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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Hao Jiang Haiwei Lai Si Wu Baojian Hua School of Software Engineering Suzhou Institute for Advanced Research University of Science and Technology of China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

JavaScript is rapidly being deployed as binaries in security-critical embedded domains, including IoT devices, edge computing, and smart automotive applications. Ensuring the security of JavaScript binaries in these domains necessitates comprehensive binary code analysis. However, despite the urgent need, a universal approach to analyzing JavaScript binaries is lacking due to the bytecode heterogeneity across the various JavaScript virtual *** this paper, to fill this gap, we present the first grammar-free, universal program analysis approach tailored for JavaScript binaries. We first design a syntax-independent intermediate representation called JasByte to encode diverse JavaScript binaries. We then develop a universal translator equipped with a set of APIs to transform JavaScript binaries into JasByte. We design a suit of program analysis algorithms for error detection, debugging, and fuzzing, to identify bugs in JavaScript VMs. We design and implement a software prototype JasFree and conduct extensive evaluations. Our results show that JasFree effectively enables construction of diverse static and dynamic analysis by reducing the overhead from 660.38% to 290.84%, outperforming the state-of-the-art tool Jalangi2. Moreover, JAS-FREE facilitates effective mutation of JasByte, resulting in the detection of 25 new vulnerabilities, all of which were missed by existing methods.

关键词： Privacy Translation Heuristic algorithms software algorithms Prototypes Transforms Fuzzing software Security Vehicle dynamics

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A Feature Fusion-Based Approach for Mammographic Mass Classification Using Deep Learning 1

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5th International Conference on Innovative Computing, IC 2022

作者： ur Rehman, Khalil Li, Jianqiang Pei, Yan Yasin, Anaa Ali, Saqib Saeed, Yousaf School of Software Engineering Beijing University of Technology Beijing China Beijing Engineering Research Center for IoT Software and Systems Beijing China Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

Breast cancer seems to be the most deadly cancer for females globally. Mammographic masse detection is essential for the early diagnosis of breast cancer. The manual detection of breast masses using texture analysis from digital mammograms is hard because of its diverse patterns. Automatic detection of breast masses from mammograms with computer algorithms at early phases could help physicians to avoid unnecessary biopsies. In the current study, we investigated the limitations of texture analysis for mass detection by creating an MR taxon filter bank using Laplacian of Gaussian. We created a deep taxon feature fusion bank for the EfficinetNet DCNN to classify benign and malignant masses. The experimental results were examined on the public CBIS-DDSM and the local PINUM dataset. Our method yielded 0.96 and 0.98 accuracies, respectively. The results findings illustrated that our method significantly outperformed than the MobileNet, AlexNet, DenseNet, and previous studies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mammography

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A Deep Learning-Based Approach for Mammographic Architectural Distortion Classification 1

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5th International Conference on Innovative Computing, IC 2022

作者： ur Rehman, Khalil Li, Jianqiang Pei, Yan Yasin, Anaa Ali, Saqib School of Software Engineering Beijing University of Technology Beijing China Beijing Engineering Research Center for IoT Software and Systems Beijing China Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

Breast cancer is the most deadly cancer in females globally. Architectural distortion is the third most often reported irregularity on digital mammograms among the masses and microcalcification. Physically identifying architectural distortion for radiologists is problematic because of its subtle appearance on the dense breast. Automatic early identification of breast cancer using computer algorithms from a mammogram may assist doctors in eliminating unwanted biopsies. This research presents a novel diagnostic method to identify AD ROIs from mammograms using computer vision-based depth-wise CNN. The proposed methodology is examined on private PINUM 2885 and public DDSM 3568 images and achieved a 0.99 and 0.95 sensitivity, respectively. The experimental findings revealed that the proposed scheme outperformed SVM, KNN, and previous studies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mammography

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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WaShadow: Effectively Protecting WebAssembly Memory Through Virtual Machine-Aware Shadow Memory

WaShadow: Effectively Protecting WebAssembly Memory Through ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhuochen Jiang Baojian Hua School of Software Engineering Suzhou Institute for Advanced Research University of Science and Technology of China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

WebAssembly (Wasm) is an emerging binary instruction set architecture designed for secure binary program execution and is rapidly being deployed across various security-critical domains, such as edge computing and smart contracts. However, despite its security-oriented design, Wasm remains susceptible to vulnerabilities, including integer overflows and memory corruption, due to the lack of effective protection mechanisms, which undermines its security *** this paper, we present WaShadow, the first approach for effective Wasm protection using virtual machine-aware shadow memory. Our key insight is that, since Wasm is a virtual instruction set, we can leverage memory layout information in the underlying Wasm VMs to enforce the protection. Specifically, we first extend the Wasm VMs with shadow memory to record memory information and track the status of linear memory, by introducing two new pseudo Wasm instructions for inserting and performing sanity checks on canaries in the linear memory. We then design a static binary instrumentation method to instrument Wasm binaries with canary instructions. Finally, we implement these canary pseudo-instructions through virtual machine extensions as well as a set of vulnerability detection algorithms as security plugins. We implemented a software prototype for WaShadow and conducted extensive experiments to evaluate its effectiveness, usability, and overhead on micro and real-world benchmarks. Experimental results demonstrated that WaShadow is effective in protecting Wasm linear memory against various memory vulnerabilities, with an average code size increase of 26.5% and an execution time penalty of 108.5%.

关键词： Instruments Instruction sets Layout Prototypes Virtual machines software Security Protection Detection algorithms Usability

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Predicting the Air Pollution Level and Creating an Awareness Platform 5

Predicting the Air Pollution Level and Creating an Awareness...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Nuha, M.N. Wishvajith, G.K.N.P. Caldera, Amitha Weerasinghe, N.T. Weeratunga, G.S. Lokuliyana, Shashika Sri Lanka Institute of Infromation Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Infromation Technology Department of Computer Systems & Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study proposes a comprehensive method to reduce the problems posed by global air pollution. To measure pollution levels and raise public awareness, the research uses a comprehensive strategy that incorporates image processing, time series analysis, chatbot, and facemask recognition. Time series analysis is used to build prediction models by recognizing historical patterns, and image processing techniques are used to examine cloud pictures for precise pollution predictions. Chatbots provide real time communication and may educate the public on environmental facts, health risks, and safety procedures. Additionally, it has a facemask identification system that monitors and promotes the use of masks. Importantly this not only encourages users to take action to reduce pollution but also utilizes predictive models to help mitigate pollution's consequences. These components result in improved public health, environmental conservation, and sustainability. The outcomes reflect significant advantages, with evidence of accuracy and its positive impact on both awareness and proactive measures to combat air pollution. © 2023 IEEE.

关键词： Time series analysis

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Impersonated Human Speech Chatbot with Adaptive Frequency Spectrum 3

Impersonated Human Speech Chatbot with Adaptive Frequency Sp...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Chettiar, Gautam Shukla, Amogh Nalwaya, Preet Sethi, Karman Prakash, Surya Vellore Institute of Technology Dept. of Communication Engineering Vellore India Department of Software Systems Vellore Institute of Technology Vellore India Indian Institute of Information Technology Allahabad Department of Electronics and Communication Engineering Prayagraj India

ISBN: (纸本)9781665453578

Recent trends in artificial intelligence and natural language processing models have led to the generation of highly efficient and versatile intelligent chatbot models., which have the potential to supplant human speech to the level of conversational proficiency. The proposed method creates a chatbot model that trains itself on open conversation datasets and aims to impersonate without compromising the emotional sentiments in the voice. These datasets extract from the applications such as WhatsApp., Telegram., Messenger., or any other chatting platform. Datasets convert to a machine-readable format., which is dynamically updated in real-time during the conversation., and then using speech conversion algorithms convert the reply into the desired individual's voice. The proposed model's conversational ability depends on the amount of conversation data., which gives the output in the person's voice frequency. By using an NLP-based chatbot trained on personalized data using KNN., and handling misses by pipelining the chatbot inputs to the GPT-2 model., the model can generate human-like replies even if there is data insufficiency. The natural replies are complemented with matching human voice and tone characteristics by using the vocoder model., which matches the spectral characteristics of the target voice onto the required voice. This opens a plethora of commercial and therapeutic applications that provide excellent insights into implementing natural communication models for humanoid and robotics innovations. © 2023 IEEE.

关键词： Sentiment analysis

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PatrolGo: Efficient Security Patrol Route Planning to Catch Intruders 3

PatrolGo: Efficient Security Patrol Route Planning to Catch ...

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Han, Jinpeng Chen, Xiaoguang Yang, Manzhi Huai, Rouxing Xi'an Jiaotong University School of Software Engineering Xi'an China Macau University of Science and Technology Macau Institute of Systems Engineering China Huairou Academy of Parallel Sensing Beijing China

ISBN: (纸本)9798350318470

Solving security patrol route planning in large-scale realistic scenarios is a very challenging problem. Here we propose a route planning algorithm PatrolGo based on Monte Carlo Tree Search (MCTS). In this algorithm, the strategy network selects the actions of security resources, and the situation network evaluates the current situation. These deep neural networks are training from the confrontation between PatrolGo and the attacker model. We assume that the attacker and the defender act alternately, and the attacker model is fixed. Specifically, we first apply MCTS to the grid security game scenario. Furthermore, we added the deep neural network to the MCTS algorithm to enhance its efficiency in selecting and evaluating stages. Finally, the PatrolGo algorithm tree search process can provide prescriptive security resources action. We provide a new scheme for security patrol route planning. The simulation results show that the scheme has state-of-the-art performance and effective strategic prescript. © 2023 IEEE.

关键词： Deep neural networks

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for Computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Gloss: Guiding Large Language Models to Answer Questions from System Logs 31

Gloss: Guiding Large Language Models to Answer Questions fro...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Huang, Shaohan Liu, Yi Qi, Jiaxing Shang, Jing Xiao, Zhiwen Fung, Carol Wu, Zhihui Yang, Hailong Luan, Zhongzhi Qian, Depei Sino-German Joint Software Institute Beihang University Beijing China China Mobile Information Technology Center Beijing China Concordia Institute for Information Systems Engineering Concordia University Quebec Canada

ISBN: (纸本)9798350330663

System logs contain valuable information and they have emerged as one of the most crucial data sources for system monitoring aimed at enhancing service quality. IT support teams and system administrators are in dire need of an intelligent log-based QA system to help them quickly identify, diagnose, and resolve issues. In this paper, we propose a novel method for constructing log-based question-answering (QA) data using large language models, addressing challenges associated with limited dataset size and diversity in existing log-based QA systems. Our pipeline consists of three steps: generating questions, answering log questions, and refining question-answer pairs. The purpose of the generating questions is to create a diverse set of log-related queries that cover a wide range of potential issues. The second step, answering log questions, aims to extract relevant information from the logs to address the generated questions. This step ensures accurate and context-aware responses. Refining question-answer pairs is intended to improve the overall quality and consistency of the generated log-based QA data. We present a case study using ChatGPT to generate a new dataset, LogQuAD, containing over 28,000 question-answer pairs derived from more than 31,000 raw logs, representing a significant increase compared to existing datasets like LogQA. In our experimental setting, we sample half of the data as the training set and use memory-effect fine-tuning to fine-tune the model, named Gloss. Experimental results show that our method can generate high-quality log-based QA data, leading to improved performance of log-based QA models. Notably, our fine-tuned 7B model outperforms the LLaMA-65B model. This approach can potentially save valuable time for IT support teams and system administrators, enabling proactive problem resolution and optimal system performance. © 2024 IEEE.

关键词： Large datasets

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