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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Monika Arya Bhupesh Kumar Dewangan Tanupriya Choudhury Ketan Kotecha Sanjana Dewangan Akhilesh Gaidhane Department of Computer Science and Engineering Bhilai Institute of Technology Durg India Department of Computer Science and Engineering OP Jindal University Raigarh India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India School of Science OP Jindal University Raigarh India Department of Mathematics SAGES M.G. Ward Kondagaon India

In today's digital era, various real-world applications generate data in streams, and these data streams are of two types stationary data streams, which are static, and non-stationary data streams that are dynamic in nature. Non-stationary data streams are pro-to-concept drifts. To overcome this issue, an adaptable streaming feature selection technique for non-stationary data streams is proposed in this paper. The selection of relevant feature space from non-stationary data streams is crucial for enabling intelligence through various data mining tasks, specifically using data classification. Feature selection improves the overall performance of the classifier while increasing the computational overhead. The proposed approach aims to enable streaming feature selection whenever the classification accuracy is underperforming to enhance the classification accuracy, while the model remains stable if there is no change in the concepts. The proposed approach is adaptive and ameliorates the classifier performance by reducing the error rate during changes in the concept with time. This adaptability to concept drift improves the accuracy by approximately 16% and reduces the computation time by 50%. The proposed model avoids overfitting of the model.

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RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer 10th

RAST: A Reward Augmented Model for Fine-Grained Sentiment Tr...

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10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021

作者： Hu, Xiaoxuan Zhang, Hengtong Zhao, Wayne Xin Li, Yaliang Gao, Jing Wen, Ji-Rong School of Information Renmin University of China Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette United States Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Engineering University at Buffalo Buffalo United States Alibaba Group Hangzhou China

ISBN: (纸本)9783030884796

In this paper, we propose a novel model RAST (Reward Augmented Sentiment Transfer) for fine-grained sentiment transfer. Existing methods usually suffer from two major drawbacks, i.e., blurre d sentiment distinction and unsatisfactory content preservation. Considering the above issues, we design two kinds of rewards to better control sentiment and content. Specially, we develop a pairwise comparative discriminator that enforces to generate sentences with clear distinctions for different sentiment intensities. Moreover, we utilize an effective sampling strategy to obtain pseudo-parallel sentences with minor changes on the input sentence to enhance content preservation. Experiments on a benchmark dataset show that the proposed model outperforms several competitive approaches. © 2021, Springer Nature Switzerland AG.

关键词： computers

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Natural Language Communication with a Conversational Teachable Agent

arXiv

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arXiv 2022年

作者： Love, Rachel Law, Edith Cohen, Philip R. Kulić, Dana The Department of Electrical and Computer Systems Engineering Monash University Australia The David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada Openstream Inc. The Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Australia The Department of Electrical and Computer Systems Engineering Department of Mechanical and Aerospace Engineering Monash University Australia

Conversational teachable agents offer a promising platform to support learning, both in the classroom and in remote settings. In this context, the agent takes the role of the novice, while the student takes on the role of teacher. This framing is significant for its ability to elicit the Protégé effect in the student-teacher, a pedagogical phenomenon known to increase engagement in the teaching task, and also improve cognitive outcomes. In prior work, teachable agents often take a passive role in the learning interaction, and there are few studies in which the agent and student engage in natural language dialogue during the teaching task. This work investigates the effect of teaching modality when interacting with a virtual agent, via the web-based teaching platform, the Curiosity Notebook. A method of teaching the agent by selecting sentences from source material is compared to a method paraphrasing the source material and typing text input to teach. A user study has been conducted to measure the effect teaching modality on the learning outcomes and engagement of the participants. The results indicate that teaching via paraphrasing and text input has a positive effect on learning outcomes for the material covered, and also on aspects of affective engagement. Furthermore, increased paraphrasing effort, as measured by the similarity between the source material and the material the teacher conveyed to the robot, improves learning outcomes for participants. © 2022, CC BY.

关键词： Students

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A Smart Electronic Wheelchair Design Using artificial intelligence and Intelligent Sensors Association

A Smart Electronic Wheelchair Design Using Artificial Intell...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： R Balamurugan D. Kanchana B. Anni Princy J Jaisudha P. Malathi E Glory Department of Artificial Intelligence and Machine Learning AJK College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Applications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India Department of Biosciences Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair’s controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair’s DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user’s pulse rate, as well as the wheelchair’s functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner.

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Imbalanced Least Squares Regression with Adaptive Weight Learning

SSRN

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SSRN 2022年

作者： Li, Yanting Jin, Junwei Ma, Jiangtao Zhu, Fubao Jin, Baohua Liang, Jing Chen, C.L. Philip College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China School of Computer Science and Engineering South China University of Technology China

Least squares regression (LSR) has demonstrated promising performance in various classification tasks owing to the effectiveness and efficiency. However, there are some deficiencies that seriously plague its application in imbalanced data scenarios. The first is the success of LSR heavily relies on the balanced class distribution, more fundamentally, a severely imbalanced class distribution may damage the effectiveness of the algorithm seriously. Secondly, the utilized binary label matrix in conventional LSR model may be too strict to learn a discriminative transform matrix for imbalanced learning. To address the above issues, in this paper an adaptive weight learning mechanism and a novel label relaxation constraint are proposed and incorporated into the framework of LSR to tackle the imbalanced classification problem. Specifically, the weight of each sample can be adaptively obtained according to the original distribution information of the imbalanced data, in which the importance of the minority class samples can be better reflected with greater weights. Then a new label relaxation matrix is constructed to widen the margins between different classes. Further, we provide an iterative algorithm to solve the resulting optimization problem. Extensive experimental results on diverse imbalanced datasets show that the proposed method outperforms the majority of imbalanced learning approaches. © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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Expression of Concern for: A Novel Dynamic Watermarking for Secure data Protection from Cyber Theft Based on artificial intelligence Supervision

Expression of Concern for: A Novel Dynamic Watermarking for ...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Ravikiran Madala R. Monica Sulakshana Sagar Malwade Selvakumaran. S Shilpa Jivan Budhavale N. Sathyabalaji Information Technology University of Cumberlands Durham North Carolina United States Department of Electronics and Communication Engineering Nandha Engineering College Erode TamilNadu India Department of Computer Engineering Dr.Vishwanath Karad School of Polytechnic and Skill Development Pune Maharashtra India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai TamilNadu India Department of Computer Engineering Dr. Vishwanath Karad School of Polytechnic and Skill Development Pune Maharashtra India Department of Computer Science and Engineering JKK Munirajah College of Technology Erode TamilNadu India

ISBN: (数字)9798350347791

ISBN: (纸本)9798350347807

One of the centre innovations of the Fourth Modern Insurgency (otherwise called Industry 4.0) is artificial intelligence (computer based intelligence), which can be utilized to shield Web associated gadgets from dangers, assaults, harm, and undesirable access. The current range of cyber security issues can be deftly handled by utilising well-known simulated intelligence techniques such as AI and profound learning procedures, the possibility of normal language handling, information portrayal and thinking, as well as the possibility of information or rule-based master frameworks displaying. In view of these man-made intelligence strategies, we give an exhaustive examination of "Simulated intelligence driven cyber security" in this article, which may be helpful for clever cyber security government agencies and the board. The cyber security registration method can be made more computerised and intelligent than the standard security frameworks by the security intelligence exhibiting in light of such simulated intelligence ***, we present various choices for future examination that fall inside the domain of our examination. At last, this paper's primary objective is to give direction and references to industry specialists in the field of cyber security, especially from a canny registering or simulated intelligence based specialized viewpoint.

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RankCSE: Unsupervised Sentence Representation Learning via Learning to Rank

arXiv

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arXiv 2023年

作者： Liu, Jiduan Liu, Jiahao Wang, Qifan Wang, Jingang Wu, Wei Xian, Yunsen Zhao, Dongyan Chen, Kai Yan, Rui Wangxuan Institute of Computer Technology Peking University China Center for Data Science AAIS Peking University China Meituan China Meta AI United States National Key Laboratory of General Artificial Intelligence China BIGAI Beijing China School of Economics Peking University China Gaoling School of Artificial Intelligence Renmin University of China China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China

Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer and pushing dissimilar ones away. However, these methods fail to capture the fine-grained ranking information among the sentences, where each sentence is only treated as either positive or negative. In many real-world scenarios, one needs to distinguish and rank the sentences based on their similarities to a query sentence, e.g., very relevant, moderate relevant, less relevant, irrelevant, etc. In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework. In particular, we learn semantically discriminative sentence representations by simultaneously ensuring ranking consistency between two representations with different dropout masks, and distilling listwise ranking knowledge from the teacher. An extensive set of experiments are conducted on both semantic textual similarity (STS) and transfer (TR) tasks. Experimental results demonstrate the superior performance of our approach over several state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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A Novel Dynamic Watermarking for Secure data Protection from Cyber Theft Based on artificial intelligence Supervision

A Novel Dynamic Watermarking for Secure Data Protection from...

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International Conference on science Technology engineering and Management (ICONSTEM)

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UniTSFace: unified threshold integrated sample-to-sample loss for face recognition 23

UniTSFace: unified threshold integrated sample-to-sample los...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Qiufu Li Xi Jia Jiancan Zhou Linlin Shen Jinming Duan National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and School of Computer Science University of Birmingham UK National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and Aqara Lumi United Technology Co. Ltd China School of Computer Science University of Birmingham UK and Alan Turing Institute UK

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://***/CVI-SZU/UniTSFace.

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Correlation-guided particle swarm optimization approach for feature selection in fault diagnosis

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Chen, Ke Wang, Wenjie Zhang, Fangfang Liang, Jing Yu, Kunjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Longmen Laboratory Luoyang471000 China Centre of Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand

A large number of features are involved in fault diagnosis, and it is challenging to identify important and relative features for fault classification. Feature selection selects suitable features from the fault dataset to determine the root cause of the fault. Particle swarm optimization (PSO) has shown promising results in performing feature selection due to its promising search effectiveness and ease of implementation. However, most PSO-based feature selection approaches for fault diagnosis do not adequately take domain-specific a priori knowledge into account. In this study, we propose a correlation-guided PSO feature selection approach for fault diagnosis that focuses on improving the initialisation effectiveness, individual exploration ability, and population diversity. To be more specific, an initialisation strategy based on feature correlation is designed to enhance the quality of the initial population, while a probability individual updating mechanism is proposed to improve the exploitation ability. In addition, a sample shrinkage strategy is developed to enhance the ability to jump out of local optimal. Results on four public fault diagnosis datasets show that the proposed approach can select smaller feature subsets to achieve higher classification accuracy than other state-of-the-art feature selection methods in most cases. Furthermore, the effectiveness of the proposed approach is also verified by examining real-world fault diagnosis problems. © 2025 Chinese Association of Automation.

关键词： Particle swarm optimization (PSO)

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