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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Lingbing Bo, Zhongpu Chen, Zhuo Zhang, Yichi Chen, Jiaoyan Lan, Yarong Sun, Mengshu Zhang, Zhiqiang Luo, Yangyifei Li, Qian Zhang, Qiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China Department of Computer Science The University of Manchester United Kingdom School of Computer Science and Engineering Beihang University China School of Computer Science Beijing University of Posts and Telecommunications China School of Software Technology Zhejiang University China

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs. © 2024 Neural information processing systems foundation. All rights reserved.

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Towards agile collaborative multi-view modeling with inconsistency tolerance

Towards agile collaborative multi-view modeling with inconsi...

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2024 Symposium on software Technologies: Applications and Foundations, STAF 2024 Workshops: 2nd International Workshop on Agile Model-Driven engineering, AgileMDE 2024, 1st Large Language Models for Model-Driven engineering Workshop, AgileMDE 2024 and 4th International Workshop on Model-Driven engineering for Smart Internet-of-Things Systems, MeSS 2024

作者： Shahvari, Yaser Sharbaf, Mohammadreza Rahimi, Shekoufeh Kolahdouz Tehrani, Sobhan Yassipour MDSE Research Group Department of Software Engineering University of Isfahan Isfahan Iran School of Arts University of Roehampton London United Kingdom Computer Science Department Faculty of Engineering University College London London United Kingdom

Model-driven engineering (MDE) copes with the complexity of software development by using the principles of separation of concerns and automatic transformation. In MDE, stakeholders from diverse domains collaborate concurrently on different models to quickly analyze, design, and generate complex software-intensive systems. To adopt an agile development approach in MDE, an inconsistency tolerance framework is needed. This framework postpones the resolution phase for incompatibilities caused by online cooperation, allowing temporary incompatibilities to be automatically fixed while requiring resolution only for the remaining conflicts at the appropriate time. In this paper, we propose a general framework for identifying and diagnosing inconsistencies, making decisions regarding inconsistency tolerance or intervention for resolution, and outlining tolerance strategies in agile systems modeled as multi-views. Our framework comprises three primary phases: detection, analysis, and tolerance. It is designed to identify inconsistencies in multi-view models, which are inherently more complex than single-view models. The tolerance phase employs strategies to tolerate inconsistencies, enhancing the flexibility of the agile development approach for collaborative multi-view modeling. © 2024 Copyright for this paper by its authors.

关键词： software design

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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AUNET (Attention-based Unified Network): Leveraging Attention Based N-BEATS for Enhanced Univariate Time Series Forecasting

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IEEE Access 2025年 13卷 95184-95217页

作者： Habib, Adria Binte Alam, Md. Golam Rabiul Uddin, Md. Zia BRAC University Department of Computer Science & Engineering Dhaka Bangladesh SINTEF Digital Software and Service Innovation Group Oslo Norway

Univariate time series forecasting is pivotal in domains such as climate modeling, finance, and healthcare, where both short-term precision and long-term reliability are essential. This study introduces AUNET (Attention-based Unified Network) - a novel extension of the N-BEATS model - integrating multi-head self-attention to capture both short-term fluctuations and long-term dependencies while reducing architectural redundancy and enhancing interpretability. Empirical evaluations on the (baseline dataset for this research) CIMIS weather dataset show that AUNET achieves a 0.3580 MAE, 0.4632 RMSE, 0.68% MAPE, and an R2 of 0.9988, outperforming baseline models including N-BEATS and attention-augmented variants by margins exceeding 87% in MAE and RMSE. AUNET's generalization capabilities are further validated on diverse domains: in finance it attains an R2 of 0.9990, and in healthcare, it outperforms baselines in RMSE and R2 despite inherent volatility. Statistical assessments confirm the significance and robustness of these improvements. Moreover, AUNET demonstrates reduced training time, lower FLOPs, and higher convergence stability. Its attention-guided modular architecture not only enhances predictive accuracy but also promotes transparency, making AUNET a compelling candidate for deployment in high-stakes, real-world forecasting tasks across varied domains. © 2013 IEEE.

关键词： Deep learning

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Alignment efficient image-sentence retrieval considering transferable cross-modal representation learning

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Frontiers of computer science 2024年第1期18卷 77-91页

作者： Yang YANG Jinyi GUO Guangyu LI Lanyu LI Wenjie LI Jian YANG School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Computing Hong Kong Polytechnic UniversityHong Kong 100872China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing 210094China 14th Research Institute of China Electronics Technology Group Corporation Nanjing 210094China guangyu.li2017@***

Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities,thereby to search similar instances in one modality according to the query from another modality in *** basic assumption behind these methods is that parallel multi-modal data(i.e.,different modalities of the same example are aligned)can be obtained in *** other words,the image-sentence cross-modal retrieval task is a supervised task with the alignments as ***,in many real-world applications,it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs,leading the non-parallel multi-modal data and existing methods cannot be used *** the other hand,there actually exists auxiliary parallel multi-modal data with similar semantics,which can assist the non-parallel data to learn the consistent ***,in this paper,we aim at“Alignment Efficient Image-Sentence Retrieval”(AEIR),which recurs to the auxiliary parallel image-sentence data as the source domain data,and takes the non-parallel data as the target domain *** single-modal transfer learning,AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel ***,AEIR learns the image-sentence consistent representations in source domain with parallel data,while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial ***,we can effectively learn the consistent representations for target domain considering both the structure and semantic ***,extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.

关键词： image-sentence retrieval transfer learning semantic transfer structure transfer

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Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

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computers, Materials & Continua 2023年第2期74卷 3727-3741页

作者： Md Zahid Hasan Ontor Md Mamun Ali Kawsar Ahmed Francis M.Bui Fahad Ahmed Al-Zahrani S.M.Hasan Mahmud Sami Azam Department of Software Engineering(SWE) Daffodil International University(DIU)SukrabadDhaka1207Bangladesh Group of Biophotomatiχ Department of Information and Communication TechnologyMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonS7N5A9SKCanada Computer Engineering Department Umm Al-Qura UniversityMecca24381Saudi Arabia Department of Computer Science American International University-Bangladesh(AIUB)KuratoliDhaka1229Bangladesh College of Engineering IT and EnvironmentCharles Darwin UniversityCasuarina0909NTAustralia

Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing *** this many women have ***,it is curable if it can be diagnosed and detected at an early stage and taken proper *** the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early *** solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell *** system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning *** build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and *** the YOLOv5 models were applied to the labeled dataset to train the *** versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early *** of the model’s variations performed *** model can effectively detect cervical cancerous cell,according to the findings of the *** the medical field,our study will be quite *** can be a good option for radiologists and help them make the best selections possible.

关键词： Cervical cancer pap-smear deep learning cancerous cell YOLOv5 model

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A Stealthy Backdoor Attack Method Based on Generative Adversarial Networks 5

A Stealthy Backdoor Attack Method Based on Generative Advers...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Tan, Yayue Yu, Gao Zhang, Yaqi Tang, Yongqing Southwest University College of Computer and Information Science School of Software Chongqing China Payment core platform Ant Group Co. Ltd Department of Payment Platform Technology Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350374407

Deep neural networks have been widely and successfully applied to many critical tasks, such as image pattern recognition, natural language processing and autonomous driving, and large amounts of training data and increasing computational power are key factors in their success. Many studies in recent years have shown that deep neural network models face many practical security threats, such as adversarial attacks and backdoor attacks, which can have very serious consequences in the real world. Aiming at the problems of stealth, backdoor trigger design and universality of current backdoor attack methods, a novel method of invisible backdoor attack based on generative adversarial network is proposed. As a backdoor attack method based on data poisoning, inspired by image steganography, this method trains an encoder to generate poisoned samples through generative adversarial network to implement backdoor attack. The secret image used to guide the generation of backdoor triggers does not need to be carefully designed, and the encoder has good generalization ability across datasets. These features not only make its attack effective and stealthy, but also greatly reduce the cost of backdoor. Extensive experimental and analytical results show that the proposed backdoor attack method is effective, stealthy, sustainable and universal. © 2024 IEEE.

关键词： Generative adversarial networks

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Autonomous Number Plate Detection System for Vehicle Identification and Tracking 2

Autonomous Number Plate Detection System for Vehicle Identif...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Nareddy, Neha Sri Polneni, Abhinaya Baithi, Rekha Kunchamwar, Pratyusha Suvarna, Sunil Kumar Battu, Durgabhavani Budda, Naveena Agarwal, Vikas Munimanda, Premchander AGVs Group Hyderabad India Malla Reddy Engineering College for Women Department of Information Technology Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta United States School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (纸本)9798350366570

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy. © 2024 IEEE.

关键词： Deep neural networks

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Privacy engineering in the Wild: Understanding the Practitioners' Mindset, Organizational Aspects, and Current Practices

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IEEE Transactions on software engineering 2023年第9期49卷 4324-4348页

作者： Iwaya, Leonardo Horn Babar, Muhammad Ali Rashid, Awais JoondalupWA6027 Australia Research Group Department of Mathematics and Computer Science Karlstad65188 Sweden The University of Adelaide Centre for Research on Engineering Software Technologies AdelaideSA5005 Australia University of Bristol Bristol Cyber Security Group REPHRAIN: National Research Centre on Privacy Harm Reduction and Adversarial Influence Online Department of Computer Science BristolBS8 1QU United Kingdom

Privacy engineering, as an emerging field of research and practice, comprises the technical capabilities and management processes needed to implement, deploy, and operate privacy features and controls in working systems. For that, software practitioners and other stakeholders in software companies need to work cooperatively toward building privacy-preserving businesses and engineering solutions. Significant research has been done to understand the software practitioners' perceptions of information privacy, but more emphasis should be given to the uptake of concrete privacy engineering components. This research delves into the software practitioners' perspectives and mindset, organizational aspects, and current practices on privacy and its engineering processes. A total of 30 practitioners from nine countries and backgrounds were interviewed, sharing their experiences and voicing their opinions on a broad range of privacy topics. The thematic analysis methodology was adopted to code the interview data qualitatively and construct a rich and nuanced thematic framework. As a result, we identified three critical interconnected themes that compose our thematic framework for privacy engineering 'in the wild': (1) personal privacy mindset and stance, categorised into practitioners' privacy knowledge, attitudes and behaviours;(2) organizational privacy aspects, such as decision-power and positive and negative examples of privacy climate;and, (3) privacy engineering practices, such as procedures and controls concretely used in the industry. Among the main findings, this study provides many insights about the state-of-the-practice of privacy engineering, pointing to a positive influence of privacy laws (e.g., EU General Data Protection Regulation) on practitioners' behaviours and organizations' cultures. Aspects such as organizational privacy culture and climate were also confirmed to have a powerful influence on the practitioners' privacy behaviours. A conducive environment f

关键词： Data privacy

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PraK Tool: An Interactive Search Tool Based on Video Data Services 30th

PraK Tool: An Interactive Search Tool Based on Video Data S...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Lokoč, Jakub Vopálková, Zuzana Stroh, Michael Buchmueller, Raphael Schlegel, Udo SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9783031533013

This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent development of interactive search interfaces and refinement techniques, which is demonstrated by a smart front-end component. The component supports common search features and allows users to exploit content-based statistics for effective filtering. We believe that video data services might be a valuable addition to the open-source VBS toolkit, especially when available for the competition on a shared server with all VBS datasets, extracted features, and meta-data behind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Video recording

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