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作者： Wu, Song Tao, Zhiheng Fan, Hao Huang, Zhuo Zhang, Xinmin Jin, Hai Yu, Chen Cao, Chun 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 State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle-aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers. We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead. © 2021 John Wiley & Sons Ltd.

关键词： Containers

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation system

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting

arXiv

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arXiv 2024年

作者： Yang, Yingnan Zhu, Qingling Chen, Jianyong College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Multivariate time series (MTS) forecasting has been extensively applied across diverse domains, such as weather prediction and energy consumption. However, current studies still rely on the vanilla point-wise self-attention mechanism to capture cross-variable dependencies, which is inadequate in extracting the intricate cross-correlation implied between variables. To fill this gap, we propose Variable Correlation Transformer (VCformer), which utilizes Variable Correlation Attention (VCA) module to mine the correlations among variables. Specifically, based on the stochastic process theory, VCA calculates and integrates the cross-correlation scores corresponding to different lags between queries and keys, thereby enhancing its ability to uncover multivariate relationships. Additionally, inspired by Koopman dynamics theory, we also develop Koopman Temporal Detector (KTD) to better address the non-stationarity in time series. The two key components enable VCformer to extract both multivariate correlations and temporal dependencies. Our extensive experiments on eight real-world datasets demonstrate the effectiveness of VCformer, achieving top-tier performance compared to other state-of-the-art baseline models. Code is available at this repository: https://***/CSyyn/VCformer. Copyright © 2024, The Authors. All rights reserved.

关键词： Random processes

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Robust UAV Policy Learning for Urban Infrastructure Surface Screening*

Robust UAV Policy Learning for Urban Infrastructure Surface ...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Bingqing Du Uddin Md. Borhan Tianxing Chen Jianyong Chen Jianqiang Li Jie Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Unmanned Aerial Vehicles (UAVs) have emerged as pivotal tools for infrastructure inspection, mitigating risks to human life and enhancing operational efficiency. Despite advancements, achieving autonomous surface coverage inspection in GPS-denied environments remains a significant challenge. This paper presents a novel approach to enhance UAV policy learning for surface screening of urban infrastructure in environments lacking GPS. Utilizing Deep Reinforcement Learning (DRL), the methodology enables UAVs to autonomously conduct surface coverage inspections more robustly and efficiently. A key innovation is the Optimal Speed Decision (OSD) model, which allows UAVs to adjust flight speed dynamically based on scene perception, enhancing efficiency and safety. Additionally, an Off-Policy Critic-Based Reward Shaping (OPCBRS) method is introduced to improve learning efficiency and performance by addressing the issue of sparse rewards using offline data. Simulation results confirm the effectiveness of these methods, with the OSD model demonstrating significant improvements in navigating complex scenarios and maintaining a balance between efficiency and robustness over traditional uniform speed policies. This research offers a robust framework for UAV-based infrastructure inspection in GPS-denied environments, advancing autonomous UAV applications.

关键词： Technological innovation Navigation Simulation Inspection Autonomous aerial vehicles Deep reinforcement learning Robustness Real-time systems Safety Robots

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Intelligent Blocking and Prevention of SARS-CoV-2 Based on Evolutionary Reinforcement Learning

Intelligent Blocking and Prevention of SARS-CoV-2 Based on E...

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Congress on Evolutionary Computation

作者： Chengwei He Huaping Hong Lijia Ma Qingling Zhu Yuan Bai College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In recent years, the global repercussions of SARS-CoV-2 and its variants have posed significant challenges to various areas, including the economic order, transportation, healthcare, and education, and the mitigation and prevention of SARS-CoV-2 and other infectious diseases have received much attention. This paper tries to develop a rational and effective intervention strategy in response to the dynamic evolution of the SARS-CoV-2 epidemic. To address this issue, we first use the SEIR model to analyze the population distribution under the SARS-CoV-2 epidemic, and then integrate a deep Q-neural network (DQN) to reflect an economic quantification model contextualized within the SARS-CoV-2 scenario. Next, we propose an evolutionary deep reinforcement learning to generate an optimal and adaptive intervention strategy, by optimizing the weight sequence of the DQN. The experimental results show the effectiveness of the proposed method in maximizing economic benefits, while ensuring the uninterrupted functionality of medical institutions. Importantly, the learned intervention strategy exhibits the applicability to other infectious diseases.

关键词： Economics Epidemics Infectious diseases Prevention and mitigation Heuristic algorithms Sociology Loss measurement

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PAIR: protein-aptamer interaction prediction based on language models and contrastive learning framework

PAIR: protein-aptamer interaction prediction based on langua...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jun Zhang Zhiqiang Yan Hao Zeng Zexuan Zhu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Aptamers are single-stranded DNA or RNA oligonucleotides that selectively bind to specific targets, making them valuable for drug design and diagnostic applications. Identifying the interactions between aptamers and target proteins is crucial for these applications. The systematic evolution of ligands by exponential enrichment process, traditionally used for this purpose, is challenging and time-consuming. The resulting aptamers often suffer from limitations in stability and diversity. Computational approaches have shown promise in aiding the discovery of high-performance aptamers, but existing methods are usually constrained by insufficient training data and limited generalizability. Recently, advancements in pre-training large language models have offered a new avenue to mitigate the dependency on large datasets. In this study, we propose a novel method to predict aptamer-protein interactions using large language models within a contrastive learning framework. Experimental results demonstrate that our method exhibits superior generalization and outperforms existing approaches. This method holds promise as a powerful tool for predicting aptamer-protein interactions.

关键词： Proteins Drugs Protein engineering Biological system modeling Large language models Computational modeling Training data Contrastive learning Predictive models Few shot learning

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FedCrowd: Towards Promoting the Concurrency of the Server in Asynchronous Federated Learning 2

FedCrowd: Towards Promoting the Concurrency of the Server in...

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2nd International Symposium on Electrical, Electronics and Information engineering, ISEEIE 2022

作者： Xi, Wen Liao, Yuying Zhou, Bin Wang, Haiyang Gao, Liqun College of Computer Science and Technology National University of Defence Technology Changsha China National University of Defence Technology Key Lab. of Software Engineering for Complex System Changsha China

ISBN: (数字)9781665468749

ISBN: (纸本)9781665468749

Federated learning is a privacy-preserving machine learning paradigm where a global model is trained collaboratively across resource-limited devices such as mobile phones, wearable devices, and sensors while the training data remains on these devices. Recently proposed asynchronous federated learning framework FedAsync has addressed the problems occurred in synchronous federated learning such as wasting time waiting for all nodes to finish uploading or losing parts of the contribution of nodes due to some unexpected reasons. However, on the server-side, it is not suitable for concurrency due to the data race on the global model. We proposed a method named FedCrowd, where the server's concurrency is improved by elaborately coordinating the working threads to relieve the data race on the global model. Several experiments are performed to show that our method could effectively increase concurrency of the server and reduce the training time. © 2022 IEEE.

关键词： Concurrent computing Training Wearable computers Training data Machine learning Collaborative work Data models

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Handwritten Prescription Recognition Using VGG Based Architecture with Bi-LSTM

Handwritten Prescription Recognition Using VGG Based Archite...

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International Research Conference on Smart Computing and systems engineering (SCSE)

作者： Isuru Kavinda Harinda Fernando Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors’ use of Latin abbreviations, driven by time constraints during patient consultations. The resultant discrepancies in prescription interpretation can potentially expose patients to incorrect medications and dosages, leading to adverse effects. In response to this concern, this study proposes a novel system integrating Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models. Promising findings are reported in this study, achieving a loss of 0.4874 and an accuracy of 0.83 in the latest trial. The final model offers a potential solution to the persistent problem of illegible prescriptions, enhancing patient safety in the healthcare domain.

关键词： Training Handwriting recognition Recurrent neural networks Convolution Computational modeling Transfer learning Medical services

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Dynamic Bi-Elman Attention Networks (DBEAN): Dual-Directional Context-Aware Representation Learning for Enhanced Text Classification

arXiv

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arXiv 2025年

作者： Liao, Mengyao Lai, Zhenglin Xu, Dong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Text classification, a fundamental task in natural language processing (NLP), aims to categorize textual data into predefined labels. Traditional methods struggled with complex linguistic structures and semantic dependencies. The advent of deep learning, particularly recurrent neural networks (RNNs) and Transformer-based models, has significantly advanced the field by enabling nuanced feature extraction and context-aware predictions. Despite improvements, existing models exhibit limitations in balancing interpretability, computational efficiency, and long-range contextual understanding. This paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN), which integrates bidirectional temporal modelling with self-attention mechanisms. DBEAN dynamically assigns weights to critical segments of input, improving contextual representation while maintaining computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Elman neural networks

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Ethiopic Cryptography: An Efficient Hybrid Encryption Algorithm for Reducing Cybersecurity Attack 11th

Ethiopic Cryptography: An Efficient Hybrid Encryption Algori...

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11th EAI International Conference on Advancement of science and Technology, ICAST 2023

作者： Akele, Tadesse Birara Bahiru, Tadesse Kebede Molla, Tibebu Legesse Fentie, Shambel Dessale Belay, Tadesse Destaw Marye, Almaz Yemataw Getaneh, Mersha Derese Department of Information System Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Information Technology Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Deparment of Software Engineering Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Computer Science Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia

ISBN: (纸本)9783031641503

Cryptography is an essential tool in ensuring the security of data during network communication. Network and service providers prioritize data security to ensure secure data exchanges. Ethiopic cryptography is a unique encryption method that utilizes the Amharic language, prevalent in Ethiopia. Unlike Latin alphabets, Amharic consists of 287 characters, making decryption challenging. The encryption process involves converting the text to Amharic and then mapping it to a pair of randomly chosen two-bit English alphabets, creating an intermediate cipher. The Ethiopic cryptography algorithm employs a hybrid approach, using the RSA algorithm for secure key exchange and the Advanced Encryption Standard algorithm for text confidentiality. The algorithm comprises three phases—translation, mapping, and encryption—contributing to its effectiveness in securing data over the network. Decryption requires familiarity with the Amharic language and mapping details. The algorithm demonstrates a stronger avalanche effect (97%) in comparison with Blowfish and DES. The algorithm is proposed and evaluated using the design science research methodology, and its performance is validated through simulation using MATLAB and BunnaScript tools, considering factors like the avalanche effect, time complexity, and memory consumption. The evaluation shows that the suggested solution operates faster, requires lesser time for data encryption, and uses less memory than other methods in use. Overall, the proposed Ethiopic cryptography algorithm proves to be an efficient and effective method for securing data during network communication, offering stronger text confidentiality and an improved avalanche effect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATLAB

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