Reviewerly/exHarmony huggingface dataset:
Bad data identification and fault diagnosis of smart substation based on secondary system information redundancy

Abstract Secondary system is an important link that affects the reliable operation of power system. However, the current improvement measures for accurate data acquisition and reliable operation in secondary systems are mainly concentrated at the equipment level. The solution at the equipment level not only increases the complexity of the system, but also can only optimize a single link or problem, which is difficult to improve the overall system level. In order to enhance the information accuracy, operation and maintenance precision and operation reliability of smart substation secondary system, this paper proposes bad data identification and fault diagnosis methods based on secondary system information redundancy. Firstly, according to the analysis of secondary information redundancy, this paper constructs the data information redundancy model of the smart substation secondary system. Then the data information state estimation method based on the least square method and the bad data identification method based on the information redundancy are proposed. Finally, case analysis is carried out to verify that the proposed method can effectively increase the information accuracy of smart substation, which also provides new research route and foundations for secondary system fault diagnosis.
Multi-label Detection Method of Facial Protection Based on Deep YOLO Network

Wearing face protectors is an important means of personal safety protection, and the corresponding detection technology plays a critical role in the face of major public security incidents. At present, the detection methods of face protectors do not fully utilize the features of protectors on the face and have the problem that it is impossible to detect the wearing situation of two or more types of face protectors at the same time. This paper integrates the features of the face region and the region of protectors, takes the fused features of the two regions as the basis of object classification, and converts the multi-label classification into multi-channel binary classification problems, which effectively reduces the difficulty of detection. On this basis, a face protectors multi-label detection method based on deep YOLO network is proposed. The experimental results show that this method can detect the multi-label information of the wearing situation of the face protectors, and effectively improve the accuracy of the detection of face protectors.
An Investigation of Social Networking Sites for Language Learning and the User Experience

This chapter investigates the user experience of the language learning platform Busuu as a tool for learning Spanish as a Foreign Language (SFL). Social constructionism has been highlighted by previous research as the theory on which Busuu is based, however, Álvarez concluded that Busuu constitutes an ecological system of nested semiotic spaces where pedagogical elements and principles from different theories of language learning interweave in complementary ways. Following a review of existing research, the chapter analyses data arising from a study involving a mixed group of university students who used the premium version of Busuu for four weeks. Data were collected via pre- and post-tests, a user experience questionnaire, and individual interviews, and were analysed using descriptive statistics and thematic analysis. Findings suggest that Busuu did not fully satisfy the requirements of a social network in terms of ease of participation, communication, and collaboration. Further research is required to explore the implications of usability testing for the design of effective SNSLL.
A Lightweight and Secure Vehicular Edge Computing Framework for V2X Services

Vehicle-to-everything (V2X) communications over cellular networks have a great potential for enabling intelligent transportation systems (ITSs), and supporting advanced services such as autonomous driving. However, such services have stringent QoS and security/privacy requirements. Even though the use of blockchain can ensure security and privacy for V2X services, blockchain-based solutions suffer from the issues of high latency, low scalability, and high computation power for mining. To overcome these challenges, we propose a lightweight and secure vehicular edge computing framework. The LS-VEC framework leverages directed acyclic graphs (DAGs) for recording transactions for edge resource allocation and micro-transactions for pricing VEC resources. In addition, an auction theory-based game-theoretic approach is proposed for allocation and pricing of edge resources used for supporting computation offloading.
Improved Untyped IntelliSense for JavaScript with Type Carriers and Binders

In general, IntelliSense refers to source-code editing facilities that assist software developers in navigating, understanding, avoiding errors, and speeding-up the overall programming process. It is essentially the crossroad of context-sensitive help, intelligent recommendations, auto corrections and adaptive prompting for the programming task. We focus on untyped languages, in particular JavaScript, where the quantity of untyped code constantly grows, even though typing frameworks like Type Script are popular, and the recent ECMA 6 standard favors for typed classes. We present an improved technique for editing-time type inference, built on top of Visual Studio Code IDE and its Language Server Protocol. We introduce type carriers and binders that can capture all possible types of variables, returning results correctly matching the particular source location from which a query is made. We show how this approach offers more informative and descriptive feedback, effectively improving the overall intelligence of JavaScript editors.
A Novel Architectural Method for Producing Dynamic Gaze Behavior in Human-Robot Interactions

We present a novel integration between a computational framework for modeling attention-driven perception and cognition (ARCADIA) with a cognitive robotic architecture (DIARC), demonstrating how this integration can be used to drive the gaze behavior of a robotic platform. Although some previous approaches to controlling gaze behavior in robots during human-robot interactions have relied either on models of human visual attention or human cognition, ARCADIA provides a novel framework with an attentional mechanism that bridges both lower-level visual and higher-level cognitive processes. We demonstrate how this approach can produce more natural and human-like robot gaze behavior. In particular, we focus on how our approach can control gaze during an interactive object learning task. We present results from a pilot crowdsourced evaluation that investigates whether the gaze behavior produced during this task increases confidence that the robot has correctly learned each object.
Federated Learning for Colorectal Cancer Prediction

The availability of datasets pertaining to various fields has increased significantly in the past decade, but there still exists a problem in getting datasets pertaining to the medical field as most of the data needs to be confidential and there exists laws which ensure a patient's data privacy. Federated learning (FL) proves to solve this problem via a client-server architecture by enabling distributed training of clients, without any data exposure. In this paper, we apply the FedAvg (FederatedAveraging) [1] algorithm on the PathMNISTv2 [2] dataset for predicting colorectal cancer. We also present a refined convolutional neural network (CNN) architecture for accurate predictions on the PathMNISTv2 dataset. We have studied the effects on IID (Independent and Identically Distributed) and Non-IID (Non-Identically Independently Distributed) distributions in a distributed environment. We have also compared these results with a centralized model and demonstrate that FedAvg achieves similar results in a distributed setting. We anticipate our study to enable additional healthcare studies driven by vast and diverse data, and illustrate the efficacy of FL at such magnitude and task complexity as a paradigm shift for multi-site partnerships, eliminating the need for data sharing.
Stereo Image Compression Using Recurrent Neural Network With A Convolutional Neural Network-Based Occlusion Detection

In this work, we propose an end-to-end trainable recurrent neural network for stereo image compression. The recurrent neural network allows variable compression rates without retraining the network due to the iterative nature of the recurrent units. The proposed method leverages the fact that stereo images have overlapping fields of view, i.e., mutual information, to reduce the overall bit rate. Each image in the stereo pair has its separate encoder and decoder network. We propose to share the mutual information between the stereo pair networks by warping the hidden states of one of the stereo image network’s recurrent layers to the other stereo image network’s recurrent layers. Moreover, we also improve the quality of the shared mutual information by eliminating the wrong information by estimating occlusion maps using a convolutional neural network. The proposed method results show significant bit rate savings compared to the single image compression baseline model and traditional codecs.
Camera Calibration Based on Phase Estimation

We propose a novel camera calibration method based on phase estimation of a periodic grid pattern that is more accurate and robust to image blur, noise, and uneven illumination. Instead of intensity-based geometric feature detection from the calibration image, we extract the phase information to acquire 3-D-to-2-D point correspondence. The calibration procedure includes three steps: 1) estimate the tilt angles of input images based on a pyramid geometry model and remap the images to a canonical fronto-parallel plane to correct perspective distortion; 2) use a highly accurate phase estimation method to localize control points; and 3) apply an iterative refinement method to compute the camera parameters until convergence. Using the perspective correction and phase estimation method together help increase the accuracy of control point localization and consequently of camera calibration. Synthetic as well as real images from high accuracy patterns etched on ceramic plates and low accuracy patterns printed on article show that the proposed method outperforms the most widely-used methods, in terms of accuracy and robustness.
Evaluation of Air Traffic Support Capability of Small and Medium Airport Based on Entropy and Cloud Model

The air traffic control support capability of the airport is the key link to ensure the normal operation of flights and airports. It is one of the core elements to promote the positive development of small and medium-sized airports. Therefore, by putting the air traffic control operation into consideration, the evaluation index of air traffic control assurance ability in small and medium-sized airports is constructed from four dimensions: safety management, running environment, personnel evaluation, facilities, and equipment. Based on determining a certain range of flights, the entropy weight-cloud model is used to evaluate the air traffic control support capability of small and medium-sized airports. The example shows that the method can evaluate the support capability of small and medium-sized airports scientifically and accurately, and it is feasible and effective.
IMPLEMENTATION OF BLENDED LEARNING USING ZOOM MEETING

Rapid technology advancements have an impact on education, one of which is blended learning. Blended learning is a combination of physical classroom activities and online technology-assisted learning activities. The goal of this study was to learn more about the utilization of Zoom Meeting application for blended learning, especially in present pandemic. Literary studies are used in the research process. Through Google Scholar, secondary data was gathered from articles, journals, and books, yielding seven journal articles. The conclusion of this study were the Zoom Meeting application may be used in the implementation of blended learning for all levels of education, as well as in training for teachers and lecturers, and the community at the Community Learning Activity Center. This application can increase student learning result; learning process was not monotonous and can grab students' attention; and it has a favorable impact on training students' learning independence. The teacher and community training is also going smoothly and efficiently. The issues include a less stable internet network; its consumes a lot of internet quota; a 45-minute meeting time limit for the first user; educators' and students' lack of understanding of how to use the application; the government's uneven distribution of facilities and infrastructure for delivering free internet, and a lack of training for instructors or lecturers on how to use it in the classroom.
Research on adaptive matched filtering algorithm of active sonar echo signal

Matched filter is the best detector of active sonar in the background of Gaussian white noise, but its detection performance is limited, and often can not meet the detection requirements under the condition of lower signal-to-noise ratio (SNR). In order to detect the echo signal under the condition of lower signal-to-noise ratio, an adaptive matched filter algorithm based on conventional matched filter, t0 filter, correlation denoising and adaptive line spectrum enhancement processing is proposed in this paper. The matched filter output signal is first processed by t0 filtering, then by correlation denoising and adaptive line spectrum enhancement. After triple processing, the echo signal and interference can be separated under the background of low SNR, which significantly improves the output SNR. The simulation results show that the algorithm proposed in this paper can significantly improve the echo detection ability under the condition of weak SNR, and can detect the -20db echo signal after beamforming, and the performance is significantly better than the traditional matched filter.
3D Human Pose Estimation in Weightless Environments Using a Fisheye Camera

Three-dimensional (3D) human pose estimation is one of the most basic tasks for human-interacting robots. Especially in weightless environments such as the International Space Station (ISS), wherein objects may move with a higher degree of freedom compared to on the ground, a camera with a wider field of view (FOV) is crucial in improving the probability of capturing surrounding humans. To this end, we propose a learning-based 3D human pose estimation (3D-HPE) using a fisheye camera targeted at weightless environments. One impediment is that 3D-HPE trained on existing datasets are incapable of addressing the adverse effects of strong fisheye distortion and weightlessness as existing human detection and pose estimation datasets are recorded on the ground using typical rectilinear cameras. To overcome this difficulty, we integrate virtual camera projection to perform a detected human-centered undistortion from fisheye to rectilinear images. We also include upside-down augmentation during training to improve the performance toward weightlessness. Our results show that these two techniques successfully mitigate the adverse effects of weightlessness and fisheye distortion.
PLT-Net: A 2-Layer SP-Network using a Modified Playfair Cipher and the Logistic-Tent Map

The Playfair cipher is a classic digraph substitution cipher. Over the years, it has been modified in many ways to increase its security. Most of its modifications are basically substitution ciphers with limited confusion and diffusion, and are vulnerable to chosen-plaintext attacks, ciphertext-only attacks, and even frequency analysis. In this paper, we introduce a modification of the Playfair cipher, Playfair Logistic-Tent Network (PLT-Net), that uses two 5X19 key tables simultaneously to randomly encrypt the text. The key tables consist of all printable characters. This modification has been used in combination with the chaotic logistic-tent map as a permutation cipher, to create a 2-layer substitution-permutation network with better security, where the encrypted text is scrambled in each layer to break the digraphs. The combination of a substitution cipher and a permutation cipher has produced an algorithm that is free from the drawbacks of both. Cryptanalysis has been performed on the PLT-Net to showcase its strengths. It has better confusion and diffusion, as changing a single character, either in the key or in the plaintext, completely changes the ciphertext.
Adaptive Retinex image defogging algorithm based on the depth of field information

The traditional Retinex algorithm is used to restore the image without the same depth of field information, which leads to the loss of local depth of field information. Firstly, the depth of field estimation of the foggy image is obtained by using BTS depth learning model; Then, the average gradient of the image is used as the optimal evaluation standard, and different Gaussian filter scales are used for Retinex enhancement for the block processing of the foggy image, to calculate the optimal Gaussian filter scale and its corresponding average value of depth of field estimation; Then, the statistical data are fitted by the gradient descent method to obtain the parameter model of the depth of field estimation and Gaussian filter scale, and the model is applied to the single-scale Retinex defogging algorithm to block the foggy image; Finally, the adaptive contrast stretching of colorless bias is realized by calculating the mean and mean square deviation, plus a parameter to control the image dynamics, and the image block edge is made more continuous by using bilinear interpolation mapping to obtain an enhanced defogging image. The experimental results show that the evaluation indexes such as image standard deviation, average brightness, information entropy, and square gradient are improved by 34.2%, 6.67%, 11.57%, and 1 ~ 2 times respectively, the actual effect contrast is high, the image details remain intact, and the excessive enhancement is restrained.
Support Vector Machine

The more the dimensions of a feature space, the more is the computing power needed to classify. Support vector machines (SVMs) main advantages are (1) their effectiveness in a high-dimensional space and in cases where the number of dimensions is higher than the number of instances in the dataset, and (2) their low use of memory and hence their memory efficiency.
MCTrack: Multi-Head Contrastive Network for Visual Tracking

Siamese trackers based on deep networks impress by their advanced performance. However, the targets often have different appearances due to deformation, fast motion and so on. In this work, we propose a multi-head contrastive network, which constructs a separate embedding space for each layer in the backbone. The goal is to learn to capture the invariance of different appearances of the same target. Further, we propose global contextual consistency loss, which not only maintains the consistency of semantic information, but also maintains consistency in the spatial relationship and channel relationship of feature representations. Finally, we performed an ablation study to demonstrate the effectiveness of the proposed multi-head contrastive network. We achieve the state-of-the-art performance on five benchmark datasets, OTB-100, VOT2016, VOT2018, UAV123 and GOT10K. In particular, we achieve an AUC score of 70.9% on OTB-100.
A hybrid artificial intelligence system for securing a maritime zone based on historical and real-time data analysis

Securing an area where a maritime event (e.g., a sailing race) occurs is essential to ensure the smooth running of such event. We discuss and detect the risk which may be caused by a ship passing close to a protected area. Based on information provided by fused data coming from a radar, an AIS and cameras, a rule-based classifier has been developed. It aims to analyse real-time data like position, speed and course in order to detect potential threats beforehand. The expert system developed here is built on a two-stage event detection where threats rules are aggregated from simple event detection. In addition, a historical maritime traffic data set is used to train a simple but efficient machine learning model based on hexagonal hierarchical geospatial indexing system H3.
Making Music Using Two Quantum Algorithms

Computers have and continue to shape the sound and music landscape that we experience. Whether that be as tools to make music with or inspiration for writing and composing music, the impact of computation in a sound generation is not difficult to find. This chapter provides the background and specific details of a collaboration formed in 2021 between the Quantum Engineering Technology Labs—a quantum computing and technology research group at the University of Bristol—and music artist, producer and audio engineer Simon Small. The goal of the collaboration was to explore how the data and concepts used in the research at the university could be ‘sonified’ to create sounds or even make music.
Research on the Role of Pronunciation laws in Chinese Named Entity Recognition(NER)

Named entity recognition is the basic task of natural language processing. It is widely used in information extraction and knowledge map construction. According to the unique pronunciation characteristics of Chinese, the word vector is trained by international phonetic alphabet, Pinyin and dialect phonetic alphabet. The word vector has the ability to represent the position and direction of the context. In the experiment, the word vector is embedded in the model constructed based on transformer. Through the comparative experiment, the experiment is embedded in the resume data set and Weibo data set. Compared with the other three models as the baseline, the F1 value is the highest after embedding the pronunciation regular word vector. The quantitative and qualitative analysis is carried out on the recognition results of the test set to further verify the effectiveness of Chinese pronunciation law in improving the performance of Chinese named entity recognition.