Reviewerly/exHarmony huggingface dataset:
Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

Falls are the contributing factor to both fatal and nonfatal injuries in the elderly. Therefore, pre-impact fall detection, which identifies a fall before the body collides with the floor, would be essential. Recently, researchers have turned their attention from post-impact fall detection to pre-impact fall detection. Pre-impact fall detection solutions typically use either a threshold-based or machine learning-based approach, although the threshold value would be difficult to accurately determine in threshold-based methods. Moreover, while additional features could sometimes assist in categorizing falls and non-falls more precisely, the estimated determination of the significant features would be too time-intensive, thus using a significant portion of the algorithm’s operating time. In this work, we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial sensors. The proposed network was introduced to address the limitations of feature extraction, threshold definition, and algorithm complexity. After training on a large-scale motion dataset, the KFall dataset, and straightforward evaluation with standard metrics, the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%, respectively. In addition, we have investigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network (CNN), a long short-term memory neural network (LSTM), and a hybrid model (CNN-LSTM). The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models (CNN, LSTM, and CNN-LSTM) with significant improvements.
Recognition of Handwritten Words from Digital Writing Pad Using MMU-SNet

In this paper, Modified Multi-scale Segmentation Network (MMU-SNet) method is proposed for Tamil text recognition. Handwritten texts from digital writing pad notes are used for text recognition. Handwritten words recognition for texts written from digital writing pad through text file conversion are challenging due to stylus pressure, writing on glass frictionless surfaces, and being less skilled in short writing, alphabet size, style, carved symbols, and orientation angle variations. Stylus pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines, angles, curves, and bends. The small change in dots, curves, and bends in the Tamil alphabet leads to error in recognition and changes the meaning of the words because of wrong alphabet conversion. However, handwritten English word recognition and conversion of text files from a digital writing pad are performed through various algorithms such as Support Vector Machine (SVM), Kohonen Neural Network (KNN), and Convolutional Neural Network (CNN) for offline and online alphabet recognition. The proposed algorithms are compared with above algorithms for Tamil word recognition. The proposed MMU-SNet method has achieved good accuracy in predicting text, about 96.8% compared to other traditional CNN algorithms.
Introduction to the Special Section on Cyber Security in Internet of Vehicles

introduction Share on Introduction to the Special Section on Cyber Security in Internet of Vehicles Authors: Ching-Hsien Hsu Department of Computer Science and Information Engineering, Asia University, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Wufeng, Taichung, Taiwan Department of Computer Science and Information Engineering, Asia University, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Wufeng, Taichung, Taiwan 0000-0002-2440-2771Search about this author , Amir H. Alavi Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA 0000-0002-7593-8509Search about this author , Mianxiong Dong Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Hokkaido, Japan Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Hokkaido, Japan 0000-0002-2788-3451Search about this author Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 4November 2022 Article No.: 81pp 1–6https://doi.org/10.1145/3584746Published:15 March 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Improved Practical Byzantine Fault Tolerance Algorithm Based on Supply Chain

The combination of blockchain technology and supply chain meets the needs of effective management and transparency of supply chain information, and promotes the development of all walks of life. However, the Practical Byzantine Fault Tolerant algorithm (PBFT) is not suitable for large-scale supply chain due to its poor scalability, arbitrary selection of master nodes and simple grouping. To solve this problem, this paper proposes a Re-PBFT algorithm (Reputation-PBFT, Re-PBFT) based on reputation-value assessment group. Firstly, the reputation value of the node is evaluated according to the historical transaction record of the node. Secondly, the nodes are grouped according to their reputation value, and the nodes with high reputation value are selected as the master nodes to construct a double-layer PBFT algorithm to improve their efficiency in multi-node situation. Finally, the impeachment mechanism is established to optimize the nodes of the consensus group. Experimental results show that Re-PBFT algorithm has higher scalability and reliability than PBFT algorithm, and the efficiency of the algorithm is also improved.
Chaotic Image Encryption Using Piecewise-Logistic-Sine Map

The classical one-dimension chaotic maps are not adequately secure, and some improved maps usually have a high time complexity. To address these problems, this paper presents a new Piecewise-Logistic-Sine map (PLSM). The bifurcation diagrams, Lyapunov exponent, and running time of the proposed PLSM are analyzed, and the outcomes show that it has better chaotic behavior with low time complexity. Furthermore, diffusion by certain rules is vulnerable to attack, so a PLSM-based image encryption scheme with random exclusive OR diffusion is proposed. Using a 256-bit secret key, the initial value and parameters of PLSM are calculated by the key distribution method. Then four chaotic sequences are generated by PLSM, they are used in three rounds of random exclusive OR diffusion. This scheme can spread a small change in the original image to all pixels, and the performance evaluation shows the security of this image encryption scheme.
STAR: An STGCN ARchitecture for Skeleton-Based Human Action Recognition

Skeleton-based human action cognition (HAR) has drawn increasing attention recently. As an emerging approach for skeleton-based HAR tasks, Spatial-Temporal Graph Convolution Network (STGCN) achieves remarkable performance by fully exploiting the skeleton topology information via graph convolution. Unfortunately, existing GCN accelerators lose efficiency when processing STGCN models due to two limitations. (1) At the dataflow level, the hardware parallelism of GCN accelerators cannot match the computation parallelism of STGCN models, leading to computing resource under-utilization. (2) At the computation level, GCN accelerators fail to exploit the inherent temporal redundancy in STGCN models. To overcome the limitations, this paper proposes STAR, an STGCN architecture for skeleton-based human action recognition. STAR is designed based on the characteristics of different computation phases in STGCN. For limitation (1), a spatial-temporal dimension consistent (STDC) dataflow is proposed to fully exploit the data reuse opportunities in all the different dimensions of STGCN. For limitation (2), we propose a node-wise exponent sharing scheme and a temporal-structured redundancy elimination mechanism, to exploit the inherent temporal redundancy specially introduced by STGCN. To further address the under-utilization induced by redundancy elimination, we design a dynamic data scheduler to manage the feature data storage and schedule the features and weights for valid computation in real time. STAR achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.48\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.98\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.54\times $ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$103.88\times $ </tex-math></inline-formula> energy savings on average over the HyGCN, AWB-GCN, TPU, and Jetson TX2 GPU.
Dual Gradient Based Snow Attentive Desnowing

In this paper, we propose a novel dual gradient-based desnowing algorithm that can accurately remove snow from a scene by characterizing snow particles. To localize snow in an image, we present a gradient-based snow activation map that can be estimated using snow classification. To recognize various patterns in the shapes and trajectories of snow particles, we introduce a gradient-based snow edge map. Using these two gradients, we estimate an accurate snow attention mask that is subsequently used for snow removal. In addition, we propose a translucency-aware context restoration network to handle various degrees of snow transparency and thus, prevent our method from losing the image context information during desnowing. Experimental results demonstrate that the proposed method considerably outperforms other state-of-the-art desnowing algorithms quantitatively and qualitatively.
Quality Analysis of Photovoltaic System Using Descriptive Statistics of Power Performance Index

The performance evaluation (PE) of the Photovoltaic(PV) system is an index representing the efficiency and reliability of the system. Most PE indicators evaluate the ratio of theoretically calculated power generation to actually measured power generation. The closer the ratio is to 1, the more ideal the PV system is. PV system varies depending on weather conditions and regional characteristics, especially on the types of sensors and measuring variables. Floating Photovoltaics(FPVs) and Marine photovoltaics(MPVs) vary with the environmental variables more as it is installed on the water and sea. In this paper, on the contrary to the existing PE methods, the most accurate regression model considering ambient temperature, relative humidity, and wind speed was used to predict the power in order to improve the accuracy. The optimal PE method for the PV system to easily and accurately detect failures of the PV system is proposed. Data from three FPVs in the same environment were analyzed for 1 year. The PV power prediction model including the wind speed and relative humidity was used to improve accuracy. The quality diagnosis was performed with an improved PE and the impact of various events can be represented through this. In this paper, the distribution of the corresponding Power Performance Index (PPI) values was analyzed using a descriptive statistic method, and indicators in terms of quality control were presented. The PV power generation state can be determined by the location of the average and median values of the boxplot. The fluctuation of PV power generation was identified using the size change of Inter Quartile Range (IQR), which represents the degree of data scattering. As a result, it was confirmed that failure occurred in the system when the IQR value is 2 times bigger than the normal IQR value.
Rapid Response System Based on Graph Attention Network for Predicting In-Hospital Clinical Deterioration

In-hospital clinical deterioration is a major worldwide healthcare burden in the intensive care units (ICUs), as it requires rapid intervention. Rapid response systems (RRSs) are widely used in many hospitals for the early detection of clinical deterioration to prevent cardiac arrest. Recently, with the increasing use of deep learning (DL) and electronic health records (EHR), many DL models have been developed for the intensive care domain, such as prediction of cardiac arrest, sepsis, or transferring to ICU. However, most existing methods do not explicitly learn the structure of multivariate time-series data, and this leads to high false-alarm rates and low sensitivity. In this research, we propose a novel DL-based framework that interpolates high-dimensional sequential data. Our approach combines two graph neural networks with an attention mechanism to learn the complex dependencies among multivariate time series. The experiments were conducted on two datasets: a private clinical dataset collected from Chonnam National University Hospital (CNUH) and a public dataset from the University of Virginia (UV). The experimental results show the potential performance of our model compared to some other related research.
The Prospects of Quantum Computing for Quantitative Finance and Beyond

Quantum computation holds the promise of highly efficient algorithms, provides exponential speedups for specific technologically significant problems, plays the most promising role in quantum technological progress, and has transformative potential in numerous industry sectors. In quantum computing, finance is recognized as one of the most beneficial areas because of the potential for many financial cases that can be solved by quantum algorithms suitable for noisy-intermedia-scale quantum(NISQ) computers. The benefits of quantum computing are colossal time and computational memory reduction with which the computational tasks are performed, which leads to the accuracy of the computations. This paper presents a comprehensive summary of quantum computing for traditional and Blockchain financial applications.
Characterizing Consumer Smartphone Apps for Virtual Reality–Based Exposure Therapy: Content Analysis

In vivo exposure therapy is the most effective treatment for phobias but is often impractical. Virtual reality exposure therapy (VRET) can help overcome critical barriers to in vivo exposure therapy. However, accessible mobile software related to VRET is not well understood.The purpose of our study is to describe the landscape of accessible smartphone apps with potential utility for clinical VRET.We conducted a content analysis of publicly available smartphone apps related to virtual reality on the Google Play Store and the Apple App Store as of March 2020.The initial search yielded 525 apps, with 84 apps (52 on the Google Play Store and 32 on the Apple App Store) included for analysis. The most common phobic stimulus depicted was bodies of water or weather events (25/84, 29.8%), followed by heights (24/84, 28.6%), and animals (23/84, 27.4%). More than half of the apps were visually abstract (39/84, 53.5%). Most apps were free to use (48/84, 57.1%), while the rest were free to try (22/84, 26.2%) or required payment for use (14/84, 16.7%), with the highest cost for use being US $6. The average overall app rating was 2.9 stars out of 5, but the number of ratings ranged from 0 to 49,233. None of the 84 apps advertised compliance with the Health Insurance Portability and Accountability Act, offered the ability to monitor data, provided clinician control over variables in the app experiences, or explicitly stated use by or development with clinicians.None of the smartphone apps reviewed were explicitly developed for phobia therapy. However, 16 of the 84 included apps were considered ideal candidates to investigate further as part of treatment due to their accessibility, depiction of phobia-relevant stimuli, low or no cost, and high user scores. Most of these apps were visually abstract and free to use, making them accessible and potentially flexible as part of clinical exposure hierarchies. However, none of the apps were designed for clinical use, nor did they provide tools for clinician workflows. Formal evaluation of these accessible smartphone apps is needed to understand the clinical potential of accessible VRET solutions.
FSL: federated sequential learning-based cyberattack detection for Industrial Internet of Things

Abstract Industrial Internet of Things (IIoT) brings revolutionary technical supports to modern industries. However, today’s IIoT still faces the challenges of modeling varying time-series in common data isolation while considering data security. To accurately characterize industrial dynamics, we propose a possible solution based on federated sequence learning (FSL) with cyber attack detection capabilities. Under a federated framework, FSL constructs a collaborative global model without violating local data integrity. Taking advantages of the locally sequential modeling, FSL captures the intrinsic industrial time-series responses. Furthermore, data heterogeneity among distributed clients is also considered, which is important to maintenance a robust but sensitive attack detection. Experiments on classic distributed datasets demonstrate that FSL is capable to accurately model data heterogeneity caused by data isolation and dynamics of time-series. Real IIoT attack detection experiments using a distributed testbed show that our FSL provides better detection performances for industrial time-series sensory data compared to existing methods. Therefore, the proposed attack detection approach FSL is promising in real IIoT scenarios in terms of feasibility, robustness and accuracy.
The creation of breast lesion models for mammographic virtual clinical trials: a topical review

Abstract Simulated breast lesion models, including microcalcification clusters and masses, have been used in several studies. Realistic lesion models are required for virtual clinical trials to be representative of clinical performance. Multiple methods exist to generate breast lesion models with various levels of realism depending on the application. First, lesion models can be obtained using mathematical methods, such as approximating a lesion with 3D geometric shapes or using algorithmic techniques such as iterative processes to grow a lesion. On the other hand, lesion models can be based on patient data. They can be either created starting from characteristics of real lesions or they can be a replica of clinical lesions by segmenting real cancer cases. Next, various approaches exist to embed these lesions into breast structures to create tumour cases. The simplest method, typically used for calcifications, is intensity scaling. Two other common approaches are the hybrid and total simulation method, in which the lesion model is inserted into a real breast image or a 3D breast model, respectively. In addition, artificial intelligence-based approaches can directly grow breast lesions in breast images. This article provides a review of the literature available on the development of lesion models, simulation methods to insert them into background structures and their applications, including optimisation studies, performance evaluation of software and education.
PrintAssist—a conversational human-machine interface for 3D printers

Abstract 3D printers are no longer found only in industry, universities or makerspaces but now are increasingly used in domestic settings. Personal fabrication will increase in the coming years, and 3D printing will play an important role in this process. Due to technology and price development, 3D printers are becoming established among casual users at home. However, there are still many hurdles in the use of 3D printers that interfere with their appropriation in everyday life. In this paper, we investigate how chatbots can overcome these hurdles and support onboarding to 3D printing. Furthermore, we explore how chatbots can be used as a human–machine interface and facilitate interaction with 3D printers for both novice and expert users. In a research-through-design approach, we have created a fully functional chatbot that introduces users to 3D printing and helps them perform typical tasks when operating 3D printers.
Global-and-Local sampling for efficient hybrid task self-supervised learning

Siamese-architecture-based self-supervised learning usually suffers from relatively high computational consumption and unsatisfactory performance because of its slow convergence and poor feature extraction capability. To alleviate these issues, we propose a self-supervised method, called SSL2, that is both efficient and effective. Specifically, a global and local sampling method is proposed to increase the number of samples while maintaining connections between semantic features. More significantly, SSL2 maintains low computational complexity and facilitates the establishment of mapping relationships between global comprehensive and local detailed features compared with other methods. In addition, an information retainer projection head (IRPH) is proposed to further balance the information between detailed inconsistency and semantic consistency. Finally, hybrid tasks are embedded into SSL2 to optimize the model so that it can effectively leverage the data provided by global and local sampling. Extensive qualitative and quantitative evaluations of various types of benchmarks illustrate that SSL2 outperforms existing self-supervised frameworks in commonly used computer vision tasks. Specifically, SSL2 achieved satisfactory performance with linear classification on ImageNet, outperforming MoCo-v2 by 2.2% with fewer calculations, and it also achieved competitive results compared with other state-of-the-art methods.
Accurate bundle matching and generation via multitask learning with partially shared parameters

How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted widespread attention in e-commerce owing to the increased satisfaction of both users and providers. Bundle matching and bundle generation are two representative tasks in bundle recommendation. The bundle matching task is to correctly match existing bundles to users while the bundle generation is to generate new bundles that users would prefer. Although many recent works have developed bundle recommendation models, they fail to achieve high accuracy since they do not handle heterogeneous data effectively and do not learn a method for customized bundle generation. In this paper, we propose BundleMage, an accurate approach for bundle matching and generation. BundleMage effectively mixes user preferences of items and bundles using an adaptive gate technique to achieve high accuracy for the bundle matching. BundleMage also generates a personalized bundle by learning a generation module that exploits a user preference and the characteristic of a given incomplete bundle to be completed. BundleMage further improves its performance using multi-task learning with partially shared parameters. Through extensive experiments, we show that BundleMage achieves up to 6.6% higher nDCG in bundle matching and 6.3× higher nDCG in bundle generation than the best competitors. We also provide qualitative analysis that BundleMage effectively generates bundles considering both the tastes of users and the characteristics of target bundles.