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DFIL: an Efficient Web Service Prediction Method based on Deep Feature Interactive Learning 25

DFIL: an Efficient Web Service Prediction Method based on De...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Zhu, Ke Wang, Zhixin Jiang, Tao Xiao, Yingyuan Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China He'nan University of Economics and Law School of Computer and Information Engineering Zhengzhou China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

As a representative of nonfunctional features, the Quality-of-Service (QoS) plays an important role in recommending the best service to users. To obtain the QoS value of web services, researchers have proposed many web service QoS prediction methods. However, most existing methods, e.g., the collaborative filtering method, leverage the historical user service invocation information to predict the QoS value, and only consider the QoS information of similar users or services, and ignore the attributes and characteristics of users and services. In this paper, we propose a Deep Feature Interaction Learning method, called DFIL for short, for web service QoS prediction. Specifically, (1) DFIL constructs a feature extraction method to effectively obtain the features of users and services. (2) DFIL trains a novel neural network for QoS prediction, which can accurately find the potential relationship between users and services based on the feature information, and provide good web service recommendation. © 2022 IEEE.

关键词： Quality of service

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A Data science Approach to Precision Medicine: Allostatic Load as a Predictor of Cardiovascular Disease 7

A Data Science Approach to Precision Medicine: Allostatic Lo...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Ikome, Otto M. Melapu, Venkata K. Kwembe, Tor A. Watson, Ella Jackson State University Department of Computer Science Engineering and Technology Jackson United States Jackson State University Department of Mathematics and Statistical Sciences Jackson United States Jackson State University Jackson Public School System Jackson United States

ISBN: (纸本)9798350342154

Introduction: Precision medicine, tailored to individual genetic and environmental attributes, has emerged as a practical approach in healthcare. Allostatic load (AL), reflecting the body's response to stressors, holds promise in predicting susceptibility to chronic illnesses like cardiovascular disease (CVD). This study explored the relationship between CVD and AL, employing big data analytics and machine learning to enhance prediction-accuracy. Objectives: 1. Develop a predictive model using AL measures for CVD prediction. 2. Create a clinical AL tool for personalized medicine. 3. Demonstrate the relationship between AL scores and CVD. Methods: Data from the National Center for Health Statistics encompassed demographic, health, and clinical variables. AL was computed using a count-based method, considering factors like blood pressure, body mass index, glucose, lipids, etc. Machine learning (ML) algorithms, including random forest and logistic regression, were employed to predict CVD based on AL factors. Missing data were managed using imputation methods tailored to variable distributions. Results: The count-based AL method correlated more with age than the weighted method. Both methods showed significant correlations with age, emphasizing the association between AL and aging. AL was positively correlated with CVD, with an 82% accuracy rate in predicting CVD using the random forest algorithm. Black/African American populations displayed the highest correlation between AL and CVD. Conclusions: This study highlights the potential of AL as a predictor for CVD and supports its integration into clinical practice. AL provides a comprehensive view of physiological health and empowers healthcare providers to make informed decisions, particularly in vulnerable populations. Big data analytics and ML enhance prediction accuracy and offer insights into the complex factors contributing to CVD risk. The convergence of healthcare, statistics, and computer programming is essential

关键词： Machine learning

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The Parables of the Mustard Seed and the Yeast: Extremely Low-Budget, High-Performance Nighttime Semantic Segmentation 39

The Parables of the Mustard Seed and the Yeast: Extremely Lo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Shiqin Xu, Xin Chen, Haoyang Jiang, Kui Wang, Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)157735897X

Nighttime Semantic Segmentation (NSS) is essential to many cutting-edge vision applications. However, existing technologies overly rely on massive labeled data, whose annotation is time-consuming and laborious. In this paper, we pioneer a new task focusing on exploring the potential of training strategy and framework design with limited annotation to achieve high-performance NSS. Insufficient information at very low labeling budgets can easily lead to under-optimization or overfitting of the model. Our solution comprises two main components: i) a novel region-based active sampling strategy called Contextual-Aware Region Query (CARQ), which identifies highly informative target nighttime regions for labeling;and ii) an innovative Fragmentation Synergy Active Domain Adaptation framework (FS-ADA), which progressively broadcasts the limited annotation to the unlabeled regions, achieving high performance with a minimal annotation budget. Extensive experiments demonstrate that our method outperforms state-of-the-art UDA-NSS & ADA-SS methods across four day-to-nighttime benchmarks, and generalizes well to foggy, rainy, & snowy scenes. In particular only with 1% target nighttime data annotation, our method is on par with the mainstream fully-supervised methods on the BDD100K-Night val dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Budget control

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A Contribution to the Spatial Analysis of Territorial systems Based on Graph-Structured Data

A Contribution to the Spatial Analysis of Territorial System...

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Metrology for Living Environment (MetroLivEn), IEEE International Workshop on

作者： Simone Corrado Federico Romaniello Francesco Scorza School of Engineering Laboratory of Urban and Regional System Engineering (LISUT) University of Basilicata Potenza Italy Department of Mathematics Computer Science and Economics University of Basilicata Potenza Italy

ISBN: (数字)9798350385014

ISBN: (纸本)9798350385021

This work aims to present the methodological framework within which the T4Y project is developing as a contribution to the spatial analysis of complex systems related to tourism. The mapping of tourist ecosystems from a territorial perspective is a complex phenomenon due to the difficulty in understanding the interactions between tourists and the territory. Through the integration of AI-based techniques, the study aims to propose an approach based on graph-structured data to manage the complexity of geospatial information and improve planners' ability to abstract the possible relationships between elements of the territory. The approach utilizes graph-based data modeling while preserving spatial connections. It has been demonstrated how, through a bipartite graph approach, it is possible to decode the territorial components while taking into account the spatial connections between different pieces of information. Furthermore, the implementation of Graph Neural Networks (GNN) has been discussed as a way to facilitate a more integrated and context-aware analysis. This work aims to contribute to the ongoing debate on complex systems in spatial planning and provide valuable tools for policymakers and planners to experiment with scenarios for spatial development.

关键词： Ecosystems Metrology Graph neural networks Data models Planning Geospatial analysis Complexity theory

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Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management 3

Cloud-Based IoT Solutions for Smart Grids: Advancing Smart T...

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3rd IEEE International Conference on Electrical Power and Energy systems, ICEPES 2024

作者： Antonius Alijoyo, Franciskus Vidhate, Deepak A. Kaur, Chamandeep Nivedan, V. Rani, V. Kanpur Balakumar, A. School of Business and Information Technology STMIK LIKMI Bandung Indonesia Dr. Vithalrao Vikhe Patil College of Engineering Department of Information Technology Maharashtra Ahmednagar India Jazan University Department of Computer Science Jazan Saudi Arabia Sri Ramakrishna College of Arts & Science Department of Electronics and Communication System Coimbatore India Hindustan Institute of Technology and Science Chennai India K.Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350390728

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future. © 2024 IEEE.

关键词： Cloud platforms

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AccelES: Accelerating Top-K SpMV for Embedding Similarity via Low-bit Pruning

AccelES: Accelerating Top-K SpMV for Embedding Similarity vi...

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IEEE Symposium on High-Performance computer Architecture

作者： Jiaqi Zhai Xuanhua Shi Kaiyi Huang Chencheng Ye Weifang Hu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computing National University of Singapore Singapore

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication (SpMV) methods, which suffer from high latency due to inefficient memory access patterns. This paper identifies these critical gaps and introduces AccelES, a novel approach that significantly enhances the efficiency of Top-K SpMV. Our method employs a two-stage calculation scheme: the first stage utilizes a compact, low-bit dataset to quickly identify the most relevant entries, while the second stage performs full-precision calculations solely on this pruned subset, thereby minimizing computational overhead. Furthermore, AccelES incorporates innovative matrix representations, Ultra-CSR and Random-CSR, which optimize memory bandwidth utilization. Experimental results demonstrate that AccelES accelerates performance, surpassing state-of-the-art FPGA, GPU, and CPU solutions by factors of 3.4×, 2.5×, and 153.3×, respectively, under controlled conditions. These advancements not only enhance processing speed but also significantly improve real-time performance in recommendation systems, establishing AccelES as a pivotal contribution to the field of Top-K sparse matrix-vector multiplication.

关键词： Quantization (signal) High dimensional data Memory management Graphics processing units Bandwidth Real-time systems Sparse matrices Recommender systems Field programmable gate arrays Optimization

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Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

Multi-View Representation Learning for Multi-Instance Learni...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhao, Lu Yuan, Liming Li, Zhenliang Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using 1 times1 convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance;ii) Compared to building multi-view representations directly from scratch, extracting them automatically using 1 times1 convolutions is more economical, and may be more effective since 1 times1 convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability. © 2022 IEEE.

关键词： Convolution

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A Thermal-Aware Scheduling Algorithm for Reducing Thermal Risks in DAG-Based Applications in Cyber-Physical systems 2nd

A Thermal-Aware Scheduling Algorithm for Reducing Thermal R...

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Ali, Irfan Shehzad, Muhammad Naeem Bashir, Qaisar Elahi, Haroon Awais, Muhammad Naeem Geman, Oana Liu, Pin Department of Computer System Engineering Institute of Business Administration Sukkar Sukkur65200 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan Intel Corporation AustinTX78746 United States Department of Computing Science Umeå University Umeå9036 Sweden Department of Computers Electronics and Automation Stefan Cel Mare University of Suceava Suceava720229 Romania School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9789819902712

Directed Acyclic Graph (DAG)-based scheduling applications are critical to resource allocation in the Cloud, Edge, and Fog layers of cyber-physical systems (CPS). However, thermal anomalies in DVFS-enabled homogeneous multiprocessor systems (HMSS) may be exploited by malicious applications posing risks to the availability of the underlying CPS. This can negatively affect the trustworthiness of CPS. This paper proposes an algorithm to address the thermal risks in DVFS-enabled HMSS for periodic DAG-based applications. It also improves the current list scheduling-based Depth-First and Breadth-First techniques without violating the timing constraints of the system. We test the algorithm using standard benchmarks and synthetic applications in a simulation setup. The results show a reduction in the temperature peaks by up to 30%, average temperature by up to 22%, temperature variations up to 3 times, and temperature spatial gradients by up to 4 times as compared to the conventional Depth-First Scheduling algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical system

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Detecting and Corrupting Convolution-based Unlearnable Examples 39

Detecting and Corrupting Convolution-based Unlearnable Examp...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Vulnerability Detection Based on Adapter Tuning and Enhanced Feature Learning 36

Vulnerability Detection Based on Adapter Tuning and Enhanced...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Luo, Hui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China PengCheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China

ISBN: (纸本)1891706594

Pre-trained code models have achieved promising results in the vulnerability detection field. The prevailing approach is to adapt these models with vulnerability datasets using inefficient full-model fine-tuning. These pre-trained models primarily capture the semantic features of code, while neglecting its structural characteristics. To address these issues, this paper proposes a vulnerability detection method with adapter tuning and enhanced feature learning. First, adapter modules are introduced to UniXcoder and tune only the parameters in the adapters to extract semantic features. This significantly reduces the number of training parameters and better adapts the model to downstream tasks. Then, structural features, including control and data flow information, are extracted from the Program Dependence Graph (PDG) to compensate for the limitations of pre-trained models that rely solely on semantic features. Finally, the semantic and structural features are integrated to train the detection model. The experimental results demonstrate that our proposed method outperforms state-of-the-art approaches and is highly efficient in terms of both training parameters and training data. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： Data flow graphs

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