Reviewerly/exHarmony huggingface dataset:
Recognition of Uni-Stroke Characters with Hand Movements in 3D Space Using Convolutional Neural Networks

Hand gestures are a common means of communication in daily life, and many attempts have been made to recognize them automatically. Developing systems and algorithms to recognize hand gestures is expected to enhance the experience of human-computer interfaces, especially when there are difficulties in communicating vocally. A popular system for recognizing hand gestures is the air-writing method, where people write letters in the air by hand. The arm movements are tracked with a smartwatch/band with embedded acceleration and gyro sensors; a computer system then recognizes the written letters. One of the greatest difficulties in developing algorithms for air writing is the diversity of human hand/arm movements, which makes it difficult to build signal templates for air-written characters or network models. This paper proposes a method for recognizing air-written characters using an artificial neural network. We utilized uni-stroke-designed characters and presented a network model with inception modules and an ensemble structure. The proposed method was successfully evaluated using the data of air-written characters (Arabic numbers and English alphabets) from 18 people with 91.06% accuracy, which reduced the error rate of recent studies by approximately half.
Comparison of RNN-LSTM and Kalman Filter Based Time Series Human Motion Prediction

Abstract Machine-human and machine-machine interaction is inevitable and needs to be considered apart from human-machine interaction. For the safety of machine interaction, the system is required to prevent any unexpected accident. This paper proposed a system that can forecast human motion to know the movement direction for one second. This research was conducted by using an RGB camera as a reliable alternative. The feature extraction process has been done by OpenPose to obtain the coordinate of human body parts in the frame. Then the coordinate data is converted to the movement data containing the distance and direction in the key points to the next frame for the input in the prediction method. This research aims for the human motion prediction using these methods to compare the performance on the human motion data and realize the prediction of human motion. Mainly, Kalman Filter shows more positive results than RNN-LSTM as the prediction method. Movement like hand gestures is more effortless than motions like hand gestures and steps to the left side. The validity of the system based on the RGB camera with unstable data has been confirmed based on the results.
Arcing Faults Detection in Switchgear with Extreme Learning Machine

Abstract The robustness of switchgears has critical impacts on the general efficiency of power distribution systems. Faulty switchgears lead to many unwanted complications for utility bodies, which in turn lead to even bigger issues. In this paper, a remote arcing fault sensing technique is proposed using ELM. By analysing the sonic waves emitted, the proposed method is capable to detect possible arcing faults in switchgears. Tests and experiments have been conducted to investigate the performance of the proposed algorithm in detecting these arcing faults. The obtained results are analysed in time and frequency domains. In the time domain analysis, the results show 93.75% success rate in training stage, 95.83% in validation stage, and 87.5% in testing stage. In the frequency domain analysis, the results show 93.75% success rate in training stage, 91.67% in validation stage, and 100% success rate in testing stage. It is thus concluded that the proposed algorithm is capable to identify arcing faults in switchgears.
Semantic Segmentation of Traffic Landmarks Using Classical Computer Vision and U-Net Model

Abstract Due to the deterioration of traffic landmarks such as (crosswalks, lanes, speed limits, …etc) in Mie prefecture-Japan, the sudden stop rate during road crossings reached 3.4% in the year 2020. It has been deemed necessary by the local authorities to implement a reliable maintenance process for these road signs. Such a process would rely on an automatic camera-based monitoring system in order to keep track of the deterioration status of road signs. The first step consists of achieving successful detection and segmentation of these landmarks. In this paper, we try to accomplish this initial goal by using a dataset of around 13000 high resolution images taken from Mie prefecture streets. In order to achieve our goal of landmark detection we relied on some classical computer vision techniques as well as a deep learning approach. We developed a method to separate landmarks from other objects in a given image by relying on distinctive features of these landmarks. The method in question was then used to build a semi-assisted labelling tool, which was used to annotate a set of around 182 images and used them to train a CNN model to perform semantic segmentation of the road signs. In the end we managed to achieve a successful segmentation with a dice score of 78.90% on the validation set, as well as a solid proof of concept for a CNN based approach to solve this particular problem
An effective and practical gradient inversion attack

While gradient aggregation playing a vital role in federated or collaborative learning, recent studies have revealed that gradient aggregation may suffer from some attacks, such as gradient inversion, where the private training data can be recovered from the shared gradients. However, the performance of the existing attack methods is limited because they usually require prior knowledge in Batch Normalization and could only reconstruct a single image or a small batch one. To make the attacks less restrictive and more applicable, we propose an effective and practical gradient inversion method in this paper. Specifically, we use cosine similarity to measure the difference of gradients between the synthesized and ground-truth images, and then construct an input regularization for the fully connected layer to ensure the fidelity of the image. Moreover, we apply the total variation denoising strategy to the convolution feature map for further improving the smoothness of the reconstructed image. Experimental results demonstrate that our method can reconstruct high fidelity training data on a large batch size for complex data sets, such as ImageNet.
Bayesian inductive learning in group recommendations for seen and unseen groups

Group recommendation refers to a recommendation of items to a group of users (i.e., members). When predicting relevant items, a model commonly faces unseen groups that do not appear in the training step. Recently, deep neural networks and an attention mechanism were applied to group recommendations by aggregating user preferences. However, current methods are insufficient to handle unseen groups (i.e., transductive models) or struggle to compute cost-effective attention networks and regularizations . In this study, we propose the novel Bayesian inductive learning method, called IndiG, for making recommendations to seen and unseen groups. To learn inductively, a function distribution consisting of efficient attention-based aggregation is used as shared information across groups. By incorporating a transductive model as a posterior into the proposed Bayesian method, an inductive model as a prior can learn robustly. We adopt cost-effective regularization to prevent degenerated solutions by maximizing a correlation between group representations of a transductive model and an inductive model, while decorrelating dimensions of group representations. Through experiments, we demonstrated that the proposed method outperformed other existing methods. The experiments also showed that the utilization of uncertainty on the predicted ratings of items worked effectively to improve the performance.
Offline reinforcement learning with representations for actions

Prevailing offline reinforcement learning (RL) methods limit the policy within the area supported by the offline dataset to avoid the distributional shift problem. But potential high-reward actions, which are out of the distribution of the dataset, are neglected in these methods. To address such issue, we propose a new method, which generalizes from the offline dataset to out-of-distribution (OOD) actions. Specifically, we design a novel action embedding model to help infer the effect of actions. As a result, our value function reaches a better generalization over the action space, and further alleviate the distributional shift caused by overestimation of OOD actions. Theoretically, we give an information-theoretic explanation on the improvement of the value function’s generalization over the action space. Experiments on D4RL demonstrate that our model improves the performance compared to previous offline RL methods, especially when the experience in the offline dataset is good. We conduct further study and validate that the value function’s generalization on OOD actions is improved, which reinforces the effectiveness of our proposed action embedding model.
DynHEN: A heterogeneous network model for dynamic bipartite graph representation learning

Learning node representations in graphs is a widespread problem in node classification and link prediction. Current research has focused on static heterogeneous, homogeneous networks, and dynamic homogeneous networks. However, many existing graphs, such as citation and social networks, are heterogeneous. Therefore, it is still a great challenge to obtain heterogeneous information in a dynamic heterogeneous graph to assist node representation learning. In this paper, we propose a Dynamic HEterogeneous Network (DynHEN) for user-item bipartite networks. It is a discrete dynamic graph neural network model that can be used directly for node representation learning by utilizing dynamic heterogeneous graphs. Specifically, DynHEN takes a bipartite graph at each time step as input, gets the corresponding embedding by capturing the deep heterogeneous information of the nodes while fusing the temporal information. To illustrate the effectiveness of heterogeneous information for graph representation learning, we compare with the current SOTA methods in the two type experiments of link prediction and node classification, and achieve outstanding results.
Data Sharing Method of College Dance Teaching Resource Database Based on PSO Algorithm

Aiming at the problems of long sharing time, low accuracy, recall, and F1 value in the traditional data sharing method of college dance teaching resource database, a data sharing method of college dance teaching resource database based on PSO algorithm is proposed. Multiple regression KNN method is used to eliminate the data noise of college dance teaching resource database, so as to obtain the missing value and complete the filling of incomplete data of college dance teaching resource database. Taking the preprocessed data as the basic element of transmission object statistics and analysis, establish the data transmission self-service channel of college dance teaching resource database, calculate the similarity of the data according to the unequal length sequence, and use the partial least square method to complete the feature extraction of the resource database data. According to the feature extraction results, particle swarm optimization algorithm is adopted to share the data of college dance teaching resource database. The simulation results show that the accuracy, recall, and F1 value of the data sharing method of college dance teaching resource database based on PSO algorithm are high, and the sharing time is short.
Short-Side Excursion for Oriented Object Detection

Oriented object detection has achieved a significant progress in image processing. Compared with horizontal detection methods, oriented detectors add the orientation parameter in regression to locate objects. However, existing rotation and quadrilateral representations are not appropriate for oriented two-stage methods to generate efficient oriented proposals. In this paper, we propose a novel framework to detect oriented objects, termed <i>short side excursion detection</i> (SSEDet). Inspired by the circle theorem, we propose a transformation method from horizontal rectangles to oriented ones to accurately describe oriented objects. To be specific, we exploit the offset of short sides relative to the top-right vertex to represent the orientation of rectangle. Compared with the horizontal rectangle, the representation parameters of oriented rectangle have only one more orientation parameter. Under the action of the orientation parameter, the one-to-one correspondence between representation parameters and oriented rectangle can be realized. Experimental results on commonly used datasets verify that SSEDet can generate high-quality oriented proposals.
An Improved Heterogeneous Graph Convolutional Network for Inter-Relational Medicine Representation Learning

Medicine representation learning, which aims at uncovering hidden medicine relationships has emerged as a significant technique to imitate a doctor’s cognitive reasoning process. The majority of present research focuses on the intuitive relationships between medication and diagnosis, however, ignores the inherent properties of medicines. This study uses a heterogeneous graph convolutional network (HGCN) and a spectral clustering (SC) algorithm to investigate the associated knowledge underlying clinical treatment. Based on the chronic obstructive pulmonary disease (COPD) clinical data, we construct a medicine-property heterogeneous network consisting of two types of nodes involving medicines and their properties, and three types of edges referring to the intermedicine, interproperty, and medicine–property relations. HGCN is used to aggregate the neighbor nodes’ information and then generalize the medicine and property embeddings. Then, SC is leveraged to divide these embeddings into the syndromes to which they belong. To verify the model performance, a series of experiments referring to the baseline comparison, ablation study, and parameter sensitivity test have been carried out. Compared to three baseline models and their variants on six evaluation metrics, the experimental results demonstrate that the HGCN-SC model outperforms the baseline approaches in medicine combination identification and has around 3.0% improvement in accuracy over the SC.
Blockchain-Based Autonomous Notarization System Using National eID Card

In recent years, the spread of information and communication technology has led to the emergence of e-government, which is the electronic replacement of government services.E-government is said to be compatible with blockchain technology, which has led to various studies on the possibility.Notarization, one of the functions of the government, has particularly been examined for the potential adoption of blockchain technology.However, because the notary public must authenticate the document's contents during the notarization process, they have been difficult to replace with smart contracts.In this study, we focus only on fixed date notarizations and propose a fully automated notarization system by combining a national eID card with Public Key Infrastructure and smart contracts.A fixed date is a notarization that allows a notary public to guarantee that a document existed, regardless of the authenticity of the document's content.Therefore, it can be replaced by a smart contract.Specifically, our proposed system automatically authenticates the creator and the document for electronic documents signed with a national eID card and uses the transaction receipt generated when the information is stored on the blockchain as a certificate of notarization.Verification of the signed data is done inside the blockchain by smart contracts, which eliminates the need for a verification authority.We further demonstrate the effectiveness of the proposed method in a Japanese use case as proof of concept.
StegFog: Distributed Steganography Applied to Cyber Resiliency in Multi Node Environments

This paper presents a new study about the communication system named StegFog.It is based on applying distributed steganography linked with the concept of cyberfog security.The idea of cyberfog is to secure data by splitting it into numerous fragments and spreading them to end-user devices.The design of StegFog combines steganographic methods previously investigated by the Authors -StegHash and SocialSteg Disc.The reference design of the system is proposed and analyzed in terms of security, reliability, performance and timing.To prove the feasibility of the proposed solution, the prototype of StegFog was implemented, consisting of steganographic nodes operating over BitTorrent P2P networks with a hidden communication channel established by the multimedia files shared within it.Experiments were conducted for the implemented prototype to understand the basic features of the system, such as operational aspects of the address generator constructed using the modified StegHash method, timing of the basic methods within the system -reading, writing and notifying, steganographic rate of the system and overall security provided by the system.The presented results show how system such as StegFog aligned with the cyberfog security concept could be considered as the practical protection measure for secure network communications.The challenges and research directions to extend the concept are also discussed.
Detecting Water Quality Using KNN, Bayesian and Decision Tree

In this study, we have analyzed water quality using different approaches: descriptive analysis and machine learning. First, we get the data source from kaggle website. After processing the data, we use Python sklearn package for data mining. Firstly, the machine learning data mining method is selected through description analysis. Finally, we choose KNN, Bayesian algorithm and decision tree to analyze the water data from kaggle website. The goal is to divide the data into available and unavailable by machine learning algorithm. Finally, through these three methods, we get the results of three methods and make some corresponding comparison and analysis.
An Improved Genetic Algorithm for Web Phishing Detection Feature Selection

Feature selection is a useful dimension reduction method in data preprocessing for machine learning. It is an discrete optimization problem in essence. Genetic algorithm is a meta heuristic optimization algorithm for discrete optimization problem, which simulates the biological evolution behavior in nature. The original Genetic algorithm, like many other swarm intelligence optimization algorithms, has its own defects, easy to fall into local optimum, and slow convergence speed. To improve the performance of Genetic algorithm, an improved algorithm ECGA is proposed. In order to measure the diversity of population, the information entropy of population is calculated after fitness calculation. To speed up convergence, the consensus mechanism is presented after mutation which is used as a alternate mutation operator. And finally ECGA is applied to feature selection to test its actual effect. The experimental results show that the improved Genetic algorithm is better than the feature selection algorithm proposed in recent 2 years for the web phishing detection problem. Our algorithm achieves a average F1socre of 97&#x0025;.
Research on Low Illumination Image Processing Algorithm Based on Adaptive Parameter Homomorphic Filtering

For the problems of traditional homomorphic filtering algorithm, multiple parameters, experiential param-eter values and parameter control difficultly, an adaptive pa-rameter homomorphic filtering image enhancement algorithm is proposed. On the Processing of Transfer Function, it simpli-fies the traditional Gaussian homomorphic filtering transfer function to a single parameter exponential transfer function compared with exponential transfer function, and the optimal value of the single parameter in the low illumination image is obtained by peak signal to noise ratio (PSNR) and structural similarity (SSIM), so parameters only change with the change of image, which improves the universality of the algorithm. The experimental results show that the proposed adaptive parameter homomorphic filtering algorithm has good effect on the enhancement of low-illumination images.
Evaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factor

Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. The available advanced information and communicating platform and computational capability renders smart grid prone to attacks with extreme social, financial and physical effects. The smart network enables energy management of smart appliances contributing support for ancillary services. Cyber threats could affect operation of smart appliances and hence the ancillary services, which might lead to stability and security issues. In this work, an overview is presented of different methods used in anomaly detection, performance evaluation of 3 models, the k-Nearest Neighbor, local outlier factor and isolated forest on recorded smart meter data from urban area and rural region
The Impact of COVID-19 on the Cybersecurity in Civil Aviation: Review and Analysis

The significance of cybersecurity and cyber resilience in the aviation sector cannot be ignored, and this fact has been highlighted time and again due to frequent cyberattacks prior and during the COVID-19 pandemic. This paper presents an analysis of the relevant studies that addressed the impact of the COVID-19 pandemic on cybersecurity generally, and in the aviation sector in particular, including identifying the most common increased cyber-attacks associated with the outbreak of COVID-19. Our analysis shows that commonly increased cyber-attacks associated with the outbreak of COVID-19 in aviation are phishing and ransomware. Furthermore, the likelihood of phishing and ransomware attacks is very likely during the pandemic than before it while the impact on flight operations is low, and the subsequent risk level is tolerable during the pandemic. Additionally, we propose a set of possible mitigation measures for those cyber-attacks.