, Liaoning Province, China
raw_author_name: DaZhe Zhao
raw_affiliation_string: Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Wenhua Street, Shenyang, 110819, Liaoning Province, China
raw_author_name: ZhaoXuan Gong
raw_affiliation_string: School of Computer, Shenyang Aerospace University, Daoyi South Street, Shenyang, 110135, Liaoning Province, China
raw_author_name: RongHui Ju
raw_affiliation_string: Department of Radiology, The Peoples Hospital of Liaoning Province, Wenyi Street, Shenyang, 110016, Liaoning Province, China

Abstract: Convolutional Neural Networks have been widely applied in medical image segmentation. However, the existence of local inductive bias in convolutional operations restricts the modeling of long-term dependencies. The introduction of Transformer enables the modeling of long-term dependencies and partially eliminates the local inductive bias in convolutional operations, thereby improving the accuracy of tasks such as segmentation and classification. Researchers have proposed various hybrid structures combining Transformer and Convolutional Neural Networks. One strategy is to stack Transformer blocks and convolutional blocks to concentrate on eliminating the accumulated local bias of convolutional operations. Another strategy is to nest convolutional blocks and Transformer blocks to eliminate bias within each nested block. However, due to the granularity of bias elimination operations, these two strategies cannot fully exploit the potential of Transformer. In this paper, a parallel hybrid model is proposed for segmentation, which includes a Transformer branch and a Convolutional Neural Network branch in encoder. After parallel feature extraction, inter-layer information fusion and exchange of complementary information are performed between the two branches, simultaneously extracting local and global features while eliminating the local bias generated by convolutional operations within the current layer. A pure convolutional operation is used in decoder to obtain final segmentation results. To validate the impact of the granularity of bias elimination operations on the effectiveness of local bias elimination, the experiments in this paper were conducted on Flare21 dataset and Amos22 dataset. The average Dice coefficient reached 92.65% on Flare21 dataset, and 91.61% on Amos22 dataset, surpassing comparative methods. The experimental results demonstrate that smaller granularity of bias elimination operations leads to better performance.
Lagrangian particle tracking in the presence of obstructing objects

Authors:
raw_author_name: Thomas Rockstroh
raw_affiliation_string: LaVision GmbH, Anna-Vandenhoeck-Ring 19, Goettingen, 37081, GERMANY

Abstract: Abstract Volumetric flow measurement techniques have become the state-of-the-art for characterizing a broad range of different flow fields. Still, certain major limitations are present, which hinders the application of these techniques for some of the more complex flow configurations. In particular, flow measurements involving the presence of obstructing objects require time consuming measurement strategies and careful adjustment of the experimental equipment to avoid inaccurate measurement results. Within this study, these limitations are mitigated by the use of a known object's shape and position in the form of depth maps for commonly used Lagrangian particle tracking (LPT) schemes like Shake-the-Box (STB) as well as in volume self-calibration (VSC) methods. The incorporation of these depth maps is computationally inexpensive and straight forward to implement. In order to evaluate the performance of this novel object-aware Lagrangian particle tracking (OA-LPT) approach, synthetic as well as experimental test data is created and the reconstruction quality is evaluated. It is shown, that OA-LPT is capable of providing full flow-field information, whereas the default STB implementation fails to correctly reconstruct particles in the partly-occluded regions.
Applying Findable, Accessible, Interoperable, and Reusable Principles to Biospecimens and Biobanks

Authors:
raw_author_name: Jennifer A. Byrne
raw_affiliation_string: New South Wales Health Statewide Biobank, New South Wales Health Pathology, Camperdown, New South Wales, Australia.; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia.

Abstract: The importance of stimulating greater sharing of data for use and reuse in health research is widely recognized. To this end, the findable, accessible, interoperable, and reusable (FAIR) principles for data have been developed and widely accepted in the research community. Research biospecimens are a resource that leads to much of this health research data but are also a form of data. Therefore, the FAIR principles should apply to biospecimens. Nevertheless, there is a widespread problem of not sharing biospecimen resources that is clearly visible within the research arena. The impacts of this are likely to include diversion of precious research funds into compiling duplicate biospecimen cohorts, detraction from research productivity as researchers compete for and create duplicate resources, and deterrence of attempts to assess research reproducibility. This article explores some of the barriers that may limit availability of FAIR biospecimens. These barriers relate to the type of biospecimen collections and the characteristics of the custodians that influence their intention and interest in sharing. Barriers also relate to the ethical, legal, and social issues concerning collections, the research context of the collections, and cost and expertise involved in repurposing collections to enable sharing. Several solutions to increase sharing are identified. Some have recently been implemented, including enhancing biospecimen locators with tools to guide researchers and facilitating transfer of research collections to centralized biobank infrastructures at the conclusion of projects. New proposed solutions include improving search capabilities within publication databases, and introduction of evidence-based justifications for all new collections into peer-reviewed grant competition processes. It is recognized that there are both scientific factors and practical reasons that can impose limits to sharing biospecimens. However, funding availability, productivity, and progress in health research all stand to benefit from improved sharing of research biospecimen collections.
Smart Voting Web Based Application Using Aadhar

Authors:
raw_author_name: Lokesh .

Abstract: Abstract: Elections are a crucial part of any democratic system, and it is essential to ensure that the voting process is conducted in a fair and transparent manner. In traditional paper-based elections, the process is time-consuming, resource-intensive, and prone to errors. In this context, the use of technology can significantly improve the efficiency, security, and accuracy of the voting process. In this project, we propose an online voting system that uses face recognition technology to identify and authenticate voters. The proposed system allows voters to cast their votes remotely, eliminating the need for physical ballot boxes and reducing the cost and time involved in the voting process. The system works by capturing the facial image of the voter and passing it to the server unit for verification. The server compares the facial image with the information stored in the database and verifies the identity of the voter. If the identity is verified, the voter is allowed to cast the vote; otherwise, an error message is displayed on the screen, and the person is not allowed to poll the vote. The system is designed to be secure and tamper-proof. The use of face recognition technology makes it difficult for anyone to impersonate another person and cast a fraudulent vote. The system also ensures that each voter can cast only one vote, and the voting process is conducted in a transparent and fair manner. Overall, the proposed online voting system offers numerous advantages over traditional paper-based voting systems, including increased efficiency, reduced cost, improved security, and transparency.
A New Novel Approach for Sentiments Analysis using Contextual Mining and Supervised Learning

Authors:
raw_author_name: Shashi Bhushan

Abstract: Textual data mining is used to anticipate the sentiment of a user based on a similar book. Using conditional probability distributions, the rating similarity between books can be quantified by textual mining. In supervised learning, the input and output data are sent to the machine learning model in tandem to maximize accuracy. In this paper, a cloud-based suggested system is incorporated which detects and recommends similar types of content based on the ranking of books. A cloud recommendation system is a means of determining which service is best suited to the user's tastes and requirements. The work uses specific formats such as graphics or text, and the networks built throughout the process may help uncover the links between the words. Textual mining is used to calculate book-ranging similarities and recommendations. From the comparison result, the proposed logistic regression technique has maximum accuracy which is 82% as compared to the existing technique The model shows all recommended books that are like the input book are displayed, while all other types of books are hidden from view. Also, positive, or negative masking can be determined by comparing the intensity of the extracted images.
eLeap and End-Semester Report System for Teaching, Learning, and Assessing Program Education Outcome in Faculty of Engineering Unimas

Authors:
raw_author_name: Yanuar Zulardiansyah Arief
raw_author_name: Nazreen Junaidi

Abstract: 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.
A 2+1 line-scan camera measurement method for efficient and accurate narrow-space 3-D scanning with motion compensation

Authors:
raw_author_name: Luyao Ma
raw_author_name: jigui zhu
raw_author_name: Yang Linghui
raw_author_name: Haoyue Liu
raw_author_name: Shuo Yang

Abstract: 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

Authors:
raw_author_name: Qian Qu
raw_affiliation_string: Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USA
raw_author_name: Ronghua Xu
raw_affiliation_string: Department of Applied Computing, Michigan Technological University, Houghton, MI 49931, USA
raw_author_name: Deeraj Nagothu
raw_affiliation_string: Intelligent Fusion Technology, Inc., Germantown, MD 20874, USA
raw_author_name: Yu Chen
raw_affiliation_string: Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USA
raw_author_name: Xiaohua Li
raw_affiliation_string: Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USA
raw_author_name: Erik Blasch
raw_affiliation_string: The U.S. Air Force Research Laboratory, Rome, NY 13441, USA
raw_author_name: Erika Ardiles-Cruz
raw_affiliation_string: The U.S. Air Force Research Laboratory, Rome, NY 13441, USA
raw_author_name: Genshe Chen
raw_affiliation_string: Intelligent Fusion Technology, Inc., Germantown, MD 20874, USA

Abstract: 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

Authors:
raw_author_name: Jiawang Liu
raw_affiliation_string: State Key Laboratory of Public Big Data, College of Compute Science and Technology, Guizhou University, Guiyang 550025, China
raw_author_name: Changgen Peng
raw_affiliation_string: State Key Laboratory of Public Big Data, College of Compute Science and Technology, Guizhou University, Guiyang 550025, China
raw_author_name: Weijie Tan
raw_affiliation_string: Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China; State Key Laboratory of Public Big Data, College of Compute Science and Technology, Guizhou University, Guiyang 550025, China
raw_author_name: Chenghui Shi
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China

Abstract: 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.
HistoriView: Implementation and Evaluation of a Novel Approach to Review a Patient Using a Scalable Space-Efficient Timeline without Zoom Interactions

Authors:
raw_author_name: V Castro
raw_affiliation_string: Department of Research Information Science and Computing, Mass General Brigham, Somerville, Massachusetts, United States
raw_author_name: Vivian Gainer
raw_affiliation_string: Department of Research Information Science and Computing, Mass General Brigham, Somerville, Massachusetts, United States

Abstract: 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

Authors:
raw_author_name: Qingliang Zeng
raw_affiliation_string: College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Liang Wang
raw_affiliation_string: College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Abstract: 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.