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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Shiri, Fatemeh Guo, Xiao-Yu Far, Mona Golestan Yu, Xin Haffari, Gholamreza Li, Yuan-Fang Department of Data Science & AI Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia School of Electrical Engineering and Computer Science University of Queensland Australia

ISBN: (纸本)9798891761643

Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs' spatial understanding and reasoning capabilities. Our analyses on object-relationship and multi-hop reasoning reveal several important findings. Firstly, bounding boxes and scene graphs, even synthetic ones, can significantly enhance LMMs' spatial reasoning. Secondly, LMMs struggle more with questions posed from the human perspective than the camera perspective about the image. Thirdly, chain of thought (CoT) prompting does not improve model performance on complex multi-hop questions involving spatial relations. Lastly, our perturbation analysis on GQA-spatial reveals that LMMs are much stronger at basic object detection than complex spatial reasoning. We believe our new benchmark dataset and in-depth analyses can spark further research on LMMs spatial reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Multi‐granularity re‐ranking for visible‐infrared person re‐identification

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 770-779页

作者： Yadi Wang Hongyun Zhang Duoqian Miao Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghaiChina Department of Electrical and Computer Engineering University of AlbertaEdmontonAlbertaCanada System Research Institute Polish Academy of SciencesWarsawPoland

Visible‐infrared person re‐identification(VI‐ReID)is a supplementary task of single‐modality re‐identification,which makes up for the defect of conventional re‐identification under insufficient *** is more challenging than single‐modality ReID because,in addition to difficulties in pedestrian posture,camera shoot-ing angle and background change,there are also difficulties in the cross‐modality *** works only involve coarse‐grained global features in the re‐ranking calculation,which cannot effectively use fine‐grained ***,fine‐grained features are particularly important due to the lack of information in cross‐modality re‐*** this end,the Q‐center Multi‐granularity K‐reciprocal Re‐ranking Algorithm(termed QCMR)is proposed,including a Q‐nearest neighbour centre encoder(termed QNC)and a Multi‐granularity K‐reciprocal Encoder(termed MGK)for a more comprehensive feature *** converts the probe‐corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality *** takes a coarse‐grained mutual nearest neighbour as the dominant and combines a fine‐grained nearest neighbour as a supplement for similarity *** experiments on two widely used VI‐ReID benchmarks,SYSU‐MM01 and RegDB have shown that our method achieves state‐of‐the‐art ***,the mAP of SYSU‐MM01 is increased by 5.9%in all‐search mode.

关键词： computer vision recognition

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Pairwise Alignment Improves Graph Domain Adaptation 41

Pairwise Alignment Improves Graph Domain Adaptation

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Shikun Zou, Deyu Zhao, Han Li, Pan Department of Electrical and Computer Engineering Georgia Institute of Technology GA United States School of Data Science University of Science and Technology of China Hefei China Department of Computer Science University of Illinois Urbana-Champaign Champaign United States

Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing. This work delves into Graph Domain Adaptation (GDA) to address the unique complexities of distribution shifts over graph data, where interconnected data points experience shifts in features, labels, and in particular, connecting patterns. We propose a novel, theoretically principled method, Pairwise Alignment (Pair-Align) to counter graph structure shift by mitigating conditional structure shift (CSS) and label shift (LS). Pair-Align uses edge weights to recalibrate the influence among neighboring nodes to handle CSS and adjusts the classification loss with label weights to handle LS. Our method demonstrates superior performance in real-world applications, including node classification with region shift in social networks, and the pileup mitigation task in particle colliding experiments. For the first application, we also curate the largest dataset by far for GDA studies. Our method shows strong performance in synthetic and other existing benchmark datasets. Copyright 2024 by the author(s)

关键词： Graph theory

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Explainable AI in Blockchain System for Decentralized Governance

Explainable AI in Blockchain System for Decentralized Govern...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Chahar, Suman Kaur, Kawaljit Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh University Institute of Computing Chandigarh University Mohali India Galgotias University Department of Computer Science and Engineering Greater Noida India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798331508685

Decentralized governance is going through radical changes due to the innovative use of blockchain which allows decisions to be made in a transparent, secure and more im- portantly, immutable way, without the authority of a single individual or party. Thus, the introduction of artificial intelligence (AI) in blockchain governing systems complicates such thing as transparency and trust, especially in the case of deep AI systems. But more on this later - this is where Explainable AI or XAI comes into the picture. XAI methods enable understanding of the reasons behind the choices made by AI models and solves the "black box"problem that is predominant in machine learn- ing. When applied to decentralised governance, XAI provides clarifications of AI's decision making in such areas as voting, resource distribution and the enactment of smart contracts. This way, the stakeholders are confident that the decision-making is fair and impartial and this is made possible by the use of XAI. This paper aims to find out more about the applicability of XAI concept in blockchain -based governance systems in terms of bias and fairness in automated decisions and explanation of the decisions made. The reasons why interpretability plays the crucial role in decentralized systems are explained with the help of the comparison between the classical artificial intelligence and the Xin Intelligent. The study also looks at various use cases such as, Decentralized Autonomous Organizations (DAOs), and voting systems implemented on blockchain platforms and how XAI can enhance the performance of the governance systems. As this study shows, XAI is vital in building trust, improving responsibility, and guaranteeing balanced majorities in decentralized blockchain governance structures. © 2025 IEEE.

关键词： Smart contract

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High-Efficiency Asymmetrically Designed Three-Phase Virtual 48-Pulse Power Supply for Electrolytic Hydrogen

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IEEE Transactions on Power Electronics 2025年第8期40卷 10335-10342页

作者： Tian, Hanlei Cui, Haoran Han, Wei Sathik M, Jagabar Liang, Guozhuang Chen, Maolin Mekhilef, Saad Electrical Engineering School of Hebei University of Science and Technology Shijiazhuang050091 China Sustainable Energy and Environment Thrust Guangzhou511453 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong SRM Institute of Science and Technology Faculty of Engineering and Technology Kattankulathur603203 India Sichuan University College of Electrical Engineering Chengdu610065 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

To address the low-voltage, high-current requirements in hydrogen production applications, a virtual 48-pulse three-phase rectifier is proposed, and achieves the equivalent performance of four parallel 12-pulse rectifiers (12-PR). The proposed approach builds upon the conventional 12-PR design and incorporates novel asymmetric current injection units (ACIU). The ACIU is installed on the dc side of the ac-dc converter, generating a specific current at the dc link to modulate and enhance the operating modes of the 12-PR. Consequently, the 12-pulse is extended to 48, resulting in an almost sinusoidal input current with a total harmonic distortion of 3.2% and an output current with a ripple rate of 2.3%, thus significantly improving power quality. Furthermore, the asymmetric design of the current injection units eliminates the need for a phase-shifting transformer. The efficacy of the proposed rectifier for electrolytic hydrogen production is validated using a 2.2-kW experimental prototype. © 1986-2012 IEEE.

关键词： Voltage Hydrogen Rectifiers Production Windings Circuits Inductors Transformers Harmonic analysis Electrochemical processes

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Retinal Image Analysis for Early Deduction of Diabetic Retinopathy 9

Retinal Image Analysis for Early Deduction of Diabetic Retin...

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9th International Conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Chinnasamy, A. Selvakumari, P. SRMIST School of Computing Department of Data Science and Business Systems Chennai India Anna University Chennai Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397376

The prevalence of DR is steadily rising, which necessitates the automatic disease severity extraction and classification. About 2% of those with this illness are entirely blind because of the diabetic mellitus problem and 10% get vision impairment after 15 years of diabetes as a result of the DR complication. It is also a significant contributor to blindness in both middle aged and older age groups. The patient may develop to severe stages of irreversible blindness if the condition is not detected early. The growing number of diabetic patients face a major issue due to a lack of ophthalmologists. It is suggested that an automated DR screening system be created to aid the ophthalmologist in making decisions. One of the primary symptoms of the DR is hard exudates. The detection of hard exudates is crucial for screening purposes and aids in disease monitoring and diagnosis. Thus, utilising Lloyd's clustering technique, this work offered a unique techniques to segment the exudates and irregularities in DR were found. © 2023 IEEE.

关键词： Mean square error

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Innovative Deep Learning Techniques for Solving Complex computer Vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jayachitra, T. Sathya, S. Ramesh, P.S. Jothikumar, R. Gouri, M.S. Hussain, Mohammed Waheeduddin Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruttani India Vellore Institute of Technology Department of Smart Computing Vellore India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Baselios Mathews - Ii College of Engineering Department of Computer Science and Engineering Sasthamcotta Kollam India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India

ISBN: (纸本)9798350375442

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications. © 2024 IEEE.

关键词： Stochastic systems

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3D Based CNN for Improved Segmentation from the Hyperspectral Images 1

3D Based CNN for Improved Segmentation from the Hyperspectra...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Narmadha, T. Arockia Raj, A. Saravanan, P. Kumar, A. Suresh Sekaran, Ramesh Gowri Shankar, J. School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering - Data Science Madanapalle India School of Computing Science and Engineering Galgotias University Delhi NCR India

ISBN: (纸本)9798350341737

In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to evaluate the validity of the model, it is applied to three separate datasets, each of which is then subjected to one of four distinct dimensionality reduction strategies. The purpose of this is to determine which of these four models is the most effective at reducing the number of dimensions. According to the findings, the 3D-CNN model is superior to both the AlexNet and CNN models in terms of the accuracy of its segmentations. In addition to this, the findings demonstrate that the 3D-CNN models perform better than the other approaches in terms of segmentation accuracy with a smaller number of datasets. © 2023 IEEE.

关键词： Image enhancement

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