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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Pontes, Felipe Arruda Schukat, Michael Curry, Edward University of Galway Insight Centre for Data Analytics Galway Ireland University of Galway School of Computer Science Galway Ireland

ISBN: (纸本)9798350324457

Multimedia Event Processing (MEP) systems play a critical role in various Internet of Things (IoT) applications, including Smart Cities and Health and Safety, by processing large amounts of multimedia data streams. These systems often leverage state-of-the-art Deep Neural Network (DNN) models to enhance their capabilities. However, the growth of Cloud and Edge Big data applications has imposed a significant environ-mental burden, further intensified by the substantial energy consumption associated with certain DNN model *** study addresses the environmental impact of growing Big data stream applications and focuses on optimising MEP systems to mitigate this issue. We tackle uncertainties arising from user-defined Quality of Service (QoS) interpretations and service worker measurement imprecisions by using uncertainty-aware solutions for the service selection problem in order to improve the QoS within MEP *** results reveal substantial advantages in employing uncertainty-aware strategies. These approaches consistently enhance QoS metrics, outperforming their uncertainty-oblivious counterparts. Specifically, we report improvements in more than 67%, 69%, and 20% of the scenarios, on average, for energy consumption, latency, and accuracy, respectively. These enhancements become evident within just three hours of processing, resulting in energy savings of up to 1.2 kilowatt-hours and latency reductions of 213 seconds, with a 0.29% average loss in query accuracy. These strategies improve system efficiency and ecological sustainability while incurring a small accuracy trade-off. When extrapolated over a year, the environmental benefits become even more noticeable, surpassing the energy requirements for a 1000 Km electric vehicle round-trip from Amsterdam to Paris and back. © 2023 IEEE.

关键词： Big data Deep Neural Networks Multimedia Streaming Sustainability

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A Deep Graph Convolutional Network based Web API Classification Approach for Mashup Creation 25

A Deep Graph Convolutional Network based Web API Classificat...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Lianyong Yan, Boyuan Zheng, Jiaqi Xu, Xiaolong Dou, Wanchun Zhou, Xiaokang Zhang, Jie School of Computer Science Qufu Normal University China China Nanjing University Department of Computer Science and Technology China School of Computer and Software Nanjing University of Information Science and Technology China Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Japan

ISBN: (纸本)9798350330014

Software developers create mashups of their interest by searching, selecting, and combining web Application Programming Interfaces (APIs) with diverse functionalities. However, achieving the selection of APIs relies on a crucial prerequisite: accurate classification of APIs. However, the traditional web API classification methods are often based on a single piece of API function's information such as manual annotations and description documents which decreases the accuracy of API classification results significantly, especially for the web API sharing communities with sparse data (e.g., ***). To tackle this issue, we propose BDGCN which improves some existing web API classification approaches by adding isomorphic graph of strong correlation among different web APIs on the basis of API description documents. Concretely, at the first, we integrate the information of multiple dimensions of web APIs as embedded inputs in which we then build an API-API strongly correlated isomorphic network to strengthen the association among API individuals, and to initialize the embedding vector from the description of each API by tuning the pre-trained Bidi-rectional Encoder Representation from Transformers (BERT) model. Besides, in order to smooth the impact of high-order features between neighboring nodes, a deep graph convolutional network is introduced in this paper that is consistent with the problem. In fact, we set up a categorization layer and utilize cross-entropy function to achieve accurate classification of APIs. Experimental results demonstrate the superiority of our method compared to the existing methods. © 2023 IEEE.

关键词： Classification (of information)

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SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization 31

SparseCoder: Identifier-Aware Sparse Transformer for File-Le...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Wang, Yanlin Huang, Yanxian Guo, Daya Zhang, Hongyu Zheng, Zibin School of Software Engineering Sun Yat-sen University China School of Computer Science and Technology Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350330663

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization, file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism. Furthermore, we verify the generality of SparseCoder on other code understanding tasks, i.e., code clone detection and code search, and results show that our model outperforms baseline models in both tasks, demonstrating that our model can generate better code representations for various downstream tasks. Our

关键词： Semantics

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable data with Item- and Attribute-level Signals

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ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings 15

Predicting Protein-Protein Interactions Using Self-Attention...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Oviya, I.R. Shanmukha Sravya, N. Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350370249

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model's performance is assessed through 5-fold cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of 92.28% and 94.08%, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise 38

Resfusion: Denoising Diffusion Probabilistic Models for Imag...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Zhenning Zheng, Haoshuai Xu, Chen Dong, Changsheng Pan, Bin Xie, Xueshuo He, Along Li, Tao Fu, Huazhu College of Computer Science Nankai University China School of Statistics and Data Science Nankai University China Haihe Lab of ITAI China Institute of High Performance Computing A*STAR Singapore

Recently, research on denoising diffusion models has expanded its application to the field of image restoration. Traditional diffusion-based image restoration methods utilize degraded images as conditional input to effectively guide the reverse generation process, without modifying the original denoising diffusion process. However, since the degraded images already include low-frequency information, starting from Gaussian white noise will result in increased sampling steps. We propose Resfusion, a general framework that incorporates the residual term into the diffusion forward process, starting the reverse process directly from the noisy degraded images. The form of our inference process is consistent with the DDPM. We introduced a weighted residual noise, named resnoise, as the prediction target and explicitly provide the quantitative relationship between the residual term and the noise term in resnoise. By leveraging a smooth equivalence transformation, Resfusion determine the optimal acceleration step and maintains the integrity of existing noise schedules, unifying the training and inference processes. The experimental results demonstrate that Resfusion exhibits competitive performance on ISTD dataset, LOL dataset and Raindrop dataset with only five sampling steps. Furthermore, Resfusion can be easily applied to image generation and emerges with strong versatility. Our code and model are available at https://***/nkicsl/Resfusion. © 2024 Neural information processing systems foundation. All rights reserved.

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Effective In-Context Learning for Named Entity Recognition

Effective In-Context Learning for Named Entity Recognition

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Jin Guo, Qian Liang, Jiaqing Li, Zhixu Xiao, Yanghua Fudan University Shanghai Key Laboratory of Data Science School of Computer Science Shanghai China Fuyang Normal University College of Information Engineering Anhui China Renmin University of China School of Information Suzhou China

ISBN: (纸本)9798350386226

Named entity recognition aims to identify meaningful words or phrases from text. Traditional NER methods depend heavily on training datasets to learn entity features, which often leads to poor performance in recognizing low-resource entities. Large language models offer a promising solution in low-resource scenarios, but they struggle with entity boundary recognition and often suffer from hallucination issues due to their reliance on general-purpose corpora. In this paper, we propose a novel approach that enhances LLMs' performance in NER by integrating a boundary regularity extraction algorithm and an explanatory path extraction method. The boundary regularity algorithm leverages ChatGPT's summarization capabilities to articulate entity boundaries in natural language, improving boundary detection accuracy. The explanatory path method constructs logical reasoning steps from text to entity recognition, guiding LLMs to generate more accurate predictions and mitigating hallucination problems. Extensive experiments on three public datasets demonstrate that our method outperforms current mainstream LLMs in accurately recognizing entities from unstructured text. These findings validate the effectiveness of our approach and its potential to address key challenges in NER. © 2024 IEEE.

关键词： Large language models Low-resource Named entity recognition

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A Dual-Defense Self-balancing Framework Against Bilateral Model Attacks in Federated Learning 24th

A Dual-Defense Self-balancing Framework Against Bilateral Mo...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Xiang Yao, Aiting Pal, Shantanu Jiang, Frank Li, Xuejun Xu, Jia Dong, Chengzu Chen, Xuefei Zhang, Xiuyi Liu, Xiao Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui China Deakin University Melbourne Australia School of Data Science Lingnan University Hong Kong

ISBN: (纸本)9789819615247

With the rapid expansion of Artificial Intelligence (AI) services, smart devices generate a large amount of user data at the edge network, which urgently needs to be protected while effectively extracting information. Federated learning (FL) is an important technology for handling dispersed data and strict privacy requirements in this context. However, the security threats caused by model inversion attacks and poisoning attacks can affect the mutual trust between the client and server. Yet, for these two types of attacks, the existing defense mechanisms are contradictory in terms of whether the model parameters are publicly disclosed. In addition, the data distribution of the clients is imbalanced which will increase the bias of model, reducing its practicality. To address this issue, this study proposes a dual defense self-balanced federated learning (DDSFL) framework, aiming to introduce a novel lightweight defense mechanism during the model parameter aggregation stage, combating these two types of attacks simultaneously by applying differential privacy and adjusting learning rates. In addition, this method also integrates a middleware-based reordering algorithm to enhance the robustness of the framework. Experimental results show that DDSFL effectively improves the ability to resist imbalanced data, forged data, and malicious behavior, significantly enhancing the generalization performance and security of the FL system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Multi-Head Self-Attention Based Arabic News Recommendation System 6

Multi-Head Self-Attention Based Arabic News Recommendation S...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Sabrina, Karboua Harrag, Fouzi Meziane, Farid Departement of Computer Science Mechatronics Laboratory Ferhat ABBAS University Setif 1 Setif Algeria Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

ISBN: (纸本)9798331522810

This paper introduces an Arabic News Recommendation System (ANRS) designed to enhance personalized news recommendations for Arabic-speaking users. By leveraging a multi-head self-Attention mechanism, the proposed model effectively captures complex dependencies within Arabic text, addressing challenges posed by the language's rich morphology and contextual nuances. The news encoder uses multi-head self-Attention to learn the meaning of Arabic news articles by analyzing interactions between words in titles, body text. Meanwhile, the user encoder models user preferences by examining previously read articles, also using multi-head self-Attention to capture connections between articles. Trained on a dataset of Arabic news articles and 15 million user interaction logs, ANRS achieves a high prediction accuracy of 98.6%, significantly outperforming traditional and deep learning-based Arabic recommendation models. © 2024 IEEE.

关键词： Recommender systems

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FasMe: Fast and Sample-efficient Meta Estimator for Precision Matrix Learning in Small Sample Settings 38

FasMe: Fast and Sample-efficient Meta Estimator for Precisio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tan, Xiao Wang, Yiqin Shen, Yangyang Shen, Dian Wang, Meng Duan, Peibo Wang, Beilun School of Computer Science and Engineering Southeast University Nanjing China College of Design and Innovation Tongji University Shanghai China Department of Data Science & AI Monash University Melbourne Australia

Precision matrix estimation is a ubiquitous task featuring numerous applications such as rare disease diagnosis and neural connectivity exploration. However, this task becomes challenging in small sample settings, where the number of samples is significantly less than the number of dimensions, leading to unreliable estimates. Previous approaches either fail to perform well in small sample settings or suffer from inefficient estimation processes, even when incorporating meta-learning techniques. To this end, we propose a novel approach FasMe for Fast and Sample-efficient Meta Precision Matrix Learning, which first extracts meta-knowledge through a multi-task learning diagram. Then, meta-knowledge constraints are applied using a maximum determinant matrix completion algorithm for the novel task. As a result, we reduce the sample size requirements to O(log p/K) per meta-training task and O(log |G|) for the meta-testing task. Moreover, the hereby proposed model only needs O(p log ϵ−1) time and O(p) memory for converging to an ϵ-accurate solution. On multiple synthetic and biomedical datasets, FasMe is at least ten times faster than the four baselines while promoting prediction accuracy in small sample settings. © 2024 Neural information processing systems foundation. All rights reserved.

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