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International Electrical Engineering Congress (iEECON)

作者： Jiran Sithiyopasakul Tuanjai Archevapanich Saran Sithiyopasakul Attasit Lasakul Boonchana Purahong Chawalit Benjangkaprasert School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

The integration of cloud computing and the Internet of Things (IoT) holds transformative potential across diverse industries. Performance assessment is essential to gauge the quality and efficiency of cloud computing and IoT systems. This paper presents a comprehensive performance evaluation of cloud computing and IoT systems, focusing on three major platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Experimental results encompass various scenarios, including normal operation, heavy load conditions, IoT applications, and scalability testing. The outcomes reveal distinct performance metrics such as response time, throughput, latency, and reliability for each cloud platform.

关键词： Performance evaluation Cloud computing Web services System performance Throughput Internet of Things Time factors

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An Improved Multi-hop LEACH Protocol Based on Fuzzy Logic for Wireless Sensor Networks

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Journal of computers (Taiwan) 2022年第3期33卷 1-16页

作者： Liu, Qing-Xue Hu, Huang-Shui Yao, Mei-Qin Wang, Chu-Hang College of Computer Science and Engineering Jilin University of Architecture and Technology Changchun130114 China College of Computer Science and Engineering Changchun University of Technology Changchun130012 China College of Computer Science and Technology Changchun Normal University Changchun130032 China

As an important kind of clustering protocols in wireless sensor networks (WSNs), LEACH and its variants have been demonstrated to efficiently balance the energy consumption, simplify the routing task and extend the network lifetime. Especially, the multi-hop LEACH type protocols have proved to be effective approaches for high energy efficiency, reliability as well as scalability. However, data transmission in hop-by-hop mode increases the energy consumption and end-to-end delay. In this paper, an improved multi-hop LEACH protocol based on fuzzy logic called IMF-LEACH is proposed, which uses a fuzzy logic controller with residual energy, length of data, and distance to BS as fuzzy descriptors to determine the hop count. Moreover, the hop count is used for each CH to find its optimal next-hop intermediate CH with more residual energy and less number of members. Simulations are conducted in MATLAB to evaluate the performance of the proposed protocol, and the results show that IMF-LEACH maximizes the network lifetime and outperforms its counterparts consistently. © 2022 computer Society of the Republic of China. All rights reserved.

关键词： Energy efficiency

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EVOAL: A Domain-Specific Language-Based Approach to Optimisation

EVOAL: A Domain-Specific Language-Based Approach to Optimisa...

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Congress on Evolutionary Computation

作者： Bernhard J. Berger Christina Plump Rolf Drechlser Institute of Embedded Systems Hamburg University of Technology Hamburg Germany DFKI GmbH-Cyber-Physical Systems Bremen Germany University Bremen - Computer Architecture Bremen Germany

Adapting optimisation algorithms, such as evolutionary algorithms, to a problem is a necessity. The required collection and exchange of domain information is an important but tedious task in real-world projects involving several experts from different areas of expertise (e.g. the domain and the optimisation area). This paper presents a structured approach that allows the experts to systematically provide their knowledge using domain-specific languages. The presented approach defines a different language for the involved experts that enables them to add their knowledge and use the information provided by other experts. The languages are extensible, allowing the addition of new optimisation aspects without changing the actual language. These languages are the front-end to a versatile open-source optimisation tool, that we built, enabling the actual execution of the optimisation. It additionally provides features for surrogate models as well as data generation and different benchmarks for evaluation. Conducting a user study, we show that the language is suitable to express the domain knowledge and domain experts can use the language to describe their domain knowledge after a short introduction. This way, the approach reduces the effort for domain experts in providing their information. As a side effect, the complete configuration of the optimisation execution through these languages allows an easy and reliable reproduction.

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Multimodal model for pneumonia detection based on enhanced stacking MOE 4

Multimodal model for pneumonia detection based on enhanced s...

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4th International Conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： Liu, Zhenzheng Liu, Zihao Zhang, Mingxun Men, Lianjie Li, Jianpeng Shandong University of Traditional Chinese Medicine School of Medicine Jinan China Shandong University of Traditional Chinese Medicine First Clinical Medical College Jinan China Shandong University of Traditional Chinese Medicine School of Traditional Chinese Medicine Jinan China Shandong University of Traditional Chinese Medicine College of Pharmacy Jinan China Jilin University of Architecture and Technology School of Electrical and Computer Jilin China

ISBN: (纸本)9798331534622

The use of artificial intelligence technology for pneumonia detection has always been an important topic in medical imaging, and integrating and processing various medical data sources to enhance the early diagnosis and identification of pneumonia has become a key challenge in the healthcare field. This study proposes a novel pneumonia prediction model with a hybrid architecture, which integrates the most advanced large language models and a stacking MOE architecture. The model combines the pre-trained ResNet-50 on ImageNet for effective image feature extraction, while using BERT to extract and analyze textual data from patient self-reports. This study uses a small-scale dataset to test pneumonia prediction based on X-ray images and patient self-reported text. The experimental results show that the hybrid model outperforms the single-modality models in all evaluation metrics. Additionally, this study conducted an ablation test on the stacking MOE and LLMs components, demonstrating the effectiveness of each component. The research results provide a feasible approach for the potential of multimodal artificial intelligence models in accurately detecting pneumonia and contribute to the development of flexible clinical diagnostic tools. © 2024 IEEE.

关键词： Clinical research

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Stochastic Task Offloading Problems for Edge Computing 17th

Stochastic Task Offloading Problems for Edge Computing

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Ding, Kexin Zhong, Zhi Zhu, Jie Nanjing University of Posts and Telecommunications Nanjing210003 China Institute of Computing Technology Academy of Sciences State Key Laboratory Chinese of Computer Architecture Beijing100864 China

ISBN: (纸本)9789819923557

The edge-cloud computing is extensively deployed to provide convenient computing. A stochastic task offloading problem in edge-cloud environment is considered. For the problem under study, a greedy simulated annealing heuristic algorithm based online offloading framework (SAOF) is proposed. In consideration of the stochastic arrival of independent tasks and the heterogeneity of resources, SAOF offloads tasks to cloud servers or edge servers dynamically. During the offloading process, each server preemptively schedules the uncompleted tasks according to the latency-sensitivity of the task. The optimization objective is to minimize the total weighted response time. Experiments are delicately designed on the testing instances with various settings. SAOF is compared with the baseline algorithm. Experimental results indicate that SAOF can more effectively reduce the total weighted response time of all tasks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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DU-DARTS: Decreasing the Uncertainty of Differentiable architecture Search 32

DU-DARTS: Decreasing the Uncertainty of Differentiable Archi...

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32nd British Machine Vision Conference, BMVC 2021

作者： Lu, Shun Hu, Yu Yang, Longxing Sun, Zihao Mei, Jilin Zeng, Yiming Li, Xiaowei Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Tecent ADlab China

Differentiable Neural architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency. However, the competition of candidate operations in DARTS introduces high uncertainty for selecting the truly important operation, thus leading to serious performance collapse. In this work, we decrease the uncertainty of differentiable architecture search (DU-DARTS) by enforcing the distribution of architecture parameters to approach the one-hot categorical distribution and by replacing the zero operation with a gate switch. Without any extra search cost, our method achieves state-of-the-art performance with 2.32%, 16.74%, and 24.1% test error on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively. Moreover, DU-DARTS can robustly find an excellent architecture on NAS-Bench-1Shot1 and NAS-Bench-201, which further demonstrates the effectiveness of our method. The source code is available at https://***/ShunLu91/DU-DARTS. © 2021. The copyright of this document resides with its authors.

关键词： computer vision

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Enhancing OSN Security: Detecting Email Hijacking and DNS Spoofing Using Energy Consumption and Opcode Sequence Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第2期49卷 333-354页

作者： Rawat, Romil Borana, Kamal Gupta, Shweta Ingle, Mandakini Dibouliya, Ashish Bhardwaj, Purvee Rawat, Anjali LabGeoInf Research LABoratory in GEOmatics and INFormation systems Rome Italy Department of CSE SVIIT SVVV Indore India Indore India Data Architecture Webster Bank United States Rabindranath Tagore University MP Bhopal India Department of Computer and Communication Technology University of Extremadura Spain

The rapid increase in automation within Online Social Networks (OSNs) has led to a surge in cyber threats, notably Email Hijacking and DNS (Domain Name System) Spoofing, which leverage malicious scripts to manipulate traffic, steal credentials, and evade detection. Traditional security mechanisms fail to effectively identify such automation-based attacks, necessitating an advanced detection framework. Objective & Purpose-This study introduces the Automated Social Network Attack Detection Model (ASNADM), which combines Energy Consumption Footprint (EComp-FP) Analysis and Automated Software Opcode Sequence Analysis (ASOSA-OSM- opcode sequence mining) for high-precision OSN security. EComp-FP detects deviations in power consumption linked to malicious automation tools, while ASOSA-OSM analyzes opcode sequences to differentiate between benign and attack behaviors. The Self-Adaptive Fuzzy Pattern Matching Clustering (SAFPMC) Algorithm enhances classification accuracy, reducing false alarms and improving real-time threat detection. Methodology and Dataset-The model was rigorously evaluated using the SPEMC-15K-E (Spam Email Classification dataset in English) dataset (15,000 samples: 7,500 benign, 7,500 malicious). EComp-FP achieved 99.87% accuracy with a 1.4W power deviation, while ASOSA-OSM attained 99.81% accuracy, detecting automation tools with an Opcode Frequency Variance (OFV) of 8.7 in malicious samples versus 3.5 in benign ones. The hybrid EComp (Energy Consumption) + OSA (Opcode Sequence Analysis) model outperformed both standalone methods, achieving 99.93% accuracy, 99.91% F1-score, a false positive rate of just 0.07%, and a false negative rate of 0.05%. Among classifiers, the Self-Adaptive Soft Fuzzy C-Means (SSFCM) Hybrid model achieved the highest performance, with 99.93% accuracy, 99.85% precision, 99.9% recall, and the lowest misclassification rate of 0.05%, surpassing Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Mach

关键词： Tweets

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ArmorCLIP: A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models

arXiv

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arXiv 2024年

作者： Li, Yijun Liang, Yuhan Niu, Yumeng Shen, Qianhe Liu, Hangyu Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Faculty of Engineering University of Bristol BristolBS8 1TH United Kingdom Computer Science and Technology Beijing University of Civil Engineering and Architecture Beijing102612 China Computer Science Mathematics & Statistics University of Toronto Mississauga MississaugaL5L 1C6 Canada Xi'an Tieyi ICC Shanxi Xian710054 China

The robustness of Vision-Language Models (VLMs) such as CLIP is critical for their deployment in safety-critical applications like autonomous driving, healthcare diagnostics, and security systems, where accurate interpretation of visual and textual data is essential. However, these models are highly susceptible to adversarial attacks, which can severely compromise their performance and reliability in real-world scenarios. Previous methods have primarily focused on improving robustness through adversarial training and generating adversarial examples using models like FGSM, AutoAttack, and DeepFool. However, these approaches often rely on strong assumptions, such as fixed perturbation norms or predefined attack patterns, and involve high computational complexity, making them challenging to implement in practical settings. In this paper, we propose a novel adversarial training framework that integrates multiple attack strategies and advanced machine learning techniques to significantly enhance the robustness of VLMs against a broad range of adversarial attacks. Experiments conducted on real-world datasets, including CIFAR-10 and CIFAR-100, demonstrate that the proposed method significantly enhances model robustness. The fine-tuned CLIP model achieved an accuracy of 43.5% on adversarially perturbed images, compared to only 4% for the baseline model. The neural network model achieved a high accuracy of 98% in these challenging classification tasks, while the XGBoost model reached a success rate of 85.26% in prediction tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly

arXiv

引用

arXiv 2024年

作者： Kyaw, Alexander Htet Jeon, Se Hwan Smith, Miana Gershenfeld, Neil Department of Architecture The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Center for Bits and Atoms Massachusetts Institute of Technology CambridgeMA02139 United States

We present a system that transforms speech into physical objects by combining 3D generative Artificial Intelligence (AI) with robotic assembly. The system leverages natural language input to make design and manufacturing more accessible, enabling individuals without expertise in 3D modeling or robotic programming to create physical objects. We propose utilizing discrete robotic assembly of lattice-based voxel components to address the challenges of using generative AI outputs in physical production—such as design variability, fabrication speed, structural integrity, and material waste. The system interprets speech to generate 3D objects, discretizes them into voxel components, computes an optimized assembly sequence, and generates a robotic toolpath. The results are demonstrated through the assembly of various objects, ranging from chairs to shelves, which are prompted via speech and realized within 5 minutes using a 6-axis robotic arm. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic assembly

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Experiments and modeling on thermal performance evaluation of standalone and M-cycle based desiccant air-conditioning systems

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Energy Reports 2024年 11卷 1445-1454页

作者： Bhat, Showkat Ahmad Sajjad, Uzair Hussain, Imtiyaz Yan, Wei-Mon Raza, Hafiz M.U. Ali, Hafiz Muhammad Sultan, Muhammad Omar, Hani Azam, Muhammad Waheed Bozzoli, Fabio Huang, Nen-Fu Center of Innovative Incubator National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Energy and Refrigerating Air-Conditioning Engineering National Taipei University of Technology Taipei10608 Taiwan Research Center of Energy Conservation for New Generation of Residential Commercial and Industrial Sectors National Taipei University of Technology Taipei10608 Taiwan Department of Agricultural Engineering Bahauddin Zakariya University Bosan Road Multan60800 Pakistan Dhahran31261 Saudi Arabia Faculty of Information Technology Zarqa University Zarqa Jordan Department of Architecture and Engineering University of Parma Parco Area delle Science 181/A Parma43124 Italy IRC-Renewable Energy and Power Systems King Fahd University of Petroleum and Minerals Dammam Dhahran Saudi Arabia

This study aims to evaluate the performance of standalone and Maisotsenko-cycle based desiccant air-conditioning systems with silica gel through experimental and explainable artificial intelligence methods. In order to examine the desiccant systems' performance, deep neural networks have been constructed based on the optimal hyper-parameters provided by the Bayesian surrogacy. The influential factors, such as regeneration air temperature and relative humidity, primary air temperature and relative humidity, among other system features like regeneration temperature and humidity, are chosen as the inputs using feature engineering and correlation heat maps. The results indicated that with the right framework and training data, the proposed model can forecast the performance of the standalone and M-cycle-based desiccant air conditioning systems with a great accuracy (i.e. R2 = 0.99). In addition, regeneration temperature and humidity, and inlet regeneration air temperature are considered to be the primary parameters that affect the thermal performance of Maisotsenko-cycle and stand-alone desiccant systems. The regeneration conditions are shown to have a positive correlation with the primary and regeneration outlet temperatures while having a negative correlation with the primary and regeneration outlet relative humidity. The primary inlet relative humidity has the greatest impact on the primary outlet relative humidity while the regeneration inlet temperature has the highest impact on the regeneration outlet relative humidity of the system. In addition, the primary inlet relative humidity is found to have positive correlations with the outlet regeneration and primary relative humidity and outlet regeneration temperature. Also, the primary air inlet temperature is found to be directly associated with the outlet primary relative humidity and temperature for the considered systems. © 2024 The Authors

关键词： Air conditioning

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