Reviewerly/exHarmony huggingface dataset:
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition

Authors:
raw_author_name: Harish Haresamudram
raw_affiliation_string: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
raw_author_name: Irfan Essa
raw_affiliation_string: School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
raw_author_name: Thomas Plötz
raw_affiliation_string: School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA

Abstract: 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

Authors:
raw_author_name: Li Xue
raw_affiliation_string: School of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China
raw_author_name: Yongmin Zhong
raw_affiliation_string: School of Engineering, RMIT University, Bundoora, VIC 3082, Australia
raw_author_name: Yulan Han
raw_affiliation_string: School of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China

Abstract: 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

Authors:
raw_author_name: Ángel Llamazares
raw_affiliation_string: Department of Electronics, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
raw_author_name: Miguel Antunes
raw_affiliation_string: Department of Electronics, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
raw_author_name: Pedro A. Revenga
raw_affiliation_string: Department of Electronics, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
raw_author_name: Luis M. Bergasa
raw_affiliation_string: Department of Electronics, Universidad de Alcalá, 28805 Alcalá de Henares, Spain

Abstract: 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)

Authors:
raw_author_name: Yuzhong Wang
raw_author_name: Axiang Yu
raw_author_name: Yayun Cheng
raw_author_name: Jiaran Qi

Abstract: 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

Authors:
raw_author_name: Guo Lai Jia
raw_affiliation_string: Shanghai Caojing Thermal Power Co., LTD., Shanghai, 201507
raw_author_name: Zhou Hong
raw_affiliation_string: Shanghai Caojing Thermal Power Co., LTD., Shanghai, 201507
raw_author_name: Xu Shuai
raw_affiliation_string: Shanghai Caojing Thermal Power Co., LTD., Shanghai, 201507

Abstract: 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

Authors:
raw_author_name: Audita Nuvriasari

Abstract: 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.
Effective Document Image Rectification via a Deep Learning Framework

Authors:
raw_author_name: Hsiau-Wen Lin
raw_author_name: Hwei Jen Lin
raw_author_name: Yoshimasa Tokuyama
raw_author_name: Chou-Wei Kong

Abstract: International Journal of Pattern Recognition and Artificial IntelligenceAccepted Papers No AccessEffective Document Image Rectification via a Deep Learning FrameworkHsiau-Wen Lin, Hwei Jen Lin, Ph. D.Yihjia Tsai, Yoshimasa Tokuyama, and Chou-Wei KongHsiau-Wen Lin, Hwei Jen Linhttps://orcid.org/0000-0002-1539-233X Search for more papers by this author , Yihjia Tsai Search for more papers by this author , Yoshimasa Tokuyama Search for more papers by this author , and Chou-Wei Kong Search for more papers by this author https://doi.org/10.1142/S0218001423510230Cited by:0 (Source: Crossref) Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 14 November 2023 Accepted 2 December 2023 PDF download
TCN-GAWO: Genetic Algorithm Enhanced Weight Optimization for Temporal Convolutional Network

Authors:
raw_author_name: Qi Xi
raw_affiliation_string: No. 55, Xiangjiang Avenue West Shanwei, ?? 516600 China
raw_author_name: Jing Wang
raw_affiliation_string: No. 55, Xiangjiang Avenue West, Magong Street Shanwei, Guangdong 516625 China
raw_author_name: Mian Li
raw_affiliation_string: 800 Dong Chuan Road Shanghai, 200240 China

Abstract: Abstract This paper proposes TCN-GAWO, a Genetic Algorithm enhanced Weight Optimization method for Temporal Convolutional Network (TCN). TCN-GAWO combines the evolutionary process of the genetic algorithm with the gradient-based training, and can achieve higher predication/fitting accuracy than traditional TCN. Performances of TCN-GAWO are also more stable. In TCN-GAWO, multiple TCNs are generated with random initial weights first, then these TCNs are trained individually for given epochs, next the selection-crossover-mutation procedure is applied among TCNs to get the evolved offspring. Gradient-based training and selection-crossover-mutation are taken in turns until convergence. The TCN with the optimal performance is then selected. Performances of TCN-GAWO are thoroughly evaluated using realistic engineering data, including CMPASS dataset provided by NASA and jet engine lubrication oil dataset provided by airlines. Experimental results show that TCN-GAWO outperforms existing methods for both datasets, demonstrating the effectiveness and the wide range applicability of the proposed method in solving time series problems.
Detection and Validation of Macro-Activities in Human Inertial Signals Using Graph Link Prediction

Authors:
raw_author_name: Victor Pankratius
raw_affiliation_string: Robert Bosch GmbH, 70469 Stuttgart-Feuerbach, Germany

Abstract: With the continuous development of new wearable devices, sensor-based human activity recognition is enjoying enormous popularity in research and industry. The signals from inertial sensors allow for the detection, classification, and analysis of human activities such as jogging, cycling, or swimming. However, human activity recognition is often limited to basic activities that occur in short, predetermined periods of time (sliding windows). Complex macro-activities, such as multi-step sports exercises or multi-step cooking recipes, are still only considered to a limited extent, while some works have investigated the classification of macro-activities, the automated understanding of how the underlying micro-activities interact remains an open challenge. This study addresses this gap through the application of graph link prediction, a well-known concept in graph theory and graph neural networks (GNNs). To this end, the presented approach transforms micro-activity sequences into micro-activity graphs that are then processed with a GNN. The evaluation on two derived real-world data sets shows that graph link prediction enables the accurate identification of interactions between micro-activities and the precise validation of composite macro-activities based on learned graph embeddings. Furthermore, this work shows that GNNs can benefit from positional encodings in sequence recognition tasks.
Design and application of transport planning analysis and data service platform based on flexible architecture technology

Authors:
raw_author_name: Shuo Zhang
raw_affiliation_string: Planning and Research Institute of the Ministry of Transport (China)
raw_author_name: Mingchen Gu
raw_affiliation_string: Planning and Research Institute of the Ministry of Transport (China)
raw_author_name: Hong Liu
raw_affiliation_string: Planning and Research Institute of the Ministry of Transport (China)
raw_author_name: Xinghua Huang
raw_affiliation_string: Planning and Research Institute of the Ministry of Transport (China)

Abstract: In order to solve the problems of insufficient sharing and multiplexing of integrated transportation planning services, weak ability of multi-user large-scale concurrent services of high-frequency data and spatial data, lack of customized sub-scene collaborative application functions, design and develop Transport planning analysis and data service platform(i.e., “the platform”) based on flexible architecture technology. The platform bases on SpringCloud mature distributed micro-service technology framework, integrates advanced digital and intelligent technologies of comprehensive transportation planning space-time big data governance, land space resource elements and visualization, initially builds a basic data center for transportation planning, a technical support experimental platform for transportation space planning and an electronic map for space planning that integrates data analysis, platform support, and model application. The construction and application of the platform comprehensively improved the scientific nature of the evaluation of comprehensive transportation spatial planning, and has initially played an important role in assisting the connection of transportation sector planning and territorial spatial planning.
FR-PatchCore: An Industrial Anomaly Detection Method for Improving Generalization

Authors:
raw_author_name: Zhiqian Jiang
raw_affiliation_string: School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
raw_author_name: Yu Zhang
raw_affiliation_string: School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
raw_author_name: Yong Wang
raw_affiliation_string: School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
raw_author_name: Jinlong Li
raw_affiliation_string: School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
raw_author_name: Xiaorong Gao
raw_affiliation_string: School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 611756, China

Abstract: In recent years, a multitude of self-supervised anomaly detection algorithms have been proposed. Among them, PatchCore has emerged as one of the state-of-the-art methods on the widely used MVTec AD benchmark due to its efficient detection capabilities and cost-saving advantages in terms of labeled data. However, we have identified that the PatchCore similarity principal approach faces significant limitations in accurately locating anomalies when there are positional relationships between similar samples, such as rotation, flipping, or misaligned pixels. In real-world industrial scenarios, it is common for samples of the same class to be found in different positions. To address this challenge comprehensively, we introduce Feature-Level Registration PatchCore (FR-PatchCore), which serves as an extension of the PatchCore method. FR-PatchCore constructs a feature matrix that is extracted into the memory bank and continually updated using the optimal negative cosine similarity loss. Extensive evaluations conducted on the MVTec AD benchmark demonstrate that FR-PatchCore achieves an impressive image-level anomaly detection AUROC score of up to 98.81%. Additionally, we propose a novel method for computing the mask threshold that enables the model to scientifically determine the optimal threshold and accurately partition anomalous masks. Our results highlight not only the high generalizability but also substantial potential for industrial anomaly detection offered by FR-PatchCore.
Application of Metaverse Technology in Entrepreneurship Education: A Systematic Review

Authors:
raw_author_name: Melor Md Yunus

Abstract: A systematic review of the application of metaverse technology in entrepreneurship education can help relevant teaching and research personnel better grasp the current development trends, and better utilize cutting-edge technologies to support the development of entrepreneurship education in teaching and practice. Therefore, this review aims to review the latest trends and research with the question "What issues can metaverse technology be applied in entrepreneurship education?". This system review adopts Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) , and collects data based on two core journal databases, Scopus and Web of Science, while using Note Express and Mendeley as supporting databases. A total of 27 articles were identified through a systematic search of ("metaverse*" OR "cyberspace*" OR "metaverse technology*") AND ("entrepreneurship education*" OR "enterprise education*" OR "entrepreneurial education*"). The results shown that metaverse related technologies are changing entrepreneurial intentions, make it easier for entrepreneurs to access entrepreneurial resources, and improving the efficiency of entrepreneurship education. The exploration of these issues also lays the foundation for subsequent researchers to study how to apply cutting-edge technologies to entrepreneurship education.
Recent Advances in Group-Based Trajectory Modeling for Clinical Research

Authors:
raw_author_name: Daniel S. Nagin
raw_affiliation_string: Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA; email: 
raw_author_name: Jonathan Elmer
raw_affiliation_string: Departments of Emergency Medicine, Critical Care Medicine, and Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

Abstract: Group-based trajectory modeling (GBTM) identifies groups of individuals following similar trajectories of one or more repeated measures. The categorical nature of GBTM is particularly well suited to clinical psychology and medicine, where patients are often classified into discrete diagnostic categories. This review highlights recent advances in GBTM and key capabilities that remain underappreciated in clinical research. These include accounting for nonrandom subject attrition, joint trajectory and multitrajectory modeling, the addition of the beta distribution to modeling options, associating trajectories with future outcomes, and estimating the probability of future outcomes. Also discussed is an approach to selecting the number of trajectory groups. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 20 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
The temporally-topological defect modes in photonic time crystals

Authors:
raw_author_name: SHAKEEL AHMED
raw_author_name: MARYAM JAMIL
raw_author_name: Qiong Wang

Abstract: In this paper, we investigate the properties of temporally-topological defect modes (TTDMs) (or temporally-topological interface states) in the topological photonic time crystal (PTC) systems. The PTC systems are constructed by the cascade of multiple sub-PTCs that possess temporal inversion symmetries and different topologies. The cases of two-, three-, and multiple-sub-PTC for the topological PTC system are studied. By transfer matrix method, we find that the TTDMs appear when the topological signs of the corresponding gaps in the sub-PTCs are different. The positions of TTDMs can be adjusted by changing the modulation strength of the refractive index, the time duration, and the period of the sub-PTCs. Moreover, the number of TTDMs is one less than the number of sub-PTCs. In addition, the robustness of the systems is also studied. We find that the topological PTC systems have good robustness, especially on the random configuration of the refractive index and time duration for the temporal slabs in the systems. Such research may provide a new degree of freedom for PTC applications, such as novel PTC lasers, tunable band-stop or band-suppression PTC filters, and many others, in the field of integrated photonic circuits for optical communications.