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9th IEEE International Conference on Smart Cloud, SmartCloud 2024

作者： Ding, Minjie Shen, Ying Chen, Mingang Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350389500

Natural language processing (NLP) is rapidly developing. A series of Large Language Models (LLMs) have emerged, represented by ChatGPT, which have made significant breakthroughs in natural language understanding and generation, enabling fluent dialogue with humans, understanding human intentions, and completing complex tasks. However, in addition to the fairness and toxicity of traditional language models, some new problems, including hallucination, have also emerged in LLMs, making them hard to use. evaluating LLMs manually is challenging due to subjectivity and inefficiency. In this paper, we focused on the fuzzy matching, toxicity detection, and hallucination detection in the evaluation of LLMs automatically, and fine-tune the Mixtral-8x7B Model, which can be deployed in private cloud environment, and prove the effectiveness of our method through experiments. © 2024 IEEE.

关键词： Toxicity

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PSO-Based Black-Box Lane Detection Adversarial Attack 2

PSO-Based Black-Box Lane Detection Adversarial Attack

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2nd International Conference on Artificial Intelligence, Human-computer Interaction and Robotics, AIHCIR 2023

作者： Fang, Hongtao Wang, Ruiyun Ma, Zeyu Chen, Mingang Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating shanghai China Shanghai Normal University Shanghai Key Laboratory of Computer Software Testing and Evaluating shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating shanghai China

ISBN: (纸本)9798350360363

Autonomous driving has achieved rapid development and promising performance by employing machine learning algorithms to automatically execute various maneuvers. Lane detection is one of the foremost tasks for vehicles, with a direct impact on driving decisions. Lane detection is a crucial component of autonomous driving, working in conjunction with lane centering control (LCC) and adaptive cruise control (ACC) to enable advanced driving assistance functions. At first thought, lane detection may seem immune to attacks. However, is this really the case? In this paper, we conduct experiments on lane detection modules and reveal their susceptibility to attacks exploiting hypersensitivity. Specifically, an excessively sensitive lane detector may mistake small markings for valid lanes, causing the vehicle to follow the wrong path. Through experiments, we demonstrate the vulnerability of lane detection models to adversarial attacks, even under black-box conditions where a change in one pixel can induce errors. We discuss the contrast between gradient-based and heuristic search-based optimization methods for black-box attacks and demonstrate the superiority of heuristic approaches for this task. Leveraging particle swarm optimization (PSO), we carry out lane detection attacks to address the research gap for black-box lane attacks. © 2023 IEEE.

关键词： Autonomous vehicles

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An Evaluation System for Large Language Models based on Open-Ended Questions 11

An Evaluation System for Large Language Models based on Open...

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11th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Cao, Zhiyuan Ma, Zeyu Chen, Mingang Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (纸本)9798350376982

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. © 2024 IEEE.

关键词： Natural language processing systems

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IVIFUZZER: A Fuzzing method for vulnerability exploitation on the operating interface of the In-Vehicle Infotainment system 5

IVIFUZZER: A Fuzzing method for vulnerability exploitation o...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Wu, Jianhua Zhang, Xin Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9798350385557

As an important information system of intelligent and connected vehicles, most In-Vehicle Infotainment systems are set up and manage the vehicles through user interaction. This paper presents a Fuzzing method IVIFUZZER for vulnerability exploitation on the operation interface of the In-Vehicle Infotainment system. With the help of automated robotic arm and improved OCR technology, collecting system leakage information and directional detection of the identified input points, 6 zero day vulnerabilities were found. The experimental results show that the proposed IVIFUZZER method is effective. © 2024 IEEE.

关键词： Vehicles

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ALQ: An Efficient Active Learning Recommendation Algorithm 6

ALQ: An Efficient Active Learning Recommendation Algorithm

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Song, Ao Lei, Zhou Chen, Shengbo Shanghai University College of Computer Engineering and Science Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9798350316520

With the rapid development of the Internet, the problem of information overload is becoming more and more serious, which makes it difficult for users to choose the content they are interested in from a large number of information. With the emergence of recommendation systems, the historical interaction information between users and information and items is used to provide users with topics of interest, which not only alleviates the problem of information overload, but also brings great satisfaction to users. However, training a model that can make accurate recommendations is not an easy task. In addition to an appropriate training strategy, it also requires a large amount of data support. Getting data, especially labeled data, takes a lot of time and money. Therefore, this paper proposes a new active learning algorithm. Firstly, the recommendation problem was transformed into a PU learning problem, and then the method was introduced in the positive sample learning process to achieve the purpose of increasing positive sample data, thereby reducing the cost of data labeling. Through experiments, the proposed method reduces the cost of data labeling in the recommendation task and further verifies the rationality of transforming the recommendation problem into a PU learning problem. © 2024 IEEE.

关键词： Recommender systems

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SFPL: Improving CRS via Prompt Learning based Semantic Fusion Module 4

SFPL: Improving CRS via Prompt Learning based Semantic Fusio...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

ISBN: (纸本)9798350363586

With the popularity of the Internet and the massive increase of content production, users are faced with massive information and content, and it is often difficult to accurately and efficiently find the content that meets their needs. This information overload motivates the emergence of recommendation systems that help users filter and recommend relevant content. Dialogue recommendation systems (CRS) aim to dynamically recommend high-quality items to users while eliciting user preferences by means of dialogue. CRS mainly consists of two parts, namely, the dialogue module and the recommendation module. An efficient CRS is to solve the problems existing in the two sub-tasks of dialogue and recommendation, namely, generating appropriate responses in dialogue and predicting user favorite items in recommendation. In addition, it is necessary to adopt a suitable method to integrate these two modules to ensure the accuracy of CRS recommendation. This paper proposes an efficient way of semantic fusion module to solve the integration problem of two modules, and proves the effectiveness of our method through experiments © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Measuring Method of Function Size of Information System 26

Research on Measuring Method of Function Size of Information...

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26th ACIS International Winter Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Chunye, Du Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing &evaluating Shanghai China

ISBN: (纸本)9798350345865

With the rapid development of software industry, a variety of new technologies are produced. Research and development in cloud computing, big data, artificial intelligence, the Internet of Things, blockchain, and other related fields continue to prosper. The increasing lines of code, function points, escalating scale of software systems, and the rising complexity of software development collectively complicate software cost estimation. This article studies how to estimate the size, workload, and cost of an information system using functional scale measurement methods. © 2023 IEEE.

关键词： Information systems

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Harmonizing Visual Text Comprehension and Generation 38

Harmonizing Visual Text Comprehension and Generation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Zhen Tang, Jingqun Wu, Binghong Lin, Chunhui Wei, Shu Liu, Hao Tan, Xin Zhang, Zhizhong Huang, Can Xie, Yuan East China Normal University China ByteDance China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

In this work, we present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. Simultaneously generating images and texts typically results in performance degradation due to the inherent inconsistency between vision and language modalities. To overcome this challenge, existing approaches resort to modality-specific data for supervised fine-tuning, necessitating distinct model instances. We propose Slide-LoRA, which dynamically aggregates modality-specific and modality-agnostic LoRA experts, partially decoupling the multimodal generation space. Slide-LoRA harmonizes the generation of vision and language within a singular model instance, thereby facilitating a more unified generative process. Additionally, we develop a high-quality image caption dataset, DetailedTextCaps-100K, synthesized with a sophisticated closed-source MLLM to enhance visual text generation capabilities further. Comprehensive experiments across various benchmarks demonstrate the effectiveness of the proposed approach. Empowered by Slide-LoRA, TextHarmony achieves comparable performance to modality-specific fine-tuning results with only a 2% increase in parameters and shows an average improvement of 2.5% in visual text comprehension tasks and 4.0% in visual text generation tasks. Our work delineates the viability of an integrated approach to multimodal generation within the visual text domain, setting a foundation for subsequent inquiries. Code is available at https://***/bytedance/TextHarmony. © 2024 Neural information processing systems foundation. All rights reserved.

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MA-MFIF: When misaligned multi-focus Image fusion meets deep homography estimation

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Multimedia Tools and Applications 2025年第12期84卷 10877-10898页

作者： Zhao, Baojun Luo, Fei Fuentes, Joel Ding, Weichao Gu, Chunhua School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Information Technologies Universidad del Bio-Bio Chillán3780000 Chile

Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning. However, one of the persistent challenges in this field is the processing of misaligned data, which can negatively impact the fusion results. To overcome this problem, a novel fusion framework with pre-registration is proposed for the fusion of misaligned multi-focus images. For pre-registration, content-aware deep homography estimation is used, which performs transfer learning on a real multi-focus image dataset to adapt to registration under defocused conditions. For fusion, a fusion module with dual-branch feature interaction is utilized to avoid invalid feature fusion and trained on real light field dataset to achieve better fusion performance. Qualitative and quantitative experimental results show that the proposed method has a 2-3 percentage point improvement in multiple evaluation metrics compared to existing advanced registration and fusion methods, and a maximum improvement of 4.83 percentage points in fusion performance when tested independently on the Lytro dataset. Additionally, We find that the value of the Qcv metric is greatly influenced by the alignment status of the input images, leading to its inability to reflect the fusion quality of aligned images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Integrating Extractive and Abstractive Models for Code Comment Generation 23

Integrating Extractive and Abstractive Models for Code Comme...

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23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Sun, Weisong Hu, Yuling Xu, Yingfei Chen, Yuchen Fang, Chunrong Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9798350319583

Code comments play an essential role in aiding developers understand and maintain source code. Current code comment generation techniques can be classified into categories: extractive methods and abstractive methods. Extractive methods use text retrieval techniques to extract important code tokens to constitute comments. Such comments contain important factual details articulated explicitly in code tokens, but are poor in naturalness. Abstractive methods usually regard code comment generation as a neural machine translation task. By leveraging powerful deep learning-based language models, abstractive methods can generate comments that resemble human writing. However, compared with natural language, programming language code is more complex. Comments generated by abstractive methods often leave out important factual details. In this paper, we propose a novel method for code comment generation by integrating extractive and abstractive models. Our extractive model is built on the Latent Semantic Analysis (LSA) model, effectively extracting important factual details in code snippets. Meanwhile, our abstractive model is built on a deep learning-based encoder-decoder model, enabling it to generate concise and human-written-like comments. We evaluate the effectiveness of our method, called ICS, by conducting extensive experiments on the CodeSearchNet dataset involving six programming languages. The results demonstrate that ICS outperforms state-of-the-art techniques in three widely used metrics: BLEU, METEOR, and ROUGE-L. Moreover, the outcomes of the human evaluation indicate that the comments generated by ICS exhibit superior naturalness and informativeness, and closely align with the provided code snippets. © 2023 IEEE.

关键词： Deep learning

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