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Enhanced Human-machine Interactive learning for Multimodal Emotion Recognition in Dialogue System 5

Enhanced Human-Machine Interactive Learning for Multimodal E...

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5th International conference on Algorithms, computing and Artificial Intelligence, ACAI 2022

作者： Leung, Clement H. C. Deng, James J. Li, Yuanxi China MindSense Technologies Limited Hong Kong Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781450398343

Emotion recognition has been well researched in mono-modality in the past decade. However, people express their emotion or feelings naturally via more than one modalities like voice, facial expressions, text, and behaviors. In this paper, we propose a new method to model deep interactive learning and dual modalities (e.g., speech and text) to conduct multimodal emotion recognition. An unsupervised triplet-loss objective function is constructed to learn representation of emotional information from speech audio. We extract text emotional feature representation by transfer learning of text-To-Text embedding from T5 pre-Trained model. Human-machine interaction like user feedback plays a vital role in improve multimodal emotion recognition in dialogue system. Deep interactive learning model is constructed by explicit and implicit feedback. Human-machine interactive learning enhanced transformer model can achieve higher levels of accuracy and precision than their non-interactive counterparts. © 2022 ACM.

关键词： Emotion Recognition

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machine learning for Security of Cyber-Physical Systems and Security of machine learning: Attacks, Defences, and Current Approaches 5th

Machine Learning for Security of Cyber-Physical Systems and ...

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5th International conference on Recent Innovations in computing , ICRIC 2022

作者： Jabeen, Ruxana Singh, Yashwant Sheikh, Zakir Ahmad Department of Computer Science and Information Technology Central University of Jammu Rahya Suchani Bagla Jammu and Kashmir181143 India

ISBN: (纸本)9789819906000

the scope of cyber-physical systems (CPS) is extending and is currently being utilized in many critical infrastructures. Any sort of security reach in such infrastructures could trigger human as well as financial loss. So, the security of these systems is of utmost importance. the feasibility of many approaches has been explored by researchers to ensure the security of CPS, one such approach is based on intelligent methods like machine learning (ML) and deep learning (DL). the involvement of intelligent methods also extends the attack surface of CPS, so there is a need to make them secure as well because they are also vulnerable to many types of adversarial attacks. thus in this work, we review the existing approaches of adversarial attacks like Momentum-based, GAN-based, Fast Gradient Sign Method, Momentum Iterative Fast Gradient Sign Method, the variant of Natural Evolution Strategies, zero-order (ZOO) random position ascent method, Deep fool, and many more. Moreover, we also explored some defence strategies like Modifying Data, Modifying Models, Using Auxiliary Tools, etc., that could be utilized to prevent adversarial attacks. Moreover, we have proposed a methodology for ensuring the security of CPS and the security of defender (intelligent model, i.e. ML and DL) itself. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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Application and Comparative Analysis of Traditional machine learning and Deep learning in Transmission Line Fault Classification 5

Application and Comparative Analysis of Traditional Machine ...

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5th IEEE Advanced Information Management, Communicates, Electronic and Automation Control conference, IMCEC 2022

作者： He, Qian Chen, Li Tang, Wei He, Gong Tan, Shan Shu, Ya Jiang, Lijuan State Grid Chongqing Electric Power Company Chongqing China College of Electrical Engineering Chongqing University Chongqing China College of Engineering Technology Southwest University Chongqing China

ISBN: (纸本)9781665479677

With the continuous expansion of power scale, safe, reliable and stable power supply plays a vital role in social and economic development. the stability of power transmission system directly affects the security and stability of power grid. the stability of the operation state of the transmission line directly affects the security and stability of the power grid. Once the transmission line fails, it will endanger personal safety and cause significant economic losses. If some behaviors can be detected and classified in time, they can be repaired in time to prevent further deterioration. With the increase of computing power, deep learning has better performance than traditional machine learning in many aspects. however, the performance of deep learning in transmission line fault diagnosis and classification has not been effectively proved. Four classifiers based on traditional intelligent algorithms: traditional machine learning algorithm: SVM, adaptive boosting, decision tree (DT), Gaussian Process (GP) and four classifiers based on deep learning: Full Progressive Network (FCN), Deep Gated Current and Progressive Network Hybrid Model (GRU-FCN), Perception Time, ResCNN, are used respectively to identify transmission line fault diagnosis and fault classification. the performance of traditional intelligent algorithm and depth learning is obtained by comparing the results. It is found that the classification results of the classifier based on depth learning are more accurate. Applying the deep learning algorithm to the fault diagnosis of transmission line short circuit can improve the stability of transmission line operation. © 2022 IEEE.

关键词： Electric power transmission

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2022 5th International conference on Computational Intelligence and Networks, CINE 2022

2022 5th International Conference on Computational Intellige...

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5th International conference on Computational Intelligence and Networks, CINE 2022

ISBN: (纸本)9781665464659

the proceedings contain 29 papers. the topics discussed include: a distributed software agent-oriented traceability milieu for blockchain-enabled supply chain;distributed hash table with extensible remote procedure calls;an approach for word segmentation from a line segment in Odia text using quartiles;improving learner's comprehension using entailment-based question generation;breast cancer prediction using long short-term memory algorithm;multiple feature selection frameworks based on evolutionary computing and ensemble learning for software defect prediction;futuristic opportunities and challenges for cognitive tutoring systems;smart prediction of severity in vehicular crashes: a machine learning approach;continuous cloud user authentication by efficient facial recognition;diagnosing skin lesion using multi-modal analysis;Hindi document extractive summarization: neural method on a new data set;outlier and trend detection using approximate median and median absolute deviation;and a lightweight DoS and DDoS attack detection mechanism-based on deep learning.

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From Demo to Design in Teaching machine learning 22

From Demo to Design in Teaching Machine Learning

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5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Bilstrup, Karl-Emi Kjær Kaspersen, Magnus Høholt Assent, Ira Enni, Simon Petersen, Marianne Graves Department of Computer Science Aarhus University Aarhus Denmark

ISBN: (纸本)9781450393522

the prevalence of artificial intelligence (AI) and machine learning (ML) technologies in digital ecosystems has led to a push for AI literacy, giving everybody, including K-12 students, the necessary knowledge and abilities to engage critically with these new technologies. While there is an increasing focus on designing tools and activities for teaching machine learning, most tools sidestep engaging with the complexity and trade-offs inherent in the design of ML models in favor of demonstrating the power and functionality of the technology. In this paper, we investigate how a design perspective can inform the design of educational tools and activities for teaching machine learning. through a literature review, we identify 34 tools and activities for teaching ML, and using a design perspective on ML system development, we examine strengths and limitations in how they engage students in the complex design considerations linked to the different components of machine learners. Based on this work, we suggest directions for furthering AI literacy through adopting a design approach in teaching ML. © 2022 ACM.

关键词： Economic and social effects

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Some Hands-on Approaches to Fake Political News Detection 22

Some Hands-on Approaches to Fake Political News Detection

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5th International conference on Signal Processing and machine learning, SPML 2022

作者： Simpson, Tyler Yang, Feng-Jen Computer Science Department Florida Polytechnic University United States

ISBN: (纸本)9781450396912

there has been a recent growth in interest surrounding the identification of misinformation in online news, specifically in the case of political news. this task was previously human-based, one which has various amounts of efficacy and time commitment for teams of fact-checkers taking up to a week to check the veracity of a single statement. Given recent research and development in artificial intelligence, this is no longer the case. this research has led to a plethora of ideas and techniques to create an ideal fact-checking in an automatic manner, which autonomously discerns the veracity of a given statement quickly. A tool like this is a massive boon to teams of fact-checkers, helping them expedite the process and allowing any individual to check claims made online. this paper will explore various supervised machine learning approaches for this intended purpose and how they interact with numerical features extracted from a dataset of journalistic writing focusing on the efficacy and efficiency thereof. © 2022 Association for computing machinery.

关键词： learning algorithms

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machine learning-Based Real-Time Localization and Automatic Trapping of Multiple Microrobots in Optical Tweezer 5

Machine Learning-Based Real-Time Localization and Automatic ...

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5th International conference on Manipulation, Automation, and Robotics at Small Scales (MARSS)

作者： Ren, Yunxiao Keshavarz, Meysam Anastasova, Salzitsa Hatami, Ghazal Lo, Benny Zhang, Dandan Imperial Coll London Hamlyn Ctr Robot Surg London SW7 2AZ England Univ Bristol Dept Engn Math Bristol BS8 1TH Avon England Bristol Robot Lab Bristol BS34 8QZ Avon England

ISBN: (数字)9781665459730

ISBN: (纸本)9781665459730

Optical Tweezer (OT) is an attractive tool for biological studies, which has been used for cell manipulation and tissue engineering. However, the high intensity of laser beam may damage the target biological objects or specimens. To this end, indirect optical manipulation using optical microrobots has become a promising research direction. To enhance the efficiency of indirect manipulation, automatic localization and trapping of multiple microrobots is significant, which paves a way for closed-loop control. For microrobots localization, a modified YOLOv4-tiny neural network model is developed by integrating ConvNext block for feature extraction, while Gaussian modelling is used to optimize the coordinates of the output of the detection head. To avoid expensive manual annotation for model training, self-supervised learning method is employed with Mosaic data augmentation to eliminate the need of collecting a large amount of data. To determine the optimal trapping points for the laser spots, a machine learning-based ellipse detection method is developed based on arc-support groups and k-means algorithm. We conducted experiments to evaluate the effectiveness of the proposed methods. Compared to mainstream object detection algorithms, our proposed microrobot localization method has higher accuracy and enhanced computational efficiency. the proposed ellipse detection method can detect spherical structures of localized microrobots effectively with 96.77% of success rate. the code is open-source and released on: https://***/ClouseYunxiao/MicrorobotsDetection_TrappingPoints

关键词： Location awareness Training Biomedical optical imaging machine learning algorithms Robot kinematics Tissue engineering Optical computing

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Novel Prediction in Storm Surge Using Ensemble machine learning Algorithms 5

Novel Prediction in Storm Surge Using Ensemble Machine Learn...

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5th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Ian, Vai-Kei Tse, Rita Tang, Su-Kit Pau, Giovanni Macao Polytechnic University Faculty of Applied Sciences China Macao Polytechnic University Eng. Res. Ctr. of Appl. Technol. on Mach. Transl. and Artif. Intelligence of Ministry of Education China Disi University of Bologna Department of Computer Science and Engineering Bologna Italy Ucla Computer Science Department Los AngelesCA United States

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Storm surge elevates the water level, resulting in floods that cause significant harm to our lives and property. Predicting possible occurrences of storm surge using machine learning technology has long piqued the interest of the different communities. However, it is observed that the accuracy rate of predictions is insufficient. this paper aims to develop a framework for evaluating the performance of various machine learning models. Various algorithms are being used to forecast storm surge incidents in Hong Kong and Kaohsiung. the inclusion of trend changes in atmosphere pressure and wind components throughout the implementation of the algorithms to the dataset gathered over the past is one of the innovative aspects of this work. these inclination characteristics are very vital and crucial to capture important correlations and features for the potential occurrence of storm surges. this study yields two major findings. First, even the basic method of an ensemble algorithm produces better results than any single algorithm. Second, determining the best performing ensemble models for those storm surge events is achievable based on the technique findings for the data analyzed. © 2022 IEEE.

关键词： Water levels

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5th International conference on Inventive Computation Technologies, ICICT 2022 - Proceedings

5th International Conference on Inventive Computation Techno...

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5th International conference on Inventive Computation Technologies, ICICT 2022

ISBN: (纸本)9781665408370

the proceedings contain 243 papers. the topics discussed include: provably secure pseudonyms based authentication protocol for wearable ubiquitous computing environment;analysis of dc motor speed control and interfacing it with drowsiness detection;height and weight estimation of an individual from virtual visuals;projective synchronization of uncertain chaotic systems with different dimensions and external noise disturbance;design and implementation of a rescue and surveillance robot using cross-platform application;design of voice controlled multifunctional computer numerical control (CNC) machine;Bangla font recognition using transfer learning method;electroencephalography (EEG) based home automation for physically challenged people using brain computer interface (BCI);optimization of redirection algorithm based on cache technology in programming implementation of college students' mental health education information platform;a resistance detection device for microelectronic semiconductors: from signal detection to fusion analysis;and research on positioning accuracy of welding robot based on visual data mining and pattern fusion analysis algorithm.

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Diagnose Colon and Lung Cancer Histopathological Images Using Pre-Trained machine learning Model 5

Diagnose Colon and Lung Cancer Histopathological Images Usin...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Maheshwari, Ullagadi Kiranmayee, B.V. Suresh, Chalumuru VNRVJIET Telangana India

ISBN: (纸本)9798350398267

Lung cancers and colon cancers are two of the leading causes of morbidity and mortality in human being. One of the essential elements to determining the type of cancer is the histopathological diagnosis. One of the most hazardous and severe diseases that people experience worldwide is colon and lung cancer, which has spread to become a common medical issue. It is very important to make a reliable and early discovery in order to reduce the danger of death. the difficulty of the task ultimately depends on the histopathologists' experience. Recent times have seen a rise in the popularity of deep learning, which is now appreciated in the interpretation of medical imaging. As a result, artificial intelligence will soon become a useful technology. In order to identify lung cancers and colon cancer using histopathological pictures and more effective augmentation strategies, this research aims to utilize and modify the current pre-trained Convolutional Neural Network (CNN) based model. From the LC25000 dataset, the results were obtained. Precision, recall, f1score, and accuracy are all used to estimate the model performances. the findings show that the pre-trained and improved pre-trained models produced impressive outcomes ranging from 93% to 97% accuracy. © 2022 IEEE.

关键词： Image classification

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