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31st Australasian Conference on Information Systems, ACIS 2019

作者： Ataei, Pouya Litchfield, Alan School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Service and Cloud Computing Research Lab. Auckland University of Technology Auckland New Zealand

Today, we live in a world that produces data at an unprecedented rate. The significant amount of data has raised lots of attention and many strive to harness the power of this new material. In the same direction, academics and practitioners have considered means through which they can incorporate data-driven functions and explore patterns that were otherwise unknown. This has led to a concept called Big Data. Big Data is a field that deals with data sets that are too large and complex for traditional approaches to handle. Technical matters are fundamentally critical, but what is even more necessary, is an architecture that supports the orchestration of Big Data systems;an image of the system providing with clear understanding of different elements and their interdependencies. Reference architectures aid in defining the body of system and its key components, relationships, behaviors, patterns and limitations. This study provides an in-depth review of Big Data Reference Architectures by applying a systematic literature review. The study demonstrates a synthesis of high-quality research to offer indications of new trends. The study contributes to the body of knowledge on the principles of Reference Architectures, the current state of Big Data Reference Architectures, and their limitations. © 2020 authors.

关键词： Big data

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Combining Bilstm and Self-Attention Mechanism for Code Search

SSRN

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SSRN 2022年

作者： Cao, Ben Liu, Jianxun School of Computer Science and Technology Hunan University of Science and Technology Hunan411100 China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan411100 China

With the wide application of deep learning in code search, especially the proposed code search model based on attention mechanism, the accuracy of code search has been greatly improved. However, the attention mechanism only captures the attention weight relationship between two words in the code fragment, without considering the contextual semantic relationship that exists between words in the code fragment, which can help improve the accuracy of code search. To address this problem, this paper proposes a model that combining BiLSTM and self-attention mechanisms for code search(CBLSAM-CS). The model first captures the contextual relationship of each word in the code fragment by a recurrent neural network, and then uses the self-attentive mechanism to extract deep-level features of the sequence. In order to verify the effectiveness of the proposed model, the paper conducts an experimental comparison with three other baseline models, on the basis of a public dataset containing 18 million code *** experimental results show that the proposed model in this paper achieves 92.4% and 93.5% in MRR value and NDCG metrics, respectively, which are better than the baseline ***, it shows that the CBLSAM-CS model proposed in this paper can effectively improve the accuracy and efficiency of code search. © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Multi-Layer Self-Attention Residual Network for Code Search

SSRN

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SSRN 2022年

作者： Hu, Haize Professor, Jianxun Liu School of Computer Science and Technology Hunan University of Science and Technology Hunan411100 China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan411100 China

To improve the efficiency of programming, software developers usually search existing code snippets in open source code repositories to modify and reuse them. Therefore, how to get the right code snippet from the open source code repository quickly and accurately is the focus of current software development research. Nowadays, code search is one of the solutions. Traditional information retrieval techniques have been gradually abandoned by researchers due to their low search efficiency and accuracy. In recent years, the code search methods based on machine learning alternatives have been widely used and researched due to the better search results. Various code search models have been proposed, especially the self-attention model, which has greatly improved the accuracy of code search. The self-attentive model for feature extraction mainly relies on the weights of each word in the code sequence unit, while ignoring the structural relationships among the code sequences, which contain rich feature information in these components. To compensate the shortage of self- attention mechanism, a multi-layer self- attention residual network based code search model (MSARN-CS) is proposed in this paper. In the MSARN-CS model, not only the weight of each word in the code sequence unit is considered, but also the effect of embedding between code sequence units is calculated. In addition, an optimization model of residuals is introduced to compensate for the loss of information in the code sequences during the model training. To verify the search effectiveness of the MSARN-CS model, three other baseline models are compared on the basis of extensive source code data. The experimental results show that the MSARN-CS model has better search results compared with the baseline model. Therefore, the MSARN-CS model proposed in the paper can effectively improve the accuracy of code search and enhance the programming efficiency of developers. © 2022, The Authors. All rights reserved.

关键词： Efficiency

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Secrecy Analysis of UAV Control Information Transmission via NOMA

Secrecy Analysis of UAV Control Information Transmission via...

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IEEE Conference on Wireless Communications and Networking

作者： Zhaoxin Feng Huabing Lu Nan Zhao Zhaoyuan Shi Yunfei Chen Xianbin Wang School of Information and Communication Engineering Dalian University of Technology Dalian China Key Lab. of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China Department of Engineering University of Durham Durham UK Department of Electrical and Computer Engineering Western University London ON Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Unmanned aerial vehicle (UAV) assisted wireless communication is essential for the next-generation mobile networks. In coping with the increased dynamics in UAV networks, the design of control information transmission is essential, requiring ultra reliability, low latency, and high security. In this paper, considering both the large-scale path loss and the Nakagami-m small-scale fading, we investigate the secrecy performance of UAV control information transmission in a NOMA ground-air network with an external flying eavesdropper. A spherical secrecy protection zone is set, and the closed-form expressions for average secure BLER and average achievable secrecy throughput are derived. After that, the asymptotic performance in the high SNR regime is analyzed to get more insights. Ultimately, simulation results verify the accuracy of analysis.

关键词： Wireless communication NOMA Accuracy Simulation Information processing Autonomous aerial vehicles Throughput

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Grrln: Gated Recurrent Residual Learning Networks for Code Clone Detection

SSRN

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SSRN 2022年

作者： Zhang, Xiangping Xiao, Qiaoxiang Hu, Haize Liu, Jianxun Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan411100 China School of Computer Science and Technology Hunan University of Science and Technology Hunan Xiangtan411100 China

Code clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which is determine the code pairs are clone or not with a pre-defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manner on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement-level tree sequence derived from the whole AST. Then, a gated recurrent neural network with residual connections is adopted to fully extract semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real-world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves generally better performance than state-of-the-art methods and meanwhile needs significant less time and memory costs. © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

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G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks

arXiv

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arXiv 2022年

作者： Wan, Zhongwei Yin, Yichun Zhang, Wei Shi, Jiaxin Shang, Lifeng Chen, Guangyong Jiang, Xin Liu, Qun Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Science China University of Chinese Academy of Sciences China Huawei Noah's Ark Lab Hong Kong Huawei Cloud Computing Zhejiang Lab China

Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this Domain-Adaptive Pre-Training (DAPT;Gururangan et al. (2020)) tends to forget the previous general knowledge acquired by general PLMs, which leads to a catastrophic forgetting phenomenon and sub-optimal performance. To alleviate this problem, we propose a new framework of General Memory- Augmented Pre-trained Language Model (GMAP), which augments the domain-specific PLM by a memory representation built from the frozen general PLM without losing any general knowledge. Specifically, we propose a new memory-augmented layer, and based on it, different augmented strategies are explored to build the memory representation and then adaptively fuse it into the domain-specific PLM. We demonstrate the effectiveness of G-MAP on various domains (biomedical and computer science publications, news, and reviews) and different kinds (text classification, QA, NER) of tasks, and the extensive results show that the proposed G-MAP1 can achieve SOTA results on all tasks. © 2022, CC BY.

关键词： Computational linguistics

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Satellite Federated Fine-Tuning for Foundation Models in Space computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collab.rative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science 20

A Multi-Dimensional Assessment Model and Its Application in ...

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21st Annual Conference of the Special Interest Group in Information technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China 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

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the Parallel Programming Principles and Practice Class of Huazhong University of Science and technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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Disentangled Hypergraph Collab.rative Filtering for Social Recommendation

Disentangled Hypergraph Collaborative Filtering for Social R...

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IEEE International Conference on Web Services (ICWS)

作者： Xiao Liu Shunmei Meng Qianmu Li Xiaolong Xu Lianyong Qi Wanchun Dou Jing Zhang Xuyun Zhang Nanjing University of Science and Technology China State Key Lab. for Novel Software Technology Nanjing University China School of Computer and Software Nanjing University of Information Science and Technology China College of Computer Science and Technology China University of Petroleum (East China) China School of Cyber Science and Engineering Southeast University China School of Computing Macquarie University Australia

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collab.rative filtering, to disentangle the representations of users and items at the granularity of social users’ intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user’s intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores.

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Federated Linear Bandit Learning via Over-the-air Computation

Federated Linear Bandit Learning via Over-the-air Computatio...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jiali Wang Yuning Jiang Xin Liu Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China Automatic Control Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings.

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