检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Srivastava, Gautam Yun, Unil Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Distributed Systems and Informatic Devices Silesian University of Technology Poland Department of Math and Computer Science Brandon University Brandon Canada Department of Computer Science and Engineering Sejong University Korea Republic of

Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Researchers have used various techniques to predict power consumption, but existing models lack discussion of collaborative learning and privacy issues among multiple clients. To address these issues, we propose Multi-Head Heterogeneous Federated Learning (MHHFL) systems that consist of multiple head networks, which independently act as carriers for federated learning. In the federated period, each head network is embedded into 2-dimensional vectors and shared with the centralized source pool. MHHFL then selects appropriate source networks and blends the head networks as knowledge transfer in federated learning. The experimental results show that the proposed MHHFL systems significantly outperform the benchmark and state-of-the-art systems and reduce the prediction error by 24.9% to 94.1%. The ablation studies demonstrate the effectiveness of the proposed mechanisms in the MHHFL (head network embedding and selection mechanisms), which significantly outperforms traditional federated average and random transfer. © 2025, CC BY-NC-SA.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Securing ML-based Android Malware Detectors: A Defensive Feature Selection Approach against Backdoor Attacks

Securing ML-based Android Malware Detectors: A Defensive Fea...

引用

Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE/ACM International Symposium on

作者： Bartłomiej Marek Kacper Pieniążek Filip Ratajczak Wojciech Adamczyk Bartosz Bok Mateusz Krzysztoń Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wrocław Poland Department of Distributed Systems Research and Academic Computer Network (NASK-PIB) Warsaw Poland

ISBN: (数字)9798350377514

ISBN: (纸本)9798350377521

This work investigates the vulnerability of ML-based Android malware detectors to backdoor attacks, and proposes a novel feature selection method to reduce system vulnerability. We present a realistic attack scenario and enhance the state-of-the-art genetic algorithm for trigger generation. The algorithm is used to design a feature selection method that prioritises attributes less prone to exploitation by attackers. The solution was evaluated using a dataset from the Koodous platform. The results show that the proposed improvements to the trigger selection algorithm were effective, resulting in a 10 percentage point increase. Furthermore, the proposed feature selection method significantly reduced the vulnerability of the ML system by 30%, without affecting the quality of the malicious application detection task execution.

关键词： Training Measurement Operating systems Computational modeling Neural networks Machine learning Predictive models Feature extraction Malware Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract)

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

引用

International Conference on Data Engineering

作者： Wei Fang Haipeng Jiang Hengyang Lu Jun Sun Xiaojun Wu Jerry Chun-Wei Lin Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Distributed Systems and IT Devices Silesian University of Technology Poland

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.

关键词： Runtime Itemsets Heuristic algorithms Sociology Approximation algorithms Search problems Topology

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Black Box: Neural Cryptanalysis with XAI

Unveiling the Black Box: Neural Cryptanalysis with XAI

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Yue-Tian Goi Shu-Min Leong Raphaël C.-W. Phan Shangqi Lai Ana Sălăgean School of Information Technology Monash University Distributed Systems Security Group CSIRO's Data61 Australia Department of Computer Science Loughborough University UK

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

At CRYPTO'19, Gohr[1] presented ResNet-based neural distinguishers (ND) for the round-reduced SPECK32/64 cipher. However, due to the black-box use of such deep learning models, it is hard for humans to understand why these distinguishers work, impeding advancements in cryptanalytic knowledge. In this work, we aim to effectively adapt eXplainable Artificial Intelligence (XAI) techniques, notably Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to gain a detailed understanding of the important features useful in Gohr's neural distinguishers.

关键词： Deep learning Adaptation models Ciphers Correlation Additives Explainable AI Closed box Manuals Encryption Cybernetics

来源：评论

学校读者我要写书评

暂无评论

Locality Sensitive Hash Aggregated Nonlinear Neighborhood Matrix Factorization for Online Sparse Big Data Analysis

引用

ACM/IMS Transactions on Data science 2021年第4期2卷 1–27页

作者： Li, Zixuan Li, Hao Li, Kenli Wu, Fan Chen, Lydia Li, Keqin College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Department of Electric Engineering Mathematics and Computer Science Distributed Systems Delft University of Technology Delft Netherlands Department of Computer Science State University of New York New Paltz United States

Matrix factorization (MF) can extract the low-rank features and integrate the information of the data manifold distribution from high-dimensional data, which can consider the nonlinear neighborhood information. Thus, MF has drawn wide attention for low-rank analysis of sparse big data, e.g., Collaborative Filtering (CF) Recommender systems, Social Networks, and Quality of Service. However, the following two problems exist: (1) huge computational overhead for the construction of the Graph Similarity Matrix (GSM) and (2) huge memory overhead for the intermediate GSM. Therefore, GSM-based MF, e.g., kernel MF, graph regularized MF, and so on, cannot be directly applied to the low-rank analysis of sparse big data on cloud and edge platforms. To solve this intractable problem for sparse big data analysis, we propose Locality Sensitive Hashing (LSH) aggregated MF (LSH-MF), which can solve the following problems: (1) The proposed probabilistic projection strategy of LSH-MF can avoid the construction of the GSM. Furthermore, LSH-MF can satisfy the requirement for the accurate projection of sparse big data. (2) To run LSH-MF for fine-grained parallelization and online learning on GPUs, we also propose CULSH-MF, which works on CUDA parallelization. Experimental results show that CULSH-MF can not only reduce the computational time and memory overhead but also obtain higher accuracy. Compared with deep learning models, CULSH-MF can not only save training time but also achieve the same accuracy performance. © 2022 Association for Computing Machinery.

关键词： Matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Exploiting Open Data for Co2 Estimation Via Artificial Intelligence and Explainable Ai

SSRN

引用

SSRN 2024年

作者： Bilotta, Stefano Palesi, Luciano Alessandro Ipsaro Nesi, Paolo University of Florence Department of Mathematics and Computer Science "U. Dini" Firenze50134 Italy University of Florence Distributed Systems and Internet Technology DISIT Lab Firenze50139 Italy

In present paper, a general CO2 emission estimation approach is developed by means of a large range of open data as predictions. Such open data are related to human activity regarding services and (socio-economic) conditions arising in a given (urban) area. The proposed model focuses on fine-scale prediction to better understand the dynamics of emissions via machine (or deep) learning approaches and taking into account an innovative study based on open data sources including city services. The goal is to understand how specific predictors can contribute to or mitigate CO2 emissions in the observed area. To this end, the impact of several features has been analyzed in order to identify the key factors influencing emissions. Thus, a formal study has been conducted to perform feature relevance analysis by using eXplainable AI (XAI) approach. The proposed model is useful to define targeted policies reducing the pollutant impact of cities, promote a more ecologically sustainable urban lifestyle and improve sustainable urban planning. Both solution and model have been improved to be more flexible by using transfer learning techniques. This study and its related results have been produced and validated by exploiting the Snap4City framework for smart city, mobility and transport and data analytics. In particular, such results deal with the metropolitan cities of Florence and Bologna. © 2024, The Authors. All rights reserved.

关键词： Open Data

来源：评论

学校读者我要写书评

暂无评论

Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

引用

Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

来源：评论

学校读者我要写书评

暂无评论

Predicting Conflict Zones on Terrestrial Routes of Automated Guided Vehicles with Fuzzy Querying on Apache Kafka

Predicting Conflict Zones on Terrestrial Routes of Automated...

引用

International Conference on Data science and Advanced Analytics (DSAA)

作者： Bożena Małysiak-Mrozek Mario Bas Vaidy Sunderam Stanisław Kozielski Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science Emory University Atlanta GA USA

In today’s world, smart factories are a coexisting element of smarticizing cities. Smart manufacturing of today relies on the automation of many component tasks of the production process. Automated guided vehicles (AGVs) that transport materials on the production lines are important elements of this automation. Appropriate management of a fleet of AGVs requires avoiding collisions. However, prediction and early detection of approaching collision points on the transportation routes not only prevent collisions but also enables adjusting the AGV operation and improving its flow. In this paper, we demonstrate the use of fuzzy sets and linguistic variables in collision prevention by processing AGV data streams with Apache Kafka. We extend the capabilities of Apache Kafka and ksqlDB towards fuzzy stream processing and use fuzzy KSQL queries to predict collisions. Our experiments prove that fuzzy querying against AGV data streams does not consume much time and computational resources, and we can successfully avoid collisions by predicting future positions of the AGV for various densities of data streams and widths of time windows.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ZigBee Network for AGV Communication in Industrial Environments

ZigBee Network for AGV Communication in Industrial Environme...

引用

International Conference on Data science and Advanced Analytics (DSAA)

作者： Jarosław Flak Tomasz Skowron Rafał Cupek Marcin Fojcik Dariusz Caban Adam Domański Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Computer Science Electrical engineering Western Norway University of Applied Sciences Bergen Norway

Automated Guided Vehicles (AGVs) are a key component of many modern industrial systems. AGVs are supposed to communicate with each other in real time using wireless networks. In this article, the advantages and disadvantages of the ZigBee wireless network related to the control of AGVs are considered. We analyze the performance of the ZigBee network programmed with both C# and Python libraries to control ZigBee devices. The throughput and signal strength are presented and discussed depending on the transmission speed of the serial port, the payload size, and the presence and distance from the obstacles. The results of the experiments show the effective values of these parameters, the methods of using C# and Python, and the reliability of the throughput up to a certain point in network devices.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing

引用

Procedia computer science 2024年 238卷 385-395页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

关键词： cloud computing edge computing dataset generation energy-efficiency green computing machine learning internet of things power consumption modeling resource management scheduling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：