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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Wang, Zheng Long, Cheng Cong, Gao Jensen, Christian S. School of Computer Science and Engineering Nanyang Technological University Singapore Aalborg University Department of Computer Science Denmark

ISBN: (纸本)9798350317152

Increasing and massive volumes of trajectory data are being accumulated that may serve a variety of applications, such as mining popular routes or identifying ridesharing candidates. As storing and querying massive trajectory data is costly, trajectory simplification techniques have been introduced that intuitively aim to reduce the sizes of trajectories, thus reducing storage and speeding up querying, while preserving as much information as possible. Existing techniques rely mainly on hand-crafted error measures when deciding which point to drop when simplifying a trajectory. While the hope may be that such simplification affects the subsequent usability of the data only minimally, the usability of the simplified data remains largely unexplored. Instead of using error measures that indirectly may to some extent yield simplified trajectories with high usability, we adopt a direct approach to simplification and present the first study of query accuracy driven trajectory simplification, where the direct objective is to achieve a simplified trajectory database that preserves the query accuracy of the original database as much as possible. Specifically, we propose a multi-agent reinforcement learning based solution with two agents working cooperatively to collectively simplify trajectories in a database while optimizing query usability. Extensive experiments on four real-world trajectory datasets show that the solution is capable of consistently outperforming baseline solutions over various query types and dynamics. © 2024 IEEE.

关键词： Trajectories

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Meta-learning with orthogonal softmax layer (MLOSL) for small sample hyperspectral image classification

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Multimedia Tools and Applications 2025年 1-30页

作者： Amoako, Prince Yaw Owusu Cao, Guo Shi, Hao Arthur, John Kingsley Agyenim, Yaw Oti Boateng School of Computer Science and Engineering Nanjing University of Science and Technology Xiaolingwei Nanjing City210094 China Department of Computer Science Valley View University Accra Oyibi Ghana ETSU Municipal Center East Tennessee State University Johnson CityTN United States

Recent research on Hyperspectral image classification (HSIC) has been much concerned with small sample size due to the challenges of obtaining large labeled data. Coupled with this is the challenge with optimization of the models’ structure and computational complexities. Meta-learning techniques have been proposed to improve classification accuracy on small training data samples. The existing meta-learning techniques still require several pre-collected labeled hyperspectral images (HSI) source datasets of large labeled samples for meta-training, which is undoubtedly a huge task and time-consuming. We propose a meta-learning with orthogonal softmax layer (MLOSL) for small sample HSIC. Orthogonality is introduced to address the structural and computational complexities of deep convolutional neural network (CNN) models for HSIC. An unsupervised meta-learning task construction extracts multiview spectral dimension features of different bands from the same sample, whereas the spatial dimension multiview features are extracted through data augmentation. The orthogonal softmax layer captures a wider variety of patterns in the data by promoting variation among the weight vectors, potentially improving generalization to unobserved samples and minimizing computational time. Label smoothing is added to the classification loss to lessen the impact of incorrectly classified samples and class imbalance. Experimental outcomes on four target datasets compared to existing state-of-the-art models indicate the competitiveness of the proposed model's small sample HSIC performance and its suitability in time-constraint applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Unlocking Soundscapes: Harnessing Machine Learning for Sound Classification 9

Unlocking Soundscapes: Harnessing Machine Learning for Sound...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Hidayat, Alexis Njoo, Davin Bennett Putrajaya Adrian, Gerald Davin Setyoko, Dimas Elang Wijanarko, Bambang Dwi School of Computer Science Bina Nusantara University Computer Science Department Semarang Campus Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

The concept of Smart Cities has been implemented in several major areas to improve the lives of citizens. One of the technologies used is sound classification, which monitors and identifies levels of urban noise. Convolutional Neural Networks (CNNs) have proven to be an effective method for sound classification because they can recognize complex patterns in audio data. Various architectures of Convolutional Neural Networks, such as ResNet, DenseNet, and others, exist for this purpose. This research aims to identify the CNN architecture that performs best in urban sound classification. The methodology involves training and testing existing CNN architecture and evaluating them based on several parameters, including the number of parameters, memory usage, convergence speed, and training time. Experimental results show that the performance of the ResNet family tends to degrade when the network depth increases. The best performance was achieved by the Resnet-18 architecture, with accuracy, precision, recall and F1-Score values of 91.5%, 92.2%, 91.2%, 91.6%. On the other hand, the use of complex features from various scales causes the DenseNet and GoogleNet architectures to be unableto achieve the best performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Graph Contrastive Learning for Truth Inference 40

Graph Contrastive Learning for Truth Inference

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Liu, Hao Liu, Jiacheng Tang, Feilong Li, Peng Chen, Long Yu, Jiadi Zhu, Yanmin Gao, Min Yang, Yanqin Hou, Xiaofeng Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong East China Normal University School of Data Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Aizu School of Computer Science and Engineering Japan Simon Fraser University School of Computing Science Canada

ISBN: (纸本)9798350317152

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques. © 2024 IEEE.

关键词： Crowdsourcing

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A Global Medical data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4379-4387页

作者： Mishra, Vinaytosh Gupta, Kishu Saxena, Deepika Singh, Ashutosh Kumar Thumbay Institute for Ai in Healthcare Gulf Medical University Ajman United Arab Emirates National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965 Japan University of Economics and Human Sciences Department of Computer Science Warsaw01-043 Poland Indian Institute of Information Technology Bhopal Department of Computer Science and Engineering Bhopal462003 India

Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs. © 1975-2011 IEEE.

关键词： K-means clustering

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in Engineering & sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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A study on artificial intelligence for monitoring smart environments

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Materials Today: Proceedings 2023年 80卷 2009-2013页

作者： D., Karthika Department of Computer Science School of Computing Science Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai India

Wireless networking has made enormous improvements. These developments have brought about new paradigms of wireless networking and communications Environmental protection has been in recent years a more intelligent and linked system for all facets of a global city. With the rise in data gathering, machine learning (ML) approaches can be used to boost the knowledge and the skill of an application. As the numbers increase and technology develops, the number of available data increases. Smart collection and interpretation of these Big data is the underground to the rising of smart Internet of Things IoT apps. This study discovers the diverse methods of machine learning that resolve data difficulties in smart cities. The discussion takes place on applications such as air quality, water pollution, radiation pollution, smart buildings, smart transport, etc., which pose genuine environmental challenges. Adequate monitoring is needed to ensure sustainable growth in the world by safeguarding a healthy society. The potential and challenges in particular the role of machine learning technology for the Internet of Things and Big data Analytics. © 2021

关键词： data Analytics

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Predicting ChatGPT's Ability to Solve Complex programming Challenges

Predicting ChatGPT's Ability to Solve Complex Programming Ch...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Ho, Nguyen May, James Ngo, Bao Formato, Jack Ngo, Linh Ho, Van Long Bui, Hoang Loyola University Maryland Department of Computer Science United States West Chester University Department of Computer Science United States Oberlin College Department of Computer Science United States International University School of Computer Science and Engineering Viet Nam

ISBN: (纸本)9798350362480

The recent emergence of Large Language Model (LLM)-based tools such as OpenAI's ChatGPT and Google's Gemini has sparked excitement across the software development industry, and offered promises to transform the software development process. Despite the enthusiasm, it remains uncertain whether these tools are already good enough at coding to replace the role of software developers. Currently, no studies have provided insights into the performance of LLMs, such as understanding which characteristics of a programming task might affect an LLM's performance, or predicting how an LLM will handle new programming challenges. In this work, we address these challenges by first creating a data collection framework to gather 3,323 programming tasks from Kattis, a widely-used programming challenge platform. We then use OpenAI's ChatGPT to solve these programming tasks. The solutions obtained from ChatGPT are submitted back to Kattis to evaluate their correctness and effectiveness. Next, we use the collected data, including both problem and solution information, to analyze the task characteristics that significantly influence ChatGPT's performance. Building on the analysis, we develop predictive models that can forecast the efficacy of ChatGPT on new programming problems. Our analysis indicates that factors such as the difficulty level of a programming challenge, or the readability complexity of a problem description can significantly affect the efficacy of ChatGPT. Finally, the experimental results show that our predictive model can correctly predict ChatGPT performance with an accuracy of up to 90% for easy problems, and up to 79% for difficult problems. © 2024 IEEE.

关键词： Software design

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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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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic Butterfly Optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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