Reviewerly/exHarmony huggingface dataset:
Self-Supervised Human Activity Recognition With Localized Time-Frequency Contrastive Representation Learning

In this article, we propose a self-supervised learning solution for human activity recognition with smartphone accelerometer data. We aim to develop a model that learns strong representations from accelerometer signals, in order to perform robust human activity classification, while reducing the model's reliance on class labels. Specifically, we intend to enable cross-dataset transfer learning such that our network pretrained on a particular dataset can perform effective activity classification on other datasets (successive to a small amount of fine-tuning). To tackle this problem, we design our solution with the intention of learning as much information from the accelerometer signals as possible. As a result, we design two separate pipelines, one that learns the data in time-frequency domain, and the other in time-domain alone. In order to address the issues mentioned above in regards to cross-dataset transfer learning, we use self-supervised contrastive learning to train each of these streams. Next, each stream is fine-tuned for final classification, and eventually the two are fused to provide the final results. We evaluate the performance of the proposed solution on three datasets, namely, MotionSense, human activities and postural transitions data (HAPT), and heterogeneity human activity recognition (HHAR), and demonstrate that our solution outperforms prior works in this field achieving F1 scores 0.934, 0.911, and 0.826, respectively. We further evaluate the performance of the method in learning generalized features, by using MobiAct dataset for pretraining and the remaining three datasets for the downstream classification task, and show that the proposed solution achieves better performance in comparison with other self-supervised methods in cross-dataset transfer learning.
Decentralized Data-Privacy Preserving Deep-Learning Approaches for Enhancing Inter-Database Generalization in Automatic Sleep Staging

Automatic sleep staging has been an active field of development. Despite multiple efforts, the area remains a focus of research interest. Indeed, while promising results have reported in past literature, uptake of automatic sleep scoring in the clinical setting remains low. One of the current issues regards the difficulty to generalization performance results beyond the local testing scenario, i.e. across data from different clinics. Issues derived from data-privacy restrictions, that generally apply in the medical domain, pose additional difficulties in the successful development of these methods. We propose the use of several decentralized deep-learning approaches, namely ensemble models and federated learning, for robust inter-database performance generalization and data-privacy preservation in automatic sleep staging scenario. Specifically, we explore four ensemble combination strategies (max-voting, output averaging, size-proportional weighting, and Nelder-Mead) and present a new federated learning algorithm, so-called sub-sampled federated stochastic gradient descent (ssFedSGD). To evaluate generalization capabilities of such approaches, experimental procedures are carried out using a leaving-one-database-out direct-transfer scenario on six independent and heterogeneous public sleep staging databases. The resulting performance is compared with respect to two baseline approaches involving single-database and centralized multiple-database derived models. Our results show that proposed decentralized learning methods outperform baseline local approaches, and provide similar generalization results to centralized database-combined approaches. We conclude that these methods are more preferable choices, as they come with additional advantages concerning improved scalability, flexible design, and data-privacy preservation.
Tuna Swarm Algorithm With Deep Learning Enabled Violence Detection in Smart Video Surveillance Systems

In smart video surveillance systems, violence detection becomes challenging to ensure public safety and security. With the proliferation of surveillance cameras in public areas, there is an increasing need for automated algorithms that can accurately and efficiently detect violent behavior in real time. This article presents a Tuna Swarm Optimization with Deep Learning Enabled Violence Detection (TSODL-VD) technique to classify violent actions in surveillance videos. The TSODL-VD technique enables the recognition of violence and can be a measure to avoid chaotic situations. In the presented TSODL-VD technique, the residual-DenseNet model is applied for feature vector generation from the input video frames and then passed into the stacked autoencoder (SAE) classifier. The SAE model is enforced to recognize the events into violence and non-violence events. To improve the violence detection effectiveness of the TSODL-VD procedure, the TSO protocol is utilized as a hyperparameter optimizer for the residual-DenseNet model. The performance validation of the TSODL-VD procedure has experimented on a benchmark violence dataset. The experimental results demonstrate that the TSODL-VD technique accomplishes precise and rapid detection outcomes over the recent state-of-the-art approaches.
A Progressive Feature Enhancement Deep Network for Large-Scale Remote Sensing Image Superresolution

The pursuit of super-resolution (SR) with large upscaling factors such as 8×, for enhancing the spatial resolution of low-resolution (LR) remote sensing images is a persistent and challenging problem. To address this issue, we propose the Progressive Feature Enhancement SR (PFESR) network with an 8× upscaling factor. Given the limited high-frequency information provided by a single LR image, we propose an improved style transfer technology to generate auxiliary details that aid in the recovery of high-resolution (HR) images. Additionally, multi-scale texture features are extracted through the Visual Geometry Group (VGG) feature extraction (VFE) block. To efficiently fuse various features, we combine hard and soft attention mechanisms. Finally, we use a hierarchical fusion block to address the progressive fusion problem of multiple scale features. Experiments on three datasets demonstrate that our method achieves state-of-the-art performance and exhibits good robustness in 8× and higher scale SR tasks.
3D Hand Mesh Reconstruction from Monocular Image by using Intermediate Representations

In this paper we propose a complete end-to-end network architecture to estimate 3D hand mesh from RGB hand images. We use ResNet-50 to extract the image features, and for better regression of model parameters later, we obtain some 2D feature maps, such as 2D heatmap and mask images, through some convolutional layers. In the model parameter regression part, we use the fully connected layer for iterative regression of the model parameters.
Customized Training and Application of Monocular Depth Ranging for Assisted Driving

In this paper, we propose a monocular depth ranging method based on a hardware-software integrated system, which composes a deep learning algorithm with a Jetson Nano-based sensor. Firstly, we use the existing model to realize the transformation from single-image to depth map. Secondly, we use the KITTI dataset to fine tune the depth transformation model. Thirdly, we invoke the physical camera and apply the reconstructed model to implement a joint hardware and software robot system. Finally, we conduct a series of experiments to verify the effectiveness of our customized hardware-software system with robot cars.
Zero Trust Framework In Financial Sector: The Handling Of Machine Learning Based Trust Management

Acceleration in technology transformation for financial sectors has been impacting the way how relevant institutes should store and protect their data and resources from untrusted entities. Zero Trust as an enhanced security model in which every access request must be verified on a per-session basis is believed to be an effective way of providing fine-tuned and granulated secure access control compared to the traditional network perimeter-based security model, where approval of access requests is based on the network location. In this paper, we proposed a machine learning-based trust management model that is integrated into a Zero Trust framework, aiming to develop a practical example specifically for financial sectors by offering a guide on how trust management could be handled in the zero trust architecture.
A modified M-sequence with dynamic mapping and its autocorrelation

The authors focus on a kind of modified M-sequence to accelerate the development of stream ciphers. This paper proposes a dynamic mapping scheme and considers the correlation property to evaluate a periodic property of the sequence. As a result, it is found that there is a symmetricity and the authors show its proof theoretically for further improvement.
Mask Generation with Meta-Learning Classifier Weight Transformer Network for Few-Shot Image Segmentation

This paper proposes a meta-learning classification weight transfer network to generate masks as a few-shot image segmentation architecture. It generates good prior masks via a pretrained classification weight transfer architecture, and uses a pretrained feature extraction architecture on query images and support images. The network architecture exploits a top-down path in a feature augmentation module to adaptively transfer information from fine to coarse features for extracting features from query images. Finally, the classification module predicts the segmentation of the query image. The experimental results show that using the mean intersection of joints (mIOU) as the evaluation mechanism, the accuracy of the 1-shot experimental results is 1.7% higher than that of the baseline. In the 5-shot experimental results, the accuracy is also improved by 2.6%. Therefore, compared with the baseline, it clearly shows that the mask generated by the meta-learning classification weight transfer network can effectively help improve the performance of few-shot image segmentation system.
Automatic Recognition of Container Serial Code

The recognition of serial code on a container is a key to manage the containers. Traditional approach to identifying the code manually is tedious and error prone. To tackle the problem, scholars have proposed various approaches based on traditional text detection and text recognition methods from the images to automatically recognize the code in the past. However, the approaches usually suffered from low recognition accuracy due to complex environments or inexpensive photographic equipment. In recent years, deep learning techniques have proved effective in real-time object detection and recognition. In this study, we propose to use deep learning coupled with image processing techniques to tackle the problem. Experimental results indicate the proposed approach is feasible and promising.
Analyzing on Reward and Fee Effect on the Size of Mining Pools - A Case of Bitcoin System

Cryptocurrency is a blockchain application that has attracted the attention by many users. The proof-of-work consensus mechanism is adopted by several cryptocurrencies, such as Bitcoin, Litecoin, and Dogecoin. This work discusses how miners choose mining pools (MPs) and how MP operators adjust pool fees to maximize their revenue. This work proposes a mining difficulty adjustment method and a randomly distributed round-batch computing power competition algorithm for mining simulation. The simulation results show that by increasing the pool fees, the large MP can increase the revenue by about 60%, and the ability to withstand the risk of changes in computing power. Small MPs can sacrifice about 35% of the revenue in exchange for the willingness of miners to stay by lowering the fees.
Development of a Smart Gate for the University Library with Multiple Sensors

The author has been developing a smart gate for the main entrance of the Library of Konan University. Two sensors are used. One is the FeliCa card reader that can read the information of the ID card issued by the university, and the other is a 2D LiDAR to verify the passage at the end of the gate, that are cooperatively used with the support of a display for the users. The design is intended to adhere the universal design. The system is of very low cost and has various extension capabilities because the author and the university can share any information of the system.
A Novel Deep Convolutional Neural Network Pooling Algorithm for Small floating objects detection

The problem of floating debris in rivers and oceans is growing. To clean floating objects on the water more effectively, IoT-based unmanned boats were chosen for autonomous cleaning. However, the strong light reflections of riverside objects on the water surface pose challenges for vision-based object detection systems to detect small targets. By modifying the pooling module in Spatial Pyramid Pooling and using the TS-YOLO structure to retain the original spatial pyramid advantage, we improve the accuracy of floating litter for detecting objects on rivers. In the experimental results, our proposed method was tested on Pascal VOC, FLOW, and WIDER FACE, which showed good detection capability on mAP with 2.86%, 1%, and 2.28% improvement over the original YOLOv4.
A Data Monitoring System with Human Action Recognition for Long-Term Care Institutions

In this paper, we apply wireless technologies to implement automatic abnormal event detection and E-clinical cart information systems for long-term care institutions. The E-clinical cart can follow medical personnel, easing the physical workload of manually pushing the clinical cart. For autonomous patrol work, some abnormal actions, such as falls and squats, can be detected using the proposed human action recognition with few-shot data. Moreover, the proposed monitoring platform with a graphical user interface (GUI) can improve the E-quality and efficiency of nursing work. The experimental results show that the intelligent data monitoring system can effectively carry out the task assignment of the E-clinical cart and event monitoring and warning in long-term care institutions.
Generating Virtual Head-Mounted Gyroscope Signals From Video Data

Human activity recognition (HAR) using the deep learning method has caught the attention of researchers thanks to its automatic feature extraction and accurate prediction capabilities. However, for applications based on a wearable sensor, such as an inertial measurement unit (IMU), the process of collecting and hand-labeling large amounts of data is complicated and labor-intensive, meaning that there is a limited amount of data available for model training. Therefore, there is a need to propose and develop data augmentation approaches to generate high quality data for the growth of HAR research. We propose a head-mounted virtual gyroscope signal generator to alleviate the problems caused by the lack of data in head movement-related applications. Unlike previous work, our system only generates head-motion related gyroscope data, minimizing system complexity. We trained a deep-learning model in a head motion-based application with different generated sensor data ratios, and show the viability of our proposed data generation method.
Research on Full-text Recommendation System Based on Language Technology Applied In Library

In recent years, recommendation systems have been widely used in the commercial field, and many e-commerce platforms and merchants have adopted the model of recommendation systems as a way to sell goods. However, when the product's features are insufficient, or the user's evaluation of the product is not much, it is difficult for the recommender system to recommend the product that meets the customer's expectations. For example, when recommending books in the library, you will find out that the books cannot be like clothes and can use rich and diverse ontology features such as brand, material, color, and length as the basis for the recommendation system. In addition, we found that only a few users will Evaluate or comment on books during the user's borrowing process. To sum up, this study will explore how to recommend books through natural language processing in the case of insufficient book features and insufficient user ratings and reviews.
Hybrid Learning: When Centralized Learning Meets Federated Learning in the Mobile Edge Computing Systems

Federated learning is a new artificial intelligence technology with which an edge server can orchestrate with multiple end users to train a global model collaboratively. Under this setting, users only upload the locally trained parameters instead of their local data, substantially reducing communication costs and boosting data privacy. Nonetheless, federated learning mainly relies on users’ local training, overlooking the abundant computing resources owned by the edge server. To exploit the edge server’s processing power, we propose a hybrid learning paradigm that consists of centralized and federated learning components. This scheme uploads a portion of users’ data for centralized learning when the local model is trained under federated learning. We derive a theoretical upper bound for the model accuracy, which can be used to assess the performance of the proposed new learning paradigm. To balance the computation and communication resources for a good model accuracy performance, we establish a joint optimization problem of model accuracy, latency, and energy consumption. We also devise the corresponding joint optimization algorithm to solve the problem. Experiment results show that compared with centralized and federated learning, the proposed hybrid learning algorithm can effectively improve the model accuracy and significantly reduce computation and communication resources.
3D object detection with low resolution depth map

Obtaining high-resolution depth map from radar and lidar for training depth estimation network is a common approach in recent years, although the expensive price and high computational complexity cause it hard to be apply in real-world applications. In this research, we propose a novel concept that only requires low resolution depth map for training, but able to reach similar performance comparing to high-resolution depth map-based approaches. Refers to the experimental results, which evaluated on KITTI dataset, our proposed concept decreases 7% and 2% on the mAP of car and pedestrian comparing to the original approach in CaDDN, although it achieves similar performance on visualization result.
Collection platform for audience sensor data in live music performances using mobile lines and clouds

Fans of live music support artists on stage with cheers and applause. Before the COVID-19 pandemic, live music performances were characterized by audience cheers, and artists could feel the fans' excitement through these cheers. However, with the spread of COVID-19, it has become difficult for fans to cheer, and such sharing has become challenging. In this paper, we propose a sensor worn on the wrist to record fans' excitement using an accelerometer. Our method allows artists and fans to share excitement, momentum, and status during online live performances during the COVID-19 pandemic. We created a prototype implementing our proposed method and built an analysis platform to collect data securely in the cloud.
Homomorphic Encryption-Based Similarity Calculation for Mobile Data Usage Prediction

For improving security, this study proposes a mobile data usage prediction method based on homomorphic encryption (HE)-based similarity calculation without plaintext storage. The cosine similarity, angular similarity, Tanimoto similarity, and Euclidean similarity based on HE according to ElGamal cryptography are proposed and combined in a machine learning method to consider the mobile data usage during the last periods for predicting the mobile data usage during the next period.
Motion based DBSCAN for object tracking in satellite video

Moving object tracking in satellite imagery is a hot topic in remote sensing. The size of the object's motion is tiny in scale compared to the entire frame. Because satellite images are in continuous motion, the momentum of the motion of the moving object is disturbed by the satellite motion. In order to detect moving objects in satellite images, this paper proposes an improved algorithm for target tracking in satellite images based on the DBSCAN to track targets. The experimental results show that objects are successfully tracked in satellite images using the algorithm proposed in this study.
Contour Based Hand Segmentation Using Deep Convolutional Neural Networks

In modern society, the interaction between human and machine has become more and more important. Compare with the traditional interface, operation with the gesture is more intuitive and attractive. Deep learning can share the computations and the parameters by sharing the convolution layers, in order to eliminate the multi-stage system. For hand gesture recognition task, hand detection and gesture classification can share the same image features. In this work, we focus on hand detection and segmentation task, enforce the data on extreme situations scene, and use iterative training to further reduce the errors. Finally, we achieve a robust hand segmentation model with F1 score over than 90%.
Real-Time Dynamic Sign Language Recognition Using LSTM Based on MediaPipe Hand Data

Sign Language is one of the important communication methods for the hearing-impaired person. As it is hard to learn, it has been researched several times throughout various motion capture technologies. In this paper, MediaPipe Hand, Google’s AI framework, and LSTM, a type of RNN, were used for training our dynamic Sign Language recognition system. Unlike in previous researches, our trained model translates the dynamic Sign Language in real-time with displaying the translated sentences. Sign language differs from existing English grammar in terms of language structure. To solve this problem, there is a way to use Neural Machine Translation, but due to the limitation of the number of words used in this research, we used a simple rule-based structure converting the recognized word sequences into sentences with English grammar.
Prevention Mechanisms for Stolen Access Token Injection Attack in Financial-grade API

In this paper, we will discuss the challenges of Financial-grade API, focusing on prevention mechanisms for "Cuckoo’s Token Attack" and delve into the current security authentication mechanisms of OAuth 2.0, Financial-grade API 2.0 and OpenID Connect. Finally, we found that the OpenID Connect method can mitigate the attack by using a one-time token and issuer identifier, but a one-time authentication method without the OpenID Connect requirement is also needed.
A Profiling Guided Optimization Scheme for Accelerating Quantum Simulations

We propose the concept of a profiling guided optimization scheme to boost the performance of quantum simulations. The key idea of the proposed scheme is that a quantum simulator is used to collect the quantum-wise information during the simulations, and the collected profiling data are fed to a quantum compiler to guide the generation of a more efficient quantum code version achieving higher simulation speed. Our result shows our proposed scheme achieves a speed-up by a factor of 1.36 when simulating a 28-bit quantum circuit.