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IEEE International Conference on software Reliability engineering Workshops (ISSRE Wksp)

作者： Xinying Liu Jianhui Jiang Long Li School of Software Engineering Tongjj University Shanghai China RAMS Technology Lab Huawei Technologies Co. Ltd. Shenzhen China

ISBN: (纸本)9781665426046

Edge computing can effectively overcome the problems of long transmission distance and high response delay of traditional cloud computing because it can offload computing tasks to edge or cloud. However, edge resources are relatively limited, so the design of an appropriate task scheduling mechanism is critical. Furthermore, ensuring reliability in edge computing is also an urgent problem to be solved. For a 3-layer architecture with local device layer, edge layer, and cloud layer, this paper presents a computing offloading and task scheduling approach with fault-tolerance for minimizing redundancy. It consists of three procedures, i.e., offloading decision, task scheduling, and minimizing redundancy. The offloading decision algorithm is used to decide which layer the task will be executed. As for the primary-backup task scheduling algorithm, the execution time, the energy consumption, the CPU utilization and the reliability are considered for the task scheduling of edge and cloud. To meet the reliability requirement of an application, the minimizing redundancy algorithm is used during the replication process. The experimental results obtained by using EdgeCloudSim show that the proposed approach is superior to other methods given in [21] [24] [38], in terms of execution time, energy consumption and redundancy.

关键词： Cloud computing Energy consumption Scheduling algorithms Redundancy Fault tolerant systems Scheduling software reliability

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Finding Simplex Items in Data Streams 39

Finding Simplex Items in Data Streams

引用

39th IEEE International Conference on Data engineering, ICDE 2023

作者： Fan, Zhuochen Guo, Jiarui Li, Xiaodong Yang, Tong Zhao, Yikai Wu, Yuhan Cui, Bin Xu, Yanwei Uhlig, Steve Zhang, Gong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Theory Lab Hong Kong Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom

ISBN: (纸本)9798350322279

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate"the two machine learning models through end-to-end experiments. We have released our source code at GitHub. © 2023 IEEE.

关键词： Denial-of-service attack

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Recommender System for Telecom Packages Based on the Deep & Cross Network

Research Square

引用

Research Square 2021年

作者： Shi, Congming Wang, Wen Wei, Shoulin Lv, Feiya School of Software Engineering Anyang Normal University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology China

With the evolution of 5G technology, the telecom industry has influenced the livelihood of people and impacted the development of the national economy significantly. To increase revenue per customer and secure long-term contracts of users, telecommunications firms and enterprises have launched different types of telecom packages to satisfy the varying requirements of users. Although several recommender systems have been proposed in recent years for telecommunication package recommendation, extracting effective feature information from large and complex consumption data remains challenging. Considering the telecom package recommendation problems, traditional recommendation methods either use complex expert feature engineering or fail to perform end-to-end deep learning training. In this study, a recommender system based on deep & cross network (DCN), deep belief network (DBN), Embedding, and Word2Vec is proposed using the powerful learning abilities of deep learning. The proposed system fits the telecom package recommender system in terms of click-through rate prediction to provide a potential solution for the recommendation challenges faced by telecom enterprises. The proposed model can effectively capture the finite order interactional features and deep hidden features. Additionally, the text information in the data is completely used to further improve the recommendation ability of the model. Moreover, the proposed method does not require feature engineering. We conducted comprehensive experiments using real-world datasets, and the results verify that our method can generate improved recommendation accuracy in comparison with those observed in DBN, DCN, deep factorization machine, and deep neural network models individually. © 2021, CC BY.

关键词： Recommender systems

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Ppt4j: Patch Presence Test for Java Binaries

Ppt4j: Patch Presence Test for Java Binaries

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International Conference on software engineering (ICSE)

作者： Zhiyuan Pan Xing Hu Xin Xia Xian Zhan David Lo Xiaohu Yang The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China The Hong Kong Polytechnic University Hong Kong China School of Computing and Information Systems Singapore Management University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor.

关键词： Java Semantics Feature extraction Libraries Security software measurement Data mining

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Self-supervised consensus representation learning for attributed graph

arXiv

引用

arXiv 2021年

作者： Liu, Changshu Wen, Liangjian Kang, Zhao Luo, Guangchun Tian, Ling School of Computer Science and Engineering University of Electronic Science and Technology of China The Noah's Ark Lab Huawei Technologies Company Limited School of Information and Software Engineering University of Electronic Science and Technology of China

Attempting to fully exploit the rich information of topological structure and node features for attributed graph, we introduce self-supervised learning mechanism to graph representation learning and propose a novel Self-supervised Consensus Representation Learning (SCRL) framework. In contrast to most existing works that only explore one graph, our proposed SCRL method treats graph from two perspectives: topology graph and feature graph. We argue that their embeddings should share some common information, which could serve as a supervisory signal. Specifically, we construct the feature graph of node features via k-nearest neighbour algorithm. Then graph convolutional network (GCN) encoders extract features from two graphs respectively. Self-supervised loss is designed to maximize the agreement of the embeddings of the same node in the topology graph and the feature graph. Extensive experiments on real citation networks and social networks demonstrate the superiority of our proposed SCRL over the state-of-the-art methods on semi-supervised node classification task. Meanwhile, compared with its main competitors, SCRL is rather efficient. The source code is available at https://***/topgunlcs98/SCRL. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

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Structure-Preserving Graph Representation Learning

Structure-Preserving Graph Representation Learning

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IEEE International Conference on Data Mining (ICDM)

作者： Ruiyi Fang Liangjian Wen Zhao Kang Jianzhuang Liu School of Information and Software Engineering University of Electronic Science and Technology of China The Noah’s Ark Lab Huawei Technologies Company Limited School of Computer Science and Engineering University of Electronic Science and Technology of China

Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an adequate way. Most existing methods focus on local structure and fail to fully incorporate the global topological structure. To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs. Specifically, to reduce the uncertainty and misinformation of the original graph, we construct a feature graph as a complementary view via k-Nearest Neighbor method. The feature graph can be used to contrast at node-level to capture the local relation. Besides, we retain the global topological structure information by maximizing the mutual information (MI) of the whole graph and feature embeddings, which is theoretically reduced to exchanging the feature embeddings of the feature and the original graphs to reconstruct themselves. Extensive experiments show that our method has quite superior performance on semi-supervised node classification task and excellent robustness under noise perturbation on graph structure or node features. The source code is available at https://***/uestc-lese/SPGRL.

关键词： Representation learning Uncertainty Source coding Perturbation methods Feature extraction Robustness Data mining

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Prospect of 5G application in Civil Airport

Prospect of 5G Application in Civil Airport

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IEEE International Conference on Civil Aviation Safety and Information technology (ICCASIT)

作者： Ningning Le Yinger Zheng Qian Huang Yanqiu Pei Big Data Analysis and Application Center China Academy of Civil Aviation Science and Technology Beijing China Service Lab Huawei Research Institute Huawei Technology Co. Ltd Beijing China College of Geoscience and Surveying Engineering China University of Mining and Technology Beijing China

ISBN: (纸本)9781728125992

With the rapid growth of China's civil aviation transportation volume, the airport has entered the era of big data in which data is massive, diversified, and converted at high speeds. Achievements for intelligent application of big data based on 5G technology have become an indispensable component of smart airports. Based on the characteristics of 5G technology, this paper combines various state-of-art technologies including Discrete Global Grid Systems, Complex Network, Artificial Intelligence, to prospect the application scenarios of 5G in airport, as efficient and secure wide-area wireless communication system, IoT-based smart monitoring, airport unmanned driving, high-speed entertainment experience, intelligent passenger service.

关键词： 5G New data technology application prospect Civil airport Airports big data 5G mobile communication PASSENGER SERVICES Grid systems Prospecting Wireless communication systems High speed

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Ppt4J: Patch Presence Test for Java Binaries

arXiv

引用

arXiv 2023年

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China The Hong Kong Polytechnic University Hong Kong Hong Kong School of Computing and Information Systems Singapore Management University Singapore

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4J (Patch Presence Test for Java Binaries). Ppt4J is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4J, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4J achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4J on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2023, CC BY-NC-ND.

关键词： Open source software

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引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jia Cui Weiping Wang Dan Meng Zhenyan Liu University of Chinese Academy of Sciences Beijing China National Engineering Lab of Info-Security Technology Institute of Information Engineering CAS School of Software Beijing Institute of Technology Beijing China Center of Computer Application Research Institute of Computing Technology CAS

ISBN: (纸本)9781479976164

Similarity join plays an important role in many applications, such as data cleaning and integration, to address the poor data quality problem. Most of the existing studies focused on performing similarity join on static datasets but few studies realized running it on dynamic data streams. With the development of network technology, the data accessing paradigm has transferred from disk-oriented mode to online data streams, which makes performing similarity join in continuous query on data streams become a novel query processing paradigm. Different from static dataset, data stream is unbounded, continuous and unpredictable. The significant differences pose serious challenges, such as real-time query performance. To this end, we study the problem of continuous similarity join on data streams in this paper, which is based on edit distance metric and filter-and-verify framework with sliding-window semantics. Two subcases of this problem are studied, including self similarity join on a single data stream and similarity join on two streams. We introduced the basic window based sliding window model to facilitate the update of sliding window and its index. More details of our method, including signature extraction schemes, filtering and verification algorithms, re-evaluation strategies are discussed respectively. Finally, extensive experimental results show that our method works efficiently on real data streams.

关键词： Filtering algorithms Filtering Query processing Indexing Heuristic algorithms Real-time systems

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Pyscribe - Learning to Describe Python Code

SSRN

引用

SSRN 2022年

作者： Guo, Juncai Liu, Jin Wan, Yao Zhao, Yanjie Li, Li Liu, Kui Klein, Jacques Bissyandé, Tegawendé F. School of Computer Science Wuhan University Wuhan China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Faculty of Information Technology Monash University VIC Australia Huawei Software Engineering Application Technology Lab Hangzhou China SnT Centre University of Luxembourg 1855 Luxembourg

Code comment generation, which aims to describe the functionality of source code in natural language, plays an important role in automatic software development. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on Abstract Syntax Trees (ASTs). However, they can not well capture both the long-distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present to the community a prototype tool called PYSCRIBE, which expands the Transformer model to a new encoder-decoder architecture to incorporate triplet positions into the node-level and edge-level features of AST towards automatically generating comments for Python code. To the best of our knowledge, this paper makes the first attempt to consider the edges of AST as an explicit component for better code representation, as to generate higher qualified comments. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. In the decoder, we perform a two-stage decoding process over the extracted node and edge features to generate comments. To evaluate the effectiveness of PYSCRIBE, we resort to a large dataset of code-comment pairs by mining Jupyter notebooks from GitHub, for which we have made it publicly available to support further studies. The evaluation results show that PYSCRIBE is indeed effective, outperforming the state-of-the-art by achieving an average BLEU score (i.e., av-BLEU) of ≈0.28. © 2022, The Authors. All rights reserved.

关键词： Large dataset

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