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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jan Blaha Tomáš Vintr Jan Mikula Jiří Janota Tomáš Rouĉek Jiří Ulrich Fatemeh Rekabi-Bana Laurenz Alexander Fedotoff Martin Stefanec Thomas Schmickl Farshad Arvin Miroslav Kulich Tomáš Krajník Faculty of Electrical Engineering Czech Technical University in Prague Czechia Computer Science Department Durham University United Kingdom Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czechia Department of Zoology Artificial Life Lab Institute of Biology University of Graz Graz Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Honeybees are one of the most important pollinators in the ecosystem. Unfortunately, the dynamics of living honeybee colonies are not well understood due to their complexity and difficulty of observation. In our project “RoboRoyale”, we build and operate a robot to be a part of a bio-hybrid system, which currently observes the honeybee queen in the colony and physically tracks it with a camera. Apart from tracking and observing the queen, the system needs to monitor the state of the honeybee comb which is most of the time occluded by workerbees. This introduces a necessary tradeoff between tracking the queen and visiting the rest of the hive to create a daily map. We aim to collect the necessary data more effectively. We evaluate several mapping methods that consider the previous observations and forecasted densities of bees occluding the view. To predict the presence of bees, we use previously established maps of dynamics developed for autonomy in human-populated environments. Using data from the last observational season, we show significant improvement of the informed comb mapping methods over our current system. This will allow us to use our resources more effectively in the upcoming season.

关键词： Biological system modeling Robot vision systems Ecosystems Data collection Cameras Data models Complexity theory Biomedical monitoring Monitoring Intelligent robots

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection

Towards Effective and Efficient Error Handling Code Fuzzing ...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Kang Chen Ming Wen Haoxiang Jia Rongxin Wu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology (HRUST) Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Jin YinHu Laboratory Wuhan China School of Informatics Xiamen University Xiamen China School of Computer Science and Technology HUST Wuhan China Cluster and Grid Computing Lab

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL- F I are effective that contribute significantly to its overall performance.

关键词： Codes computer bugs Fuzzing Software systems Software reliability Testing

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Cost Efficient Service Mesh Controller Placement for Edge Native Computing

Cost Efficient Service Mesh Controller Placement for Edge Na...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yuepeng Li Deze Zeng Lvhao Chen Lin Gu Weiyin Ma Feng Gao School of Computer Science China University of Geosciences Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Research Center for Cyber-Physical-Social System Zhejiang Lab Hangzhou China

ISBN: (纸本)9781665435413

Cloud native computing featured by microservice has been regarded as a compelling trend in cloud application development. Edge computing, as an alternative or complemen-tary to cloud computing, is potential to expand the microservice to edge computing, simplifying the development and deployment of edge applications. Despite that, there is still a challenge on how to manage the microservices efficiently in the open and heterogeneous distributed environment. To this end, service mesh provides a potential solution in efficient microservices management. However, as a traditional cloud-oriented architecture, it can not be applied into edge computing directly since the centralized controller policy. To address this problem, in this paper, we propose an edge service mesh architecture with distributively deployed controllers for edge native computing. We further inves-tigate the problem on how to deploy these distributive controllers in a cost efficient manner with the consideration of control cost and the synchronization cost. The problem is formulated into a non-linear optimization form and then linearized into an integer linear programming (ILP) problem. To tackle the computation complexity, we then come up with a customized k-means based algorithm (i.e., ck-means) in polynomial computation complexity. The experimental results verify the efficiency of our ck-means algorithm in comparison with the traditional k-means algorithm.

关键词： Cloud computing Costs Heuristic algorithms Microservice architectures computer architecture Synchronization Computational complexity

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What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding

arXiv

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arXiv 2024年

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony Brook NY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown Heights NY United States

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Enhancing Acceptance of Automated Vehicles Through Gamification: Insights from a Driving Simulator Study

SSRN

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SSRN 2025年

作者： Li, Chenchang Yin, Rao Yang, Bo Guan, Muhua Xie, Kun Nakano, Kimihiko Institute of Industrial Science University of Tokyo Tokyo153-8505 Japan School of Design & Innovation China Academy of Art Hangzhou311100 China School of Computer Science & System Engineering Kyushu Institute of Technology Fukuoka820-8502 Japan Department of Civil and Environmental Engineering Transportation Informatics Lab Old Dominion University NorfolkVA23529 United States

Automated vehicles hold great potential to revolutionize transportation, but subjective user acceptance remains a major obstacle to their widespread adoption and the realization of anticipated safety benefits. Recognizing this challenge, this study explores the role of gamification in enhancing user acceptance of automated vehicles and promoting safer driver engagement. By designing a gamified interaction framework and implementing it in a driving simulator experiment, the research addresses three core questions: Does gamification work? Why does it work? How can it be effectively implemented? In the experiment, participants experienced automated driving scenarios with integrated gamified interactions during overtaking tasks. Data on users' attitudes, behaviors, and eye movements were collected and analyzed through qualitative and quantitative methods. Furthermore, the study incorporated design research techniques, including interviews and user journey mapping, into the scientific framework to derive meaningful and actionable insights. The results reveal that gamification significantly improves user acceptance by enhancing Perceived Ease of Use and strengthening Behavioral Intentions. At a deeper mechanistic level, gamification enhances users' situational awareness and trust by increasing their perception efficiency during interactions and raising their risk tolerance, thereby shifting the balance between safety and reward. Based on these mechanisms and further findings, the study proposes three design strategies to maximize the benefits of gamification: designing system transparency, tailoring experiences to individual user characteristics, and leveraging incentive mechanisms to engage and motivate users effectively. This study's findings provide theoretical foundations and practical design directions for leveraging gamification to enhance human-vehicle interactions, utimately contributing to the development of safer and more efficient automated driving systems. © 2

关键词： Eye movements

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Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting

arXiv

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arXiv 2024年

作者： Ünsal, Serbulent Özdemir, Sinem Kasap, Bünyamin Kalaycı, M. Erşan Turhan, Kemal Doğan, Tunca Acar, Aybar C. Department of Biostatistics and Medical Informatics Faculty of Medicine Graduate School of Health Sciences Karadeniz Technical University Trabzon Turkey Graduate School of Informatics Middle East Technical University Ankara Turkey Health Sciences University Trabzon Kanuni Training and Research Hospital Medical Microbiology Laboratory Trabzon Turkey Biological Data Science Lab Dept. of Computer Engineering Department of Computer Engineering Hacettepe University Ankara Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey

In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings. The challenge of predicting protein functions is compounded by the limited availability of labeled data. Traditional machine learning models already struggle in such cases, and while deep learning models excel with abundant data, they also face difficulties when data is scarce. HOPER addresses this issue by integrating three distinct modalities—protein sequences, biomedical text, and protein-protein interaction (PPI) networks—to create a comprehensive protein representation. The model utilizes autoencoders to generate holistic embeddings, which are then employed for PFP tasks using transfer learning. HOPER outperforms existing methods on a benchmark dataset across all Gene Ontology categories, i.e., molecular function, biological process, and cellular component. Additionally, we demonstrate its practical utility by identifying new immune-escape proteins in lung adenocarcinoma, offering insights into potential therapeutic targets. Our results highlight the effectiveness of multimodal representation learning for overcoming data limitations in biological research, potentially enabling more accurate and scalable protein function prediction. HOPER source code and datasets are available at https://***/kansil/HOPER © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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Trash to Treasure: Using text-to-image models to inform the design of physical artefacts

arXiv

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arXiv 2023年

作者： Smith, Amy Schroeder, Hope Epstein, Ziv Cook, Michael Colton, Simon Lippman, Andrew School of Electronic Engineering and Computer Science Queen Mary University London United Kingdom MIT Media Lab United States Department of Informatics King's College London United Kingdom SensiLab Faculty of Information Technology Monash University Australia

Text-to-image generative models have recently exploded in popularity and accessibility. Yet so far, use of these models in creative tasks that bridge the 2D digital world and the creation of physical artefacts has been understudied. We conduct a pilot study to investigate if and how text-to-image models can be used to assist in upstream tasks within the creative process, such as ideation and visualization, prior to a sculpture-making activity. Thirty participants selected sculpture-making materials and generated three images using the Stable Diffusion text-to-image generator, each with text prompts of their choice, with the aim of informing and then creating a physical sculpture. The majority of participants (23/30) reported that the generated images informed their sculptures, and 28/30 reported interest in using text-to-image models to help them in a creative task in the future. We identify several prompt engineering strategies and find that a participant's prompting strategy relates to their stage in the creative process. We discuss how our findings can inform support for users at different stages of the design process and for using text-to-image models for physical artefact design. © 2023, CC BY.

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Relational Message Passing for Fully Inductive Knowledge Graph Completion

Relational Message Passing for Fully Inductive Knowledge Gra...

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International Conference on Data Engineering

作者： Yuxia Geng Jiaoyan Chen Jeff Z. Pan Mingyang Chen Song Jiang Wen Zhang Huajun Chen College of Computer Science and Technology Zhejiang University Hangzhou China Donghai Laboratory Zhoushan China Department of Computer Science The University of Manchester Manchester United Kingdom School of Informatics The University of Edinburgh Edinburgh United Kingdom NAIE PDU Huawei Technologies Co. Ltd. Xi’an China AZFT Joint Lab for Knowledge Engine Zhejiang University Hangzhou China

In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. Some recent methods have achieved good performance, but they (i) usually can only predict triples involving unseen entities alone, failing to address more realistic fully inductive situations with both unseen entities and unseen relations, and (ii) often conduct message passing over the entities with the relation patterns not fully utilized. In this study, we propose a new method named RMPI which uses a novel Relational Message Passing network for fully Inductive KGC. It passes messages directly between relations to make full use of the relation patterns for subgraph reasoning with new techniques on graph transformation, graph pruning, relation-aware neighborhood attention, addressing empty subgraphs, etc., and can utilize the relation semantics defined in the KG’s ontological schema. Extensive evaluation on multiple benchmarks has shown the effectiveness of RMPI’s techniques and its better performance compared with the existing methods that support fully inductive KGC. RMPI is also comparable to the state-of-the-art partially inductive KGC methods with very promising results achieved. Our codes, data and some supplementary experiment results are available at https://***/zjukg/RMPI.

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Framework for enhanced respiratory disease identification with clinical handcrafted features

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computers in Biology and Medicine 2025年 195卷

作者： Md. Ibrahim Patwary khokan Tasnim Jahan Tonni Md. Awlad Hossen Rony Kaniz Fatema Md. Zahid Hasan Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Dhaka 1216 Bangladesh

Respiratory disorders cause approximately 4 million deaths annually worldwide, making them the third leading cause of mortality. Early detection is critical to improving survival rates and recovery outcomes. However, chest X-rays require expertise, and computational intelligence provides valuable support to improve diagnostic accuracy and support medical professionals in decision-making. This study presents an automated system to classify respiratory diseases using three diverse datasets comprising 18,000 chest X-ray images and masks, categorized into six classes. Image preprocessing techniques, such as resizing for input standardization and CLAHE for contrast enhancement, were applied to ensure uniformity and improve the visual quality of the images. Albumentations-based augmentation methods addressed class imbalances, while bitwise segmentation focused on extracting the region of interest (ROI). Furthermore, clinically handcrafted feature extraction enabled the accurate identification of 20 critical clinical features essential for disease classification. The K-nearest neighbors (KNN) graph construction technique was utilized to transform tabular data into graph structures for effective node classification. We employed feature analysis to identify critical attributes that contribute to class predictions within the graph structure. Additionally, the GNNExplainer was utilized to validate these findings by highlighting significant nodes, edges, and features that influence the model's decision-making process. The proposed model, Chest X-ray Graph Neural Network (CHXGNN), a robust Graph Neural Network (GNN) architecture, incorporates advanced layers, batch normalization, dropout regularization, and optimization strategies. Extensive testing and ablation studies demonstrated the model's exceptional performance, achieving an accuracy of 99.56 %. Our CHXGNN model shows significant potential in detecting and classifying respiratory diseases, promising to enhance diagnostic

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OCTAPUS: Empowering Next Generation Central Offices with Cross-Layer Network Intelligence Towards a Reconfigurable Data Plane 13th

OCTAPUS: Empowering Next Generation Central Offices with Cro...

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Gatzianas, M. Kalfas, G. Vagionas, C. Vargemidou, M. Papadakis, A. Saliou, F. Theodoropoulou, E. Lyberopoulos, G. Piscione, P. Abdulwahed, A. Giardina, P. Gavrilut, V. Alsted, C. Gardes, F. Zeimpekis, I. Domínguez, T. Yu, X. Chakraborty, I. Atwi, H. Dris, S. Argyris, N. Bakopoulos, P. Petridou, S. Miliou, A. Pleros, N. Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Center for Interdisciplinary Research and Innovation Thessaloniki Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece Orange 2 Avenue Pierre Marzin Lannion22307 France Athens Greece Nextworks Srl Via Livornese 1027 Pisa56122 Italy Comcores ApS Haldor Topsøes Allé 1 Lyngby Denmark Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Teem Photonics Meylan France NVIDIA Photonic Systems Lab Ermou 56 Athens10563 Greece

ISBN: (纸本)9783031632266

Considering the challenges faced by the current Central Offices, this paper presents a novel architecture and related technological aspects of the Next Generation Central Offices (NGCOs), as envisioned within the OCTAPUS research project, towards the flexible support of the emerging capacity and latency demanding services. First, the main functional blocks of the OCTAPUS NGCO Data and Control planes are presented, with emphasis on the dynamicity of the optical paths within the NGCO and its reconfiguration capabilities. Then, focusing on the Control plane, we describe in detail its hierarchical structure, the role of the associated entities and the interactions among them. An example of network provisioning to support multiple concurrent next-generation services of realistic use cases in a real urban area is used to stress the need for NGCOs to adapt to different service requirements and temporal fluctuations in traffic demands, while catering for time-sensitive services with stringent delay requirements. © IFIP International Federation for Information Processing 2024.

关键词： Next generation networks

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