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7th International conference on Electronic Information Technology and Computer engineering (EITCE)

作者： Cai, Xiaodong Xue, Yun Zhang, Yanyan Ye, Qing Guilin Univ Elect Technol Guilin Peoples R China

ISBN: (纸本)9798400708305

The recommender system's overall suggestion results may be erroneous and weakly robust due to the sparse interaction behavior between users and items in the system and the noise inherent in interaction samples. This research suggests a graph comparison learning recommendation method based on knowledge graph augmentation to address the aforementioned issues. First, a knowledge graph-based data improvement method is created to make use of the rich entity attribute data and help the recommendation system solve its data sparsity issue. In order to help the model better capture the crucial information contained in the nodes and to lessen the effect of noisy links between interaction samples on the quality of node representation, a graph self-attentive augmented network is also proposed. In order to test the algorithm's efficacy, experiments are carried out on three datasets: Yelp2018, Amazon-Book, and MIND. The results show that the recommendation algorithm is effective in enhancing recommendation accuracy in the case of small sample sizes and the presence of noisy data scenarios. The Recall metrics are improved by 8.59%, 7.08%, and 15.47%, respectively, compared with the state-of-the-art model.

关键词： computer software and theory recommendation algorithms graph neural networks comparative learning personalized recommendations knowledge graph

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VGX: Large-Scale Sample Generation for Boosting Learning-based software Vulnerability Analyses 24

VGX: Large-Scale Sample Generation for Boosting Learning-Bas...

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44th ACM/IEEE International conference on software engineering, ICSE 2024

作者： Nong, Yu Fang, Richard Yi, Guangbei Zhao, Kunsong Luo, Xiapu Chen, Feng Cai, Haipeng Washington State University United States Hong Kong Polytechnic University Hong Kong The University of Texas at Dallas United States

ISBN: (纸本)9798400702174

Accompanying the successes of learning-based defensive software vulnerability analyses is the lack of large and quality sets of labeled vulnerable program samples, which impedes further advancement of those defenses. Existing automated sample generation approaches have shown potentials yet still fall short of practical expectations due to the high noise in the generated samples. This paper proposes VGX, a new technique aimed for large-scale generation of high-quality vulnerability datasets. Given a normal program, VGX identifies the code contexts in which vulnerabilities can be injected, using a customized Transformer featured with a new value-flow-based position encoding and pretrained against new objectives particularly for learning code structure and context. Then, VGX materializes vulnerability-injection code editing in the identified contexts using patterns of such edits obtained from both historical fixes and human knowledge about real-world vulnerabilities. Compared to four state-of-the-art (SOTA) (i.e., pattern-, Transformer-, GNN-, and pattern+Transformer-based) baselines, VGX achieved 99.09-890.06% higher F1 and 22.45%-328.47% higher label accuracy. For in-the-wild sample production, VGX generated 150,392 vulnerable samples, from which we randomly chose 10% to assess how much these samples help vulnerability detection, localization, and repair. Our results show SOTA techniques for these three application tasks achieved 19.15-330.80% higher F1, 12.86-19.31% higher top-10 accuracy, and 85.02-99.30% higher top-50 accuracy, respectively, by adding those samples to their original training data. These samples also helped a SOTA vulnerability detector discover 13 more real-world vulnerabilities (CVEs) in critical systems (e.g., Linux kernel) that would be missed by the original model. © 2024 ACM.

关键词： Deep learning

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Enhanced BERT with Graph and Topic Information for Short Text Classification 35

Enhanced BERT with Graph and Topic Information for Short Tex...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Zhang, Tong Tang, Ailing Yan, Rong College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Mongolian Information Processing Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China

Short text classification is an important natural language processing task due to the prevalence of short text on the internet and social media platforms. In this paper, we propose a novel graph-based short text classification method named GBBM (Graph-BERT-BTM Model) that leverages the powerful representation ability of graph data to capture the structural features of short text. In this work, we incorporate topic information to enrich and expand the feature space for the short text and compare our proposed method on five publicly available short text datasets with five existing models. Experimental results indicate the superiority of our proposed method. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Graphic methods

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Survey on Data Ingestion for AutoML 35

Survey on Data Ingestion for AutoML

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Mac’Hamilton Renaux Alves, Gabriel Maciel, Alexandre Magno Andrade Department of Computer Engineering University of Pernambuco Recife Brazil

Automated machine learning (AutoML) is an increasingly popular approach to building machine learning (ML) models without the need for extensive human intervention. One key component of AutoML is automated data ingestion, which involves automatically collecting, cleaning, and preparing data for use in ML models. This paper aims to analyze the literature in order to identify how automated data ingestion is being developed in the literature. To achieve this goal, a survey was conducted on the state-of-the-art of automated data ingestion using a method based on a systematic literature review, in order to identify the existing practices. A total of 12 articles were initially found, however, after applying filters, only six of them were ultimately utilized in the research, showing that visual data navigation and validation as well as metadata inference are important features for automated data ingestion focused in AutoML. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Machine learning

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BDC:Using BERT and Deep Clustering to Improve Chinese Proper Noun Recognition 35

BDC:Using BERT and Deep Clustering to Improve Chinese Proper...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Ma, Yuanchi He, Hui Niu, Zhendong Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Institute of Technology Institute of Engineering Medicine Beijing China

Proper noun recognition is a sub-task in named entity recognition. However, few methods have been specifically applied to the Chinese. The reason is that most of the existing deep clustering methods rely on manually labeled training sets, which take a long time in the learning process. And due to the wide and large-scale nature of the proprietary domain and the lack of word boundaries, recognizing Chinese specialized terms from unstructured text remains challenging. In this paper, we design an unsupervised method to improve Chinese proper noun recognition. The first step is to implement the word separation for Chinese, followed by a BERT-based improved word characterization method to obtain word vectors. Finally, we use the autoencoder-based deep clustering method to complete the extraction of proper nouns from books. We have done comparison experiments on the public dataset and our selected professional book data respectively, and the result is an improvement of our method in both the accuracy and F1 values. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Cluster analysis

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Topic and Speaker-aware Hierarchical Encoder-Decoder Model for Dialogue Generation 35

Topic and Speaker-aware Hierarchical Encoder-Decoder Model f...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Hu, Qitao Wei, Xiao School of Computer Engineering and Science Shanghai University China

As one of the most common social behavior in human society, communication in multi-turn conversation or dialogue system has always been a research focuses of natural language processing (NLP). The quality of downstream tasks in multi-turn dialogue is often determined by the result of dialogue context modeling. For dialogue generation, the context information will determine the consistency and diversity of the generated responses. However, the current research on dialogue generation increasingly relies on external information rather than mining from the dialogue content itself. In this paper, we propose a topic and speaker-aware hierarchical encoder-decoder (TSHED) model to capture the topic and speaker information flow in the context for response generation with the hierarchical transformer-based framework. Specifically, we obtain semantic information of each utterance at word-level and then apply topic and speaker-aware attention to model context at utterance-level. Experimental results on two open-domain datasets show that TSHED significantly improves the quality of responses and outperforms strong baselines. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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Coding Agents: A Comprehensive Survey of Automated Bug Fixing Systems and Benchmarks 14

Coding Agents: A Comprehensive Survey of Automated Bug Fixin...

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14th IEEE International conference on Communication Systems and Network Technologies, CSNT 2025

作者： Puvvadi, Meghana Arava, Sai Kumar Santoria, Adarsh Chennupati, Sesha Sai Prasanna Puvvadi, Harsha Vardhan Adobe Systems Inference Platform San Jose United States Adobe Systems San Jose United States IIT Mandi Dept. of Electrical Engineering Mandi India Rippling San Jose United States Shell India Markets Pvt. Ltd. Bangalore India

ISBN: (纸本)9798331531935

One of the trickiest problems in software engineering is automating software issue fixes, which calls for a thorough comprehension of contextual relationships, code semantics, and dynamic debugging techniques. The development of automatic program repair (APR) is examined in this survey, which traces a path from early template and constraint-based approaches to more recent developments powered by large language models (LLMs). Three main paradigms are compared here: retrieval-augmented approaches that integrate external knowledge sources, agent-based systems that use multi-agent frameworks, and agentless systems that use simplified repair pipelines. Real-world benchmarks that mimic actual engineering workflows and repository-level difficulties, such as SWE-bench, CODEAGENT-BENCH, and CodeRAG-Bench, are used to assess these cutting-edge technologies. This study demonstrates how agentic, agentless, and retrieval- augmented systems use LLMs to achieve previously unheard- of precision and scalability by following the shift from localized, single-file solutions to solving complicated, multi-file, and repository-wide difficulties. According to our findings, while complex agent architectures have potential, straightforward test- time scaling frequently produces better outcomes, especially when paired with containerized environments that allow for parallel exploration. Additionally, the survey looks at industrial applications, emphasizing effective connections with quality assurance and DevOps procedures. In order to further the development of more resilient and flexible APR frameworks that blend in perfectly with contemporary software engineering practices, we conclude by highlighting important issues in context handling and validation and suggesting future research directions in improved contextual models, human-AI collaboration, and multi-modal debugging systems. © 2025 IEEE.

关键词： software design

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A knowledge Graph-based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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Context, Content, Consent – How to Design User-Centered Privacy Explanations 35

Context, Content, Consent – How to Design User-Centered Pri...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Brunotte, Wasja Droste, Jakob Schneider, Kurt Leibniz University Hannover Software Engineering Group Hannover Germany Leibniz University Hannover Cluster of Excellence PhoenixD Hannover Germany

In the context of the ongoing digitalization of society, human values such as privacy, ethics and trust are becoming increasingly important. Digital systems are entering private and professional spaces, which in turn affects the privacy of their end users. Hence, there is a need for conveying privacy information in a transparent and understandable manner, with the user in the focus. Lawmakers introduced privacy policies as a means of communicating privacy information. However, those documents have proven to be practically useless for end users. Privacy policies are long, vague, ambiguous and use complex language, such as legal terms, which often require profound background knowledge. Explainability has shown potential as a means to increase transparency and foster trust in software systems. based upon the foundation of explainability, we developed a layered concept for user-centered privacy explanations, which is implemented within a high-fidelity software prototype. Finally, we tested and evaluated our concept by conducting an interactive user study with 61 participants. The results of our study suggest that our layered design concept enabled participants to understand the privacy aspects they regarded as important. We conclude that our approach seems to be an appropriate way to communicate complex privacy information to end users. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Conveying

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Generating Explanations to Understand and Repair Embedding-based Entity Alignment 40

Generating Explanations to Understand and Repair Embedding-B...

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40th IEEE International conference on Data engineering, ICDE 2024

作者： Tian, Xiaobin Sun, Zequn Hu, Wei Nanjing University State Key Laboratory for Novel Software Technology Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China

ISBN: (纸本)9798350317152

Entity alignment (EA) seeks identical entities in different knowledge graphs, which is a long-standing task in the database research. Recent work leverages deep learning to embed entities in vector space and align them via nearest neighbor search. Although embedding-based EA has gained marked success in recent years, it lacks explanations for alignment decisions. In this paper, we present the first framework that can generate explanations for understanding and repairing embedding-based EA results. Given an EA pair produced by an embedding model, we first compare its neighbor entities and relations to build a matching subgraph as a local explanation. We then construct an alignment dependency graph to understand the pair from an abstract perspective. Finally, we repair the pair by resolving three types of alignment conflicts based on dependency graphs. Experiments on a variety of EA datasets demonstrate the effectiveness, generalization, and robustness of our framework in explaining and repairing embedding-based EA results. © 2024 IEEE.

关键词： Embeddings

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