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Multi-Dimensional Research Software Categorization

computing in science and Engineering 2025年

作者： Hasselbring, Wilhelm Druskat, Stephan Bernoth, Jan Betker, Philine Felderer, Michael Ferenz, Stephan Hermann, Ben Lamprecht, Anna-Lena Linxweiler, Jan Prat, Arnau Rumpe, Bernhard Schoening-Stierand, Katrin Yang, Shinhyung Kiel University Kiel24098 Germany Berlin12489 Germany University of Potsdam Potsdam14476 Germany Helmholtz Centre for Infection Research Department for Epidemiology Brunswick Germany University of Cologne Cologne51147 Germany Carl von Ossietzky Universität Oldenburg Department of Computer Science Oldenburg26129 Germany Secure Software Engineering Group TU Dortmund Dortmund44227 Germany TU Braunschweig Braunschweig38106 Germany Braunschweig38108 Germany RWTH Aachen University Germany University of Hamburg Hub of Computing and Data Science Hamburg22761 Germany

Research software has been categorized in different contexts to serve different goals. We start with a look at what research software is, before we discuss the purpose of research software categories. We propose a multi-dimensional categorization of research software. We present a template for characterizing such categories. As selected dimensions, we present our proposed role-based, readiness-based, developer-based, and dissemination-based categories. Since our work has been inspired by various previous efforts to categorize research software, we discuss them as related works. We characterize all these categories via the previously introduced template, to enable a systematic comparison. We report on the multi-dimensional categorization of selected research software examples. © 1999-2011 IEEE.

关键词： Aerospace engineering Analytical models data collection data models data visualization Monitoring Numerical models Software Software development management Software engineering

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Sparse Gaussian process regression via compactly supported kernels: A trade-off between accuracy and computational efficiency

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Information sciences 2025年 717卷

作者： Esmaeilbeigi, Mohsen Chatrabgoun, Omid Cavoretto, Roberto De Rossi, Alessandra Shafa, Maryam Faculty of Mathematical Sciences and Statistics Malayer University 65719-95863 Malayer Iran Faculty of Interdisciplinary Science and Technology Malayer University Malayer Iran School of Computing Mathematics and Data Science Coventry University Coventry CV1 5FB United Kingdom Department of Mathematics “G. Peano” University of Torino via Carlo Alberto 10 Torino 10123 Italy Member of the INdAM Research group GNCS Italy

Gaussian Process Regression (GPR) is a powerful kernel-based learning model, but its application to large datasets is hindered by the computational demands of handling a full kernel matrix. This paper introduces a novel approach to mitigate these challenges by employing Compactly Supported Radial Kernels (CSRKs) to create a sparse kernel matrix. CSRKs help to improve the conditioning of the kernel matrix during GPR training and prediction, thereby reducing both computational costs and memory usage. One key aspect of using CSRKs is the ability to control the sparsity of the kernel matrix by adjusting the kernel's support. However, a significant challenge arises due to a trade-off between maintaining sparsity and achieving high prediction accuracy. Our work explores this trade-off in detail, demonstrating that the advantage of CSRKs in inducing sparsity can be diminished if accuracy is prioritized. To address this, we propose the integration of expert knowledge through a suitable sparsification functional (SF) during GPR training, coupled with Maximum Likelihood Estimation (MLE). This approach aims to strike a balance between accuracy and sparsity, leading to more efficient learning. We validate our method through extensive numerical simulations on both 2D regular and irregular domains with noisy datasets. Additionally, we conduct a comparative study on real data to assess the impact of our modified MLE on the predictive performance of GPR models. The results consistently show that our proposed method yields sparser, better-conditioned kernel matrices compared to traditional techniques. © 2025 Elsevier Inc.

关键词： Compact support kernels Gaussian process regression Modified maximum likelihood estimation Sparsification functional

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Collecting the Public Perception of AI and Robot Rights

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Proceedings of the ACM on Human-Computer Interaction 2020年第CSCW2期4卷 1-24页

作者： Lima, Gabriel Kim, Changyeon Ryu, Seungho Jeon, Chihyung Cha, Meeyoung School of Computing Kaist Data Science Group Ibs Daejeon Korea Republic of School of Computing Kaist Daejeon Korea Republic of Dept. of Computer Science Dept. of Mathematics Hkust Hong Kong SAR Hong Kong Graduate School of Science and Technology Policy Kaist Daejeon Korea Republic of Data Science Group Ibs School of Computing Kaist Daejeon Korea Republic of

Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities."Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate that even though online users mainly disfavor AI and robot rights, they are supportive of protecting electronic agents from cruelty (i.e., favor the right against cruel treatment). Furthermore, people's perceptions became more positive when given information about rights-bearing non-human entities or myth-refuting statements. The style used to introduce AI and robot rights significantly affected how the participants perceived the proposal, similar to the way metaphors function in creating laws. For robustness, we repeated the experiment over a more representative sample of U.S. residents (N=164) and found that perceptions gathered from online users and those by the general population are similar. © 2020 ACM.

关键词： Intelligent robots

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Human perceptions on moral responsibility of ai: A case study in ai-assisted bail decision-making 21

Human perceptions on moral responsibility of ai: A case stud...

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2021 CHI Conference on Human Factors in computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Lima, Gabriel Grgic-Hlaca, Nina Cha, Meeyoung School of Computing Kaist Data Science Group Ibs Korea Republic of

ISBN: (纸本)9781450380966

How to attribute responsibility for autonomous artifcial intelligence (AI) systems' actions has been widely debated across the humanities and social science disciplines. This work presents two experiments (N=200 each) that measure people's perceptions of eight diferent notions of moral responsibility concerning AI and human agents in the context of bail decision-making. Using real-life adapted vignettes, our experiments show that AI agents are held causally responsible and blamed similarly to human agents for an identical task. However, there was a meaningful diference in how people perceived these agents' moral responsibility;human agents were ascribed to a higher degree of present-looking and forward-looking notions of responsibility than AI agents. We also found that people expect both AI and human decision-makers and advisors to justify their decisions regardless of their nature. We discuss policy and HCI implications of these fndings, such as the need for explainable AI in high-stakes scenarios. © 2021 ACM.

关键词： Decision making

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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Detection and Localization of Stationary Waves on Venus Using a Self-Supervised Anomaly Detection Model

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第1期2卷

作者： Husnu Baris Baydargil Jose Eduardo Oliveira Silva Yeon Joo Lee Meeyoung Cha Data Science Group Institute for Basic Science (IBS) Daejeon Republic of Korea Planetary Atmospheres Group Institute for Basic Science (IBS) Daejeon Republic of Korea School of Computing Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Max Planck Institute for Security and Privacy (MPI-SP) Bochum Germany

The atmosphere of Venus features stationary waves of planetary scale, which are believed to be caused by wind flowing over elevated terrains. Detecting these waves can provide valuable insight into atmospheric dynamics. However, manual detection is time-consuming, especially with the increasing volume of new images. To address this, we propose a self-supervised anomaly detection model based on a vector-quantized generative adversarial network to identify and localize stationary atmospheric waves in spacecraft images of Venus. Our model is developed over Akatsuki images of Venus and is trained on smaller cropped images containing features other than stationary waves, allowing it to learn the underlying patterns of normal cloud features. We enhance the model with spatial attention modules, skip connections, and latent space transformation to improve image reconstruction and avoid mixing stationary wave features with artifacts. For inference, we use an overlapping sliding window approach, considering the reconstruction loss of each grid slice to identify anomalies. The model records bounding boxes with high reconstruction loss, localizes stationary waves, and draws the final bounding box. Experiments demonstrate that our model outperforms benchmark models using ultraviolet imaging (UVI) and longwave infrared imaging (LIR) acquired by the Akatsuki Venus orbiter, achieving an area under the curve (AUC) score of 94.78% for UVI and 92.84% for LIR images, and an intersection over union score of 66.53% for UVI and 61.49% for LIR images. The ablation study shows consecutive performance improvements with each added module. Propose a self-supervised model for detecting stationary atmospheric waves on Venus through anomaly detection in Akatsuki images Localize stationary waves by analyzing overlapping image patches, identifying regions with significant original-reconstruction deviations Streamline the identification of stationary waves, eliminating the constraints of date and or

关键词： Venus stationary wave deep learning anomaly detection localization

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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

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Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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Motor imagery enhances performance beyond the imagined action

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Proceedings of the National Academy of sciences of the United States of America 2025年第20期122卷 e2423642122页

作者： Gippert, Magdalena Shih, Pei-Cheng Heed, Tobias Howard, Ian S. Jamshidi Idaji, Mina Villringer, Arno Sehm, Bernhard Nikulin, Vadim V. Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig 04103 Germany Sony Computer Science Laboratories 141-0022 Tokyo Japan Department of Psychology and Centre for Cognitive Neuroscience University of Salzburg 5020 Salzburg Austria School of Engineering Computing and Mathematics Faculty of Science and Engineering University of Plymouth Plymouth PL4 8AA United Kingdom Machine Learning Group Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany Machine Learning Group Institute of Software Engineering and Theoretical Computer Science Electrical Engineering and Computer Science Faculty Technical University Berlin 10587 Berlin Germany Department of Neurology Martin Luther University of Halle-Wittenberg Halle (Saale) 06120 Germany

Motor imagery is frequently utilized to improve the performance of specific target movements in sports and rehabilitation. In this study, we show that motor imagery can facilitate learning of not only the imagined target movements but also sequentially linked overt movements. Hybrid sequences comprising imagined and physically executed segments allowed participants to learn specific movement characteristics of the executed segments when they were consistently associated with specific imagined segments. Electrophysiological recordings revealed that the degree of event-related synchronization in the alpha and beta bands during a basic motor imagery task was correlated with imagery-evoked motor learning. Thus, both behavioral and neural evidence indicate that motor imagery's benefits extend beyond the imagined movements, improving performance in linked overt movements. This provides decisive evidence for the functional equivalence of imagined and overt movements and suggests applications for imagery in sports and rehabilitation.

关键词： adaptation force field motor imagery motor learning motor sequence

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Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter 31

Adversarial Color Enhancement: Generating Unrestricted Adver...

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31st British Machine Vision Conference, BMVC 2020

作者： Zhao, Zhengyu Liu, Zhuoran Larson, Martha Data Science Group Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands

We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification. Our approach, Adversarial Color Enhancement (ACE), generates unrestricted adversarial images by optimizing the color filter via gradient descent. The novelty of ACE is its incorporation of established practice for image enhancement in a transparent manner. Experimental results validate the white-box adversarial strength and black-box transferability of ACE. A range of examples demonstrates the perceptual quality of images that ACE produces. ACE makes an important contribution to recent work that moves beyond Lp imperceptibility and focuses on unrestricted adversarial modifications that yield large perceptible perturbations, but remain non-suspicious, to the human eye. The future potential of filter-based adversaries is also explored in two directions: guiding ACE with common enhancement practices (e.g., Instagram filters) towards specific attractive image styles and adapting ACE to image semantics. Code is available at https://***/ZhengyuZhao/ACE. © 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Image enhancement

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dataset of Quotation Attribution in German News Articles 30

Dataset of Quotation Attribution in German News Articles

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Petersen-Frey, Fynn Biemann, Chris House of Computing and Data Science & Language Technology Group Universität Hamburg Germany

ISBN: (纸本)9782493814104

Extracting who says what to whom is a crucial part in analyzing human communication in today's abundance of data such as online news articles. Yet, the lack of annotated data for this task in German news articles severely limits the quality and usability of possible systems. To remedy this, we present a new, freely available, creative-commons-licensed dataset for quotation attribution in German news articles based on WIKINEWS. The dataset provides curated, high-quality annotations across 1000 documents (250,000 tokens) in a fine-grained annotation schema enabling various downstream uses for the dataset. The annotations not only specify who said what but also how, in which context, to whom and define the type of quotation. We specify our annotation schema, describe the creation of the dataset and provide a quantitative analysis. Further, we describe suitable evaluation metrics, apply two existing systems for quotation attribution, discuss their results to evaluate the utility of our dataset and outline use cases of our dataset in downstream tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： annotation attribution dataset German news quotation quote

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