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17th International Conference on Design science Research in Information Systems and Technology (DESRIST)

作者： Cahenzli, Marcel Univ St Gallen Muller Friedbergstr 8 CH-9000 St Gallen Switzerland

ISBN: (纸本)9783031065156;9783031065163

Students that first learn about and wish to apply Design science Research (DSR) perceive difficulties in communicating DSR research designs. This, however, is an important communication use case, since more senior design researchers need to gain a good understanding of the DSR research design propositions in order to provide adequate feedback and thus, support the new generation of design researchers. This study features an artefact that fills junior design researchers' unsatisfied need for support in presenting DSR research designs. The artefact was built based on knowledge from the problem environment (i.e., a research methods course) and the emerging body of literature on DSR communication. It is evaluated in a natural field experiment, and the results indicate that the artefact is useful. A contribution of this article is the artefact itself, which is presented explicitly and can be re-used freely by DSR instructors.

关键词： Design science Research DSR communication Teaching support

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Verifying the Product of Generalized Boolean Matrix Multiplication and Its Applications to Detect Small Subgraphs 18th

Verifying the Product of Generalized Boolean Matrix Multip...

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18th International Symposium on Algorithms and Data Structures, WADS 2023

作者： Hon, Wing-Kai Tsai, Meng-Tsung Wang, Hung-Lung Computer Science National Tsing Hua University Hsinchu Taiwan Information Science Academia Sinica Taipei City Taiwan Computer Science and Information Engineering National Taiwan Normal University Taipei City Taiwan

ISBN: (纸本)9783031389054

Given three n by n integer matrices A, B, and P, determining whether the product AB equals P can be done in randomized O(n2) time by Freivalds’ algorithm. In this paper, we consider some generalized Boolean matrix multiplication AB= Pf, which is defined to be setting the entry pij of Pf for i, j∈ [ n] as the value of a given function f of the entries on the ith row of A and jth column of B. We show that, for a family of functions f, it takes deterministic O(n2) time to verify whether the generalized product Pf contains only False entries. Then, we present how to apply such a result to detect small subgraphs efficiently, including: Detect any designated colored 4-cycle in randomized O(n2) time for n-node k-edge-colored (for k= O(1 ) ) complete graphs with maximum color degree at most 2 (including 1-edge-colored general graphs), which unifies several independently discovered algorithms each corresponding to a customized Ramsey-type theorem. As a complementary result, we show that if the maximum color degree is at least 3, any combinatorial algorithm that solves this problem requires Ω (n3-Ε) time for any constant Ε> 0, assuming the hardness of triangle *** any designated 4-node induced subgraph in randomized O(n2) time for n-node triangle-free graphs. In contrast, the known best algorithm for this problem on general graphs needs triangle time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Matrix algebra

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Enhancing Knowledge-Aware Recommendation with Contrastive Learning 9th

Enhancing Knowledge-Aware Recommendation with Contrastive L...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Xinyue Gao, Hui School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783031466601

Knowledge graph serves as a side information, bringing diversity and interpretability to the recommendation. A well-developed recommender system can efficiently capture user and item characteristics, accurately reflecting user preferences. However, supervised signals with graph structure are extraordinarily sparse, and the collaborative and knowledge graphs contain irrelevant edges, exacerbating noise propagation and reducing the robustness of recommendations. To address the above issues, we propose a model for enhancing Knowledge-aware Recommendation with Contrastive Learning (KRCL), including two contrastive learning tasks and three functional modules. Specifically, we construct two views, using TransR and TATEC to optimize knowledge representations from distance and semantic aspects, respectively. After the item-side knowledge is augmented, we remove unreliable interaction edges from collaborative graph to reduce noise propagation. We then perform contrastive learning on the output node representations of different views through graph propagation. To further tap the latent interest of users, we consider users/items that exhibit similar representations as semantic neighbors, treating them as positive pairs in contrastive learning. The structural and semantic contrastive tasks are eventually integrated in a multi-task learning manner to jointly boost the recommendation performance. To validate the effectiveness of our method, we conduct extensive experiments on three benchmark datasets. Experimental results demonstrate that our KRCL significantly outperforms previous state-of-the-art baselines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recommender systems

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A Genetic-Muted Leader Scheme with CNN-Based IDS for Industrial Internet of Things Networks 1

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9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Reddy, Boyella Mala Konda Abdul Azeez Khan, A. Sathick, K. Javubar Raj, L. Arun Department of Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Applications B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

The industrial industry has adopted web and cloud-based technologies widely, which has accelerated the IoT ecosystem’s expansion. Industrial Internet of Things (IIoT) has established a large network due to the sheer volume of information and linked equipment. IIoT networks are made to be vulnerable in terms of cybersecurity. So as to ensure the security of the IIoT networks, intrusion detection systems (IDS) must be developed. This study suggests an IDS-based on a Genetic-Muted Leader Scheme (GMLS)-CNN for usage in IIoT networks. The data collection and preprocessing steps improve the detection accuracy. The next step is to classify data using convolutional neural networks (CNNs), with GMLS used to fine-tune the parameters for the most accurate results. The two datasets such as UNSW-NB15 and X-IIoTID and named as TD were used to distinguish normal and problematic data, which allowed the study to evaluate several categorization techniques. Among the other schemes, the GMLS-CNN model had the maximum accuracy rate for IDSis 98.7% (X-IIoTID) and 95.21% (UNSW-NB15). Additionally, the model’s success in correctly detecting attacks based on the various types of attacks reported in the datasets was assessed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics): Preface

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics) 2014年 8620 LNCS卷 V-VI页

作者： Bikakis, Antonis Fodor, Paul Roman, Dumitru University College London Department of Information Studies Gower Street London WC1E 6BT United Kingdom Stony Brook University Department of Computer Science Stony Brook NY 11794 United States SINTEF University of Oslo Forskningsveien 1 0314 Oslo Norway

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Vision-Based Categorical Object Pose Estimation and Manipulation 16th

Vision-Based Categorical Object Pose Estimation and Manipul...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Meng, Qiwei Liao, Jianfeng Jun, Shao Xu, Nuo Xu, Zeming Sun, Yinan Sun, Yao Zhu, Shiqiang Gu, Jason Song, Wei Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China School of Mechanical Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964826

Object manipulation and environment interaction are of great significance for intelligent robots, especially service robots working under unstructured household and office scenarios. This paper proposes a novel approach for categorical unseen object grasping and manipulation. Different from recently popular end-to-end reinforcement learning methods, we develop models for geometric primitive abstraction of target objects, and accordingly estimate their pose as well as generate task-orientated grasp points. Such design emphasizes visual perception in guiding robotic manipulation, thereby enhancing model interpretability and reliability during implementation. In addition, we also conduct object grasping experiments both under simulation and real-world settings, which further verify the effectiveness and superiority of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intelligent robots

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Towards Online Anomaly Detection in Steel Manufacturing Process 23rd

Towards Online Anomaly Detection in Steel Manufacturing Pro...

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23rd International Conference on Computational science, ICCS 2023

作者： Jakubowski, Jakub Stanisz, Przemyslaw Bobek, Szymon Nalepa, Grzegorz J. Department of Applied Computer Science AGH University of Science and Technology Krakow30-059 Poland ArcelorMittal Poland Krakow31-752 Poland and Mark Kac Center for Complex Systems Research Jagiellonian University ul. prof. Stanislawa Lojasiewicza 11 Kraków30-348 Poland

ISBN: (纸本)9783031360268

Data generated by manufacturing processes can often be represented as a data stream. The main characteristics of these data are that it is not possible to store all the data in memory, the data are generated continuously at high speeds, and it may evolve over time. These characteristics of the data make it impossible to use ordinary machine learning techniques. Specially crafted methods are necessary to deal with these problems, which are capable of assimilation of new data and dynamic adjustment of the model. In this work, we consider a cold rolling mill, which is one of the steps in steel strip manufacturing, and apply data stream methods to predict distribution of rolling forces based on the input process parameters. The model is then used for the purpose of anomaly detection during online production. Three different machine learning scenarios are tested to determine an optimal solution that fits the characteristics of cold rolling. The results have shown that for our use case the performance of the model trained offline deteriorates over time, and additional learning is required after deployment. The best performance was achieved when the batch learning model was re-trained using a data buffer upon concept drift detection. We plan to use the results of this investigation as a starting point for future research, which will involve more advanced learning methods and a broader scope in relation to the cold rolling process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cold rolling mills

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics) 2014年 8460 LNCS卷 VII-VIII页

作者： Magoutis, Kostas Pietzuch, Peter University of Ioannina FORTH-ICS Dept. of Computer Science and Engineering 45110 Ioannina Greece Imperial College London Dept. of Computing Queen's Gate 180 London SW7 2AZ United Kingdom

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JavaBIP meets VerCors: Towards the Safety of Concurrent Software Systems in Java 1

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26th International Conference on Fundamental Approaches to Software Engineering (FASE) Held as Part of the European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Bliudze, Simon van den Bos, Petra Huisman, Marieke Rubbens, Robert Safina, Larisa Univ Lille INRIA CNRS Cent LilleUMR 9189 CRIStAL 9189 F-59000 Lille France Univ Twente Formal Methods & Tools Enschede Netherlands

ISBN: (数字)9783031308260

ISBN: (纸本)9783031308253;9783031308260

We present "Verified JavaBIP", a tool set for the verification of JavaBIP models. A JavaBIP model is a Java program where classes are considered as components, their behaviour described by finite state machine and synchronization annotations. While JavaBIP guarantees execution progresses according to the indicated state machines, it does not guarantee properties of the data exchanged between components. It also does not provide verification support to check whether the behaviour of the resulting concurrent program is as (safe as) expected. This paper addresses this by extending the JavaBIP engine with run-time verification support, and by extending the program verifier VerCors to verify JavaBIP models deductively. These two techniques complement each other: feedback from run-time verification allows quicker prototyping of contracts, and deductive verification can reduce the overhead of run-time verification. We demonstrate our approach on the "Solidity Casino" case study, known from the Verify This Collaborative Long Term Challenge.

关键词： computer software

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A Review on Network Layer Attacks in Wireless Sensor Network and Defensive Mechanisms 7th

A Review on Network Layer Attacks in Wireless Sensor Network...

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7th International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2023

作者： Touhami, Sana Belghachi, Mohamed Touhami, Achouak Department of Mathematics and Computer Science Tahri Mohammed University Bechar Algeria Department of Mathematics and Computer Science Ali Kafi University Center Tindouf Algeria Information and Telecommunication Laboratory Tahri Mohamed University Bechar Algeria Energetic in Arid Zones Laboratory Tahri Mohamed University Bechar Algeria

ISBN: (纸本)9783031606281

Wireless sensor networks (WSN) are considered as one of the most widely used networks for all-inclusive applications. They are organized into many sensor nodes. The deployment of nodes in these networks is not secure, and this may lead to security attacks. In this paper we are deliberating different types of attacks in network layer and defensive mechanisms against those attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Network layers

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