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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Sibi Chakkaravarthy Sethuraman Aditya Mitra Anisha Ghosh Rakesh Thoppaen Suresh Babu Centre of Excellence Artificial Intelligence and Robotics (AIR) School of Computer Science and Engineering VIT-AP University India Hexaware Technologies United States of America

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Passwordless authentication has revolutionized secure merchant payments, discarding reliance on passwords and PINs. Utilizing W3C Web Authentication (WebAuthn) and Client to Authenticator Protocol (CTAP), it employed public key cryptography to uniquely verify a user’s device and identity, constituting the FIDO authentication standard. Its growing popularity prompts widespread adoption, yet device attestation ties it to a specific device, hindering user flexibility. FIDO Passkeys attempt to address this by synchronizing cryptographic keys, enabling passwordless authentication from any device. However, Passkeys face challenges like proprietary encryption algorithms, reliance on specific cloud providers, and suboptimal cross-platform key synchronization. To overcome these limitations, this paper introduces a novel private key management system—Transferable User Secret on Hardware Key (TUSH-Key). TUSH-Key facilitates seamless, cross-platform synchronization for passwordless logins, aligning with FIDO2 specifications, thereby addressing the drawbacks of FIDO Passkeys.

关键词： Protocols Authentication W3C Passwords Public key cryptography Hardware Pins Synchronization Standards Faces

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Designing Interaction with AI for Human Learning: Towards Human-Machine Teaming in Radiology Training 23

Designing Interaction with AI for Human Learning: Towards Hu...

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Proceedings of the 35th Australian computer-Human Interaction Conference

作者： Margot Brereton Aloha Hufana Ambe David Lovell Laurianne Sitbon Tara Capel Alessandro Soro Yue Xu Catarina Moreira Benoit Favre Andrew Bradley School of Computer Science Queensland University of Technology Australia School of Computer Science Queensland University of Technology (QUT) Australia University of Technology Sydney Australia Aix-Marseille University France School of Electrical Engineering & Robotics Queensland University of Technology (QUT) Australia

ISBN: (纸本)9798400717079

We explore the design of systems that enable humans and machines to operate as teams, exercising their different and complementary abilities to work and learn together. Machine Learning (ML) is now widely used in diverse applications such as medical image reading and autonomous vehicles, but typically, ML systems are not designed with human learning in mind, sometimes eroding or supplanting human skills, creating a whole that is less than the sum of its parts. We propose a new approach to ML/AI system design to foster human-machine mutual learning: synergistic interactions in which machines help people think critically and gain wisdom, while people help improve machine models by reframing ML tasks and immersing them in human-machine-human systems which provide feedback to the AI model while helping humans to learn. By explicitly aiming to increase human skill and wisdom, teaming goes beyond “human-in-the-loop” approaches where humans serve primarily to enhance machine performance. We contribute a conceptual model for human-machine teaming design and use a case study in radiology training to identify five critical considerations for interaction design and for how to make AI interactive: (1) human-machine dialogue (2) labelling and attention (3) problem framing (4) biases, values and affect (5) ethics, agency and human choice.

关键词： HCI Human Machine Teams Human-Centred Artificial Intelligence Human-Centred Machine Learning Machine-Learning Mutual human-machine learning Radiology Training

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SCASAN: Spatial Central Attention-Spectral Attention Network Based Feature Extractor for Hyperspectral Image Classification

SCASAN: Spatial Central Attention-Spectral Attention Network...

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Chinese Automation Congress (CAC)

作者： Wei Zhao Hui Zhang Yihong Cao Lizhu Liu Yingjian Su School of Robotics Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

Due to the influence of space, time, environmental changes, and types of ground objects on the lighting conditions of high-light falling images, Hyperspectral image (HSI) classification is challenging. Recently, attention-based HSI classification has received increasing attention. It uses both spatial attention and spectral attention to capture the required spatial-spectral features, thereby achieving better classification accuracy. However, it is challenging to connect the two attention mechanisms without compromising the spatial-spectral features. In addition, how to obtain the attention weight matrix which determines the importance of each channel or spatial location is also an important problem in the use of attention mechanisms. In response to the above issues, the Spatial Central Attention-Spectral Attention network (SCASAN) is designed to efficiently model the spatial-spectral information for HSI classification. In this network, we propose the novel spectral weight spatial attention (SWSA) module to solve the connection problem between spatial attention and spectral attention. Furthermore, we utilize a spatial central attention network (spatial CAN) to obtain a spatial attention weight matrix to avoid the impact of non-homogeneous adjacent pixels on the classified pixel. Our proposed approach demonstrates superior performance in hyperspectral image classification compared to state-of-the-art methods, as evidenced by comprehensive experiments conducted on three publicly available datasets.

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Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter 6

Social Media Analysis using Sentiment Analysis on COVID-19 f...

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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Iqbal Maulana, Fairuz Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter. © 2022 IEEE.

关键词： COVID-19 python sentiment analysis social media twitter

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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TFENet: Topological Feature Extraction Network for 6D Object Pose Estimation

TFENet: Topological Feature Extraction Network for 6D Object...

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Chinese Automation Congress (CAC)

作者： Zhihao Zhang Zhengrong Li Jiacheng Lin Zhiyong Li Wenrui Chen College of Computer Science and Electronic Engineering Hunan University Changsha China School of Robotics Hunan University Changsha China

Grasping is an essential and challenging skill for robots to interact with their environment. Accurately obtaining the 6D pose information of the grasped object is the key to successful grasping. Due to the influence of illumination changes and object textures, the previous methods had problems with low utilization of depth information and inaccurate feature information. We study feature information extraction and fusion to address these issues and proposed a topological feature extraction network (TFENet). Firstly, we design TFE module to achieve global feature extraction from object point clouds, through which features can be identified to use depth information efficiently. Secondly, we design a MFF module to fuse color and geometric embedding better. Finally, experiments show that the proposed method outperforms existing methods with two datasets, LineMOD and YCB-Video.

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning

Improving the Performance of Backward Chained Behavior Trees...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Mart Kartasev Justin Salér Petter Ögren Robotics Perception and Learning Lab. School of Electrical Engineering and Computer Science Royal Institute of Technology (KTH) Sweden

In this paper we show how to improve the performance of backward chained behavior trees (BTs) that include policies trained with reinforcement learning (RL). BTs represent a hierarchical and modular way of combining control policies into higher level control policies. Backward chaining is a design principle for the construction of BTs that combines reactivity with goal directed actions in a structured way. The backward chained structure has also enabled convergence proofs for BTs, identifying a set of local conditions to be satisfied for the convergence of all trajectories to a set of desired goal states. The key idea of this paper is to improve performance of backward chained BTs by using the conditions identified in a theoretical convergence proof to configure the RL problems for individual controllers. Specifically, previous analysis identified so-called active constraint conditions (ACCs), that should not be violated in order to avoid having to return to work on previously achieved subgoals. We propose a way to set up the RL problems, such that they do not only achieve each immediate subgoal, but also avoid violating the identified ACCs. The resulting performance improvement depends on how often ACC violations occurred before the change, and how much effort, in terms of execution time, was needed to re-achieve them. The proposed approach is illustrated in a dynamic simulation environment.

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SuperEdge: Towards a Generalization Model for Self-Supervised Edge Detection

arXiv

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arXiv 2024年

作者： Leng, Kai Zhang, Zhijie Liu, Jie Sui, Wei Boukhers, Zeyd Yang, Cong Li, Zhijun School of Computer Science and Technology Harbin Institute of Technology China School of Future Science and Engineering Soochow Universit China Fraunhofer Institute for Applied Information Technolog Sankt Augustin Germany Horizon Robotics

Edge detection is a fundamental technique in various computer vision tasks. Edges are indeed effectively delineated by pixel discontinuity and can offer reliable structural information even in texture-less areas. State-of-the-art heavily relies on pixel-wise annotations, which are labor-intensive and subject to inconsistencies when acquired manually. In this work, we propose a novel self-supervised approach for edge detection that employs a multilevel, multi-homography technique to transfer annotations from synthetic to real-world datasets. To fully leverage the generated edge annotations, we developed SuperEdge, a streamlined yet efficient model capable of concurrently extracting edges at pixel-level and object-level granularity. Thanks to self-supervised training, our method eliminates the dependency on manual annotated edge labels, thereby enhancing its generalizability across diverse datasets. Comparative evaluations reveal that SuperEdge advances edge detection, demonstrating improvements of 4.9% in ODS and 3.3% in OIS over the existing STEdge method on BIPEDv2. Copyright © 2024, The Authors. All rights reserved.

关键词： Edge detection

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Augmented Feature Representation with Parallel Convolution for Cross-domain Facial Expression Recognition 16th

Augmented Feature Representation with Parallel Convolution ...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Yang, Fan Xie, Weicheng Zhong, Tao Hu, Jingyu Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (纸本)9783031202322

Facial expression recognition (FER) has made significant progress in the past decade, but the inconsistency of distribution between different datasets greatly limits the generalization performance of a learned model on unseen datasets. Recent works resort to aligning feature distributions between domains to improve the cross-domain recognition performance. However, current algorithms use one output each layer for the feature representation, which can not well represent the complex correlation among multi-scale features. To this end, this work proposes a parallel convolution to augment the representation ability of each layer, and introduces an orthogonal regularization to make each convolution represent independent semantic. With the assistance of a self-attention mechanism, the proposed algorithm can generate multiple combinations of multi-scale features to allow the network to better capture the correlation among the outputs of different layers. The proposed algorithm achieves state-of-the-art (SOTA) performances in terms of the average generalization performance on the task of cross-database (CD)-FER. Meanwhile, when AFED or RAF-DB is used for the training, and other four databases, i.e. JAFFE, SFEW, FER2013 and EXPW are used for testing, the proposed algorithm outperforms the baselines by the margins of 5.93% and 2.24% in terms of the average accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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