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Consistency Regularization for Unsupervised Domain Adaptation in Semantic Segmentation 21st

Consistency Regularization for Unsupervised Domain Adaptatio...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Scherer, Sebastian Brehm, stephan Lienhart, Rainer Univ Augsburg Machine Learning & Comp Vis Lab Augsburg Germany

ISBN: (纸本)9783031064272;9783031064265

Unsupervised domain adaptation is a promising technique for computer vision tasks, especially when annotating large amounts of data is very costly and time-consuming, as in semantic segmentation. Here it is attractive to train neural networks on simulated data and fit them to real data on which the models are to be used. In this paper, we propose a consistency regularization method for domain adaptation in semantic segmentation that combines pseudo-labels and strong perturbations. We analyse the impact of two simple perturbations, dropout and image mixing, and show how they contribute enormously to the final performance. Experiments and ablation studies demonstrate that our simple approach achieves strong results on relevant synthetic-to-real domain adaptation benchmarks.

关键词： Domain adaptation Semi-supervised learning Unsupervised learning Semantic segmentation Synthetic data

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Toward Prediction of Traffic Accidents Using Formal Concept Analysis of Actual Accidents and Related data 21

Toward Prediction of Traffic Accidents Using Formal Concept ...

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21st international conference on Machine learning and Cybernetics, ICMLC 2022

作者： Kotani, Shogo Nakamura, Masaki Sakakibara, Kazutoshi Motoyoshi, Tatsuo Hoshikawa, Keisuke Toyama Prefectural University Faculty of Engineering 5180 Kurokawa Imizu-shi Toyama939-0398 Japan

ISBN: (数字)9781665488327

ISBN: (纸本)9781665488327

This study uses Formal Concept Analysis (FCA) to investigate factors of traffic accidents by analyzing actual traffic accident data including its date, place, injury severity, road shape, accident summary in a natural language, etc for each accident. FCA is a mathematical theory of data analysis based on formal contexts and concept lattices. We gather data related to each of the traffic accidents such as land use districts, traffic volumes, and so on, translate them into a binary context table as an input of FCA, and analyze conceptual structures as an output of FCA to investigate traffic accident factors. © 2022 IEEE.

关键词： Formal concept analysis

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GAN-Loc: Empowering Indoor Localization for Unknown Areas via Generative Fingerprint Map 21

GAN-Loc: Empowering Indoor Localization for Unknown Areas vi...

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21st Annual IEEE international conference on Sensing, Communication, and Networking, SECON 2024

作者： Yoon, JinYi You, Yeawon Kang, Dayeon Kim, Jeewoon Lee, HyungJune Department of Computer Science Virginia Tech BlacksburgVA United States Department of Cyber Security Ewha Womans University Seoul Korea Republic of Manning College of Information & Computer Sciences University of Massachusetts AmherstMA United States Department of Computer Science University of California IrvineCA United States Department of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9798331519186

As indoor localization becomes a necessity to provide intelligent location-based services for in-building users, fingerprint-based positioning has been widely adopted in numerous Wi-Fi-equipped devices. However, its reliance on extensive offline site surveys limits its application in unexplored environments without prior fingerprint sampling. To address the challenge, we propose GAN-Loc, a novel generative fingerprint-based framework. We extract the underlying correlation between location and radio signal features, empowering indoor localization in un/under-explored areas, including unknown data points, newly deployed APs, or unexplored sites. It involves: 1) decomposing into a signal feature map for each AP perspective;2) learning with a set of points and their associated signal strength data;3) generating and integrating synthetic radio fingerprints;and 4) employing them into some existing localization algorithms. We evaluated GAN-Loc with extensive real-world RSSI measurements in seven different indoor places. GAN-Loc achieves the localization accuracy of up to 1.21m, 1.29m, and 1.28m for Wi-Fi, Zigbee, and BLE, respectively, compared to the accuracy of 1.47m, 1.58m, and 1.22m using an ideal ground-truth map, which is unachievable without site survey in unknown sites. © 2024 IEEE.

关键词： Gallium nitride

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Role Of Artificial Intelligence In Art 5

Role Of Artificial Intelligence In Art

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Prashanthi, Vempaty Kanakala, Srinivas Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of CSE- Data Science Hyderabad India Vnr Vignana Jyothi Institute Of Engineering And Technology Department Of Cse Hyderabad India

ISBN: (纸本)9798350330861

It is the 21st century now, and the world has been introduced to many new miraculous technologies. In such technologies 'Artificial Intelligence (AI)' is one of the highest standard technologies involving a great number of stunning techniques. AI has already run into every domain present, like education-machine learning, deep learning;healthcare, agriculture, gaming etc. Art is one such domain that has been embraced by AI techniques. This new technology of artificial intelligence in art merges the artist's imagination and technical experts, robotic intelligence together to express the beautiful creativity and emotions [1]. AIART is the technical term to signify the application of one of the most revolutionary technologies-Artificial Intelligence into Arts. This paper reviews the recent developments of how AI is associated in creating an art form and conveys the uniqueness of AIART. It briefly describes the way in which AIART is distinguished from other traditional arts like Paintings, Sculptures and Digital Arts. It also describes in what way AIART is more beneficial than other art forms. This paper analyses the necessity of AIART to help the creative minds. This paper draws a conclusion that the modern way of expression and simplicity are the key factors that distinguish AIART from other traditional arts. [1] © 2023 IEEE.

关键词： Deep learning

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Estimating the Distribution of Oral Presentation Skills in an Educational Institution: A Novel Methodology 15

Estimating the Distribution of Oral Presentation Skills in a...

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15th international conference on Computer Supported Education, CSEDU 2023

作者： Domínguez, Federico Eras, Leonardo Tomalá, Josué Collaguazo, Adriana Information Technology Center Escuela Superior Politécnica Del Litoral ESPOL Guayaquil Ecuador Faculty of Electrical and Computer Engineering Escuela Superior Politécnica Del Litoral ESPOL Guayaquil Ecuador

ISBN: (纸本)9789897586415

Mastering oral presentation skills is of paramount importance for new graduates as they navigate the competitive job market of the 21st century. Consequently, procuring the effective development of these skills in students is an essential task for higher education institutions (HEIs). We developed a technological solution that facilitates oral presentation skills learning by providing automatic and immediate feedback using machine learning algorithms on audiovisual recordings of oral presentations. We have been using this tool to record novice students’ presentations since 2017 and, by using the resulting data corpus, developed a methodology to accurately detect and evaluate posture and gaze in oral presentations. This article presents this methodology and its application on more than 3,000 recordings from more than 2,000 different students across all study programs at our university. Preliminary results provide a glimpse of the prevalence and distribution of oral presentation skills across several demographic variables. statistically significant patterns point to possible oral communication deficiencies in engineering programs at our HEI, highlighting the potential of our methodology to serve as a diagnostic tool for communication skills learning strategies. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： students

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Deep Reinforcement learning to Enhance the Energy-efficient Performance of Unmanned Aerial Vehicles-enabled Fog Radio Access Networks 21

Deep Reinforcement Learning to Enhance the Energy-efficient ...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liu, Chuanjie Ren, Siyu Du, Shuang Dai, Cheng Guo, Bing Fan, Runzhi Sichuan University School of Computer Science Chengdu China Uestc School of Aeronautic and Astronautic Chengdu China

ISBN: (纸本)9798350329223

Nowadays, the use of unmanned aerial vehicles (UAVs) as the fog access points (F-APs) is of high practical value to future fog radio access networks (F-RANs). Compared to the F-AP, UAV enabled F-AP possesses stronger line-of-sight links with the ground terminals (GTs) due to its high altitude as well as high and flexible mobility in 3D spaces. Nevertheless, unlike terrestrial F-AP that has reliable power supply, UAV- enabled F-AP in practice has limited on board power, but requires high propulsion power to stay air-borne and support high mobility. Motivated by the above considerations, this paper aims to explore how deep reinforcement learning (DRL) can be applied to enhance the energy-efficient performance of UAV- enabled F-RANs. Such DRL method supports UAV enabled F-AP to real-timely optimize the air-to-ground fog computing by reasoning network configuration in an online manner, which sheds new light on the existing works in UAV-enabled networks. In specific, the DRL based network configuration considers parameters including resource allocation, task offloading, cache deployment and 3D UAV trajectory, and get validated via simulation in this paper. In the end, the main design challenges and promising directions for future research are also discussed. © 2023 IEEE.

关键词： Reinforcement learning

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Hypergraph based Multi-Agents Representation learning for Similarity Analysis 21

Hypergraph based Multi-Agents Representation Learning for Si...

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21st international conference on Control, Automation and Systems (ICCAS)

作者： Baek, Jaeuk Lee, Changeun Elect & Telecommun Res Inst Intelligent Convergence Res Lab Daejeon 34129 South Korea

ISBN: (纸本)9788993215212

As a lot of agents with diverse sensing capabilities are expected to collaborate in the near future, the needs to process a huge number of multi-modal data are emerging to recognize global situations, events or environment. In this paper, we propose a hypergraph based multi-agents representation learning (HMARL) to obtain agent embedding vectors, which can be used to classify agents in the same region and correlate the collected data of similar properties. To this end, the proposed HMARL transforms the multi-modal data into the same graph structure with nodes and their relations. Then, a hypergraph is constructed to integrate local graphs and a hypergraph random walk is applied to obtain the sequence of adjacent agents, which is used to train the agent embedding vectors. Experiments on public datasets are provided for similarity analysis on agents and their collected data.

关键词： Hypergraph Multiple agents Random walk Representation learning Multi-modal data

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Analysis of Chinese traditional opera based on the Western music genre classification methods 25

Analysis of Chinese traditional opera based on the Western m...

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作者： Li, Yue Peng, Zhengwei Liufu, Ying Li, Guanghao Zhu, Huanyan School of Computer Science and Engineering South China University of Technology Guangzhou China Shenzhen Futu Network Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9798350330014

At a time of rapid development of Internet technology, AI capabilities are gradually penetrating into the audio field. Although the study of traditional music has a long history, the mainstream music research is currently limited to Western classical music. But besides Western music, there is other traditional classical music, Chinese opera is a representative of traditional music, and it is one of the six traditional music genres. Cantonese opera is an important part of the Chinese opera atlas. It is one of the few local operas included in the World Intangible Cultural Heritage among the more than 360 kinds of Chinese operas, so the operas processed in this experiment are all Cantonese opera excerpts. A total of 1000 audios from Cantonese Opera dataset and G TZAN dataset are collected and processed in this experiment, and the classification methods of western traditional music are applied to analyze Chinese operas. A music classification network based on deep learning is designed and implemented in which the most classical methods in music classification are used to conduct some research on Cantonese opera. Finally, the connections and differences between Chinese opera and western music are analyzed and obtained. © 2023 IEEE.

关键词： Classification (of information)

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A Novel Method with Transformers for Fine-grained Encrypted Traffic Classification 25

A Novel Method with Transformers for Fine-grained Encrypted ...

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作者： Kong, He Li, Tong Ge, Jingguo Li, Hui Yang, Liqun Lu, Zhuang Xu, Zhibin Xie, Chuanxi Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350330014

With the rise of cyber threats and data breaches, encrypted traffic has become increasingly important in ensuring online privacy and security. However, it also brings significant security challenges. Existing studies have shown that encrypted traffic from applications or web services can be identified using various methods. Nevertheless, there is limited generalization ability across multiple scenarios and difficulties in distinguishing similar traffic traces between web pages of the same application. To overcome the limitations of existing methods, we propose ANT-ET, a novel learning method tailored for fine-grained webpage analysis. ANT-ET extracts the contextual semantics from raw traffic and represents it as a semantic vector. These vector representations are subsequently utilized for classification through a classification layer and discriminator. The experimental results show that ANT-ET can effectively understand the semantics of encrypted traffic across various scenarios, achieving F1-score greater than 95% in four public encrypted traffic tasks, outperforming existing methods. In addition, we collected a real-world traffic dataset to verify the effectiveness of ANT-ET for fine-grained webpage detection. © 2023 IEEE.

关键词： Websites

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