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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati Taruna Anand D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Ambala India Department of Humanities and Social Sciences Graphic Era (Deemed to be) University Dehradun India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions.

关键词： Support vector machines Radio frequency Training Accuracy Education Symbols Feature extraction Tokenization Integrated circuit modeling Principal component analysis

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SiamIRPN: Siamese Visual Tracking with Improved Region Proposal Networks

SiamIRPN: Siamese Visual Tracking with Improved Region Propo...

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Frontiers of Robotics and software Engineering (FRSE), International Conference on

作者： Dun Cao Renhua Dai Tianyu Zhao Fayez Alqahtani Amr Tolba R. Simon Sherratt Min Zhu School of Computer and Communications Engineering Changsha University of Science and Technology Changsha Hunan China Key Lab of Broadcast Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Computer Science Department Community College King Saud University Riyadh Saudi Arabia School of Biomedical Engineering The University of Reading United Kingdom College of Information Science and Technology Zhejiang Shuren University Zhejiang China

Though introducing the Region Proposal Network (RPN) from object detection enabled Siamese trackers’ success, RPN-based trackers still struggle in challenging scenarios. We posit that the reason comes from two major limitations of the introduced RPN, where one is that the external structure of the RPN is simple and straight, the other is that the internal components used in RPN cannot cope with complex scenes. In this paper, we propose an Improved RPN (IRPN) suitable for visual tracking. Externally, we place a Convolutional Block Attention Mechanism (CBAM) to the IRPN, and internally, we adopt the advanced component groups for the IPRN. Using multiple IRPN blocks and deep architecture, we propose a Siamese tracker (SiamIRPN) having a layer-wise structure. Comprehensive experiments and ablation studies on five benchmarks (VOT-2019, OTB100, UAV123, GOT-10k, and NFS) show our proposed SiamIRPN achieves competitive performance.

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An Automatic Extraction Method of Coronary Arteries Centerline Based on Cellular Automata 20

An Automatic Extraction Method of Coronary Arteries Centerli...

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3rd International Conference on computer science and software Engineering, CSSE 2020

作者： Ma, Guangkun Yang, Jinzhu Zhao, Hong School of Computer Science and Engineering Northeastern University Shenyang China School of Software Shenyang University of Technology Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781450375528

Effectively obtaining the centerline of the coronary arteries is very important to the clinical medical diagnosis. Cellular automata are extensively used to find fine solutions in many complex hard. However, it has the problem of large amount of computation. In this paper, we propose an automatic extraction method of coronary centerline based on cellular automata. Different from the existing methods, the distance from boundary field is used to extract the centerline of the coronary arteries roughly, which can place the candidate voxel at the center of the coronary arteries and reduce the size of the data. Furthermore, the centerlines of the coronary arteries are extracted accurately by using cellular automata to find the shortest path. The proposed method can extract the centerlines of each branch of the coronary arteries simultaneously from a single source to multiple destinations. Furthermore, compared to the method in Ref [10], our proposed method works well, especially in the case of large vessel curvature, the voxels at the extracted centerline are relatively centered. In the last, quantitative evaluation is conducted on the coronary arteries and the results show that the proposed method achieves a smaller deviation distance. © 2020 ACM.

关键词： Cellular automata

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Beacon-based Indoor Fire Evacuation System using Augmented Reality and Machine Learning

Beacon-based Indoor Fire Evacuation System using Augmented R...

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IEEE International Conference on Robotic Computing (IRC)

作者： Hwawon Lee Dohyun Chung Seongmin Kim Jiwon Lim Yoonha Bahng Suhyun Park Anthony H. Smith School of Software Soongsil University Seoul Korea Industrial Security Chung-Ang University Seoul Korea Computer Science Kangwon National University Chuncheon Korea School of Software Kwangwoon University Seoul Korea School of Computer Science & Engineering Chung-Ang University Seoul Korea Computer Engineering Paichai University Seoul Korea Computer and Information Technology Purdue University West Lafayette United States

ISBN: (纸本)9781665472616

The inefficiency of fire evacuation has been the issue since the present evacuation method is unsuitable for complex buildings. In order to improve the evacuation system, this paper aims at three main components. First, Kalman Filter and deep learning models were utilized to estimate the user’s location accurately. Second, Q-learning based evacuation algorithm was designed to deal with various fire situations. Lastly, AR and a 2D map offer effective navigation systems. The proposed system offers the safest path based on accurate location with a user-friendly visual supplement.

关键词： Deep learning Location awareness Visualization Q-learning Machine learning algorithms Navigation Real-time systems

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Timeliness-Aware Multiple Rumor Sources Estimation in Dynamic Online Social Networks

SSRN

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SSRN 2024年

作者： Huang, Da-Wen Wu, Wenjie Bi, Jichao Li, Junli Gan, Chenquan Zhou, Wei College of Computer Science Sichuan Normal University Chengdu China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating the damage caused by them. However, most studies are not suitable for identifying rumor sources in dynamic OSNs with community structures. Moreover, these studies tend to neglect the impact of timeliness on the spread of rumors, potentially leading to an overestimation of rumors' propagation ability. To overcome these limitations, this paper proposes a multiple rumor sources estimation framework for OSNs. First, a community-based dynamic network model is introduced to depict the temporal nature and community feature of OSNs. Second, a meticulously designed microcosmic SIR model, incorporating the timeliness of rumor topics, is developed to unravel the complex dynamics of rumor propagation. Then, a computationally efficient multiple rumor sources estimation algorithm is proposed. This algorithm utilizes the infection information collected by sensors and applies the maximum likelihood estimation (MLE) method to identify the rumor sources. Finally, experimental results on real-world and synthetic temporal networks demonstrate the effectiveness of the proposed rumor source estimation algorithm. © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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Shooting for the Stars: How a Stem Initiative Has Evolved to Address Gender Challenges in Work and Education

SSRN

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SSRN 2023年

作者： Travers, Marie Bromell, Jenna Crowley, Katie Patten, James Vincent O’Connor, Mairéad Richardson, Ita Lero – The Science Foundation Ireland Research Centre for Software University of Limerick Limerick Ireland Johnson & Johnson Vision Care Ireland UC Limerick Ireland Lero – The Science Foundation Ireland Research Centre for Software Dept of Computer Science & Information Systems University of Limerick Limerick Ireland School of Information Systems & Technology Management UNSW Business School University of New South Wales Sydney Sydney Australia

This paper discusses science technology Engineering and Mathematics (STEM) interventions and presents our experience with a specific intervention implemented in Ireland, the University of Limerick-Lero/Johnson & Johnson WiSTEM2D (Women in STEM, Manufacturing and Design) programme. The programme has evolved based on the learnings identified in areas such as participant group dynamics, invited speaker engagement and presentation delivery, and teamwork-centred activities. Such learnings have applications across a range of STEM fields, including software engineering. We believe that initiatives, such as WiSTEM2D, which support and promote gender equality in STEM2D fields, and specifically computer science (CS) education and industry, are of key importance. Our review of the programme has identified three primary lessons: (1) individuals did not benefit as strongly as those working in groups (2) online events allow for more speakers and greater transfer of knowledge and (3) working in teams provides the students with the opportunity to socialise with other women in a work environment. For the last 3 years, the programme has seen the number of applications steadily increase. The value of the lessons learned from the WiSTEM2D programme are discussed in terms of the economic, ethical, and legal areas. © 2023, The Authors. All rights reserved.

关键词： Students

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General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment

arXiv

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arXiv 2023年

作者： Xie, Liang Yang, Yibo Wang, Wenxiao Lin, Binbin Cai, Deng He, Xiaofei Liang, Ronghua Colleague of Computer Science and Technology Zhejiang University of Technology Hangzhou China State Key Lab of CAD & CG Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China JD Explore Academy Beijing China Fabu Inc. Hangzhou China

Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformation. There are many recent SOTA works dedicated to rotation-invariant learning for 3D point clouds. However, current rotation-invariant methods lack generalizability on the point clouds in the open scenes due to the reliance on the global distribution, i.e., the global scene and backgrounds. Considering that the output activation is a function of the pattern and its orientation, we need to eliminate the effect of the orientation. In this paper, inspired by the idea that the network weights can be considered a set of points distributed in the same 3D space as the input points, we propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) via aligning the features with the principal axes of the network weights. Our WFA algorithm provides a general solution for the point clouds of all scenes. WFA ensures the model achieves the target that the response activity is a necessary and sufficient condition of the pattern matching degree. Practically, we perform experiments on the point clouds of both single objects and open large-range scenes. The results suggest that our method almost bridges the gap between rotation invariance learning and normal methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Pattern matching

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Bayesian Decision-based Bandwidth Resource Control Optimization Algorithm for Integer-Pixel Motion Estimation in AVS3

Bayesian Decision-based Bandwidth Resource Control Optimizat...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Chen Li Guoqing Xiang Yukun Zhang Yan Cui Peng Zhang Wei Yan School of Software and Microelectronics Peking University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China School of Computer Science Peking University Beijing China Forecasting and Strategic Target Management Research Center Zhejiang Economic Information Center Hangzhou China PengCheng Laboratory Shenzhen China

Compared to the previous AVS2 standards, AVS3 enhances its coding flexibility through the utilization of partition mechanisms including Quad-tree, Binary-Tree, and Extended quad-tree, which permit adaptable Coding Unit(CU) partitioning. The increased flexibility in block partitioning imposes considerable bandwidth resource costs for the Integer-Pixel Motion Estimation (IME) process. However, some CUs show little benefit from IME in coding performance, skipping the search of these CUs will yield substantial bandwidth resource savings. In this paper, we propose a bandwidth resource control strategy based on the Bayesian decision to mitigate and regulate the bandwidth resource consumption of AVS3. By adjusting the motion estimation scheme for coding blocks as the Largest Coding Units (LCU), the bandwidth resource is minimized to a defined level with less performance loss. This proposed method can adjust bandwidth resource from 100% to 20%. Experimental results show the effectiveness of our proposed approach.

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Domain Adaptive Semantic Segmentation via Image Translation and Representation Alignment

Domain Adaptive Semantic Segmentation via Image Translation ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jingxuan Kang Bin Zang Weipeng Cao School of Computer Science University of Liverpool Liverpool UK School of Computer Science and Technology Beijing Institute of Technology Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Domain Adaptation for semantic segmentation is of vital significance since it enables effective knowledge transfer from a labeled source domain (i.e., synthetic data) to an unlabeled target domain (i.e., real images), where no effort is devoted to annotating target samples. Prior domain adaptation methods are mainly based on image-to-image translation model to minimize differences in image conditions between source and target domain. However, there is no guarantee that feature representations from different classes in the target domain can be well separated, resulting in poor discriminative representation. In this paper, we propose a unified learning pipeline, called Image Translation and Representation Alignment (ITRA), for domain adaptation of segmentation. Specifically, it firstly aligns an image in the source domain with a reference image in the target domain using image style transfer technique (e.g., CycleGAN) and then a novel pixel-centroid triplet loss is designed to explicitly minimize the intra-class feature variance as well as maximize the inter-class feature margin. When the style transfer is finished by the former step, the latter one is easy to learn and further decreases the domain shift. Extensive experiments demonstrate that the proposed pipeline facilitates both image translation and representation alignment and significantly outperforms previous methods in both GTA5 → Cityscapes and SYNTHIA → Cityscapes scenarios.

关键词： Image segmentation Adaptation models Semantics Pipelines Predictive models Linear programming Robustness

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