Reviewerly/exHarmony huggingface dataset:
A fast automatic detection and classification of voltage magnitude anomalies in distribution network systems using PMU data

Timely anomaly detection and classification in voltage signals for distribution systems allows the design of preventive and corrective actions to avoid damage or loss of equipment. In this paper, an approach that combines a robust recurrence quantification analysis (RRQA) for features’ extraction that allows anomaly detection and classification through a multiclass support vector machine (SVM) algorithm is proposed. This approach is robust to noise, is free a filtering stage, has a low computational burden and is easy to interpret, becoming viable for online monitoring of distribution systems. For its validation, case studies with the presence of voltage magnitude anomalies (VMA) events that occur during normal operation conditions are analyzed. Thus, synthetic records generated through a Monte Carlo model are compared with other algorithms based on similar strategies. Finally, the proposed approach is assessed using PMU records installed in a distribution system to show its performance in a real world environment.
Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network

Matching aerial and satellite optical images with large dip angles is a core technology and is essential for target positioning and dynamic monitoring in sensitive areas. However, due to the long distances and large dip angle observations of the aerial platform, there are significant perspective, radiation, and scale differences between heterologous space-sky images, which seriously affect the accuracy and robustness of feature matching. In this paper, a multiview satellite and unmanned aerial vehicle (UAV) image matching method based on deep learning is proposed to solve this problem. The main innovation of this approach is to propose a joint descriptor consisting of soft descriptions and hard descriptions. Hard descriptions are used as the main description to ensure matching accuracy. Soft descriptions are used not only as auxiliary descriptions but also for the process of network training. Experiments on several problems show that the proposed method ensures matching efficiency and achieves better matching accuracy for multiview satellite and UAV images than other traditional methods. In addition, the matching accuracy of our method in optical satellite and UAV images is within 3 pixels, and can nearly reach 2 pixels, which meets the requirements of relevant UAV missions.
New analytical solution of the fractal anharmonic oscillator using an ancient Chinese algorithm: Investigating how plasma frequency changes with fractal parameter values

This paper uses the two-scale fractal dimension transform and He’s formula derived from the ancient Chinese algorithm Ying Bu Zu Shu to find the approximate frequency–amplitude expression of the fractal and forced anharmonic oscillator that can be used to study the nonlinear oscillations produced by the plasma physics fractal structures. The results show how the electron frequency and wavelength change as a function of the plasma physics fractal structure. In fact, if the value of the fractal parameter is decreased, the wavelength increases, and consequently, the system frequency decreases. The introduced solution procedure sheds a bright light on the easy-to-follow steps to obtain an accurate steady-state analytical solution of fractal anharmonic nonlinear oscillators.
Research on Piano Performance Optimization Based on Big Data and BP Neural Network Technology

At present, there are many chess styles in piano education, but there is a lack of comprehensive, scientific, and guiding teaching mode. It highlights many educational problems and cannot meet the development requirements of piano education at this stage. However, the piano scoring system can partially replace teachers' guidance to piano players. This paper extracts the signal characteristics of playing music, establishes the piano performance scoring model using Big Data and BP neural network technology, and selects famous works to test the effect of the scoring system. The results show that the model can test whether the piano works fairly. It can effectively evaluate the player's performance level and accurately score each piece of music. This not only provides a reference for the player to improve the music level but also provides a new idea for the research results and the application of new technology in music teaching. This paper puts forward reasonable solutions to the problems existing in piano education at the present stage, which is helpful to cultivate high-quality piano talents. Experiments show that the application of Big Data technology and BP neural network to optimize the piano performance scoring system is effective and can score piano music accurately. This paper studies the performance scoring system and gets the model after training, which can replace music teachers and alleviate the shortage of music teachers in the market.
Secure Inter-Domain Routing Based on Blockchain: A Comprehensive Survey

The whole Internet consists of thousands of autonomous systems that transfer data with one another. The BGP plays a significant role in routing, but its behaviour is essentially naive, trusting neighbours without authenticating advertised IP prefixes. This is the main reason why BGP endures various path manipulation attacks. Recently, conventional methods for securing BGP have been implemented, i.e., BGPSec with RPKI. However, these approaches are centralised with a single point of failure that may be compromised, invalidating the whole security mechanism. There have been multiple decentralised projects dealing with various mechanisms, mostly built on Ethereum and blockchain networks. Some with ambition to strengthen existing centralised mechanisms, others to replace them. In this article, we present the first comprehensive survey on blockchain solutions to enforce BGP security, with complex explanations of their contributions and a comparison with different aspects. We explain how blockchain technology can provide an alternative to prevent the false origin of IP prefixes or hijacking AS paths. Moreover, we describe new blockchain-based attacks that BGP would face after the inclusion of blockchain into the inter-domain routing. Finally, we answer the defined research questions and discuss the potential open issues for further study.
The Hitchhiker’s Guide to Prior-Shift Adaptation

In many computer vision classification tasks, class priors at test time often differ from priors on the training set. In the case of such prior shift, classifiers must be adapted correspondingly to maintain close to optimal performance. This paper analyzes methods for adaptation of probabilistic classifiers to new priors and for estimating new priors on an unlabeled test set. We propose a novel method to address a known issue of prior estimation methods based on confusion matrices, where inconsistent estimates of decision probabilities and confusion matrices lead to negative values in the estimated priors. Experiments on fine-grained image classification datasets provide insight into the best practice of prior shift estimation and classifier adaptation, and show that the proposed method achieves state-of-the-art results in prior adaptation. Applying the best practice to two tasks with naturally imbalanced priors, learning from web-crawled images and plant species classification, increased the recognition accuracy by 1.1% and 3.4% respectively.
Angle-Only Filtering of a Maneuvering Target in 3D

We consider the state estimation of a maneuvering target in 3D using bearing and elevation measurements from a passive infrared search and track (IRST) sensor. Since the range is not observable, the sensor must perform a maneuver to observe the state of the target. The target moves with a nearly constant turn (NCT) in the XY-plane and nearly constant velocity (NCV) along the Z-axis. The natural choice for the NCT motion is to allow perturbations in speed and angular rate in the stochastic differential equation, as has been pointed out previously for a 2D scenario using range and bearing measurements. The NCT motion in the XY-plane cannot be discretized exactly, whereas the NCV motion along the Z-axis is discretized exactly. We discretize the continuous-time NCT model using the first and second-order Taylor approximations to obtain discrete-time NCT models, and we consider the polar velocity and Cartesian velocity-based states for the NCT model. The dynamic and measurement models are nonlinear in the target state. We use the cubature Kalman filter to estimate the target state. Accuracies of the first and second-order Taylor approximations are compared using the polar velocity-based and Cartesian velocity-based models using Monte Carlo simulations. Numerical results for realistic scenarios considered show that the second-order Taylor approximation provides the best accuracy using the polar velocity or Cartesian velocity-based models.
Ensuring the ethical use of big data: lessons from secure data access

<h2>Abstract</h2> Big data holds great potential for research and for society, large volumes of varied data can be produced and made available to researchers much faster compared to 'traditional' data. Whilst this potential is recognized, there are ethical concerns which users of big data must consider. With the volume and variety of information in big data, comes a greater risk of disclosure. Researchers and data access services working with highly detailed and sensitive, secure data have grappled with this for many years. The sector has developed both ethical frameworks and statistical disclosure control techniques which could be utilized by those working with big data. We discuss the challenges, present some of the frameworks and techniques and conclude with recommendations for secure data access of big data.
Indian Sign Language Recognition Using Ensemble Based Classifier Combination

Abstract Indian Sign Language (ISL) is an alternate to written and direct communication language used in India and Indian subcontinent. It is commonly used by deaf and mute people who cannot speak and hear. The ISL is a new sign language as compared to other sign languages used in developed countries. There is a need for automatic recognition of any sign language including ISL due to its application aspects in current times. The automation of ISL will helpful to both communities (those who can interact only through ISL and those who do not know ISL at all), because in many places the unblessed people facing difficulty in communicating like at airports, railway stations, banks, hospitals, and other places. In this paper, the authors have tried to develop an automatic conversion from ISL signs to text in the domain of digits. The system requires a digital camera to capture the ISL signs and the underlying software will interpret the meaning of the image. To develop such a system, pattern recognition concept including image processing techniques have been used. Important features from the ISL signs are extracted using Histogram Oriented Gradient (HOG) technique. To classify the input images, two classification techniques have been used in the experiments namely Tree Bagger and Fit Ensemble.
Tourism &amp; Hospitality Students’ Perception towards the Use of Robots in Service Organizations: A Qualitative study in India

The study examined tourism and hospitality (T&amp;H) students’ perception of the use of service robots in organizations. Using purposive sampling, 80 students of T&amp;H programs were interviewed online, and data was analyzed through thematic analysis. The findings showed that a majority of respondents had shown a favorable perception of robots. While Indian students think that robots may offer several benefits for organizations, T&amp;H entrepreneurs must make provisions to deal with the obstacles associated with robot adoption. The major challenges are employee resistance, technological glitches, services with no human touch, significant operational costs, human-robot interaction, cost of training &amp; repair, and robot-friendly organizational design. They also believe that service organizations should balance the work of employees and robots in such a way where technologies should perform repetitive tasks while employees could deliver warm and personalized services. They prefer humanoid robots in human-touch service industries to humanize services. The study also recommends that educational institutions must incorporate courses on modern technologies into academic programs to meet future challenges and job requirements of the service sector. The findings have several implications, and further research directions are suggested.
Qualitative Comparison of Image Stitching Algorithms for Multi-Camera Systems in Laparoscopy

Multi-camera systems were recently introduced into laparoscopy to increase the narrow field of view of the surgeon. The video streams are stitched together to create a panorama that is easier for the surgeon to comprehend. Multi-camera prototypes for laparoscopy use quite basic algorithms and have only been evaluated on simple laparoscopic scenarios. The more recent state-of-the-art algorithms, mainly designed for the smartphone industry, have not yet been evaluated in laparoscopic conditions. We developed a simulated environment to generate a dataset of multi-view images displaying a wide range of laparoscopic situations, which is adaptable to any multi-camera system. We evaluated classical and state-of-the-art image stitching techniques used in non-medical applications on this dataset, including one unsupervised deep learning approach. We show that classical techniques that use global homography fail to provide a clinically satisfactory rendering and that even the most recent techniques, despite providing high quality panorama images in non-medical situations, may suffer from poor alignment or severe distortions in simulated laparoscopic scenarios. We highlight the main advantages and flaws of each algorithm within a laparoscopic context, identify the main remaining challenges that are specific to laparoscopy, and propose methods to improve these approaches. We provide public access to the simulated environment and dataset.
Weather Classification Model Performance: Using CNN, Keras-Tensor Flow

Nowadays, automation is at its peak. The article provides a base to examine the weather through the machine without human intervention. This study offers a thorough classification model to forecast a weather type. Here, the model facilitates defining the best results for the weather prediction model to any climatic zones and categorizes the climate into four types: cloud, rain, shine, and sunrise. This model designs and reveals using convolution neural networks (CNN) algorithms with Keras framework and TensorFlow library. For practical implementations, use the images dataset available from the kaggle.com website. As a result, this research presents the performance of the designed and developed model. It shows accuracy, validation accuracy, losses, and validation losses approximately 94%, 92%, 18%, and 22%, respectively.
Development of an Android-based Computer Based Test (CBT) In Middle School

The teaching and learning process, especially the process of evaluating student learning outcomes, the teacher still uses a semi-conventional evaluation process where the exam process is carried out using social media to send student exam results which are done manually on paper. This kind of evaluation process is less effective and efficient, both in terms of time, the method used, and the examination process which requires a long time and inaccurate examinations. For this reason, the authors designed a Computer Based Test (CBT) application as a media for evaluating student learning outcomes. The study aims to develop an Android-based Computer Based Test whether it is valid, practical, and effective in the learning exam / evaluation process. This research method is Research and Development which is focused on developing a smartphone-based Computer Based Test, especially Android. The development model used refers to the Alessi and Trollip development model, which consists of 3 stages, namely: planning, design, and development. The total research subjects were 30 students and 1 mathematics subject teacher. The media developed was validated by media experts and material experts, with the results of validation by material experts obtained an average score of 3.7 (very valid) and by media experts an average score of 3.8 (very valid) was obtained. This means that it can be stated that the Android-based Computer Based Test is very practical in the evaluation / exam process in mathematics subjects. Based on the results of the feasibility test of the Computer Based Test, it can be concluded that the use of the Computer Based Test is feasible to be used as a medium for evaluating learning in mathematics subjects
Ship Detection in Optical Remote Sensing Images Using YOLOv4 and Tiny YOLOv4

With the advances in remote sensing domain, images with higher spatial and spectral resolution are obtained from increasing number of sensors, and they have been employed in more research fields, including object detection and tracking. In particular, the detection of marine vehicles has a significant role in civil and military applications. However, due to the varying type, size, posture, and complex background of the ships to be detected, ship target detection is still considered as a challenging task. Deep learning techniques with their wide-spread use in computer vision applications have been successfully applied to object detection problems that is important to monitor marine traffic and ensure maritime safety. In this study, a freely available aerial image dataset is utilized to train and test the two popular single-stage object detection models, namely YOLOv4 and Tiny YOLOv4, based on the “You Only Look Once” approach. Produced results were analyzed using conventional accuracy metrics, and average prediction times were also compared. The trained models were evaluated on different ship images and detections were performed. As a result of the study, mean average precision (mAP) values of 80.82% and 62.30% were obtained using YOLOv4 and Tiny YOLOv4 architectures, respectively. This indicates major performance difference between YOLOv4 and Tiny YOLOv4 models for ship detection studies.
Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System

This paper proposes a dynamic high-type control (DHTC) method based on an interval type-2 fuzzy logic controller (IT2FLC), which is used in the photoelectric tracking system to improve the steady-state accuracy and response speed. Adding integrators to the traditional multi-loop feedback control loop can increase the system type, thereby speeding up the response speed and improving the steady-state accuracy, but there is a risk of integral saturation. Switching the type dynamically according to the system state can avoid integral saturation while retaining the advantages of the high-type. Fuzzy logic control (FLC) can dynamically change the output value according to the input change and has the advantages of fast response speed and strong ability to handle uncertainties. Therefore, in this paper, the FLC is introduced into the high-type control system, and the output of the FLC is used as the gain of the integrator to control the on-off to achieve the goal of dynamic switching type, which is successfully verified in the experiment. IT2FLC introduces a three-dimensional membership function, which further improves the FLC’s ability to handle uncertainties. From the experimental results, compared with T1FLC, IT2FLC’s ability to handle uncertainties is significantly improved. In addition, in order to speed up the calculation speed of IT2FLC, this paper proposes an improved type-reduction algorithm, which is called weighted-trapezoidal Nie-Tan (WTNT). Compared with the traditional type-reduction algorithm, WTNT has faster calculation speed and better steady-state accuracy, and has been successfully applied to real-time control systems, which has good engineering application value. Finally, in order to reduce the interference of human factors and improve the automation level of the system, a multi-population genetic algorithm (MPGA) is used to iteratively optimize the parameters of the FLC, which improves the output accuracy. On the experimental platform of the flexible fast steering mirror (FFSM), the control effects of the traditional controller, T1FLC and IT2FLC are compared, which proves that the IT2FLC-DHTC system has a faster response performance, higher steady-state accuracy, and stronger ability to handle uncertainties.
Information retrieval from scientific abstract and citation databases: A query-by-documents approach based on Monte-Carlo sampling

The rapidly increasing amount of information and entries in abstract and citation databases steadily complicates the information retrieval task. In this study, a novel query-by-document approach using Monte-Carlo sampling of relevant keywords is presented. From a set of input documents (seed) keywords are extracted using TF-IDF and subsequently sampled to repeatedly construct queries to the database. The occurrence of returned documents is counted and serves as a proxy relevance metric. Two case studies based on the Scopus® database are used to demonstrate the method and its key advantages. No expert knowledge and human intervention is needed to construct the final search strings which reduces the human bias. The methods practicality is supported by the high re-retrieval of seed documents of 7/8 and 26/31 in high ranks in the two presented case studies.
Influence of the Linear Layer on the Algebraic Degree in SP-Networks

We consider SPN schemes, i.e., schemes whose non-linear layer is defined as the parallel application of t ≥ 1 independent S-Boxes over F2n and whose linear layer is defined by the multiplication with a (n · t) × (n · t) matrix over F2. Even if the algebraic representation of a scheme depends on all its components, upper bounds on the growth of the algebraic degree in the literature usually only consider the details of the non-linear layer. Hence a natural question arises: (how) do the details of the linear layer influence the growth of the algebraic degree? We show that the linear layer plays a crucial role in the growth of the algebraic degree and present a new upper bound on the algebraic degree in SP-networks. As main results, we prove that in the case of low-degree round functions with large S-Boxes: (a) an initial exponential growth of the algebraic degree can be followed by a linear growth until the maximum algebraic degree is reached; (b) the rate of the linear growth is proportional to the degree of the linear layer over Ft2n. Besides providing a theoretical insight, our analysis is particularly relevant for assessing the security of the security of cryptographic permutations designed to be competitive in applications like MPC, FHE, SNARKs, and STARKs, including permutations based on the Hades design strategy. We have verified our findings on small-scale instances and we have compared them against the currently best results in the literature, showing a substantial improvement of upper bounds on the algebraic degree in case of low-degree round functions with large S-Boxes.