Reviewerly/exHarmony huggingface dataset:
Monitoring the Physical Condition of Basketball Players using IoT and Blockchain

As a popular competitive sport, basketball has attracted the attention of many people due to its warm and sunny characteristics. Athletics means results, but not everyone gets the results they want. With the continuous development of technology, people have gradually discovered that the physical function of athletes is one of the important factors affecting the performance of sports. The long-standing principle of “three big and one small” ignores the overall physical fitness of athletes, resulting in insufficient physical coordination and slow performance improvement. Competitive sports have always followed the principle of “three big and one small,” which means emphasizing high-strength and high-intensity training, and fundamentally ignoring the impact of small muscle groups and overall physical fitness on athletes. To overcome this disadvantage, this paper aims to study the physical condition monitoring of basketball players based on the Internet of Things and blockchain. It is expected to use the Internet of Things and blockchain technology to monitor the physical function of athletes in real time, formulate scientific training plans, and improve athletes’ competition. The essence of blockchain is a decentralized database, which is used to verify the validity of messages. With the promotion of Internet technology, it is essential to store and extract information from large network information. This paper compared the results of experimental methods to show the impact of the connection between physical function training and high-level basketball traditional training, proposed a new scalable storage and privacy protection scheme to solve the problem that a large amount of data cannot be stored on the blockchain. The experimental results in this paper showed that the lowest hemoglobin content of athletes before training was 131 g/L, and the highest was 157 g/L, indicating that the athletes were generally in good physical condition at this moment. After training, the lowest hemoglobin content of athletes was 117 g/L and the highest was 131 g/L, indicating that the athlete’s physical function state declined and the training intensity was too high.
Convolution Neural Networks for the Automatic Segmentation of 18F-FDG PET Brain as an Aid to Alzheimer’s Disease Diagnosis

Our work aims to exploit deep learning (DL) models to automatically segment diagnostic regions involved in Alzheimer’s disease (AD) in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) volumetric scans in order to provide a more objective diagnosis of this disease and to reduce the variability induced by manual segmentation. The dataset used in this study consists of 102 volumes (40 controls, 39 with established Alzheimer’s disease (AD), and 23 with established mild cognitive impairment (MCI)). The ground truth was generated by an expert user who identified six regions in original scans, including temporal lobes, parietal lobes, and frontal lobes. The implemented architectures are the U-Net3D and V-Net networks, which were appropriately adapted to our data to optimize performance. All trained segmentation networks were tested on 22 subjects using the Dice similarity coefficient (DSC) and other similarity indices, namely the overlapping area coefficient (AOC) and the extra area coefficient (EAC), to evaluate automatic segmentation. The results of each labeled brain region demonstrate an improvement of 50%, with DSC from about 0.50 for V-Net-based networks to about 0.77 for U-Net3D-based networks. The best performance was achieved by using U-Net3D, with DSC on average equal to 0.76 for frontal lobes, 0.75 for parietal lobes, and 0.76 for temporal lobes. U-Net3D is very promising and is able to segment each region and each class of subjects without being influenced by the presence of hypometabolic regions.
Design of Internet of Things (IoT) System Based Smart City Model on Raspberry Pi

This research work focuses on the Internet of Things (IoT) idea utilizing an intelligent (or smart or innovative) city model with the support of Raspberry Pi. Using Raspberry Pi, this model examined the strategy and execution of a smart city established on the Internet of Things. This study attempts to create an urban IoT system utilizing Raspberry Pi to help smart cities solve domestic difficulties. The Internet of Things idea assists in analyzing the functions used to define the functions of the Raspberry Pi. The IoT is critical to creating a smart city concept in a developing nation like India. The various low-cost operations help analyze Raspberry Pi functionalities in the smart city model. A better Smart city development strategy has been presented when comparing the existing system, Raspberry Pi-based techniques.
Police Brutality and Racial Justice Narratives Through Multi-Narrative Framing: Reporting and Commenting on the George Floyd Murder on YouTube

The increasing use of social media like YouTube as a news platform provides new opportunities for the public to react to news reporting. This convergence produces multi-narrative framings of police violence-related evidence that requires further attention, especially given the potential impact on state accountability processes. Using a frame analysis of news outlets and content analysis of comments on YouTube, we identify frames, responses, and the multi-narrative framing that results from this converging environment. Our findings suggest a triumvirate of competing frames around police brutality, with mistrust of media complicating the role news media plays in accountability.
Fabrication of Nāmya: A Bend and Touch-Sensitive Flexible Smartphone-Sized Prototype

In human-computer interaction research, prototypes allow for communicating design ideas and conducting early user studies to understand user experience without developing the actual product. For investigating deformation-based interaction, functional prototyping becomes challenging due to the unavailability of commercial platforms and the marginal availability of flexible electronic components. During functional prototyping, incurred time and cost are essential factors that further depend on the ease of stiffness customization, reproduction, and upgrade. To offer these advantages, this work presents the fabrication workflow of Nāmya, a smartphone-sized flexible prototype that can detect bend gestures and touch-based inputs using off-the-shelf sensors and flexible materials. This do-it-yourself (DIY) approach to fabricating deformable prototypes focuses on addressing the challenges of selecting flexible material, type of sensor, and sensor positions. We also demonstrate that the proposed use of a flexible three-dimensional- (3D-) printed internal structure with sensor pockets and the one-part silicone cast allows the development of robust deformable prototypes. This fabrication process offers the opportunity to easily customize device stiffness, reproduce prototypes with similar physical properties, and upgrade existing prototypes.
Permutation-Based Block Code for Short Packet Communication Systems

This paper presents an approach to the construction of block error-correcting code for data transmission systems with short packets. The need for this is driven by the necessity of information interaction between objects of machine-type communication network with a dynamically changing structure and unique system of commands or alerts for each network object. The codewords of a code are permutations with a given minimum pairwise Hamming distance. The purpose of the study is to develop a statistical method for constructing a code, in contrast to known algebraic methods, and to investigate the code size. An algorithm for generating codewords has been developed. It can be implemented both by enumeration of the full set of permutations, and by enumeration of a given number of randomly selected permutations. We have experimentally determined the dependencies of the average and the maximum values of the code size on the size of a subset of permutations used for constructing the code. A technique for computing approximation quadratic polynomials for the determined code size dependencies has been developed. These polynomials and their corresponding curves estimate the size of a code generated from a subset of random permutations of such a size that a statistically significant experiment cannot be performed. The results of implementing the developed technique for constructing a code based on permutations of lengths 7 and 11 have been presented. The prediction relative error of the code size did not exceed the value of 0.72% for permutation length 11, code distance 9, random permutation subset size 50,000, and permutation statistical study range limited by 5040.
Model averaging for sparse seemingly unrelated regression using Bayesian networks among the errors

Abstract Multivariate Bayesian linear regression (MBLR) is a popular statistical tool with many applications in a variety of scientific fields. However, a shortcoming is potential model over-complexity, as the model assumes that all responses depend on the same covariates and that all errors are mutually pairwise correlated. The class of Bayesian seemingly unrelated regression (SUR) models generalizes the class of MBLR models by allowing for response-specific covariate sets. In a recent work it has been proposed to employ Gaussian graphical models for learning sparse SUR (SSUR) models with conditional independencies among the errors. The proposed SSUR model infers undirected edges among the errors, and the proposed Reversible Jump Markov Chain Monte Carlo (RJMCMC) inference algorithm relies on approximations of the marginal likelihoods. In this paper, we propose a new refined SSUR model that replaces the undirected graphs (Gaussian graphical models) by directed acyclic graphs (Gaussian Bayesian networks). Unlike the earlier proposed model, our new model is therefore able to learn some directed edges among the errors. And we derive a RJMCMC algorithm that does not require approximations of the marginal likelihoods. In particular, we present an algorithm for sampling covariance matrices that are coherent with a given directed acyclic graph. The proposed RJMCMC algorithm allows for exact Bayesian model averaging across both: the response-specific covariate sets and the directed acyclic graphs.
Decentralized Identifiers (DIDs)-Based Authentication Scheme for Smart Health Care System

The smart health care system collects users' health data, stores and shares it with other associated users in the platform. The ability to remotely access and manage smart medical equipment is handy, but it’s also perilous, because vulnerable devices may be used to spy on people or execute other criminal actions. This emphasizes the need of developing a reliable and secure authentication system. Recently, there has been a lot of interest in employing blockchain in the smart environment (e.g., Distributed Internet of Things (IoT)) for both maintaining trust and privacy-preserving. Although several proposals have been handled with blockchain-based IoT issues, there are still numerous challenges such as authentication, revocation, delays, anonymity, and impersonation. Motivated by these facts, in this work, we construct an efficient Decentralized Identifiers (DIDs)-Based Authentication Scheme for Smart Health Care. The model integrates DIDs in the Smart Health care system to provide a secure and efficient authentication service. We also demonstrate that the suggested system meets the security and privacy criteria, such as anonymity, traceability, and confidentiality, through implementation and assessment.
User-Centered Design to Enhance IoT Cybersecurity Awareness of Non-Experts in Smart Buildings

Smart buildings, building automation and operational management have increasingly begun to incorporate Internet of Things (IoT) technology. Therefore, they have become susceptible to common cyber attacks targetting IoT devices. However, there is still a lack of an effective way of monitoring the cybersecurity situation of smart devices, IoT sensors and networks. During the operational lifecycle it may also not be easy for non-experts to discern cybersecurity issues from malfunctioning or physical safety. Therefore, we propose visualization prototypes that provide both safety and cybersecurity status of IoT devices for non-expert users in smart buildings. By utilising a user-centered design method, the visualization dashboards are developed based on requirements of two user roles - House managers and Residents. The user test results have shown the capabilities and effectiveness of leveraging dashboards to increase cybersecurity awareness in smart buildings.
An Improved Sensor Anomaly Detection Method in IoT System using Federated Learning

The industrial sensor has emerged as a critical device to monitor environment condition in the manufacturing system. However, abnormal behaviors of these smart sensor may indicate some failure or potential risk during system operation, thereby increasing high availability of the entire manufacturing process. Data collected from many edge devices for detecting failure contain private data of different enterprises which is challenging current detection approaches as user privacy has attracted more concerns. Moreover, detecting anomalies in the centralized system is often more time consuming due to the response time. To overcome these issues, we proposed an anomaly detection method using a clustering federated learning framework with a long short-term memory (LSTM) to improve model performance in term of accuracy, scalability and more secure.
Dual-inferences mechanism for real-time semantic segmentation

Autonomous cars have potential developments based on technology evolution. In the street scenes, the car needs to deal with a wide range of object sizes. Existing methods generally concentrate on deploying a single inference for semantic segmentation. However, one single scale is not suitable to capture the whole information of diverse sizes. It can effectively capture the context of thin objects, but it will get problems to cover the whole information of large objects, and reversely. In this paper, we propose an approach based on multi-scale inference to tackle the above difficulty. The multi-scale mechanism proposal employs two inference scales. Each scale is processed by a specific rate set of atrous spatial pyramid pooling. The segmentation maps are added together to take advantage of all scales. We validate our networks with a series of experiments on different open datasets. The approaches achieve high accuracy while reaching the speed for real-time semantic segmentation. The results are 75.5 % mIoU at 51 FPS on Cityscapes and 42.0 % mIoU on Mapillary Vistas.
Reinforcement Learning-based Multiple Camera Collaboration Control Scheme

This paper proposes a reinforcement learning-based multiple camera control scheme to accurately monitor an object of interest or abnormal behavior. The proposed scheme recognizes the object information such as object coordinates and size using an object detection deep learning model or CCTV controller selection. The recognized object information is mapped with the cameras to be linked, and PTZ camera control values are generated and transmitted to the video surveillance system. The video surveillance system performs PTZ control according to the PTZ camera control value. The multiple camera-based collaboration control scheme can support monitoring from various angles in CCTV cameras. In addition, the accuracy of video analysis can be improved by using videos obtained from various positions or angles.
An Explainable Multi-Task Learning Approach for RF-based UAV Surveillance Systems

Unmanned aerial vehicles or drones are ubiquitous among people, which can lead to technological, security, and community safety issues that must be addressed, monitored, and avoided. Intelligence services are always on the search for potential technology and intelligent systems that can identify drones. A potential drone surveillance system must be capable of detecting, localizing, identifying, recognizing the modes, and combating unauthorized drones. In this paper, we introduce a Multi-Task Learning (MTL) neural network for drone detection, identification, and drone mode detection using Radio Frequency (RF) signals. Due to the semantic abstraction of the drone RF signals, a single-task learning method can not fully meet the demands of the current anti-drone system. Moreover, executing each of the tasks, such as drone detection, type identification, and activity recognition, individually takes longer time, which is not applicable in a real-time drone surveillance system. Therefore, this paper proposes an MTL approach leveraging convolution layers to perform three tasks in parallel. A cross-entropy loss function used as the objective function optimization to improve the accuracy of the multiple tasks. The empirical results shows that the proposed MTL model achieve a better recognition accuracy compared to the existing solutions.
Efficient Device-Edge Inference for Disaster Classification

Image classification can learn useful insights from crisis incidents and is gaining popularity in the field of disaster management. This is fueled by the recent advances in computer vision and deep learning techniques, where accurate neural network models for disaster type classification can be accrued. However, these studies quite commonly neglect the prohibitive inference workload which may hamper its wide-spread deployment, especially for model execution on low-powered edge devices. In this paper, we propose a lightweight disaster classification model that recognizes four types of natural disaster plus one non-disaster class. To support real-time applications, the proposed model is optimized with OpenVINO, which is a neural network acceleration platform. Different from existing works which focus on benchmarking at training stage, our experimental results reveal the actual performance at inference stage. Specifically, the optimized version achieves up to 23.93 frames per second (FPS), which is more than doubled the speed achieved by the original model, while sacrificing only 0.935 % of classification accuracy.
Design and Development of Server-Client Cooperation Framework for Federated Learning

Federated learning is a machine learning technique that enables distributed training without explicitly data sharing between multiple heterogeneous devices. In this paper, we propose and develop a practical federated learning framework to effectively support model deployment, aggregation, and client device monitoring. The proposed approach is designed as a micro-architecture service using container-related technologies such as Docker, Kubernetes, and Prometheus.
TCAE: Temporal Convolutional Autoencoders for Time Series Anomaly Detection

Prevalent recurrent autoencoders for time series anomaly detection often fail to model time series since they have information bottlenecks from the fixed-length latent vectors. In this paper, we propose a conceptually simple yet experimentally effective time series anomaly detection framework called temporal convolutional autoencoder (TCAE). Our model imposes dilated causal convolutional neural networks to capture temporal features while avoiding inefficient recurrent models. Also, we utilize bypassing residual connections in encoded vectors to enhance the temporal features and train the entire model efficiently. Extensive evaluation on several real-world datasets demonstrates that the proposed method outperforms strong anomaly detection baselines.
Quantifying quantum correlations in noisy Gaussian channels

The Gaussian states are essential ingredients in many tasks of quantum information processing. The presence of the noises imposes limitations on achieving these quantum protocols. Therefore, examining the evolution of quantum entanglement and quantum correlations under the coherence of Gaussian states in noisy channels is of paramount importance. In this paper, we propose and analyze a scheme that aims to specify and examine the dynamic evolution of the quantum correlations in two-mode Gaussian states submitted to the influence of the Gaussian thermal environment. We describe the time evolution of the quantum correlations in an open system consisting of two coupled bosonic modes embedded in a Gaussian thermal environment. We discuss the influence of the environment in terms of the initial parameters of the input states. The quantum correlations are quantified using Gaussian interferometric power and the Gaussian entanglement of formation. The behavior of these quantum correlations quantifiers is strictly dependent on the parameters of the input states that are employed. We show that the Gaussian interferometric power is a measurement quantifier that can capture the essential quantum correlations beyond quantum entanglement. In addition, we show that the Gaussian interferometric power is less influenced than the Gaussian entanglement of formation.
A core-based quota allocation model for the Bitcoin-refunded Blockchain network

Bitcoin operates in a Blockchain network under which a group of participants are responsible for adding new blocks to the chain. The participants are called miners and the ones who successfully add a block to the network are rewarded. As technology evolves over the years, the “mining” process has become more challenging, with miners facing longer periods of negative cash flow while still having expenses. This business architecture has driven away participants, jeopardizing its operation and hindering its progression. To solve this problem, an alternative to provide financial sustainability for miners is to join a mining pool whose main objective is to mitigate cash flow shortages by sharing the rewards obtained by the group, which in turn will be more constant. In this work, we propose a reward sharing methodology for mining pools based on the least-core of a stochastic cooperative game. A risk-averse value functional based on the Conditional Value-at-Risk (CVaR) is used to characterize the game’s certainty equivalent. Two numerical experiments were conducted in this work: (i) one based on a small, illustrative network; and (ii) one derived from real data of the Bitcoin-refunded Blockchain network. The focus of the experiment is on the incremental value of the proposed methodology over using intuitive allocations (uniform and based on computational power) and to what extent the relative increase in the chance of mining by participating as group benefits the stability of the pool. Finally, we deliberate and numerically analyze a nested procedure derived from the proposed least-core-based allocation seeking increased “fairness” in sharing the pool rewards.