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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Mingjie Yu, Zhihuan Ma, Yichao Li, Guohui Yang, Zhong School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

Visual Commonsense Reasoning (VCR) is a cognitive task, challenging models to answer visual questions, and to explain the rationale behind their answers. While Large Language Models (LLMs) offer potential for this task, VCR's complex scenes require specialized approaches to activate their commonsense reasoning abilities, as existing Multimodal LLMs struggle with VCR's visual events and unique reference tags. To address these challenges, we propose EventLens, which enhances VCR through Event-Aware Pretraining and Cross-modal Linking in Supervised Fine-tuning. First, we introduce a new pretraining stage that emulates human cognitive processes to improve LLM comprehension of complex scenarios. Second, during supervised fine-tuning, we leverage reference tags to explicitly bridge RoI features with text, maintaining semantic integrity across modalities. Additionally, instruct prompts and task-specific adapters help integrate LLMs' knowledge with new commonsense reasoning. Experimental results demonstrate competitive performance with state-of-the-art methods and ablation studies verify the effectiveness of proposed EventLens. © 2025 IEEE.

关键词： Multimedia Understanding Pretrained Language Models Visual Commonsense Reasoning

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DIEONet:Domain-Invariant Information Extraction and Optimization Network for Visual Place Recognition

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computers, Materials & Continua 2025年第3期82卷 5019-5033页

作者： Shaoqi Hou Zebang Qin Chenyu Wu Guangqiang Yin Xinzhong Wang Zhiguo Wang School of Computer Science and Technology Xinjiang UniversityUrumqi830046China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Institute of Public Security Kash Institute of Electronics and Information IndustryKashi844000China

Visual Place Recognition(VPR)technology aims to use visual information to judge the location of agents,which plays an irreplaceable role in tasks such as loop closure detection and *** is well known that previous VPR algorithms emphasize the extraction and integration of general image features,while ignoring the mining of salient features that play a key role in the discrimination of VPR *** this end,this paper proposes a Domain-invariant Information Extraction and Optimization Network(DIEONet)for *** core of the algorithm is a newly designed Domain-invariant Information Mining Module(DIMM)and a Multi-sample Joint Triplet Loss(MJT Loss).Specifically,DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group,which enhances the model’s attention to the domain-invariant static object *** Loss introduces the“joint processing of multiple samples”mechanism into the original triplet loss,and adds a new distance constraint term for“positive and negative”samples,so that the model can avoid falling into local optimum during *** demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative *** particular,the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.

关键词： Visual place recognition domain-invariant information mining module multi-sample joint triplet loss

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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Science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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Scene and Aerial Image Classification Based on Advanced Deep Learning Technique 8th

Scene and Aerial Image Classification Based on Advanced Deep...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Mehmood, Maryam Hussain, Farhan Ijaz, Asad Department of Computer and Software Engineering National University of Sciences and Technology Islamabad44000 Pakistan Department of Software Engineering National University of Modern Languages Islamabad Pakistan Department of Mechanical Engineering Mirpur University of Science and Technology MUST AJK Mirpur Pakistan

ISBN: (纸本)9789819786947

Classifying scenes and aerial imagery is a critical component in applications such as land-use analysis, land cover mapping, and remote sensing technologies. Numerous existing models leverage Convolutional Neural Networks for classification, but they often involve intricate designs with a vast number of parameters, necessitating extensive computational power and prolonged training periods. This paper introduces a refined strategy employing a pre-trained EfficientNet B0 framework. Modification of parameters and layers through careful fine-tuning has resulted in notable enhancements in the model's classification precision. The superiority of EfficientNet B0 over comparative CNN architectures was confirmed by testing with three disparate datasets, particularly within the sphere of remote sensing imagery. The EfficientNet B0's superior performance is credited to its optimal structure that strikes a balance between model complexity and efficiency. Effective fine-tuning coupled with judicious computational resource allocation yields substantial accuracy in categorization. Moreover, its adaptability to assimilate remote sensing data exemplifies its widespread applicability. This study accentuates the significance of refining prevailing deep learning models to cater to specified objectives. Tailoring pre-trained models such as EfficientNet B0 enables marked advancements in classification precision, circumventing the necessity for intricate systems or significant computational demands. This methodology is promising for elevating both the proficiency and impact of scene and aerial image classification across a range of fields, with particular implications for remote sensing endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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BugWatch: A Comprehensive Approach to Classification and Redundancy Detection in software Testing 1

BugWatch: A Comprehensive Approach to Classification and Red...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Shinde, Sandeep Chrungoo, Mahi Mane, Aditya N. Maske, Pavan R. Kulkarni, Sakshi Chat, Sachin Vishwakarma Institute of Technology Dept of Computer Engineering Pune India Dassault Medidata Software Testing California United States

ISBN: (纸本)9798350355611

Countless software programs are created daily, leading to a parallel increase in software bugs. Each institution typically maintains a dedicated quality assurance team tasked with ensuring the integrity of their developed products. However, the sheer volume of bug reports received on a daily basis presents a formidable challenge for these teams to manually address each issue. The primary aim of this research is to streamline the process of bug classification and mitigate redundancy within reported bug data. To achieve this goal, the authors explored the utilization of dif erent classification algorithms, specifically SGD, based on the issue summary of each bug. The approach involves categorizing bugs into two main groups: major and minor. Additionally, a redundancy removal process was implemented to preprocess the data, flagging sentences that exhibit significant similarity above a predetermined threshold as duplicates. The results of the study demonstrate promising outcomes, with classification accuracies of 75.93% (Feature Classification) and 90.02% (Redundancy detection) achieved using SGD classifier, which gave us the best accuracy. © 2025 IEEE.

关键词： Bug Report Classification Duplicate detection Machine Learning Natural Language Processing SGD classifier software Testing

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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Science China(Information Sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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Predictive Caching of File System for Database Workload Management 1

Predictive Caching of File System for Database Workload Mana...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Jadhav, Rohini Jadhav, Veena Pawar, Prajakta Kannaujia, Abhishek Kumar, Abhishek Gohil, Jayesh College of Engineering Department of Information Technology Pune India College of Engineering Department of Computer Engineering Pune India Bharati Vidyapeeth College of Engineering Department of Computer Engineering Lavale Pune India Veritas Principal Software Engineering India

ISBN: (纸本)9798350355611

The proactive caching technique known as 'predictive caching' attempts to improve file system performance by anticipating and pre-fetching data that is likely to be requested in the future. Conventional caching strategies, such LRU (Least Recently Used) and LFU (Least Frequently Used), base their decision on which material to keep in the cache on historical access patterns. Although these methods work well in many situations, they could not completely take advantage of the temporal and geographical proximity of file system access patterns, which could result in less-than-ideal cache use. Utilizing machine learning algorithms, statistical analysis, or heuristics, predictive caching makes predictions about future access patterns based on past data, system status, file properties, or user activity. So we propose a predictive caching technique which reduce access latency and boost system performance by pre-fetching and storing pertinent data ahead of time in anticipation of incoming data accesses. © 2025 IEEE.

关键词： Dynamic Data File Access Pattern Predictive Caching Random Forest Classifier

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SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing

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Discover Computing 2025年第1期28卷 1-17页

作者： Mohanty, Subhasish Mishra, Jyotirmaya Mohapatra, Sudhir Kumar Bejo, Seifu Detso Deferisha, Aliazar Deneke Computer Science and Engineering GIET University Odisha Gunupur India Faculty of Engineering and Technology Sri Sri University Cuttack India Department of Software Engineering CCI Wolkite University Wolkite Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducing faulty code, or mutants, into the program to assess the quality of the test suite and other testing methods. However, mutation testing faces challenges such as the generation of numerous mutants, the presence of equivalent mutants that are difficult to detect through testing, and the lack of realistic mutant creation. Literature reviews indicate significant efforts to address these issues through formal solutions and heuristic methods. Recent optimization-based methods are now recognized as cost-effective result in an optimized solution. Hence, to overcome these limitations, this study introduces SQUMUTH, a novel approach for high-order mutant generation based on the Squirrel Search Algorithm (SSA). Inspired by the foraging behavior of squirrels, SSA offers a promising solution for enhancing the efficiency and effectiveness of mutation testing. Experimental evaluations on eight well-known Java benchmark programs demonstrate that SQUMUTH outperforms existing methods. Comparative analyses of mutation scores and the rates of realistic mutants consistently show its better performance across all subject programs compared to other state-of-the-art methods such as Social Group Optimization, Binary Genetic Algorithm, and random testing. The experimental results underscore its effectiveness in generating more realistic mutants. The experimental results indicated that the proposed approach has the potential to advance software testing by improving the cost-effectiveness of mutation analysis and the quality of software systems. © The Author(s) 2025.

关键词： First-order mutants Higher-order mutants Mutation score Mutation testing software testing Squirrel search algorithm

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Exploration and Practice of Deep Learning Classroom Teaching of 'computer Network' Course Based on the Integration of Specialization and Innovation 5

Exploration and Practice of Deep Learning Classroom Teaching...

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5th Asia Conference on Information engineering, ACIE 2025

作者： Huang, Min Peng, ShaoWu Yuan, Hua School of Software Engineering South China University of Technology Guangzhou China School of Computer Science South China University of Technology Guangzhou China

ISBN: (纸本)9798331528409

In the era of rapid development of artificial intelligence technologies, traditional teaching models are unable to meet the employment needs of enterprises, and talent cultivation in universities faces more challenges, so teachers should consider how to better cultivate students' innovative abilities in classroom teaching and guiding them in practical applications. The paper focuses on teaching reform and practice of 'computer Networks' course by deep learning classroom, which mainly includes three aspects: 1) Integrating ideological and political education and innovative education into the process of professional education of in the curriculum, by which to enhance students' professional innovation awareness;2) Inviting experts from enterprises to participate in classroom teaching and practical guidance to help students learn about the ability requirements and related positions in the IT enterprises for talents in the era of AI;3) Designing practice projects and guiding students to implement them by 'design thinking' methods, and also adopting an evaluation method that can reflect the innovative ability of students. Works of the paper can better reflect the support and deepening the teaching philosophy of studentcentered, and provide an attempt to improve teaching effectiveness and talent cultivation quality. © 2025 IEEE.

关键词： Cultivation

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Deep domain adaptation by joint distribution neural matching

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Neural Computing and Applications 2025年 1-21页

作者： Hong, Zijie Chen, Sentao Wen, Lisheng Yang, Xiaowei School of Software Engineering South China University of Technology Panyu District Guangdong Guangzhou510006 China Department of Computer Science Shantou University Daxue Road Guangdong Shantou515063 China

Domain adaptation aims to learn a prediction model that performs well in a target domain with unlabeled data by harnessing the distributionally different labeled data from a source domain. In this paper, we design a simple yet powerful neural network approach named joint distribution neural matching (JOINT) to address the challenge of joint distribution difference/mismatch in domain adaptation. Our JOINT approach matches the source joint distribution and the target one in the activation space of a neural network by minimizing an estimate of the Jensen–Shannon (JS) divergence between the two joint distributions. The estimated JS divergence is derived in an explicit form, which frees our approach from solving a minimax adversarial problem when matching the distributions. Experiments on four representative large-scale visual recognition datasets show that our JOINT approach outperforms the state-of-the-art under both unsupervised and semi-supervised domain adaptation settings. Our code will be available at https://***/TitanRisen/JOINT-CODE. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep neural networks

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