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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data

Procedia computer science 2023年 227卷 1062-1069页

作者： Alam Ahmad Hidayat Arif Budiarto Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters.

关键词： deep learning forecasting sleep quality time series wearable devices

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Exploring Multi-Modal Control in Music-Driven Dance Generation

Exploring Multi-Modal Control in Music-Driven Dance Generati...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ronghui Li Yuqin Dai Yachao Zhang Jun Li Jian Yang Jie Guo Xiu Li Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory School of Computer Science and Engineering Nanjing University of Science and Technology

Existing music-driven 3D dance generation methods mainly concentrate on high-quality dance generation, but lack sufficient control during the generation process. To address these issues, we propose a unified framework capable of generating high-quality dance movements and supporting multi-modal control, including genre control, semantic control, and spatial control. First, we decouple the dance generation network from the dance control network, thereby avoiding the degradation in dance quality when adding additional control information. Second, we design specific control strategies for different control information and integrate them into a unified framework. Experimental results show that the proposed dance generation framework outperforms state-of-the-art methods in terms of motion quality and controllability.

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Expression recognition algorithm based on CM-PFLD key point detection

Expression recognition algorithm based on CM-PFLD key point ...

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2021 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2021

作者： Zhang, Chao Hu, Siquan Shi, Zhiguo School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China Beijing Big Data Center Beijing China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

Facial expression recognition is a hot research topic in artificial intelligence industry and has a good research prospect in various fields. At present, facial expression recognition processes the whole face directly, but the pixel value of non-feature region may bring some interference for feature descriptor extraction. Considering that the cartoon effect of the face can directly reflect the facial expression features. In order to make the network pay more attention to the information of the facial features and their surrounding pixels, this paper proposes an expression recognition algorithm based on key point detection of Covering multi-scale Practical Landmark Detector (CM-PFLD). Under this algorithm, this paper constructs a cartoon expression data set, which only retains the key points of facial expression information, and then classifies facial expression by directly locating the key points of facial expression information. In order to verify the feasibility of the expression recognition method in this paper. The experiment uses Fer2013 and CK data sets to produce cartoon expression data sets, and trains and compares cartoon data sets and original data sets respectively under the same network. The experimental results show that the method proposed in this paper has high detection accuracy and fast speed on standardized and neat data sets. On the data set with more unfavorable factors, the training accuracy of the two methods is similar, but the processing speed of the proposed method is faster. Experimental results show that the proposed method is feasible and effective. © 2022 SPIE.

关键词： Pixels

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Convolutional Neural Network Using Res-Net For Organic and Anorganic Waste Classification 4

Convolutional Neural Network Using Res-Net For Organic and A...

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4th IEEE International Conference of computer science and Information technology, ICOSNIKOM 2022

作者： Girsang, Abba Suganda Yunanda, Rezky Syahputra, Muhammad Edo Peranginangin, Ezra Binus Graduate Program-Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399073

Convolutional Neural network is state of the art of image recognition or image classification. However to build the robust model using CNN needs many parameters adjusted, and choosing the good combination hyperparameter which impacts taking much computation time. Genetic algorithm is one method metaheuristic which is robust for choosing the combinatorial possible hyperparameter. This model also uses ResNet-50 which is a pretrained model of convolutional neural network that consists 50 layers. By using a pretrained like ResNet-50, it will increase the performance model. CNN-ResNet with efficient genetic algorithm (EGA) to optimize the hyperparameter. The EGA algorithm utilizes transfer learning techniques in its algorithm so that the optimization process on CNN can achieve unified accuracy values quickly. The best performance model optimized using EGA outperformed the ResNet-50 model and the model optimized using GA and VLGA in classifying organic and inorganic materials. The accuracy value obtained from EGA is 97.53% with a loss of 0.08. © 2022 IEEE.

关键词： Convolutional neural networks

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Optimizing Wireless Sensor Network Topology with Deep Reinforcement Learning for Multi-Source/Destination Scenarios

Optimizing Wireless Sensor Network Topology with Deep Reinfo...

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作者： Kosei Kobayashi Masayoshi Aritsugi Pedro Henrique González Israel Mendonça Kumamoto University Graduate School of Science and TechnologyKumamotoJapan Kumamoto University Faculty of Advanced Science and TechnologyKumamotoJapan Federal University of Rio de Janeiro System Engineering and Computer Science ProgramRio de JaneiroBrazil

Wireless sensor networks are widely valued for their effectiveness in real-time data collection. As the amount of data exchanged within such networks grows, designing a robust network topology that maximizes area coverage with minimal sensors has become a critical challenge. The choice of topology impacts key network metrics, including sensor coverage, communication range, connectivity, inference, and installation and management costsIn this paper, we address the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations, presenting an optimization approach based on deep reinforcement learning. This problem is noteworthy, as sensors in various applications are often required to share data within distinct destinationsWe leverage deep reinforcement learning to effectively address the complex task of selecting optimal sensor locations. Our reinforcement learning agent dynamically learns network structure by iteratively adding and removing sensors, optimizing both sensor coverage and the total number of sensors used. Experiment across diverse scenarios demonstrate the effectiveness of our method for network planning problems of varying scales, achieving full coverage with fewer sensors than traditional approaches. Additionally, our approach also produce solutions for large instances where Mixed Integer programming solvers were not able to. Overall, our method was able to reduce the number of sensors used by up to 22.3% compared to other methods.

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Mitigating oversmoothing through reverse process of GNNs for heterophilic graphs 24

Mitigating oversmoothing through reverse process of GNNs for...

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Proceedings of the 41st International Conference on Machine Learning

作者： MoonJeong Park Jaeseung Heo Dongwoo Kim Graduate School of Artificial Intelligence Pohang University of Science and Technology (POSTECH) Pohang Republic of Korea Graduate School of Artificial Intelligence and Computer Science and Engineering Pohang University of Science and Technology (POSTECH) Pohang Republic of Korea

Graph Neural Network (GNN) resembles the diffusion process, leading to the oversmoothing of learned representations when stacking many layers. Hence, the reverse process of message passing can produce the distinguishable node representations by inverting the forward message propagation. The distinguishable representations can help us to better classify neighboring nodes with different labels, such as in heterophilic graphs. In this work, we apply the design principle of the reverse process to the three variants of the GNNs. Through the experiments on heterophilic graph data, where adjacent nodes need to have different representations for successful classification, we show that the reverse process significantly improves the prediction performance in many cases. Additional analysis reveals that the reverse mechanism can mitigate the over-smoothing over hundreds of layers. Our code is available at https://***/ml-postech/reverse-gnn.

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Protecting Intellectual Property Rights in the Digital Age: Implementing Cyber Security Measures and Strategies 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Gadekar, Amit Goyal, S.B. Muqeem, Mohd Rajawat, Anand Singh Peiyu, Yao Chee, Weiming Sandip Institute of Technology and Research Centre Nashik India Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Science & Engineering Sandip University Nashik422213 India City Graduate School City University Petaling Jaya46100 Malaysia

ISBN: (数字)9783031508875

ISBN: (纸本)9783031508868

The protection of intellectual property rights (IPR) has taken on increased importance in today’s interconnected world, but it also faces new obstacles. As patents, copyrights, trademarks, and trade secrets are increasingly represented digitally, they expose their owners to new risks in the form of cyberattacks. This paper explains why it’s crucial to use cutting-edge cybersecurity precautions and techniques when protecting intellectual property online. This book begins with a discussion of the current digital dangers to IPR, such as hacking, phishing, and unauthorized duplication and distribution, and then moves on to discuss the most effective modern cybersecurity strategies for mitigating these risks. Encryption, multi-factor authentication, IP management via blockchain, and AI-driven threat detection are just some of the methods investigated. The article also highlights the importance of cross-border cooperation, legal frameworks, and policies in bolstering IPR protection in the digital sphere. A comprehensive paradigm for protecting intellectual property rights in the digital age can be reached through the integration of technical cybersecurity measures with strong legislative and policy-driven tactics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Phishing

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Sota Furusawa Chinthaka Premachandra Dept. of Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into “learned subject (class)” and “unlearned subjects (classes)”. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments.

关键词： Accuracy Passwords Biometric authentication Generative adversarial networks Search problems Security Protection Information technology Faces Hyperspectral imaging

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： E. Jiménez-Delgado C. Quesada-López A Méndez-Porras A Lara-Petitdemange J Alfaro-Velasco Graduate Program in Computer Science and Informatics University of Costa Rica Computer Engineering Department Technological Institute of Costa Rica School of Computer Science and Informatics University of Costa Rica

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

In this study, two deep learning models for automatic tattoo detection were analyzed; a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures.

关键词： Training Analytical models Accuracy Computational modeling Transfer learning computer architecture Real-time systems Computational efficiency Convolutional neural networks Tuning

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Multi Fuzzy Criteria based Decision Model for Presidential Election in Indonesia 9

Multi Fuzzy Criteria based Decision Model for Presidential E...

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9th International Conference on Informatics and Computing, ICIC 2024

作者： Utama, Ditdit Nugeraha Ramadhan, Bagas Rizky Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta 11480 Indonesia Bina Nusantara University Research Transfer Technology Office Jakarta 11480 Indonesia

ISBN: (纸本)9798331517601

Inevery nation, the main goal of a presidential election is to choose a good leader. Citizens of a country need to thoroughly observe and assess the presidential presidential candidates before making their selection. A study was conducted to create a simple decision model using multiple fuzzy criteria, taking into account the constrained perspective of Indonesian citizens. In this context, the primary methods employed for respectively designing and developing the model were object-oriented design and fuzzy logic. According to 7 free-selected presidential candidates, the model simulation revealed that candi- date number 2 received the highest decision value, scoring 68.542 points;with a gap value of 0.49. © 2024 IEEE.

关键词： decision model fuzzy logic multi-criteria presidential election

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