Reviewerly/exHarmony huggingface dataset:
Blueprints from plane to space: outlook of next‐generation three‐dimensional histopathology

Authors:
raw_author_name: Akira Leon Yoshikawa
raw_affiliation_string: Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; Department of Pathology, Kameda Medical Center, Chiba, Japan

Abstract: Abstract Here, we summarize the literature relevant to recent advances in three‐dimensional (3D) histopathology in relation to clinical oncology, highlighting serial sectioning, tissue clearing, light‐sheet microscopy, and digital image analysis with artificial intelligence. We look forward to a future where 3D histopathology expands our understanding of human pathophysiology and improves patient care through cross‐disciplinary collaboration and innovation.
Algorithm to satisfy <i>l</i>‐diversity by combining dummy records and grouping

Authors:
raw_author_name: Keiichiro Oishi
raw_affiliation_string: Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
raw_author_name: Yuichi Sei
raw_affiliation_string: Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
raw_author_name: J. Andrew
raw_affiliation_string: Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
raw_author_name: Yasuyuki Tahara
raw_affiliation_string: Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
raw_author_name: Akihiko Ohsuga
raw_affiliation_string: Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan

Abstract: Abstract Universities and corporations frequently use personal information databases for diverse objectives, such as research and marketing. The use of these databases inherently intersects with privacy issues, which have been the subject of extensive research. Traditional anonymization techniques predominantly focus on removing or altering identifiers and quasi‐identifiers (QIDs), the latter of which, although not unique, are closely correlated with individuals. However, this modification of QIDs can often impede data analysis. In this study, we introduce an innovative anonymization algorithm that combines the dummy‐record addition technique with a grouping method while circumventing the modification of QIDs. This fusion reduces the number of dummy records required for effective anonymization. The principal contribution of this study is the algorithm's ability to reduce the number of added dummy records. The proposed algorithm not only retains a high degree of data usefulness but also successfully adheres to the ‐diversity standard, which is a critical metric in privacy security. The experimental findings demonstrate that the proposed method offers a more equitable balance between safety and utility than existing technological solutions.
Quantum computer based feature selection in machine learning

Authors:
raw_author_name: Gerhard Hellstern
raw_affiliation_string: Center of Finance, DHBW Stuttgart, Stuttgart, Germany; DHBW Ravensburg, Ravensburg, Germany
raw_author_name: Vanessa Dehn
raw_affiliation_string: Fraunhofer‐Institut für Angewandte Festkörperphysik IAF  Freiburg Germany
raw_author_name: Martin Zaefferer
raw_affiliation_string: DHBW Ravensburg, Ravensburg, Germany

Abstract: Abstract The problem of selecting an appropriate number of features in supervised learning problems is investigated. Starting with common methods in machine learning, the feature selection task is treated as a quadratic unconstrained optimisation problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. The different results in small problem instances are compared. According to the results of the authors’ study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, the authors compare the convergence behaviour of the QUBO methods via quantum computing with classical stochastic optimisation methods. Due to persisting error rates, the classical stochastic optimisation methods are still superior.
Explainable artificial intelligence-based evidential inferencing on process faults in plasma etching

Authors:
raw_author_name: Jeong Eun Choi
raw_author_name: Dohyun Kim
raw_author_name: Sang Jeen Hong

Abstract: Abstract The fault detection and classification (FDC) modeling proposed in this study is a research approach that is intended to improve the performance of plasma process models by leveraging optical emission spectroscopy (OES) data containing plasma information (PI) and enhancing model interpretability using explainable artificial intelligence (XAI) algorithms. Status variable identification data that included normal and abnormal states of bias power, pressure, SF 6 gas flow, and O 2 gas flow were collected during a silicon etching process with SF 6 , O 2 gas plasma. Additional variables were derived from the OES data and included additional PI, such as O and F radicals, which were computed using actinometry, and electron temperature and electron density computed using the line ratio method. By building a high-performance FDC model and interpreting its results using XAI algorithms, we propose solutions to the limitations of the FDC model in semiconductor plasma processes.
Reference‐Frame‐Independent Mode‐Pairing Quantum Key Distribution with Advantage Distillation

Authors:
raw_author_name: Zhongqi Sun
raw_affiliation_string: School of Science and State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
raw_author_name: Zhenhua Li
raw_affiliation_string: School of Science and State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
raw_author_name: Jiaao Li
raw_affiliation_string: School of Science and State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
raw_author_name: Haoyang Wang
raw_affiliation_string: School of Science and State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
raw_author_name: Kaiyi Shi
raw_affiliation_string: School of Science and State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
raw_author_name: Chang Liu
raw_affiliation_string: School of Science and State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China
raw_author_name: Haiqiang Ma
raw_affiliation_string: School of Science and State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

Abstract: Abstract The coordination between distance and the secure key rate is one of the main challenges in the practical application of quantum key distribution (QKD). Mode‐pairing quantum key distribution is one of the schemes that can surpass the secret key capacity for repeaterless QKD. However, the protocol utilizes phase to encode the information, which leads to the problem of active stabilization in the interferometer. In this paper, a reference‐frame‐independent mode‐pairing quantum key distribution (RFI‐MP‐QKD) is proposed as an effective scheme to solve this problem. Moreover, the performance of the RFI‐MP‐QKD protocol is improved by applying the Advantage Distillation (AD) method in data post‐processing, which separates the highly correlated raw key bits from the weakly correlated information. The simulation results show that the secure key rate of RFI‐MP‐QKD has almost no degradation for reference frame deviation angles of . Compared to RFI‐MP‐QKD without AD method, the AD method decreases the quantum bit error rate from 0.04 to 0.012 and increases the maximum transmission distance from 406 to 450 km. The scheme proposed is expected to facilitate the practical implementation of RFI‐MP‐QKD, especially in cases of concerning reference frame alignment and high channel loss.
Adaptive micro- and macro-knowledge incorporation for hierarchical text classification

Authors:
raw_author_name: Zijian Feng
raw_affiliation_string: Future Resilient Systems Programme, Singapore-ETH Centre, CREATE Campus, 138602, Singapore; Interdisciplinary Graduate Programme, Nanyang Technological University, 639798, Singapore
raw_author_name: Kezhi Mao
raw_affiliation_string: Future Resilient Systems Programme, Singapore-ETH Centre, CREATE Campus, 138602, Singapore; School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
raw_author_name: Hanzhang Zhou
raw_affiliation_string: Future Resilient Systems Programme, Singapore-ETH Centre, CREATE Campus, 138602, Singapore; Interdisciplinary Graduate Programme, Nanyang Technological University, 639798, Singapore

Abstract: Hierarchical text classification (HTC) aims to classify a text into multiple categories organized in a hierarchical structure. The state-of-the-art HTC methods usually employ graph networks, where label graphs are constructed and label representation is learned to interact with text representations for classification. In general, label graphs are built on the intrinsic label hierarchy, label semantic similarity, or label co-occurrence. Such graphs have been proven to be effective, but they only exploit knowledge from training data or simple label descriptions, without considering the vast external knowledge in the open sources. Actually, external knowledge from open sources could bring in complementary information to enhance the label graph’s representation power. Motivated by the above considerations, we explore the use of external knowledge for improving HTC in this paper. We categorize knowledge into micro-knowledge and macro-knowledge, which are defined as the fundamental concepts related to a single class label and the correlations among class labels, respectively. For tailor-made incorporation of the two types of knowledge into representation learning and classification, we propose Adaptive Micro- and Macro-Knowledge Incorporation for Hierarchical Text Classification (AMKI-HTC) model in this paper. The micro-knowledge incorporation helps capture class-relevant keywords in the text and hence produce discriminative representations While the macro-knowledge incorporation improves the accuracy of label graphs. finally a confidence maximization fusion strategy is developed for adaptive aggregation of multi-view features. extensive experiments on three benchmark HTC datasets demonstrate that AMKI-HTC consistently outperforms state-of-the-art models.
Research on industrial risk early warning algorithms based on edge computing and multimodal data

Authors:
raw_author_name: Qiao Su
raw_affiliation_string: Zhangjiakou Cigarette Factory Co., Ltd, Zhangjiakou, Heibei, China
raw_author_name: Haiyang Zhao
raw_affiliation_string: Zhangjiakou Cigarette Factory Co., Ltd, Zhangjiakou, Heibei, China
raw_author_name: Yan Chu
raw_affiliation_string: Zhangjiakou Cigarette Factory Co., Ltd, Zhangjiakou, Heibei, China
raw_author_name: Jie Li
raw_affiliation_string: Zhangjiakou Cigarette Factory Co., Ltd, Zhangjiakou, Heibei, China
raw_author_name: Xuan Lyu
raw_affiliation_string: Zhangjiakou Cigarette Factory Co., Ltd, Zhangjiakou, Heibei, China
raw_author_name: Zijuan Li
raw_affiliation_string: Zhangjiakou Cigarette Factory Co., Ltd, Zhangjiakou, Heibei, China

Abstract: Abstract To achieve real‐time and effective prediction of industrial site risks, this paper proposes an industrial risk prediction framework for multimodal data based on edge computing. First, the authors gather and annotate industrial risk multimodal data that consist of text descriptions, images, and videos. Then, the authors transfer the data to the edge server, and apply deep learning models such as Bidirectional Encoder Representations from Transformers (BERT), ResNet etc., to extract features and learn representations for text, image, and video data respectively. The authors input the fused feature data into an enhanced long short term memory (LSTM) model and train it on the dataset. Finally, the authors perform the risk prediction based on the collected multimodal data. The experimental results demonstrate that the method proposed in this paper exhibits superior performance, achieving a 1.4% enhancement in predictive accuracy.
AI Fitness Model using Deep Learning

Authors:
raw_author_name: None Gowtham R
raw_affiliation_string: Global Academy of Technology, Bangalore, India
raw_author_name: None Dr Asha K H
raw_affiliation_string: Global Academy of Technology, Bangalore, India

Abstract: The project “AI Fitness Model Using Deep Learning (YOLOv5)” aims inorder to transform the fitness business by leveraging state-of-the-art deep learning techniques, specifically YOLOv5 (You Only Look Once version 5), to develop an advanced and efficient fitness tracking system. The model is designed to accurately detect and analyze human poses, movements, and exercise routines in real-time using computer vision. By employing YOLOv5’s object detection capabilities, the AI fitness model can identify key body points, track exercise execution, and provide personalized feedback to users, enhancing their workout experience. This creative strategy not only facilitates automated performance monitoring but also enables the creation of adaptive and dynamic fitness routines tailored to individual needs, fostering improved engagement and results in the realm of health and wellness
Using Stochastic Point Pattern Analysis to Track Regional Orientations of Magmatism During the Transition to Cenozoic Extension and Rio Grande Rifting, Southern Rocky Mountains

Authors:
raw_author_name: S. P. Gaynor
raw_affiliation_string: Department of Earth Sciences, University of Geneva, Geneva, Switzerland; Department of Geosciences, Princeton University, Princeton, NJ, USA
raw_author_name: A. Ulianov
raw_affiliation_string: Institute of Earth Sciences, University of Lausanne, Lausanne, Switzerland
raw_author_name: U. Schaltegger
raw_affiliation_string: Department of Earth Sciences, University of Geneva, Geneva, Switzerland

Abstract: Abstract The southern Rocky Mountains in Colorado and northern New Mexico hosted intracontinental magmatism that developed during a tectonic transition from shortening (Laramide orogeny, ca. 75 to 40 Ma) through extension and rifting. We present a novel approach that uses stochastic weighted bootstrap simulations of a large set of new and historical geochronology data to better understand how regional anisotropies responsible for focusing magma emplacement evolved through time. This technique can detect subtle trends in directional distributions, including multi‐modal orientations, and can be filtered from regional to local scales. Our results indicate that magmatism followed first the northeast trend of the Colorado mineral belt between 75 and 40 Ma and deviated afterward. These deviations vary depending on the scale of the analysis. At the smallest scale we evaluated (&lt;75 km), the orientation of magmatism from 45 to 30 Ma rotated counter‐clockwise before aligning with the north‐south trend of the modern Rio Grande rift. Larger, regional‐scale analyses indicate magma centers between 40 to 35 Ma and 25 to 20 Ma were dominantly oriented southwest‐northeast, whereas magmatism between 35 and 25 Ma had north‐south orientation. The large areal footprint of magmatism and shifting regional patterns suggest that ancient zones of weakness in the North American lithosphere accommodated magma flow at different moments in time, rather than controlled by a retreating interface of the Farallon and North American plates.
Adaptive Trajectories’ Constant False Alarm Rate Mirror Filters and Elevation Angle Evaluation for Multiple-Input Multiple-Output Radar-Based Hand Gesture Recognition

Authors:
raw_author_name: Tzu-Jung Tseng
raw_affiliation_string: Graduate Institute of Communication Engineering, National Taiwan University, Taipei City 10617, Taiwan
raw_author_name: Jian-Jiun Ding
raw_affiliation_string: Graduate Institute of Communication Engineering, National Taiwan University, Taipei City 10617, Taiwan

Abstract: Gesture recognition technology has been quickly developed in the field of human–computer interaction. The multiple-input multiple-output (MIMO) radar is popular in gesture recognition because of its notable spatial resolution. This work proposes a MIMO radar-based hand gesture recognition algorithm with low complexity. We leverage low-complexity adaptive signal processing to extract trajectory information and minimize noise to create a system that can be applied in real-world applications with small training datasets. First, a spectrum analysis is utilized on range-Doppler maps (RDMs), and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness of noise. Then, the features related to the distance, speed, direction, and elevation angle of the moving object are determined using the proposed adaptive signal analysis techniques. For classification, the random forest algorithm is implemented. The proposed system can precisely distinguish and identify eight gestures, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anti-clockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.
The Effectiveness of Psychoeducation on Depression Literacy and Psychological Well-being of University Students

Authors:
raw_author_name: Noor Muhammad
raw_affiliation_string: Department of Psychology, Jagannath University, Dhaka-1100, Bangladesh

Abstract: Today’s modern life is a challenging issue for human beings, particularly those who have mental illnesses like depression, requiring awareness-related programs for increasing their Depression Literacy (D-Lit) and Psychological Well-being (PWB). To mark the gap, we planned to investigate the effect of web-based psychoeducation on literacy regarding depression and psychological well-being. A total of 100 depressed students were included in the sampling frame, purposively selected based on some inclusion and exclusion criteria from Jagannath University. The study was accomplished by following a pretest-posttest with a control group design where data were collected using Depression, Depression Literacy (D-Lit), and Psychological Well-being questionnaires along with a personal information form. The data were analyzed by applying an independent sample t-test, paired sample t-test, and ANCOVA. Results revealed that the level of D-Lit and PWB of the depressed students significantly increased in the post-test as compared to the pre-test in the experimental group, but no significant changes in the control group, indicating psychoeducation has an effect on increasing D-Lit and PWB. This finding explored that psychoeducation can be an immediate awareness program for improving depression-related knowledge and psychological well-being in the community. The applications of these outcomes for investigation and practice are discussed.&#x0D; Jagannath University Journal of Science, Volume 10, Number I, Jun 2023, pp. 1−10
A self-adaptive parallel image stitching algorithm for unmanned aerial vehicles in edge computing environments

Authors:
raw_author_name: Li Zhang
raw_affiliation_string: College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
raw_author_name: Ying Wang
raw_affiliation_string: College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
raw_author_name: Jie Liu
raw_affiliation_string: Division of Pest Monitoring and Forecasting, National Agricultural Technology Service Center, Beijing, China
raw_author_name: Yanhui Lu
raw_affiliation_string: State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
raw_author_name: Hongbo Qiao
raw_affiliation_string: College of Information and Management Science, Henan Agricultural University, Zhengzhou, China

Abstract: The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV.
Bribing to Escape Poverty in Africa

Authors:
raw_author_name: Simplice A. Asongu
raw_affiliation_string: Department of Economic &amp; Data Science, New Uzbekistan University, Tashkent, Uzbekistan; School of Economics, University of Johannesburg, Johannesburg, South Africa
raw_author_name: Samba Diop
raw_affiliation_string: Faculty of Economics and Management, Alioune Diop University, Bambey, Senegal

Abstract: This study assesses the nexus between bribery and poverty using the Afrobarometer survey. Our data cover 38 countries consisting of three survey rounds and a sample of 151,345 individuals. The empirical strategy is based on multi-level mixed-effects ordered logit regression. The results reveal that while poverty has a positive effect on the spread of bribery, inflation can mitigate the impact. The impact is stronger for people living without basic necessities such as food, water, and medical care. In other words, the results indicate that the impact of poverty on bribery becomes negative when inflation increases. Policy implications are discussed.