Reviewerly/exHarmony huggingface dataset:
Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

Intelligent fault detection is promising to deal with big data due to its ability in rapidly and efficiently processing collected signals and providing accurate detection results. In traditional fault detection methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise, such processes take advantage of human ingenuity but are time-consuming. Inspired by the idea of unsupervised feature learning artificial intelligence techniques are used to learn features from the raw data.As the dimensionality increases, the accuracy of fault identification methods implemented on big data decreases significantly. For supervised learning, large volume of data is needed which leads to high cost and time consuming. In this paper, an unsupervised learning approach is proposed on the basis of weighted softmax regression for fault detection using the power signals. In the proposed approach, the features are extracted from the unlabelled data. The developed approach is based on squirrel search algorithm and voting based weighted softmax regression with jaya optimization algorithm (IWSRJO_SSA). The proposed approach is simple and easy to carry out, though it attains high accuracy when compared to that of more advanced techniques. The features from the signals are extracted normally and some of the best features are selected using the squirrel search algorithm (SSA). Because of selecting the best features and using the improved weighted softmax regression, the proposed method achieves high accuracy in fault detection. Experiment on the power signal dataset shows the applicability of the proposed approach in fault detection on big data. The proposed system will be applicable on medical field, industrial field, electrical field and so on. The experimental results prove that the proposed method attains high accuracy and is superior to the other advanced methods.
Use House Ads in Content Emergencies

The Membership Management ReportVolume 18, Issue 2 p. 3-3 Publication Tip Use House Ads in Content Emergencies First published: 03 January 2022 https://doi.org/10.1002/mmr.31847Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume18, Issue2February 2022Pages 3-3 RelatedInformation
Healthcare Providers Recommender System Based on Collaborative Filtering Techniques

Finding a suitable health care provider (physicians, clinics, therapeutic centers, etc.) seems to be a time-consuming and difficult task. Before deciding, the patient should gather input from others who are in a similar situation. The latter can be determined in a variety of ways, such as by looking for individuals who have similar symptoms or who have similar attitudes toward a group of health professionals. Both ways can lead to overchoice problem, consequently, the patient can't make the right decision. In this chapter, we try to automate this process of finding a healthcare provider to solve the above-mentioned problems through the technique of recommendation systems, one of the applications of machine learning (ML). To create a predictive patient-healthcare provider rating model, multiple collaborative filtering strategies were introduced, tested, and evaluated. The algorithms for sparse matrix completions were implemented in four different ways: k-nearest neighbors (KNN) based, matrix factorization, slop one, and co-clustering. The root mean squared error (RMSE) metric was used to compare and evaluate the accuracy of the different models.
Mathematics Problem Solving Ability: An Analysis Based on IDEAL Problem Solving Viewed from Learning Style

IDEAL problem solving is model that able to improve skills in the problem-solving process and improve the ability to think.This study aims to describe the problem-solving ability student of Junior High School 8 th grades on IDEAL problem solving viewed from the learning style.This type of research is qualitative research with a case study research design.Subjects were selected by purposive sampling method.Hence, 3 students were selected and became subjects on their respective learning styles, namely visual learning style (S1), auditory learning style (S2), and kinesthetics learning style (S3).The data collection techniques of this research were tests and interview.The validity of the data using triangulation method.Data analysis includes data reduction, data presentation and conclusions.The results showed that the subject S1 could fulfill the IDEAL problem-solving steps of the first, third and fourth stages.Subject S2 are only able to fulfill the first stage.And subject S3 could fulfill the IDEAL problem solving all of steps except second stages.
Hate Speech and Minority Protection in the United States

Minority protection and hate speech have been separately analyzed and debated extensively, but the relation between the two is not a highly discussed topic. Whether it’s due to their race, religion, ethnicity, gender, or any other aspect, minority groups become a target for hate speech because of their differences. The dissemination of hateful messages results in real harm, both to the intended victims and to the society in which it is permitted to happen. Hate speech can influence those around and even change an individual’s opinion and feelings towards a minority group. This can spark other issues, such as discrimination and hate crimes. In this paper, I will analyze how hate speech is handled in the United States, and what this means in respect to minority protection.
VARNA FREE UNIVERSITY’S PROJECTS FOR DEVELOPING OF CYBERSECURITY EDUCATION AND TRAINING – “CYBERMAGICIANS” PROJECT

Varna Free University has a tradition in teaching cyber security through bachelor’s, master’s and doctoral degrees and LLL (Lifelong Learning) courses for school teachers and students. The latest Cyberwizards project stems from the need to teach teachers and students how to protect themselves online, how to manage their digital identity, what types of information to publish. The university project aims to develop training courses and research methodologies to develop effective and sustainable cyber learning solutions aimed at digital protection and security.
Characteristics of Stereo Imaging using Non-Identical Cameras for Object Detection

The fear from the continuous spreading of the Covid-19 pandemic had put lot of restrictions on the movement of goods around the world. In Egypt, the importing of goods especially electronic products from many countries including China was crucial to the research and educational purposes. The restrictions had stopped the importing of many electronic devices from China including cameras. Object detection and identification were among the hot topics of research in our university which depended mainly on imported cameras. In this paper we tackle the problem of setting up stereo cameras using old non identical cameras to do object detection. The selection of the cameras was not optional since we had to use what we found in our old laptops. OpenCV and Python programming commands were used to set the two cameras to obtain equally clear images as much as possible. A disparity map was then calculated using openCV and its accuracy was then discussed. Accuracy was dependable on the sharpness of the cameras used, Gamma parameter, number of pixels per image and matching algorithm to match the two images obtained using the stereo cameras.
Cyber-Physical Experiences

Conventionally, architects have relied on the qualities of elements, such as materiality, light, solids, and voids, to break away from the static nature of space and enhance the way users experience and perceive architecture. With the introduction of technologies such as augmented reality (AR), it is becoming easier to blend digital and physical realities and create new types of spatial qualities and experiences. These emerging technologies can be used to supplement and enhance the experience and qualities architecture provides. The authors have proposed a hybrid museum in which AR is integrated into conventional analogue methods (e.g., materiality, light) to mediate spatial experiences.
Expand the Net Force Direction Range for Aerial Hose-type Robots Using Passive Orientation Control of Nozzle Unit With Multiple Water Jets

Fluid injection has promising potential as a novel actuation approach for robots to enable the rapid switching of large forces. The proposed nozzle unit can drastically expand the injection range using a passive rotation mechanism to automatically follow the intended injection direction. Concretely, the nozzle unit consists of the passive joint to rotate and two active rotating nozzles to realize the net force. If the unit emits the jets inwardly, the nozzle posture converges to the direction of net force without any additional control. By modeling the proposed nozzle unit, we theoretically validate the stability of the proposed unit near the equilibrium point. Moreover, we demonstrate that some geometrical parameters could modify the responsiveness of the posture. The developed nozzle unit experimentally verifies that the nozzle unit can direct the net force direction in the range of 180 degrees from the upward to the downward direction.
ANALISIS TINGKAT PLAGIASI DOKUMEN SKRIPSI DENGAN METODE COSINE SIMILARITY DAN PEMBOBOTAN TF-IDF

Plagiarism is the activity of duplicating or imitating the work of others then recognized as his own work without the author's permission or listing the source. Plagiarism or plagiarism is not something that is difficult to do because by using a copy-paste-modify technique in part or all of the document, the document can be said to be the result of plagiarism or duplication.&#x0D; The practice of plagiarism occurs because students are accustomed to taking the writings of others without including the source of origin, even copying in its entirety and exactly the same. Plagiarism practices are mostly carried out by students, especially when completing the final project or thesis&#x0D; One way that can be used to prevent the practice of plagiarism is by doing prevention and detecting. Plagiarism detection uses the concept of similarity or document similarity is one way to detect copy &amp; paste plagiarism and disguised plagiarism. one of the right methods that can be done to detect plagiarism by analyzing the level of document plagiarism using the Cosine Similarity method and the TF-IDF weighting.&#x0D; This research produces an application that is able to process the similarity value of the document to be tested. Hasik testing shows that it is appropriate between manual calculations and implementation of algorithms in the application made. Use of the Literature Library is quite effective in the Stemming process. Calculations that use stemming will have a higher similarity value compared to calculations without stemming methods.
Processing-Response Dependence on the On-Chip Readout Positions in Spin-Wave Reservoir Computing

This paper reports and discusses the processing response dependence on a spin-wave reservoir chip, a natural computing device, to present one of the important steps to design a spin-wave reservoir computing hardware. As an example, we deal with a sinusoidal-square wave distinction task, where signals with a certain duration switch to each other at random. Observation of the transient response provides us with information useful for determining chip size and other parameters. Accumulation of this type of investigations will elucidate how we should design a spin-wave reservoir chip.
Some Aspects of Text Recognition from Video Document in Education 4.0

Optical character recognition in the video document or videos from is a challenging task because flipping in the unconstrained environment and at any speed can cause difficulties specially in selecting a frame that contains the required image (OBI) for readability. In this paper, we stress on the problem of recognizing the readable and whole frame by selecting the frames with optical flow. Fundamentally, optical flow is used here for segmentation of the required area from the frame. Due to the unavailability of such a dataset, we have also created a dataset of video document containing book-flipping videos. Experiments are executed on the sample video documents recorded using mobile camera. The use of optical flow on video document gives proper track of motion as it detects the boundary of the document. Here in this case a normal Indian publication textbook is used. The need to develop this technique is to help various educational reforms in Indian universities related to paper evaluation. A light-weight model and accurate results makes this technique is cost-efficient and sustainable.
3D Reconstruction with Coronary Artery Based on Curve Descriptor and Projection Geometry-Constrained Vasculature Matching

This paper presents a novel method based on a curve descriptor and projection geometry constrained for vessel matching. First, an LM (Leveberg–Marquardt) algorithm is proposed to optimize the matrix of geometric transformation. Combining with parameter adjusting and the trust region method, the error between 3D reconstructed vessel projection and the actual vessel can be minimized. Then, CBOCD (curvature and brightness order curve descriptor) is proposed to indicate the degree of the self-occlusion of blood vessels during angiography. Next, the error matrix constructed from the error of epipolar matching is used in point pairs matching of the vascular through dynamic programming. Finally, the recorded radius of vessels helps to construct ellipse cross-sections and samples on it to get a point set around the centerline and the point set is converted to mesh for reconstructing the surface of vessels. The validity and applicability of the proposed methods have been verified through experiments that result in the significant improvement of 3D reconstruction accuracy in terms of average back-projection errors. Simultaneously, due to precise point-pair matching, the smoothness of the reconstructed 3D coronary artery is guaranteed.
MaTop: An Evaluative Topic Model for Marathi

Topic modeling is a text mining technique that presents the theme of the corpus by identifying latent features of the language. It thus provides contextual information of the documents in the form of topics and their representative words, thereby reducing time, efforts, etc. Topic modeling on English corpus is a common task, but topic modeling on regional languages like Marathi is not explored yet. The proposed approach implements a topic model on Marathi corpus containing more than 1200 documents. Intrinsic evaluation of latent Dirichlet allocation (LDA) which is used to implement the topic model is carried out by coherence measure. Its value is maximum for 4 topics. The retrieved topics are related to ‘Akbar–Birbal,’ ‘Animal stories,’ ‘Advise giving stories’ and ‘general stories.’ Dendrogram and word cloud are used for visualization. The dendrogram shows topic-wise documents and word cloud show sample informative words from different stories. The proposed approach involves context while deriving the topics using synsets. Entropy value is 1.5 for varied datasets; entropy value ensures independence of topic and similarity between topics’ words.
Stable Locomotion and Tracking of the Climbing Robot

Climbing robots are used in many various tasks such as inspection, survey, and even some specific special tasks. The engineering design of the robot for automatic inspection of welding connection makes not only scientific but practical sense. To resolve this task, the robot must firmly and freely move alongside a metal surface and follow exactly the weld in real time to identify the defects. It means the dynamic design should provide the sliding and the turnover avoidance for the robot. But on the other side under the magnetic force and the weight, the friction force will cause the bilateral influence on the locomotion of the robot. The quality of the identification depends on the dynamic stability and the tracking ability of the robot car. The aim of this work is to design the robot car, which is enabled stably moving and tracking alongside the welding seam, mostly on ship hull for ultrasonic inspection.
Dynamic Gesture Recognition using LBRCN Combined with Attention Mechanism

Convolutional Neural Networks(CNNs) have good performance in action recognition. In this paper, we propose a dynamic gesture recognition method using Long-term Bidirectional Recurrent Convolutional Network(LBRCN) combined with Attention Mechanism idea. First, the weights of the foreground and background of the video frame image are redistributed through the idea of the attention mechanism, which focuses on the computing resources of the algorithm on gestures, eliminating the interference of complex situation and other motion factors. Then, bidirectional Long Short-Term Memory (LSTM) is used to obtain the motion information of the current frame relative to the previous frame and the motion information of the current frame relative to the next frame. It can better analyze the motion features of gestures, and improve the accuracy of long-time sequence video recognition. The experimental results show that the recognition accuracy rate of the LBRCN combined with attention mechanism is relatively high, and the parameter amount is relatively small on the 20BN-jester dataset.
A Simple Data-Rich IBII Test for Identifying All Orthotropic Stiffness Components at High Strain Rates

This work focuses on the development of a new simple IBII test configuration that is designed to maximize identifiability of all four orthotropic stiffness components for bone and glass fiber-reinforced polymer (GFRP) composites. A limitation of current ultra-high speed camera technology is the spatial resolution so it is desirable to explore test configurations that do not require the use of geometrical stress concentrators. Here, the heterogeneity of the kinematics is increased using three parameters: (1) impacting over a percentage of the sample’s height, (2) changing the angle of the input loading pulse, and (3) changing the material orthotropy angle relative to the specimen edges. Two identification strategies based on the virtual fields method are explored including the generalized stress–strain curves and the special optimized virtual fields. This chapter focuses on the presentation of the image deformation simulations used to find optimal test configurations.
Design of Parallel Big Data Stream Transmission System Based on Chaotic Migration Learning

Aiming at the problems of severe node congestion, low data transmission rate, and low data integrity in the current parallel data transmission process, a method for controlling the integrity of parallel data transmission in a big data environment is proposed; the integrity control principle of parallel data transmission is used to control the parallel data during transmission. Obtain and measure the two major interference factors of transmission congestion for analysis. In order to solve this problem, a parallelized big data streaming system based on migration learning is proposed. This paper studies the design of a parallel big data stream transmission system based on chaotic migration learning.
DGrid: Dense Grid Network for Salient Object Detection

The performance of salient object detection has been significantly advanced by using fully convolutional networks (FCN). However, it still remains nontrivial to take full advantage of the multi-level convolutional features for salient object detection. In this paper, a dense grid network framework (denoted $$\mathbf {DGrid}$$ ) is proposed to solve the above problem, which mainly consists of the backbone module, extended module and fusion module. Specifically, $$\mathbf {DGrid}$$ utilizes a multi-branch refinement mechanism for saliency detection. First, the backbone module is used to generate a coarse prediction map. Then, the extended module, which contains four branches, is used to improve the resolution and precision of the prediction map gradually from coarse to fine. Moreover, we proposed the densely connected strategy to fully fuse features at different levels. Finally, the fusion module densely fuses the highest level features of all branches to achieve the final saliency map. Experimental results on five widely used benchmark datasets demonstrate that $$\mathbf {DGrid}$$ can improve the accuracy of detection by maintaining a high-resolution feature branch, and it outperforms state-of-the-art approaches without any post-processing.
Sensing with few photons: the power of correlations in lossy SU(1,1) interferometers

This work explores the phase uncertainty of lossy SU(1,1) interferometers in the low gain regime. We show that coincidence measurement can reach the lowest phase uncertainty possible in the system, regardless of the internal losses.
DS4ALL: All you need for democratizing data exploration and analysis

Today, large amounts of data are collected in various domains, presenting unprecedented economic and societal opportunities. Yet, at present, the exploitation of these data sets through data science methods is primarily dominated by AI-savvy users. From an inclusive perspective, there is a need for solutions that can democratise data science that can guide non-specialists intuitively to explore data collections and extract knowledge out of them. This paper introduces the vision of a new data science engine, called DS4ALL (Data Science for ALL), that empowers users who are neither computer nor AI experts to perform sophisticated data exploration and analysis tasks. Therefore, DS4ALL is based on a conversational and intuitive approach that insulates users from the complexity of AI algorithms. DS4ALL allows a dialogue-based approach that gives the user greater freedom of expression. It will enable them to communicate using natural language without requiring a high level of expertise on data-driven algorithms. User requests are interpreted and handled internally by the system in an automated manner, providing the user with the required output by masking the complexity of the data science workflow. The system can also collect feedback on the displayed results, leveraging these comments to address personalized data analysis sessions. The benefits of the envisioned system are discussed, and a use case is also presented to describe the innovative aspects.