Reviewerly/exHarmony huggingface dataset:
Exploring 3D Object Detection for Autonomous Factory Driving: Advanced Research on Handling Limited Annotations with Ground Truth Sampling Augmentation

Authors:
raw_author_name: Matthias Reuse
raw_affiliation_string: Valeo Schalter & Sensoren GmbH, Hummendorfer Str. 74, 96317 Kronach, Germany
raw_author_name: Karl Amende
raw_affiliation_string: Valeo Schalter & Sensoren GmbH, Hummendorfer Str. 74, 96317 Kronach, Germany
raw_author_name: Martin Simon
raw_affiliation_string: Valeo Schalter & Sensoren GmbH, Hummendorfer Str. 74, 96317 Kronach, Germany
raw_author_name: Bernhard Sick
raw_affiliation_string: Intelligent Embedded Systems, Universität Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany

Abstract: Autonomously driving vehicles in car factories and parking spaces can represent a competitive advantage in the logistics industry. However, the real-world application is challenging in many ways. First of all, there are no publicly available datasets for this specific task. Therefore, we equipped two industrial production sites with up to 11 LiDAR sensors to collect and annotate our own data for infrastructural 3D object detection. These form the basis for extensive experiments. Due to the still limited amount of labeled data, the commonly used ground truth sampling augmentation is the core of research in this work. Several variations of this augmentation method are explored, revealing that in our case, the most commonly used is not necessarily the best. We show that an easy-to-create polygon can noticeably improve the detection results in this application scenario. By using these augmentation methods, it is even possible to achieve moderate detection results when only empty frames without any objects and a database with only a few labeled objects are used.
A Novel Method for Monocular Depth Estimation Using an Hourglass Neck Module

Authors:
raw_author_name: Seung-Jin Oh
raw_affiliation_string: Department of Electronic Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Republic of Korea

Abstract: In this paper, we propose a novel method for monocular depth estimation using the hourglass neck module. The proposed method has the following originality. First, feature maps are extracted from Swin Transformer V2 using a masked image modeling (MIM) pretrained model. Since Swin Transformer V2 has a different patch size for each attention stage, it is easier to extract local and global features from images input by the vision transformer (ViT)-based encoder. Second, to maintain the polymorphism and local inductive bias of the feature map extracted from Swin Transformer V2, a feature map is input into the hourglass neck module. Third, deformable attention can be used at the waist of the hourglass neck module to reduce the computation cost and highlight the locality of the feature map. Finally, the feature map traverses the neck and proceeds through a decoder, comprised of a deconvolution layer and an upsampling layer, to generate a depth image. To evaluate the objective reliability of the proposed method in this paper, we used the NYU Depth V2 dataset to compare and evaluate the methods published in other papers. As a result of the experiment, the RMSE value of the novel method for monocular depth estimation using the hourglass neck module proposed in this paper was 0.274, which was lower than those published in other papers. The lower the RMSE value, the better the depth estimation method; therefore, its efficiency compared to other techniques has been proven.
Holographic matrix method for rapid material structural feature extraction and detection

Authors:
raw_author_name: Wenfu Wang
raw_affiliation_string: Institute of Advanced Type Optoelectronic Materials and Technology, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
raw_author_name: Qidan Wang
raw_affiliation_string: Institute of Advanced Type Optoelectronic Materials and Technology, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China

Abstract: The extraction of the structural features of materials is fundamental for investigating novel properties in fields such as electronic information and biochemistry. However, existing experimental methods have limitations in analyzing material structures with sufficient depth. Therefore, rapid and accurate extraction and analysis of structural features from atomic coordinates obtained through simulation calculations are crucial for advancing the exploration of new material properties. Herein, we propose an approach for extracting the structural features of materials by combining the holographic matrix method with Bayesian optimization and tensor flow operations. The proposed algorithm efficiently classifies and statistically analyzes cluster structures within materials. Experimental validation conducted on a system comprising 8000 atoms demonstrated a correct recognition rate exceeding 99.213%. Moreover, the algorithm achieved an average recognition time of approximately [Formula: see text][Formula: see text]s. The proposed analytical framework exhibits scalability and robustness, establishing an algorithmic foundation for future advancements in big data analytics for complex materials.
How Does VR Affect Emotional Appeal and Persuasiveness of Gain Versus Loss-Framed Messages?

Authors:
raw_author_name: Mengqi Liao
raw_affiliation_string: Pennsylvania State University, University Park, USA
raw_author_name: Pejman Sajjadi
raw_affiliation_string: College of Computing and Software Engineering, Kennesaw State University, Kennesaw, GA, USA
raw_author_name: S. Shyam Sundar
raw_affiliation_string: Pennsylvania State University, University Park, USA

Abstract: Can virtual reality (VR) lead to more desirable persuasion effects than videos in communicating the science of contentious issues like climate change? And, will these effects differ between messages that offer hope versus fear? We addressed these questions with a 2 (Modality: Desktop VR vs. Unidirectional video) × 2 (framing: Gain vs. Loss) between-subjects experiment ( N = 130), and found that VR triggered more fearful responses, which in turn led to more persuasive outcomes. It also increased individuals’ attitudes toward climate change mitigation policy when the experience was loss-framed, but backfired when the experience was gain-framed. Theoretical and practical implications are discussed.
Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

Authors:
raw_author_name: Wenhao Yu
raw_affiliation_string: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
raw_author_name: Zhongxing Li
raw_affiliation_string: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
raw_author_name: Changjun Wang
raw_affiliation_string: Research Institute for Road Safety of the Ministry of Public Security, Beijing 100062, China
raw_author_name: Guangming Zhao
raw_affiliation_string: Research Institute for Road Safety of the Ministry of Public Security, Beijing 100062, China
raw_author_name: Wenhui Zhou
raw_affiliation_string: Research Institute for Road Safety of the Ministry of Public Security, Beijing 100062, China
raw_author_name: Mingyue Ma
raw_affiliation_string: Research Institute for Road Safety of the Ministry of Public Security, Beijing 100062, China

Abstract: In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.
MSIE-Net: Associative Entity-Based Multi-Stage Network for Structured Information Extraction from Reports

Authors:
raw_author_name: Qiuyue Li
raw_affiliation_string: School of Physics and Electronics, Shandong Normal University, Jinan 250358, China
raw_author_name: Hao Sheng
raw_affiliation_string: School of Physics and Electronics, Shandong Normal University, Jinan 250358, China
raw_author_name: Honglin Wan
raw_affiliation_string: School of Physics and Electronics, Shandong Normal University, Jinan 250358, China

Abstract: Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination report (MER) can enhance medical efficiency, data analysis, and scientific research. While current methods focus on reconstructing table bodies, they often overlook table headers, leading to incomplete information extraction. This paper proposes MSIE-Net (multi-stage-structured information extraction network), a novel structured information extraction method, leveraging refined attention transformers and associated entity detection to enhance comprehensive MER information retrieval. MSIE-Net includes three stages. First, the RVI-LayoutXLM (refined visual-feature independent LayoutXLM) targets key information extraction. In this stage, the refined attention accentuates the interaction between different modalities by adjusting the attention score at the current position using previous position information. This design enables the RVI-LayoutXLM to learn more specific contextual information to improve extraction performance. Next, the associated entity detection module, RIFD-Net (relevant intra-layer fine-tuned detection network), identifies each test item’s location within the MER table body. Significantly, the backbone of RIFD-Net incorporates the intra-layer feature adjustment module (IFAM) to extract global features while homing in on local areas, proving especially sensitive for inspection tasks with dense and long bins. Finally, structured post-processing based on coordinate aggregation links the outputs from the prior stages. For the evaluation, we constructed the Chinese medical examination report dataset (CMERD), based on real medical scenarios. MSIE-Net demonstrated competitive performance in tasks involving key information extraction and associated entity detection. Experimental results validate MSIE-Net’s capability to successfully detect key entities in MER and table images with various complex layouts, perform entity relation extraction, and generate structured labels, laying the groundwork for intelligent medical documentation.
Meetor: A Human-Centered Automatic Video Editing System for Meeting Recordings

Authors:
raw_author_name: Haihan Duan
raw_affiliation_string: Shenzhen MSU-BIT University, Shenzhen, China and The Chinese University of Hong Kong, Shenzhen, China and Mohamed bin Zayed University of Artificial Intelligence, Masdar City, United Arab Emirates
raw_author_name: Junhua Liao
raw_affiliation_string: Sichuan University, Chengdu, China
raw_author_name: Lehao Lin
raw_affiliation_string: The Chinese University of Hong Kong, Shenzhen, China
raw_author_name: Abdulmotaleb El Saddik
raw_affiliation_string: Mohamed bin Zayed University of Artificial Intelligence, Masdar City, United Arab Emirates and University of Ottawa, Ottawa, Canada
raw_author_name: Wei Cai
raw_affiliation_string: The Chinese University of Hong Kong, Shenzhen, China

Abstract: Widely adopted digital cameras and smartphones have generated a large number of videos, which have brought a tremendous workload to video editors. Recently, a variety of automatic/semi-automatic video editing methods have been proposed to tackle these issues in some specific areas. However, for the production of meeting recordings, the existing studies highly depend on extra equipment in the conference venues, such as the infrared camera or special microphone, which are not practical. In this paper, we design and implement Meetor, a human-centered automatic video editing system for meeting recordings. The Meetor mainly contains three parts: an audio-based video synchronization algorithm, human-centered video content flaw detection algorithms, and an automatic video editing algorithm. Two main experiments are conducted from both objective and subjective aspects to evaluate the performance of the Meetor. The experimental results on a testbed illustrate that the proposed algorithms could achieve state-of-the-art (SOTA) performance in video content flaw detection. On the other hand, the conducted user study demonstrates that Meetor could generate meeting recordings with a satisfactory quality compared with professional video editors. Moreover, we also present a practical application of the Meetor in a university campus prototype, in which the Meetor is applied in the automatic editing of lecture recordings. All in all, the proposed Meetor can be utilized in practical applications to release the workload of professional video editors.
Exploring the Role of Sociability, Ownership, and Affinity for Technology in Shaping Acceptance and Intention to Use Personal Assistance Robots.

Authors:
raw_author_name: Eileen Roesler
raw_affiliation_string: Department of Psychology, George Mason University, David King Hall, Fairfax, 22030, USA
raw_author_name: Felix Wilhelm Siebert
raw_affiliation_string: Department of Technology, Management, and Economics, Technical University of Denmark, Bygningstorvet, 116, 2800, Kongens Lyngby, Denmark

Abstract: Abstract Personal assistance robots are making inroads into our private and public life. At the same time, most humans are still unfamiliar with this technology and hesitate to accept and use it for daily tasks. Fortunately, the designs of robots can be adjusted to yield greater acceptance, subsequently enabling their utilization across various tasks. Using a scenario-based online experiment, we explored how sociability (low vs. high), ownership (private vs. public), and affinity for technology influence the acceptance and intention to use a robot for grocery shopping. Moreover, to assess users’ preference for robots’ morphology, participants were asked to choose a robot (technical vs. anthropomorphic design) that they would prefer to use in a supermarket. We found that low sociability of the service robot and a higher affective affinity for technology led to a higher level of acceptance. For more sociable robots, higher levels of anthropomorphism were preferred. Our results point to the importance of task-specific robot design that exceeds functional considerations.
Extending the Framework for Developing Intelligent Virtual Environments (FIVE) with Artifacts for Modeling Internet of Things Devices and a New Decentralized Federated Learning Based on Consensus for Dynamic Networks

Authors:
raw_author_name: Miguel Rebollo
raw_affiliation_string: Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, 46022 Valencia, Spain
raw_author_name: Jaime Andrés Rincon
raw_affiliation_string: Departamento de Digitalización, Escuela Politécnica Superior, Universidad de Burgos, 09200 Miranda de Ebro, Spain
raw_author_name: Luís Hernández
raw_affiliation_string: Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, 46022 Valencia, Spain
raw_author_name: Francisco Enguix
raw_affiliation_string: Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, 46022 Valencia, Spain
raw_author_name: Carlos Carrascosa
raw_affiliation_string: Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, 46022 Valencia, Spain

Abstract: One of the main lines of research in distributed learning in recent years is the one related to Federated Learning (FL). In this work, a decentralized Federated Learning algorithm based on consensus (CoL) is applied to Wireless Ad-hoc Networks (WANETs), where the agents communicate with other agents to share their learning model as they are available to the wireless connection range. When deploying a set of agents, it is essential to study whether all the WANET agents will be reachable before the deployment. The paper proposes to explore it by generating a simulation close to the real world using a framework (FIVE) that allows the easy development and modification of simulations based on Unity and SPADE agents. A fruit orchard with autonomous tractors is presented as a case study. The paper also presents how and why the concept of artifact has been included in the above-mentioned framework as a way to highlight the importance of some devices used in the environment that have to be located in specific places to ensure the full connection of the system. This inclusion is the first step to allow Digital Twins to be modeled with this framework, now allowing a Digital Shadow of those devices.
Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

Authors:
raw_author_name: Jun Wang
raw_affiliation_string: School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China; Sichuan Province Key Laboratory of Power Electronics Energy-Saving Technologies & Equipment, Xihua University, Chengdu 610039, China
raw_author_name: Lin Guo
raw_affiliation_string: School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China; Sichuan Province Key Laboratory of Power Electronics Energy-Saving Technologies & Equipment, Xihua University, Chengdu 610039, China
raw_author_name: Hong Peng
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, Chengdu 610039, China

Abstract: To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very difficult. To address this challenging prediction problem, in this paper, a novel method to predict the short-term PV power using a nonlinear spiking neural P system-based ESN model has been proposed. First, we combine a nonlinear spiking neural P (NSNP) system with a neural-like computational model, enabling it to effectively capture the complex nonlinear trends in PV sequences. Furthermore, an NSNP system featuring a layer is designed. Input weights and NSNP reservoir weights are randomly initialized in the proposed model, while the output weights are trained by the Ridge Regression algorithm, which is motivated by the learning mechanism of echo state networks (ESNs), providing the model with an adaptability to complex nonlinear trends in PV sequences and granting it greater flexibility. Three case studies are conducted on real datasets from Alice Springs, Australia, comparing the proposed model with 11 baseline models. The outcomes of the experiments exhibit that the model performs well in tasks of PV power prediction.
Solar Energy Forecasting With Performance Optimization Using Machine Learning Techniques

Authors:
raw_author_name: Murugesan S
raw_affiliation_string: Department of Information Technology, Tagore Engineering College, Vandalur, India
raw_author_name: M. Mahasree
raw_affiliation_string: Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India
raw_author_name: N. Bharathiraja
raw_affiliation_string: Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Rajpura, India

Abstract: The objective of this paper is proposed a forecasting technique system constitute the data integrity phase using Machine Learning (ML) techniques and post-processing output optimization to improve the performance. The outcomes demonstrated that the Photo Voltaic (PV) energy manufacture neural network performed more accurate by reducing solar irradiance prediction errors of linear regression characteristics. Through the adoption of both the optimized method settings and chosen function of ML methods were evaluated for predicting the performance. The accuracy of solar radiation estimation could be assessed in terms of statistical error measurement and verification measurements. The approach was tested in two climates hot and cold and the findings demonstrated using absolute error percentage with a contribution of 6.3% and 4.7%, respectively to improve the performance of the system based on data-driven ML algorithms and analytical post-processing.
Digital competence profiles of first year, pre-service teachers. Analysis in the Catalan university system

Authors:
raw_author_name: Mireia Usart
raw_affiliation_string: Department of pedagogy, Faculty of Educational Sciences and Pedagogy, Campus Sescelades, Universitat Rovira I Virgili, Tarragona, Spain
raw_author_name: José-Luis Lázaro-Cantabrana
raw_affiliation_string: Faculty of Psychology and Education Sciences, Universitat Oberta de Catalunya (UOC), Barcelona, Spain
raw_author_name: Teresa Romeu
raw_affiliation_string: Department of pedagogy, Faculty of Educational Sciences and Pedagogy, Campus Sescelades, Universitat Rovira I Virgili, Tarragona, Spain
raw_author_name: Mercè Gisbert-Cervera
raw_affiliation_string: Department of pedagogy, Faculty of Educational Sciences and Pedagogy, Campus Sescelades, Universitat Rovira I Virgili, Tarragona, Spain

Abstract: Like many other countries around the world, Catalonia is currently facing an urgent need for digitally competent teachers. Understanding the profile of first-year, pre-service teachers in relation to their teacher digital competence (TDC) can help design effective training programmes for future educators. The present study aims to measure the TDC profile of first-year, pre-service teachers along with the demographic variables related. A quantitative approach with a not experimental design was conducted in the 2018–19 academic year, to perform a cluster analysis. Three TDC groups emerge from a sample of 947 participants: Confident (n = 525), Average (n = 311), and Realistic (n = 111). Two variables show significant differences among groups: age and access to university. Both younger students and those coming from vocational training should be guided intensively during their initial training. On the other hand, gender and type of studies are distributed similarly across the three profiles. These findings have implications for developing pre-service teachers’ digital competence. In particular, the findings of this study highlight the need for all Catalan universities to develop a critical and common perspective, considering personal and professional factors of TDC as a key aspect for designing and implementing training in the curriculum.