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An Ensemble Model for the Analysis of Parent-Child Interactions from Text and Audio 22

An Ensemble Model for the Analysis of Parent-Child Interacti...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Nikbakhtbideh, Behnam Duffett-Leger, Linda Letourneau, Nicole Oxford, Monica Tryphonopoulos, Panagiota Moshirpour, Mohammad University of Calgary Dept. of Electrical and Software Engineering Calgary Canada University of Calgary Dept. of Nursing Calgary Canada University of Washington Dept. of Nursing Washington United States Arthur Labatt Family School of Nursing University of Western Ontario Ontario Canada University of California Irvine Dept. of Informatics Irvine United States

ISBN: (纸本)9798350345346

The interaction between parents and their children is foundational for their social and emotional development and well-being. Among measures that assess parent-child interaction quality, the Parent-Child Interaction Teaching Scale (PCI-TS) is an effective and well-established assessment tool that measures the quality of parent-child interactions. One of the significant hurdles in PCI-TS is identifying parent-child behaviors early on to aid parents in addressing initial behavioral issues. However, manual evaluations are resource-intensive and time-consuming, and restrict the accessibility of such assessments. In this research, we present an ensemble model to categorize various behavior types in parent-child interaction scales such as PCI-TS. We have collected over 2,000 minutes of video of parents and their children emerging in specific tasks such as drawing or object manipulation. These videos have been labeled based on PCI-TS by trained healthcare professionals. Our ensemble model has been trained to recognize emotions and psychological elements within parent-child interactions. This model is adjusted by multiple modalities and aspects of these psychological elements. We posit that our study introduces the first model that employs a new framework to integrate numerous behavioral factors to recognize these psychological elements within parent-child interactions using audio and text modalities. Our suggested model can discern and detect the semantic and syntactic features of audio and language in parent-child interactions according to the PCI-TS scale. The results of our evaluation showed an enhancement in performance when implementing this strategy, compared to similar approaches by 16% in F1-score. © 2023 IEEE.

关键词： audio classification behavior analysis e-health parent-child interaction text classification

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Human Activity Recognition Using intelligent Resonant Accelerometer Employing MEMS Reservoir Computing

Human Activity Recognition Using Intelligent Resonant Accele...

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international conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Takahiro Mizumoto Amit Banerjee Jun Hirotani Toshiyuki Tsuchiya Department of Micro Engineering Kyoto University Kyoto Japan

This paper reports an experimental demonstration of human activity recognition (HAR) using a MEMS accelerometer integrating a physical reservoir. The doubly clamped beam nonlinear resonator in the frequency modulated accelerometer acted as a physical reservoir. We successfully classified walking and jogging vibration without any software recognition. The physical reservoir resonator holds past input and performs nonlinear conversion for recognition and human activity data that are not linearly separable can be classified by a simple optimization of the output weights by linear regression. The results showed its good performance at the operating frequency where the nonlinear bistability occurs, with the classification accuracies reaching up to 98.8%.

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An intelligent Resource Scheduling Method with Edge Channel Deployment for BPM 19

An Intelligent Resource Scheduling Method with Edge Channel ...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Bowen Dou, Wanchun Zhou, Xiaokang Zhang, Xuyun Qi, Lianyong Dai, Fei Chen, Chaochao Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shiga University Faculty of Data Science Hikone Japan Macquarie University Department of Computing Sydney Australia College of Computer and Software Qingdao China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350346558

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users' status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method. © 2022 IEEE.

关键词： Cost effectiveness

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Building intelligent automated Real-Time data Analysis Systems Through Machine learning

Building Intelligent Automated Real-Time Data Analysis Syste...

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international conference on Futuristic Technologies (INCOFT)

作者： Sahil Rathee M. Vigenesh Kala Priydarshini Vijaykumar R Shweta Singh Jyoti Seth Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

This paper explores the capacity of using gadget-get-to-know-based facts evaluation structures to build intelligent, automated real-time statistics analysis systems. It examines the demanding situations present points evaluation systems pose and proposes a unique remedy. The proposed method leverages supervised and unsupervised devices and learning strategies to enable deep information on complex datasets. It then makes use of predictive skills to make clever selections with the aid of comparing input facts about databases. ultimately, the paper describes a case to demonstrate the effectiveness of this proposed machine and outlines implications for comparable implementations in the enterprise. This work is precious for information scientists and commercial enterprise decision-makers, as it outlines an ability to deploy reliable and accurate records analysis structures.

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Flood Risk Analysis with Deep learning

Flood Risk Analysis with Deep Learning

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international Symposium on intelligent Systems and Informatics (SISY)

作者： Saba Salmani Ghanbari Milad Mousavi Maleki Pouria Mosavi Amir Bahram Choubin James watt school of engineering University of Glasgow Glasgow United Kingdom Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary Department of Electrical Engineering Bu-Ali Sina University Hamedan Iran John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary West Azarbaijan Agricultural and Natural Resources Research Institute Urmia Iran

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Predicting flood hazard risk is crucial for reducing potential damage to infrastructures. This study uses machine learning algorithms to improve the accuracy of flood hazard risk predictions. We compare the performance of Long Short-Term Memory (LSTM) networks, Random Forest (RF), and Support Vector Machine (SVM) algorithms using a dataset to assess their effectiveness in predicting flood risk. The findings reveal that RF along with LSTM are the most accurate methods. These findings highlight the potential of machine learning algorithms, particularly RF and LSTM, in enhancing flood hazard risk analysis which offers valuable insights for risk mitigation strategies and infrastructure planning.

关键词： Radio frequency Machine learning algorithms Accuracy Computational modeling Predictive models data models Mathematical models Floods Long short term memory Context modeling

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Natural Language Processing and Deep learning Based Techniques for Evaluation of Companies' Privacy Policies 22nd

Natural Language Processing and Deep Learning Based Techniqu...

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22nd international conference on Computational Science and its Applications (ICCSA)

作者： John, Saka Ajayi, Binyamin Adeniyi Marafa, Samaila Musa Nasarawa State Univ Keffi Dept Comp Sci Keffi Nigeria Fed Univ Gusau Dept Comp Sci Gusau Nigeria

ISBN: (纸本)9783031105364;9783031105357

Companies' websites are vulnerable to privacy attacks that can compromise the confidentiality of data which, particularly in sensitive use cases like personal data, financial transaction details, medical diagnosis, could be detrimental and unethical. The noncompliance of companies with privacy policies requirements as stipulated by the various data Protection Regulations has raised lot of concerns for users and other practitioners. To address this issue, previous research developed a model using conventional algorithms such as Neural Network (NN), Logistic Regression (LR) and Support Vector Machine (SVM) to evaluate the levels of compliance of companies to general data protection regulations. However, the research performance shows to be unsatisfactory as the model's performance across the selected core requirements of the legislation attained F1-score of between 0.52-0.71. This paper improved this model's performance by using Natural Language Processing (NLP) and Deep learning (DL) techniques. This was done by evaluating the same dataset used by the previous researcher to train the proposed model. The overall results show that LSTM outperform both GRU and CNN models in terms of F1-score and accuracy. This research paper is to assist the Supervisory Authority and other practitioners to better determine the state of companies' privacy policies compliance with the relevant data protection regulations.

关键词： Privacy policies Natural language processing Deep learning Manual Annotation Machine learning Artificial intelligent Crowdsourcing

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Using Survey to Investigate the Integration of Artificial Intelligence in e-learning 22nd

Using Survey to Investigate the Integration of Artificial In...

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22nd international conference on Software engineering Research and Practice, SERP 2024, and 23rd international conference on e-learning, e-Business, Enterprise Information Systems, and e-Government, EEE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Tao, Yi Yang, Zijiang Zhang, Ethan Boyi School of Information Technology York University Toronto Canada St. Robert Catholic High School Thornhill Canada

ISBN: (纸本)9783031866432

This paper focuses on analyzing data obtained from a survey conducted among 312 management undergraduate students in China. The aim of the survey is to explore their perceptions, inclinations, and attitudes towards the use of AI in e-learning. In addition, feature importance analysis is conducted to identify the most influential factors in determining students’ attitudes towards AI in e-learning. This analysis helps uncover the key factors that significantly impact students’ perceptions and evaluations of AI usage in the daily learning process. Overall, this paper provides a comprehensive and empirical analysis of the survey data. The findings contribute to a better understanding of the key factors influencing attitudes and provide valuable insights for educational practitioners, creators of AI technology, and policymakers involved in the implementation of AI technologies in the educational industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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Lightweight Approximate Multiplier with Improved Accuracy in FPGA for Error Resilient Application

Lightweight Approximate Multiplier with Improved Accuracy in...

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international conference on VLSI Design

作者： Apurba Prasad Padhy Bishnu Prasad Das Electronics and Communication Engineering Indian Institute of Technology Roorkee Roorkee India

Approximate computing finds its application in image processing, machine learning, data mining and multimedia data processing. The paper proposes a scalable and lightweight $4\times 4$ approximate multiplier, which is designed to efficiently utilize the FPGA resources. We have divided the partial product matrix and selectively chosen the input variables for the effective usage of the LUTs and carry-chain. The bit error position due to the approximation is reduced by assigning the possible lower bits to ‘1’ which reduces the maximum error magnitude from 16 to 4. The proposed $4\times 4$ approximate multiplier achieves a reduction in area, power and latency of 26.7%, 57.6% and 60.1% respectively compared to the Xilinx multiplier IP. The proposed multiplier is used as a building block to implement $8\times 8$ approximate multipliers for image sharpening applications. We achieve a high PSNR value of 69.4 dB using the $8\times 8$ approximate multiplier.

关键词： Embedded systems Input variables Multimedia computing Machine learning Very large scale integration data processing Distance measurement

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Use Digital Twins to Support Fault Diagnosis from System-Level Condition-Monitoring data

Use Digital Twins to Support Fault Diagnosis from System-Lev...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Killian Mc Court Xavier Mc Court Shijia Du Zhiguo Zeng CentraleSupélec Université Paris-Saclay Gif-sur-Yvette France Shenzhen Weimin Institute for Reliability Systems Engineering Shenzhen China Chair on Risk and Resilience of Complex Systems Laboratoie Genie Industriel CentraleSupélec Université Paris-Saclay Gif-sur-Yvette France

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Deep learning models have created great opportunities for data-driven fault diagnosis but they requires large amount of labeled failure data for training. In this paper, we propose to use a digital twin to support developing data-driven fault diagnosis model to reduce the amount of failure data used in the training process. The developed fault diagnosis models are also able to diagnose component-level failures based on system-level condition-monitoring data. The proposed framework is evaluated on a real-world robot system. The results showed that the deep learning model trained by digital twins is able to diagnose the locations and modes of 9 faults/failure from 4 different motors. However, the performance of the model trained by a digital twin can still be improved, especially when the digital twin model has some discrepancy with the real system.

关键词： Fault diagnosis Deep learning Training Performance evaluation Motors data models Digital twins Robots Predictive maintenance

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Deep Reinforcement learning for Dependent Task Offloading in Mobile Edge Computing Systems 19

Deep Reinforcement Learning for Dependent Task Offloading in...

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作者： Gong, Bencan Jiang, Xiaowei Three Gorges University Hubei Key Laboratory of Intelligent Vision Monitoring for Hydropower Engineering Yichang China Three Gorges University College of Computer and Information Technology Yichang China

ISBN: (纸本)9798350346558

In mobile edge computing, there are usually relevant dependencies between different tasks, and traditional algorithms are inefficient in solving dependent task offloading problems and neglect the impact of the dynamic change of the channel on the offloading strategy. To solve the offloading problem of dependent tasks in dynamic network environment, this paper establishes the dependent task model as a directed acyclic graph. A Dependent Task Offloading Strategy (DTOS) based on deep reinforcement learning is proposed with minimizing the weighted sum of delay and energy consumption of network services as the optimization objective. The task-dependent offloading problem is transformed into an optimal policy problem under Markov decision processes. Multiple parallel deep neural networks (DNN) are used to generate offloading decisions, cache the optimal decisions for each round, and then optimize the DNN parameters using experience replay and incentive-driven mechanisms to obtain the optimal task offloading decisions. By using multiple parallel deep neural networks to generate offloading decisions, the impact of oscillations in the training results of individual neural networks on the algorithm is reduced. The experimental results show that, compared with the existing algorithms, the offloading decision generated by the algorithm can effectively reduce the delay and energy consumption of network services. © 2022 IEEE.

关键词： Reinforcement learning

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