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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1561 条记录，以下是21-30 订阅

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Two Hybrid Heuristic Algorithms for Optimizing data Processing Schedules in Distributed Edge computing 11

Two Hybrid Heuristic Algorithms for Optimizing Data Processi...

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11th international conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and Intelligent Connectivity

作者： Sayah, Achraf Aqil, Said Aadil, Bouchra Lahby, Mohamed Hassan II Univ Lab Appl Math & Comp Sci Higher Normal Sch Casablanca Morocco Hassan II Univ Lab Artificial Intelligence & Complex Syst Engn Natl Higher Sch Arts & Crafts Casablanca Morocco

ISBN: (纸本)9798350377873;9798350377866

In recent years, edge computing has emerged as a promising solution in the field of network computing. This architecture ensures the availability of distributed computing resources located closer to end-users and IoT devices. However, resource scheduling remains a significant challenge in edge computing, requiring effective strategies to optimize resource utilization and ensure efficient task allocation. In this paper, we propose two hybrid approaches that combine the NawazEnscore-Ham (NEH) algorithm with local search and Greedy Random Adaptive Search Procedure (GRASP) algorithm with local search for modeling and solving data traffic in distributed edge computing environments (DPSDEC). Through extensive evaluations, we consistently observe that the NEH algorithm outperforms GRASP, delivering minimized makespan and generating efficient schedules. Moreover, the NEH algorithm performs very well in less complex situations and maintains this advantage even in larger and more complex problems.

关键词： Local search Algorithm Edge computing Distributed Networks Scheduling.

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Preliminary Investigation of SSL for complex Work Activity Recognition in Industrial Domain via MoIL

Preliminary Investigation of SSL for Complex Work Activity R...

引用

ieee international conference on Pervasive computing and Communications (PerCom)

作者： Xia, Qingxin Maekawa, Takuya Morales, Jaime Hara, Takahiro Oshima, Hirotomo Fukuda, Masamitsu Namioka, Yasuo Osaka Univ Multimedia Engn Osaka Japan Toshiba Co Ltd Corp Mfg Engn Ctr Minato Japan

ISBN: (纸本)9798350304367;9798350304374

In this study, we investigate a new self-supervised learning (SSL) approach for complex work activity recognition using wearable sensors. Owing to the cost of labeled sensor data collection, SSL methods for human activity recognition (HAR) that effectively use unlabeled data for pretraining have attracted attention. However, applying prior SSL to complex work activities such as packaging works is challenging because the observed data vary considerably depending on situations such as the number of items to pack and the size of the items in the case of packaging works. In this study, we focus on sensor data corresponding to characteristic and necessary actions (sensor data motifs) in a specific activity such as a stretching packing tape action in an assembling a box activity, and try to train a neural network in self-supervised learning so that it identifies occurrences of the characteristic actions, i.e., Motif Identification Learning (MoIL). The feature extractor in the network is used in the downstream task, i.e., work activity recognition, enabling precise activity recognition containing characteristic actions with limited labeled training data. The MoIL approach was evaluated on real-world work activity data and it achieved state-of-the-art performance under limited training labels.

关键词： Activity recognition self-supervised learning wearable sensor industrial domain

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LGDCloudSim: A resource management simulation system for large-scale geographically distributed cloud data center scenarios 17

LGDCloudSim: A resource management simulation system for lar...

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ieee 17th international conference on Cloud computing (ieee CLOUD)

作者： Liu, Jiawen Xu, Yuehao Feng, Binbin Ding, Zhijun Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350368543;9798350368536

Current IaaS providers have deployed data centers worldwide, with resources continually increasing. Meanwhile, there is a rising trend in the concurrency of user requests and the diversity of user request types. To achieve better resource allocation, various complex scheduling architectures have been proposed. However, due to the challenges associated with real-world experiments, simulation systems are needed to build experimental environments for related research. As existing systems do not perform well enough, we construct LGDCloudSim. It is designed with full consideration of the characteristics of the largescale geographically distributed cloud data center scenarios. To support large-scale simulations, we propose state management optimization and operation process optimization methods. Experiments show that LGDCloudSim can simulate up to 5x10(8) hosts and 107 request concurrency. It also supports diverse scheduling architectures and different request types.

关键词： large-scale cloud geographically distributed data centers cloud simulation system resource management scheduling architecture

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Efficient Anomaly Detection Algorithm for Operational data Based on Fuzzy cognitive Map 3

Efficient Anomaly Detection Algorithm for Operational Data B...

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3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Zhao, Xin Huang, Changda Marketing Service Center of State Grid Xinjiang Electric Power Co. LTD. Urumqi830000 China

ISBN: (纸本)9798331530280

In order to improve the accuracy and efficiency of anomaly detection in operational data, an efficient anomaly detection algorithm for operational data based on fuzzy cognitive maps is proposed. Clean, denoise, and standardize raw operational data;Extracting concept nodes based on fuzzy cognitive maps;Constructing a fuzzy cognitive map model based on relational mapping to obtain the probability distribution results of operational data;Design an efficient anomaly detection algorithm for operational data through big data information fusion and association rule mining methods. The test results show that this method can improve the sensitivity and detection rate of abnormal data detection, reduce false detection rate, KL divergence, and average detection delay time. The accuracy and efficiency of abnormal detection in operational data are high. © 2024 ieee.

关键词： Fuzzy cognitive Maps

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Tutorial: Federated Learning x Security for Network Monitoring 44

Tutorial: Federated Learning x Security for Network Monitori...

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44th ieee international conference on Distributed computing Systems (ICDCS)

作者： Busnel, Yann Lavaur, Leo IMT Nord Europe IRISA Lille France IMT Atlantique IRISA Rennes France

ISBN: (纸本)9798350354720;9798350354713

Federated Learning (FL) is a Machine Learning paradigm that enables training models across distributed clients without accessing their data. In the context of network security, FL can be used to collaboratively train Intrusion Detection System (IDS) models across multiple organizations, virtually extending the local dataset of each participant. Among the new challenges raised by this approach, the heterogeneity of the clients' environments induce consequent differences in the data distributions, and therefore contributions. Further, identifying and mitigating malicious contributions is made more complex in heterogeneous environments. This tutorial introduces the audience to the principles of FL and its application to network security, and more specifically to build Collaborative Intrusion Detection Systems (CIDSs) using FL. We address open challenges on that regard, before focusing on the problem of training on heterogeneous data. Finally, we discuss the issues raised by using FL in the context of network security, with a particular focus on poisoning attacks.

关键词： Federated Learning Network Security Collaborative Intrusion Detection System Network Monitoring data poisoning

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Scalable complex Scene Understanding for Edge computing

Scalable Complex Scene Understanding for Edge Computing

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ieee international conference on Web Services (ieee ICWS)

作者： Chen, Nanxi Liu, Lili Wang, Xu Li, Jiamao Zhang, Xiaolin Shanghai Inst Microsyst & Informat Technol CAS Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350368567;9798350368550

This paper aims to develop flexible and scalable scene understanding capabilities usable on resource-constrained devices. We propose an edge computing framework to enable large-scale object detection within complex visual scenes. The framework attempts to emulate human intuition - leveraging commonsense reasoning and fast concept learning from small data. Structured knowledge about objects and their relationships guides efficient deduction of scene contents. Specifically, we develop an ontology-based scene deduction model to refine an initial scene parsing, which incorporates declarative constraints on objects, their attributes, and interactions. It represents such domain knowledge explicitly and reasons over ontological relationships. By representing this domain knowledge explicitly and reasoning over ontological relationships, the model refines an initial scene parsing to better reflect commonsense consistency. The evaluation compared the proposed method to other large-scale object detection methods. The results show that our ontology-driven deduction model scale-ups the data-driven scene parser's capability while reducing the data and compute requirements.

关键词： Scene understanding Edge computing Ontology AI services Object refinement

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Hybrid IoT Programming with data Flow Diagrams and mruby 22

Hybrid IoT Programming with Data Flow Diagrams and mruby

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2024 ieee conference on Dependable, Autonomic and Secure computing

作者： Tanaka, Kazuaki Ogura, Sota Krishnamoorthy, R. Kyushu Inst Technol Fac Informat Engn Iizuka Fukuoka 8208502 Japan Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Iizuka Fukuoka 8208502 Japan Chennai Inst Technol Ctr Adv Wireless Integrated Technol Chennai 600069 Tamil Nadu India

ISBN: (纸本)9798331522735;9798331522728

IoT is increasingly being used in different areas of society. IoT is used in society by collecting values acquired by sensor devices through networks and analysing the data. IoT software is needed to use the IoT, but efficient software development methods are desired because software for an increasingly complex society tends to become more complex. This research aims to improve the efficiency of IoT software and develop an intuitive method for developing IoT software. This paper proposes a development methodology that combines a data-flow-based programming approach with a procedural programming language. When the focus is on data, data flow programming is intuitive and efficient. On the other hand, when the focus is on data processing, it is better to use a procedural programming language because data is processed sequentially[1]. In the hybrid IoT programming environment proposed in this paper, the code of a procedural programming language is generated from the description by data flow programming, and the program is executed by a micro controller. The products of this research are available as open source software[2], [3].

关键词： data flow programming embedded software iot mruby

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LLM4HIN: Discovering Meta-path with Large Language Model for Reasoning on complex Heterogeneous Information Networks 17

LLM4HIN: Discovering Meta-path with Large Language Model for...

引用

ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th ieee international conference on Smart data, Smartdata 2024

作者： Cheng, Haoxiang Liu, Shixuan Fan, Changjun Huang, Kuihua He, Hua Wang, Xianghan Liu, Zhong National University of Defense Technology Laboratory for Big Data and Decision Changsha China

ISBN: (纸本)9798350351637

Heterogeneous Information Networks (HINs) are complex structures hosting various types of entity nodes and diverse relationships. Meta-paths are crucial in HINs for relaying detailed information and enhancing knowledge transfer. However, their discovery is challenged by the expansive meta-path space inherent in complex HINs. To address this, we introduce LLM4HIN, an innovative meta-path discovery framework that utilizes Large Language Models (LLMs) for efficient meta-path discovery. Through the use of LLMs, LLM4HIN bypasses challenges including computational limitations, semantic inconsistency, and path selection optimization traditionally associated with meta-path reasoning. LLM4HIN could efficiently discovers meta-paths by utilizing LLMs to generate these paths from a minimal subset of HIN samples. Experimental evaluation on large-scale HINs attests to the enhanced performance of the meta-paths generated by LLM4HIN in link prediction tasks compared to state-of-the-art methods. © 2024 ieee.

关键词： Modeling languages

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Quantum computing for complex Energy Systems: A Review 6

Quantum Computing for Complex Energy Systems: A Review

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6th international conference on data-Driven Optimization of complex Systems (DOCS)

作者： Heinen, Xander Chen, Hao Univ Twente Behav Management & Social Sci BMS Enschede Netherlands Univ Twente Dept High Tech Business & Entrepreneurship IEBIS Enschede Netherlands

ISBN: (纸本)9798350377859;9798350377842

Quantum computing holds promise for addressing previously unsolvable problems, particularly within complex energy systems driven by big data. This research employs a semi-systematic literature analysis to identify and categorise popular quantum algorithms with potential applications in these systems. The algorithms are divided into two main groups: quantum chemical simulation algorithms and quantum optimisation algorithms. Quantum chemical simulations can model molecules, facilitating the discovery of advanced materials and technologies for complex energy systems. Meanwhile, quantum optimisation algorithms aim to enhance energy production efficiency by optimising the grid's energy flow and smart energy storage. A significant challenge across all algorithms is the current hardware limitations, as they require more processing power than is presently available. Additionally, these algorithms necessitate precise initial parameter settings, necessitating accurate representation of real-world scenarios. Overcoming these challenges could enable quantum computing to enhance the efficiency and effectiveness of complex energy systems significantly.

关键词： Quantum computing Algorithms Quantum circuits qubits complex energy systems

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complex Semantic Tabular Interpretation using SDD-Gen 18

Complex Semantic Tabular Interpretation using SDD-Gen

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18th ieee international conference on Semantic computing (ICSC)

作者： Johnson, Matthew Stingone, Jeanette A. Bengoa, Sofia Masters, James McGuinness, Deborah L. Rensselaer Polytech Inst Tetherless World Constellat Troy NY 12180 USA Columbia Univ Dept Epidemiol New York NY USA Icahn Sch Med Mt Sinai Dept Environm Med & Publ Hlth New York NY USA Icahn Sch Med Mt Sinai Sci Comp & Data New York NY USA Rensselaer Polytechn Inst Dept Comp Sci Tetherless World Constellat Troy NY USA

ISBN: (纸本)9798350385359

Knowledge graphs have become an essential technology for both businesses and governments. They enable a wide variety of critical tasks, such as aligning diverse datasets, improving the capabilities of search engines, supporting error checking, and generating explanations using inference engines. However, populating, augmenting, and/or validating a knowledge graph can be challenging because developers need domain knowledge to understand their data and experience in ontology modeling to align concepts properly as well as experience with conflict detection and truth maintenance tools. Previous efforts have explored automatically integrating simple tabular data into knowledge graphs to lower the barrier to entry. These methods heavily rely on named entity overlap and require that tables are similar to relational tables in third normal form. While these methods have been successful under competition, these limitations make them impractical for general usage. In this paper, we introduce the semantic data dictionary generator (SDD-Gen), an algorithm that aligns complex tabular data to ontological terms for knowledge graph generation. Our methodology leverages context information from data dictionaries to make alignments, enabling us to align complex tables with few named entities and multiple subject columns.

关键词： Tabular data Knowledge Graph Semantic Annotation

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