Reviewerly/exHarmony huggingface dataset:
A survey of nature-inspired algorithm for partitional data clustering

Abstract The aim of the clustering is representing the huge amount of data objects by a smaller number of clusters or groups based on similarity. It is a task of good data analysis tool that required a rapid and precise partitioning the vast amount of data sets. The clustering problem is bring simplicity in modelling data and plays major role in the process of data mining and knowledge discovery. In the early stage, there are many conventional algorithm are used to solve the problem of data clustering. But, those conventional algorithms do not meet the requirement of clustering problem. Hence, the nature-inspired based approaches have been applied to fulfil the requirements data clustering problem and it can manage the shortcoming of conventional data clustering algorithm. This present paper is conducting a comprehensive review about the data clustering problem, discussed some of the machine learning datasets and performance metrics. This survey paper can helps to researcher in to the next steps in future.
Real-time Measurement and Prediction of Ball Trajectory for Ping-pong Robot

This paper develops a vision system to predict ball trajectory for the ping-pong robot. The stereo vision system with two cameras is used to measure ball center. A multi threshold segmentation algorithm is applied to precisely locate the center of ball in image and the triangulation algorithm is applied to compute the 3D position in the world coordinates. Six consecutive coordinates are collected and the least squares method is used to determine the initial states of the ball. From these initial states, the following flight trajectory of the ball is predicted using two physical models: aerodynamics model and rebound model. Then the states of the ball at striking point are calculated from the predicted trajectory. Experimental results show that the developed system can achieve a good predicting precision in real-time.
Application of VR image recognition and digital twins in artistic gymnastics courses

Because rhythmic gymnastics requires a combination of human movements and hand-held instruments, it is difficult to teach and requires high movement standards. Therefore, the actual course teaching is difficult. In order to improve the teaching efficiency of rhythmic gymnastics courses, based on VR image recognition technology and digital twins, this paper combines the actual teaching needs of rhythmic gymnastics to build a corresponding auxiliary teaching system. The sports database designed in this article mainly has three kinds of sports: difficulty movements, connecting movements and equipment movements. It is different from the traditional method in that each movement and the device-related connection movement correspond to a difficulty movement of the same length and close coordination, and the connection movement plays a role in smoothly connecting the two difficulty movements. In addition, the performance of the auxiliary teaching system constructed in this paper is studied through system experiments. The research results show that this system is feasible.
Focus Your Attention: A Focal Attention for Multimodal Learning

The key point in multimodal learning is to learn semantic alignment that finds the correspondence between sub-elements of instances from different modality data. Attention mechanism has shown its power in semantic alignment learning as it enables to densely associate sub-elements across different modalities. However, for each sub-element, existing attention aligns it with all the sub-elements from another modality, while most of them have no correspondence with it, i.e. irrelevant sub-elements. The irrelevant sub-elements will distract the semantic alignment if they are also attended. In this paper, we propose a novel focal attention mechanism to learn more accurate semantic alignment. The focal attention sparsely attends to a subset of sub-elements, which are identified as relevant ones according to their posterior probabilities given each sub-element from another modality. Based on the observation that relevant sub-elements mostly describe the same semantic, the posterior probability can precisely distinguish relevant and irrelevant ones by taking interactions within the same modality into consideration, such that relevant sub-elements get higher and closer posterior probabilities, while irrelevant ones get lower probabilities. Such a design learns better semantic alignment by preventing the interference of irrelevant sub-elements, and it facilitates subsequent multimodal tasks that demand semantic alignment. To validate the effectiveness of the focal attention, we conduct extensive experiments on image-text matching and text-to-image generation, and we propose a bidirectional and stacked version of focal attention for them, respectively. Experimental results on benchmarks show that the focal attention can significantly and consistently outperform state-of-the-arts.
A Knowledge Architecture Layer for Map Data in Autonomous Vehicles

Autonomous Driving (AD) systems use digital maps as a virtual sensor to perceive the environment around the car. As the field of digital maps continues to evolve, existing solutions face new challenges such as integration ability for new map formats (e.g., High Definition maps), supporting onboard and offboard deployment and providing a generic interface to access the road environmental knowledge. In this paper, we propose a knowledge architecture layer for environmental modeling and distinguish between low-level ontologies based on various map data formats and a high-level ontology for representing a generic road environment. The adequacy of the modeling is validated over two use cases: lane change notification and logical inconsistency detection. The performance is measured using real map data and it shows encouraging results for future development within onboard and offboard systems.
Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning

The growing use of information and communication technologies (ICT) in power grid operational environments has been essential for operators to improve the monitoring, maintenance and control of power generation, transmission and distribution, however, at the expense of an increased grid exposure to cyber threats. This paper considers cyberattack scenarios targeting substation protective relays that can form the most critical ingredient for the protection of power systems against abnormal conditions. Disrupting the relays operations may yield major consequences on the overall power grid performance possibly leading to widespread blackouts. We investigate methods for the enhancement of substation cybersecurity by leveraging the potential of machine learning for the detection of transformer differential protective relays anomalous behavior. The proposed method analyses operational technology (OT) data obtained from the substation current transformers (CTs) in order to detect cyberattacks. Power systems simulation using OPAL-RT HYPERSIM is used to generate training data sets, to simulate the cyberattacks and to assess the cybersecurity enhancement capability of the proposed machine learning algorithms.
A Fusion Approach to Detect Traffic Signs Using Registered Color Images and Noisy Airborne LiDAR Data

Traffic sign detection is considered as one of the active research topics in transportation and computer vision. The previous works mainly focus on detecting traffic signs in images or in mobile light detection and ranging (LiDAR) data. In this paper, we propose a novel deep learning method to accurately detect traffic signs by fusing the complementary features from registered airborne geo-referenced color images and noisy airborne LiDAR data. Specifically, we first segment the airborne color images to road and non-road segments by integrating various local features in an inequality constraint quadratic optimization model. Next, we find the corresponding road regions in LiDAR data and extract high elevated objects above the road. We then segment the extracted objects to different regions corresponding to traffic sign candidates using Euclidean distance-based clustering. Finally, we find the corresponding traffic sign candidates in color images, extract their deep features, and represent them in a convex optimization model to classify the candidates. A set of extensive experiments have been carried out on the airborne geo-referenced color images and noisy airborne LiDAR data captured by Utah State University from I-15 highway. The results show the effectiveness of the proposed method in detecting traffic signs.
Histopathology during the COVID-19 pandemic: resilience through adaptation and innovation

Histopathology departments have adapted to the challenges posed by the COVID-19 pandemic by a variety of changes including working pattern alterations, technology adoptions and incorporation of techniques.This article summarizes these adaptations and provides references to guide pathologists through the continuing pandemic.
Synchronous Constructive Cryptography

This paper proposes a simple synchronous composable security framework as an instantiation of the Constructive Cryptography framework, aiming to capture minimally, without unnecessary artefacts, exactly what is needed to state synchronous security guarantees. The objects of study are specifications (i.e., sets) of systems, and traditional security properties like consistency and validity can naturally be understood as specifications, thus unifying composable and property-based definitions. The framework's simplicity is in contrast to current composable frameworks for synchronous computation which are built on top of an asynchronous framework (e.g. the UC framework), thus not only inheriting artefacts and complex features used to handle asynchronous communication, but adding additional overhead to capture synchronous communication. As a second, independent contribution we demonstrate how secure (synchronous) multi-party computation protocols can be understood as constructing a computer that allows a set of parties to perform an arbitrary, on-going computation. An interesting aspect is that the instructions of the computation need not be fixed before the protocol starts but can also be determined during an on-going computation, possibly depending on previous outputs.
Nearly symmetric orthogonal wavelets for time-frequency-shape joint analysis: Introducing the discrete shapelet transform’s third generation (DST-III) for nonlinear signal analysis

This article introduces the third generation of an interesting tool created for time-frequency-shape (TFS) joint analysis. Called Discrete Shapelet Transform (DST-III), it improves both its predecessors, i.e., DST-I and DST-II, in such a way that nearly symmetric major shapelet functions, and consequently almost linear-phase filterbanks, are obtained. Following a brief review on important concepts, the DST-III formulation is specified in detail and complemented with a numerical example. In addition, a prototype pattern matching strategy, where a comparison with ordinary wavelets takes place, and a spike sorting application are also presented and discussed. Notably, wavelet expansions use to provide very concise signal representations, thus simplifying subsequent nonlinear signal analysis in both time and frequency and, consequently, bringing advantages for non-stationary problem solving in science and engineering. This reassures the efficacy of this new tool.
JExpert

Software development, maintenance, and evolution are increasingly challenging tasks and require skilled professionals in many different technologies. Enterprise and open source projects seek to build the best possible team composed of highly skilled developers in specific libraries. On the other hand, the identification of such skilled professionals is not a trivial task. In this paper, we introduce JExpert, an automated tool that identifies library experts from source code. This tool is designed to identify experts in specific libraries based on source code activities from GitHub projects. In a preliminary evaluation, we rely on JExpert to identify the top experts in 6 libraries that support microservice-based application development. In total, we analyzed more than 1,200 projects and 797 developers. JExpert outputs a summarized profile of each expert with 3 expertise metrics, namely, number of commits, imports, and lines of code written for the library.
Review TVET learning innovation: Augmented reality technology for virtual 3D laboratory

Abstract Currently the trend of using Virtual Augmented Reality (VAR) technology in the field of vocational studies has become a very sexy research topic with visual features that can display objects in 3D, Virtual Augmented Reality can help many things, especially in creating innovation in various industries. In this literature study, the advantages and disadvantages of VAR are clearly described in terms of developing a three-dimensional virtual laboratory for innovation in learning vocational techniques. Unfortunately, some technical problems of VAR technology cannot be fully implemented into vocational engineering learning. The purpose of this study is to show a study of the application of VAR technology with a literature review system method in an appropriate three-dimensional virtual laboratory framework. This analysis is carried out by screening a number of publications of up to 30 major studies published from 2009 to 2018. The results show the low application of VAR technology in education and the increasing application of VAR in industrial environments that lead to high complexity for selecting and developing vocational engineering learning with the VAR system. Subsequent research was also proposed to design and implement AR technology integration into a virtual three-dimensional laboratory specifically for vocational engineering.
Comparative analysis of facial recognition models using video for real time attendance monitoring system

Attendance reporting is one of the standard processes across the world in academic institutions. The key purpose is to encourage consistency in attending school which in turn improves the learning process for a student. The manual attendance system is widely used in the educational system which is time-consuming as well as laborious. The main concept behind the automatic attendance system is to apply facial recognition effortlessly compared to other biometric systems. Following three methods i.e. Histogram Oriented Gradients (HOG), Viola-Jones (Haar Cascade), and Convolution Neural Network (CNN) are analyzed based on face detection accuracy. The Viola-Jones method delivered high accuracy amongst all. For real-time attendance systems, Viola-Jones and CNN algorithms are utilized for face detection and recognition purposes respectively. A benefit of the recommended system is to overcome hurdles like moderately detectable faces, objectionable light conditions, and alignments. The proposed system achieved 94.6% accuracy on a real-time database.
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems

The prompt and accurate detection of faults and abnormalities in electric transmission lines is a critical challenge in smart grid systems. Existing methods mostly rely on model-based approaches, which may not capture all the aspects of these complex temporal series. Recently, the availability of data sets collected using advanced metering devices, such as Micro-Phasor Measurement units (μ PMU), which provide measurements at microsecond timescale, boosted the development of data-driven methodologies. In this paper, we introduce a novel deep learning-based approach for fault detection and test it on a real data set, namely, the Kaggle platform for a partial discharge detection task. Our solution adopts a Long-Short Term Memory architecture with attention mechanism to extract time series features, and uses a 1D-Convolutional Neural Network structure to exploit frequency information of the signal for prediction. Additionally, we propose an unsupervised method to cluster signals based on their frequency components, and apply multi task learning on different clusters. The method we propose outperforms the winner solutions in the Kaggle competition and other state of the art methods in many performance metrics, and improves the interpretability of analysis.
Modified graph-state codes for single-node recovery in quantum distributed storage

Distributed quantum information stored over nodes within a quantum network are prone to node failures, motivating the need for coded quantum networks. We encode $k$ qubits of quantum information over an $n$-node network in a distributed fashion, with one qubit per node, using modified graph-state codes. We devise a procedure to recover the encoded quantum information in any given arbitrary network after a detected node fails. The recovery procedure is locally within four edges of the failed node. For a network with even number of nodes, we provide a class of scalable modified graph-state codes with rate $(n\ensuremath{-}2)/n$, saturating the quantum Singleton bound. This yields higher coding rates than Grassl et al.'s well-known erasure codes and has asymptotic rate 1. For odd node size $n$, we design the network links to obtain an $(n\ensuremath{-}3)/n$ rate-optimal modified graph state code.
Improved Particle Swarm Optimization Algorithm Based on a Three-Dimensional Convex Hull for Fitting a Screw Thread Central Axis

To meet the needs of three-dimensional (3D) measurement and to achieve the high-precision fitting of the central axis of a screw thread, this study performed complex processing of the 3D structures on the surface of the thread central axis.Moreover, this study examined the problems of long processing time and low accuracy caused by the use of a point cloud with a large data volume.An improved particle swarm optimization algorithm was used to develop a fitting method for the central axis of the three-dimensional (3D) thread of a 3D convex hull.SolidWorks was used to simulate the 3D parts of a standard thread, and the Point Cloud Library was used to generate a 3D simulated point cloud for the thread surface.The maximum deviation between the fitted line and the line obtained in the two-dimensional (2D) projection method was 0.12 µm.An experiment was conducted using data regarding the tip of the plug gauge surface.The variation in the distance between the obtained straight line and the 3D point cloud was smaller than that between the obtained straight line and the 2D point cloud.Moreover, the calculation speed was higher when using the 3D method than when using the 2D method.
Detection of Fraudulence in Credit Card Transactions using Machine Learning on Azure ML

With the advancement of mobile and cloud technologies, there is a sharp increase in online transactions. Detecting fraudulent credit card transactions on a timely basis is a very critical and challenging problem in Financial Industry. Although online transactions are very convenient, they bring the risk of fraudulence on many aspects. Some of the key challenges in detecting fraudulence in online transactions include irregular behavioral patterns, skewed dataset i.e. high normal transaction to fraudulent transaction ratio, limited availability of data and dynamically changing environment. Every year people lose millions of dollars due to credit card fraud. There is a lack of quality research in this domain. We have used a dataset comprising of European cardholders which has 284,807 transactions to model our system. In this paper, we will design and develop credit card fraudulence detection system by training and testing two ML algorithms: Decision Forest (DF) and Decision Jungle (DJ) classifiers. Our results successfully demonstrate that DJ classifier delivers higher performance compared to DF classifier.
Chasing the Tail in Monocular 3D Human Reconstruction With Prototype Memory

Deep neural networks have achieved remarkable progress in single-image 3D human reconstruction. However, existing methods still fall short in predicting rare poses. The reason is that most of the current models perform regression based on a single human prototype, which is similar to common poses while far from the rare poses. In this work, we 1) identify and analyze this learning obstacle and 2) propose a prototype memory-augmented network, PM-Net, that effectively improves performances of predicting rare poses. The core of our framework is a memory module that learns and stores a set of 3D human prototypes capturing local distributions for either common poses or rare poses. With this formulation, the regression starts from a better initialization, which is relatively easier to converge. Extensive experiments on several widely employed datasets demonstrate the proposed framework's effectiveness compared to other state-of-the-art methods. Notably, our approach significantly improves the models' performances on rare poses while generating comparable results on other samples.
Real-Time Gait Reconstruction For Virtual Reality Using a Single Sensor

Embodying users through avatars based on motion tracking and reconstruction is an ongoing challenge for VR application developers. High quality VR systems use full-body tracking or inverse kinematics to reconstruct the motion of the lower extremities and control the avatar animation. Mobile systems are limited to the motion sensing of head-mounted displays (HMDs) and typically cannot offer this.We propose an approach to reconstruct gait motions from a single head-mounted accelerometer. We train our models to map head motions to corresponding ground truth gait phases. To reconstruct leg motion, the models predict gait phases to trigger equivalent synthetic animations. We designed four models: a threshold-based, a correlation-based, a Support Vector Machine (SVM) -based and a bidirectional long-term short-term memory (BLSTM) -based model. Our experiments show that, while the BLSTM approach is the most accurate, only the correlation approach runs on a mobile VR system in real time with sufficient accuracy. Our user study with 21 test subjects examined the effects of our approach on simulator sickness and showed significantly less negative effects on disorientation.
Blockchain Mobility Solution for Charging Transactions of Electrical Vehicles

Many countries in Europe are adopting a deregulated system where prosumers can subscribe with any energy supplier in an open market, independently of location. However, the mobility aspect of transactions in the existing system is not satisfactorily covered. For instance, if a person receives the service of charging an EV from a prosumer's local outlet, he cannot pay to the prosumer directly without the presence of an intermediary system. This has led to a situation where the EV owners need to have a large number of subscriptions for EV charging providers and visitors cannot pay for the electricity used there. This study evaluates this mobility gap and proposes a solution for charging transactions using blockchain technology. Furthermore, we implement a proof of concept using the Hyperledger consortium platform for the technical feasibility of the proposed approach and evaluate the performance metrics such as transaction latency and throughput.