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Detecting adversaries in Crowdsourcing 21

Detecting adversaries in Crowdsourcing

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Traganitis, Panagiotis A. Giannakis, Georgios B. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781665423984

Despite its successes in various machine learning and data science tasks, crowdsourcing can be susceptible to attacks from dedicated adversaries. This work investigates the effects of adversaries on crowdsourced classification, under the popular Dawid and Skene model. The adversaries are allowed to deviate arbitrarily from the considered crowdsourcing model, and may potentially cooperate. To address this scenario, we develop an approach that leverages the structure of second-order moments of annotator responses, to identify large numbers of adversaries, and mitigate their impact on the crowdsourcing task. The potential of the proposed approach is empirically demonstrated on synthetic and real crowdsourcing datasets.

关键词： Crowdsourcing Classification Adversaries Ensemble learning

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Estimating Lithium-Ion Battery Health Parameters Using Deep learning Techniques 1

引用

21st ISME international conference on Advances in Mechanical engineering, ISME 2023

作者： Kakati, Pallabi Dandotiya, Devendra Joshi, Anand K. Singh, Rajiv Ranjan Ramesh, C.S. Department of Electronics and Communication Engineering Presidency University Karnataka Bangalore560064 India Department of Mechanical Engineering Presidency University Karnataka Bangalore560064 India Presidency University Karnataka Bangalore560064 India

ISBN: (数字)9789819736485

ISBN: (纸本)9789819736478

Prognostics and health management (PHM) application is a viable strategy to proactively manage and reduce the risks of failure. PHM has been widely utilized in various applications, including battery systems. Battery capacity is a critical parameter that requires careful assessment to ensure safe operation and informed decision-making. Quantifiable data from the battery management system, such as the current (I), voltage (V), and temperature (T) profiles, which fluctuate with battery aging, are used to anticipate failure. Using this information, neural networks may learn the relationship between capacity and charging profiles. In the present work, a framework, utilizing multi-channel convolutional neural networks (CNN), long short-term memory (LstM), and multi-channel CNN-LstM is proposed to accurately predict the status of health that incorporates V, I, and T profiles. The proposed multi-channel CNN-LstM strategy outperforms the other two techniques, achieving a mean absolute percentage error reduction of up to 25%-58%. The potential of utilizing multi-channel data for accurate PHM of batteries can be concluded from the present work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Identifying Hand Pose Used in Sign Language Using Key-Point and Transfer learning Technique 1st

Identifying Hand Pose Used in Sign Language Using Key-Point ...

引用

1st international conference on intelligent Computing and Big data Analytics, ICICBDA 2024

作者： Ingoley, Shilpa N. Bakal, Jagdish W. Thadomal Shahani Engineering College Bandra Mumbai India Pillai HOC College of Engineering and Technology Rasayani India

ISBN: (纸本)9783031747007

Sign language recognition is now possible because of the advancements in processors, computer vision, image processing, machine and deep learning techniques. This development has made it feasible to interpret sign languages effectively by using cutting-edge technology. It has made communication possible for those with speech and hearing impairments. This will help them improve the quality of their lives when they need to connect with rest of the world. For the said cause, we have suggested a methodology. To extract the features, hand key points and transfer learning techniques with vgg16 model have been used. To prove its efficacy, we worked on three different datasets: (i) NUS hand posture datasets II (ii) ISL dataset from Kaggle and (iii) our own ISL dataset created with the help of multiple users in diverse backgrounds. Our methodology works efficiently on all three datasets and exhibits validation accuracy of 99.12, 99.89 and 99.78 respectively. Result demonstrates that it works effectively with various images and mentioned diverse datasets. Hence, we feel this approach is generalizable and can be effectively useful with different sign languages. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Energy Efficient Design of Coarse-Grained Reconfigurable Architectures: Insights, Trends and Challenges 21

Energy Efficient Design of Coarse-Grained Reconfigurable Arc...

引用

21st international conference on Field-Programmable Technology (ICFPT)

作者： Aliagha, Ensieh Goehringer, Diana Tech Univ Dresden Chair Adapt Dynam Syst Dresden Germany

ISBN: (数字)9781665453363

ISBN: (纸本)9781665453363

Coarse-Grained Reconfigurable Architectures (CGRAs) are promising solutions to achieve more performance with the end of Moore's law. Thanks to word-level programmability, they are more energy-efficient compared to FPGAs. Although ASICs can minimize energy, they suffer from high Non-Recurring engineering (NRE) costs and inflexibility. CGRAs provide near ASIC energy efficiency and are deployed in the literature to accelerate low-power and high-performance applications. However, focusing on low-power CGRAs is crucial as a high volume of data should be processed on a resource-constrained device by the development of IoT and Machine learning applications. This survey has reviewed and categorized CGRA architectures from processing elements, interconnect networks, and memory points of view and derived guidelines for energy-efficient CGRA design.

关键词： Coarse-Grained Reconfigurable Architectures (CGRAs) Energy Efficiency Signal Processing Machine learning

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Application of Artificial Intelligence Techniques to Detect Water stress 21

Application of Artificial Intelligence Techniques to Detect ...

引用

2024 IEEE/ACS 21st international conference on Computer Systems and Applications, AICCSA 2024

作者： Dorai, Marwa Abdellaoui, Mehrez Douh, Bouthaina Douik, Ali National Engineering School of Sousse University of Sousse NOCCS Laboratory Tunisia Higher Institute of Computer Science and Communication Technologies University of Sousse Tunisia Higher Institute of Agronomic Sciences of Chott Mariem University of Sousse Tunisia

ISBN: (纸本)9798331518240

Water stress is a critical issue that significantly affects agricultural productivity worldwide, particularly in the context of climate change and diminishing water resources. Conventional methods for detecting water stress, such as visual inspections and ground sensors, often lack the precision, costeffectiveness, and scalability needed for modern agricultural demands. Recent advances in artificial intelligence (AI) offer innovative solutions by leveraging complex data from multiple sources. This research aims to develop a multimodal AI model that integrates satellite imagery, drone data, and ground IoT sensors to improve the accuracy and timeliness of water stress detection. By combining Convolutional Neural Networks (CNNs) for image analysis, machine learning algorithms for sensor data interpretation, and advanced data fusion techniques, the proposed model captures water stress indicators from various perspectives, enhancing sensitivity and specificity. The expected outcomes include significant improvements in precision and responsiveness compared to traditional single-source methods. This approach aims to optimize irrigation, minimize crop losses, and support sustainable water management, providing a valuable tool for precision agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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The Effectiveness of Online-Based Authentic learning Assessment 7

The Effectiveness of Online-Based Authentic Learning Assessm...

引用

7th international conference on Sustainable Information engineering and Technology, SIET 2022

作者： Murad, Dina Fitria Wijanarko, Bambang Dwi Leandros, Riyan Murad, Silvia Ayunda Bina Nusantara University Information System Development BINUS Online Learning Indonesia Universitas Islam Syekh Yusuf Information Technology Department Indonesia

ISBN: (纸本)9781450397117

Authentic learning is one consequence of the influence of globalization and revolution on teaching. Authentic learning is one of the keys to the 21st-century learning model. Using an ethnographic approach by observing the behavior of respondents in learning assignments, this study aims to identify the effectiveness of implementing authentic learning assignments during the pandemic in primary and secondary schools. The evaluation of the research data was carried out using analysis of variance and correlational techniques. The results of this study show that there is no significant difference based on the Top 5 learning tasks, namely Effective Reading, Preparing Discussions, Making Schedules, Planning Tasks, and Identifying Goals. This is evidenced by the P-value = 0.507 which is greater than 0.5 and the F value = 0.8297 which is smaller than Fcrit = 2.387. This study also states that soft-skill learning materials are easier for students to learn than hard-skill materials. © 2022 ACM.

关键词： students

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Contrastive learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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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： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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Estimation Of Cortical Currents From Eeg Signals During N-Back Working Memory Tasks 21

Estimation Of Cortical Currents From Eeg Signals During N-Ba...

引用

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

作者： Yoshiiwa, Shinnosuke Takano, Hironobu Ido, Keisuke Morishige, Ken-Ichi Toyama Prefectural University Graduate School of Engineering Toyama Japan Toyama Prefectural University Department of Intelligent Robotics Toyama Japan Toyama Prefectural University Center for Liberal Arts and Sciences Toyama Japan

ISBN: (数字)9781665488327

ISBN: (纸本)9781665488327

Electroencephalography studies of working memory have demonstrated cortical activities and oscillatory representations without clarifying what kind of information is stored in memory representations. To answer this question, we measured scalp EEG and fMRI data while participants performed a N-back working memory task. We calculated the current intensities from the estimated cortical currents. To investigate the representation of working memory in the cortical regions, we classified information about its contents using the power spectrum during a retention period. These results indicate that our method classified (to some extent) the oscillatory representations of EEG cortical currents over multiple regions. © 2022 IEEE.

关键词： Electroencephalography

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Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection 21st

Adversarial Robustness and Feature Impact Analysis for Drive...

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21st international conference on Artificial Intelligence in Medicine (AIME)

作者： Vitorino, Joao Rodrigues, Lourenco Maia, Eva Praca, Isabel Lourenco, Andre Polytech Porto ISEP IPP Sch Engn Res Grp Intelligent Engn & Comp Adv Innovat & Dev P-4249015 Porto Portugal CardioID Technol P-1959007 Lisbon Portugal

ISBN: (纸本)9783031343438;9783031343445

Drowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times. To detect drowsiness before any impairment occurs, a promising strategy is using Machine learning (ML) to monitor Heart Rate Variability (HRV) signals. This work presents multiple experiments with different HRV time windows and ML models, a feature impact analysis using Shapley Additive Explanations (SHAP), and an adversarial robustness analysis to assess their reliability when processing faulty input data and perturbed HRV signals. The most reliable model was Extreme Gradient Boosting (XGB) and the optimal time window had between 120 and 150 s. Furthermore, the 18 most impactful features were selected and new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.

关键词： adversarial robustness explainability machine learning heart rate variability driver drowsiness detection

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Continual learning with Neuron Activation Importance 21st

Continual Learning with Neuron Activation Importance

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

作者： Kim, Sohee Lee, Seungkyu Kyung Hee Univ Dept Comp Sci & Engn 1732 Deogyeong Daero Yongin Gyeonggi Do South Korea

ISBN: (纸本)9783031064272;9783031064265

Continual learning is a concept of online learning with multiple sequential tasks. One of the critical barriers of continual learning is that a network should learn a new task keeping the knowledge of old tasks without access to any data of the old tasks. We propose a neuron activation importance-based regularization method for stable continual learning regardless of the order of tasks. We conduct comprehensive experiments on existing benchmark data sets to evaluate not just the stability and plasticity of our method with improved classification accuracy also the robustness of the performance along the changes of task order.

关键词： Continual learning Neuron importance

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