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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Babu Pravin Vishnu Shinde Neha Agrawal M. Vijaya Bhaskar K. Hemabala Sweta Priya Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Department of Artificial Intelligence & Data Science Shah and Anchor Kutchhi Engineering College Affiliated to University of Mumbai Maharashtra India Department of IT Maharaja Agrasen Institute of Technology New Delhi Department of Computer Science &Engineering SriVenkateswara Institute of Technology Anantapur Andhra Pradesh India Department of Freshmen Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tiruvathi Department of Amity School of Communication University Amity University Patna

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This work offers a thorough investigation of sentiment analysis in social media using deep learning techniques. Understanding stoner sentiment has become essential for colourful operations like marketing, public opinion research, and recommendation algorithms due to the rapid expansion of social media networks. Deep learning approaches have emerged as crucial tools for sentiment analysis tasks because of their capacity to extract intricate patterns from massive datasets. The usefulness of deep learning models, such as convolutional neural networks (RNNs), and intermittent neural networks (RNNs), and their variations, in extracting complex sentiment information from social media textbooks is investigated in this study. Additionally, the study looks at model infrastructures, point representations, and pre-processing techniques to improve sentiment analysis performance. The results show how effective deep learning techniques are in extracting sentiment directly from various social media posts, opening the door for more sophisticated sentiment-apprehensive digital processes

关键词： Deep learning Sentiment analysis Analytical models Adaptation models Text analysis Social networking (online) Neural networks Convolutional neural networks Research and development Videos

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Detecting the Human Activities of Aging People using Restricted Boltzmann Machines with Deep Learning Technique in IoT

Detecting the Human Activities of Aging People using Restric...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： M. Manimaran A. Sasi Kumar N V S Natteshan K. Baranitharan R Mahaveerakannan K Sudhakar Department of Computer Science Sri Krishna Adithya College of Arts and Science Coimbatore Department of Cloud Technology & Data Science Institute of Engineering & Technology Srinivas University Mukka Surathkal Dakshina Kannada District Department of CSE S.A. Engineering College Chennai Department of CSE VSB Engineering College Department of Computer Science and Engineering Saveetha School of Engineering Chennai Tamil Nadu India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Approximately 962,000,000 persons worldwide are 60 or older. Despite the widespread adoption of techniques for human activity detection, there has been a dearth of study into the specific challenge of identifying the tasks performed by the elderly. Given the proliferation of wearable and mobile devices, the Internet of Healthcare Things is assuming a larger role in Human Activity Recognition (HAR). This article focuses on assisting the elderly by tracking their movements in both indoor and outdoor settings. The dataset includes human actions like sitting, walking, ascending and descending stairs, standing, and lying down. Here, how deep learning might improve HAR in Internet of Home Things settings has been examined. For better HAR results, a semi-supervised deep learning framework has been developed that makes effective use of the imperfectly labelled sensor data in order to fine-tune the classifier learning model. An intelligent auto-labelling system is built on top of Deep Q-Network to enhance learning performance in Internet of Things (IoT) environment and address the issue of insufficient labelled samples. Finally, real-world data is used in trial and evaluation to prove the method’s utility and efficacy.

关键词： Deep learning Performance evaluation Schedules Tracking Medical services Stairs Safety

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Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization

arXiv

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arXiv 2022年

作者： Liu, Mengyin Zhu, Chao Gao, Hongyu Gu, Weibo Wang, Hongfa Liu, Wei Yin, Xu-Cheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Data Platform Department Tencent Shenzhen China

With the prosperity of e-commerce industry, various modalities, e.g., vision and language, are utilized to describe product items. It is an enormous challenge to understand such diversified data, especially via extracting the attribute-value pairs in text sequences with the aid of helpful image regions. Although a series of previous works have been dedicated to this task, there remain seldomly investigated obstacles that hinder further improvements: 1) Parameters from up-stream single-modal pretraining are inadequately applied, without proper jointly fine-tuning in a down-stream multi-modal task. 2) To select descriptive parts of images, a simple late fusion is widely applied, regardless of priori knowledge that language-related information should be encoded into a common linguistic embedding space by stronger encoders. 3) Due to diversity across products, their attribute sets tend to vary greatly, but current approaches predict with an unnecessary maximal range and lead to more potential false positives. To address these issues, we propose in this paper a novel approach to boost multi-modal e-commerce attribute value extraction via unified learning scheme and dynamic range minimization: 1) Firstly, a unified scheme is designed to jointly train a multi-modal task with pretrained single-modal parameters. 2) Secondly, a text-guided information range minimization method is proposed to adaptively encode descriptive parts of each modality into an identical space with a powerful pretrained linguistic model. 3) Moreover, a prototype-guided attribute range minimization method is proposed to first determine the proper attribute set of the current product, and then select prototypes to guide the prediction of the chosen attributes. Experiments on the popular multi-modal e-commerce benchmarks show that our approach achieves superior performance over the other state-of-the-art techniques. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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A large-scale study of performance and equity of commercial remote identity verification technologies across demographics

A large-scale study of performance and equity of commercial ...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Kaniz Fatima Michael Schuckers Gerardo Cruz-Ortiz Daqing Hou Sandip Purnapatra Tiffany Andrews Ambuj Neupane Brandeis Marshall Stephanie Schuckers Electrical and Computer Engineering Clarkson University Potsdam NY USA School of Data Science Univ. of North Carolina Charlotte Charlotte NC USA General Services Administration (GSA) USA DataedX Group LLC USA

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

As more types of transactions move online, there is an increasing need to verify someone’s identity remotely. Remote identity verification (RIdV) technologies have emerged to fill this need. RIdV solutions typically use a smart device to validate an identity document like a driver’s license by comparing a face selfie to the face photo on the document. Recent research has been focused on ensuring that biometric systems work fairly across demographic groups. This study assesses five commercial RIdV solutions for equity across age, gender, race/ethnicity, and skin tone across 3,991 test subjects. This paper employs statistical methods to discern whether the RIdV result across demographic groups is statistically distinguishable. Two of the RIdV solutions were equitable across all demographics, while two RIdV solutions had at least one demographic that was in-equitable. For example, the results for one technology had a false negative rate of 10.5% +/- 4.5% and its performance for each demographic category was within the error bounds, and, hence, were equitable. The other technologies saw either poor overall performance or inequitable performance. For one of these, participants of the race Black/African American (B/AA) as well as those with darker skin tones (Monk scale 7/8/9/10) experienced higher false rejections. Finally, one technology demonstrated more favorable but inequitable performance for the Asian American and Pacific Islander (AAPI) demographic. This study confirms that it is necessary to evaluate products across demographic groups to fully understand the performance of remote identity verification technologies.

关键词： Statistical analysis Error analysis Face recognition Licenses Skin Software Smart devices Faces

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MetaSlicing: A Novel Resource Allocation Framework for Metaverse

arXiv

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arXiv 2022年

作者： Chu, Nam H. Hoang, Dinh Thai Nguyen, Diep N. Phan, Khoa T. Dutkiewicz, Eryk Niyato, Dusit Shu, Tao The School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology La Trobe University Melbourne Australia The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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Attention Synchronous Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Prediction 3

Attention Synchronous Based Spatial-Temporal Graph Convoluti...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Cao, Zhuyou Luo, Tianwang Xiao, Jianhua Xiao, Hongbo Qing, Xinyi School of Computer Science and Engineering Huaihua University Hunan China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huai-hua Hunan418000 China

ISBN: (纸本)9798400716775

This study introduces AS-STGCN, a novel traffic flow prediction model that incorporates an attention mechanism and a synchronized spatio-temporal graph convolutional network. In the field of traffic prediction, deep learning models (e.g., STGCN) have outperformed traditional methods, especially in capturing temporal and spatial correlations. AS-STGCN introduces a spatio-temporal attention mechanism and simultaneous convolution, which improves the prediction accuracy and demonstrates a unique capability in capturing dynamic spatio-temporal patterns in urban traffic networks. This research is of great significance in advancing the field of traffic flow prediction and providing a more comprehensive and accurate method for predicting urban traffic dynamics. © 2023 ACM.

关键词： Convolution

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ColorByte: A real time MOT method using fast appearance feature based on ByteTrack

ColorByte: A real time MOT method using fast appearance feat...

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International Conference on Advanced Cloud and Big data (CBD)

作者： Lin Shen Mengyang Liu Caishan Weng Jinghui Zhang Fang Dong Fa Zheng Big Data Computing Center Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350309720

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. With the wide application of embedded devices, MOT methods with acceptable accuracy that performed in real-time on these weak devices is becoming more and more important. In order to realize real-time MOT on embedded IoT devices, the best trade-off between accuracy and inference latency is the key to achieve. To achieve high accuracy, popular MOT methods introduce Re-ID module to integrate with the detection-based MOT method and train two DNNs simultaneously. However, the integration causes a conflict between computing cost and training both tasks to achieve good results. To address this key issue, we design a fast appearance feature, which is a simple but relatively accurate method, to substitute cumbersome Re-ID component. Besides, ByteTrack is the new SOTA association algorithm in MOT benchmarks which introduce an extra association on objects with low score. Based on ByteTrack, we propose an improved association method to remove most of the background interference based on the results from appearance extraction and recover part of lost detection boxes after the association based on IoU. In addition, we turn down the detection threshold and release more boxes for the low sensitivity of our own feature extraction method. We evaluate our methods and achieve 70.1 MOTA, 81.9 MOTP and 69.2 IDFI with real-time running speed on NVIDIA Xavier NX.

关键词： Training Performance evaluation Sensitivity Interference Feature extraction Real-time systems Inference algorithms

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Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach

arXiv

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arXiv 2024年

作者： Wan, Yao Wan, Guanghua Zhang, Shijie Zhang, Hongyu Zhou, Pan Jin, Hai Sun, Lichao National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Huazhong University of Science and Technology China University of Leigh United States

Recent years have witnessed significant progress in developing deep learning-based models for automated code completion. Examples of such models include CodeGPT and StarCoder. These models are typically trained from a large amount of source code collected from open-source communities such as GitHub. Although using source code in GitHub has been a common practice for training deep-learning-based models for code completion, it may induce some legal and ethical issues such as copyright infringement. In this paper, we investigate the legal and ethical issues of current neural code completion models by answering the following question: Is my code used to train your neural code completion model? To this end, we tailor a membership inference approach (termed CodeMI) that was originally crafted for classification tasks to a more challenging task of code completion. In particular, since the target code completion models perform as opaque black boxes, preventing access to their training data and parameters, we opt to train multiple shadow models to mimic their behavior. The acquired posteriors from these shadow models are subsequently employed to train a membership classifier. Subsequently, the membership classifier can be effectively employed to deduce the membership status of a given code sample based on the output of a target code completion model. We comprehensively evaluate the effectiveness of this adapted approach across a diverse array of neural code completion models, (i.e., LSTM-based, CodeGPT, CodeGen, and StarCoder). Experimental results reveal that the LSTM-based and CodeGPT models suffer the membership leakage issue, which can be easily detected by our proposed membership inference approach with an accuracy of 0.842, and 0.730, respectively. Interestingly, our experiments also show that the data membership of current large language models of code, e.g., CodeGen and StarCoder, is difficult to detect, leaving amper space for further improvement. Finally, we also t

关键词： Deep learning

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A Survey of Stance Detection on Social Media: New Directions and Perspectives

arXiv

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arXiv 2024年

作者： Zhang, Bowen Dai, Genan Niu, Fuqiang Yin, Nan Fan, Xiaomao Wang, Senzhang Cao, Xiaochun Huang, Hu College of Big Data and Internet Shenzhen Technology University Shenzhen518000 China College of Computer Science and Software Shenzhen University Shenzhen518000 China Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Science and Engineering Central South University Changsha410083 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Peking University Shenzhen Graduate School Shenzhen518000 China

In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these opinions offers valuable insights for decision-making in various sectors, including marketing and politics. As a result, stance detection has emerged as a crucial subfield within affective computing, enabling the automatic detection of user stances in social media conversations and providing a nuanced understanding of public sentiment on complex issues. Recent years have seen a surge of research interest in developing effective stance detection methods, with contributions from multiple communities, including natural language processing, web science, and social computing. This paper provides a comprehensive survey of stance detection techniques on social media, covering task definitions, datasets, approaches, and future works. We review traditional stance detection models, as well as state-of-the-art methods based on large language models, and discuss their strengths and limitations. Our survey highlights the importance of stance detection in understanding public opinion and sentiment, and identifies gaps in current research. We conclude by outlining potential future directions for stance detection on social media, including the need for more robust and generalizable models, and the importance of addressing emerging challenges such as multi-modal stance detection and stance detection in low-resource languages. Copyright © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement ?

arXiv

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arXiv 2022年

作者： Wang, Xin Luo, Xinzhe Zhuang, Xiahai School of Data Science Fudan University Shanghai China Department of Electrical and Computer Engineering University of Washington Seattle United States

Multimodal groupwise registration aligns internal structures in a group of medical images. Current approaches to this problem involve developing similarity measures over the joint intensity profile of all images, which may be computationally prohibitive for large image groups and unstable under various conditions. To tackle these issues, we propose BInGo, a general unsupervised hierarchical Bayesian framework based on deep learning, to learn intrinsic structural representations to measure the similarity of multimodal images. Particularly, a variational auto-encoder with a novel posterior is proposed, which facilitates the disentanglement learning of structural representations and spatial transformations, and characterizes the imaging process from the common structure with shape transition and appearance variation. Notably, BInGo is scalable to learn from small groups, whereas being tested for large-scale groupwise registration, thus significantly reducing computational costs. We compared BInGo with five iterative or deep-learning methods on three public intrasubject and intersubject datasets, i.e. BraTS, MS-CMR of the heart, and Learn2Reg abdomen MR-CT, and demonstrated its superior accuracy and computational efficiency, even for very large group sizes (e.g., over 1300 2D images from MS-CMR in each group). Copyright © 2022, The Authors. All rights reserved.

关键词： Computational efficiency

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