Reviewerly/exHarmony huggingface dataset:
Position and time deception based on satellite navigation technology and experimental verification

Due to the characteristics of open system and weak signal power of satellite navigation signal, it is very easy to be affected by navigation deception and other interference. At present, most studies divide the influence of deception signal on the receiver into two independent aspects: positioning and timing. There is a lack of research on accurately controlling the positioning and timing of the receiver in the process of deception. In this paper, the principle of accurately controlling positioning and timing in navigation deception are analyzed, three deception scenarios are proposed and verified by experiments. The experiments show that :1) it is feasible to introduce a step or linear position deviation to the receiver without excessively affecting the timing results; 2) it is feasible to introduce a step or linear time deviation to the receiver without excessively affecting the positioning results:3) it is feasible to perform position spoofing and time spoofing on the receiver at the same time, the positioning results of the receivers were successfully pulled off by 60m in the experiment, while their timing results were pulled off by 1.5μs.
ADVANTAGES AND DISADVANTAGES OF LEARNING REPORT TEXT USING YOUTUBE VIDEOS IN PANDEMIC

This study focuses on the advantages and disadvantages of learning report text using YouTube video in Covid-19 Pandemic. The purpose of this study was to find out the advantages and disadvantages of online learning using YouTube video media during the covid-19 pandemic through student opinions about learning taking place. This study uses a qualitative descriptive method, in which this study describes students' opinions through questionnaires and interviews that have been conducted by researchers. The participants of this study were 56 students who took part in the interview and 14 students who took part in the interview, all participants were taken from the twelfth class of hotel accommodation at SMK N 4 Yogyakarta. The results of this study indicate that in learning report text using YouTube media has advantages and disadvantages. The students give a positive response to the number of benefits that can be obtained during the learning process, even though the losses that can be obtained are also quite a lot.
Design and Fabrication of Solar-powered Wheelchair with Smart Features for Mobility

This project proposes an automated wheelchair that will help physically challenged people and serve as a complete form of automatic system (means of mobility). This paper exhibits a design and fabrication of a smarter feature of a solar-powered wheelchair with two modes of the control interface. A prototype of the smarter features of the solar-powered wheelchair system has been fabricated based on the manually purposed wheelchair that is commonly available in the market with the addition of related work done in electrical and mechanical advancement. Bluetooth control interfaces a deployed to the system in the addition of manually purposed wheelchair, joystick-control interface with enhanced the interaction among the user. The mechanical design of the wheelchair, the electrical, and the controller system of the smarter feature wheelchair are presented. The wheelchair is based on a solar power supply for indoor/outdoor uses. High torque DC motor is used in our project. Additional Feature is the fingerprint, Panic alarm and Liquid crystal display. Fingerprint panic key set-up is placed in joystick itself and buzzer also used for release the sound when the panic key button is pressed. LCD shows the direction and condition of the wheelchair. This solar-powered smart featured wheelchair is planned to add up with maximum technological advancement.
Prediction Model of Heart Failure Disease Based on GA-ELM

In order to predict heart failure disease in advance and keep heart failure patients away from pain, this paper combines genetic algorithms (GA) to optimize Extreme Learning Machine (ELM) and builds a network model that predicts heart failure disease based on some of the characteristic factors that may induce heart failure. The ELM model has the characteristics of fast training speed and strong generalization ability, but its randomly generated input weights and hidden layer bias will cause instability in the prediction results. In this paper, we use GA’s principle of survival of the fittest to find the most suitable parameter configuration of the ELM algorithm, and analyzes the simulation results, and compares them with the prediction results of the original ELM model. The results show that the GA-optimized ELM model can better predict the occurrence of heart failure disease and has better predictive performance. The ELM algorithm, which has undergone parameter optimization has made up for its original defects, which may affect clinical practice and become a new assistant for doctors to predict patients with heart failure.
sRouting: Towards a Better Flow Size Estimation Performance through Routing and Sketch Configuration

Flow size estimation is highly important and beneficial to various applications, including traffic engineering and anomaly detection. Considering the resource constraints and performance requirements in place, sketches are widely used to accomplish this task. However, when sketches are applied in real-life networks, the measurement performance is often decreased due to many practical reasons, including partial deployment of sketches, unique traffic characteristics, etc. In this paper, we present sRouting, a practical framework that aims to better utilize the deployed sketches. sRouting improves the measurement performance by optimizing which flows are monitored and where. We first investigate the relationship between the accuracy of a given sketch and the total number of packets, then formulate the offline problem in sRouting as an integer linear programming problem. To solve this problem efficiently, we devise a rounding-based algorithm and provide its performance guarantees. Furthermore, to handle dynamic changes in the network, we design an online adjustment algorithm capable of responding appropriately to these changes. Through experiments using real traces and typologies, we demonstrate that sRouting can significantly improve the volume of monitored traffic and reduce the measurement error without negatively impacting the network throughput.
Challenges on Collecting Smartphone Data in Cold Environments

Smartphones can be considered the cheapest and well-penetrated devices for collecting everyday human behaviour data. However, smartphones, as any battery-dependant electronic devices, face a number of problems when exposed to below-freezing conditions, from sudden crashes and decreased battery life to challenging usage experiences such as freezing fingers especially during prolonged periods of time. In this paper, we present the results of a user survey (N=130, 59% female) exploring smartphone usage in below-freezing temperatures, including the problems caused by cold conditions and prevention mechanisms users could be willing to take to protect their device and user experience during winter months.
Research on the Path of Talent Development and Upgrading of Mining Engineering under the Background of the Intelligent Mine

The construction of intelligent mines puts forward new challenges to the talent training of Mining Engineering. The connotation and future development trend of intelligent mines were systematically expounded in the paper, summarizing the current situation and the barriers to upgrading and transforming the major. Based on the development status of intelligent mines, a curriculum system construction plan was proposed incorporating the principle of merging traditional courses, introducing in new courses, strengthening knowledge integration, highlighting practical ability training, and setting elective course groups according to the research direction. In addition, a supporting teacher construction plan and a practice base construction plan were proposed accordingly. Subsequently, a talent training and an upgrading path for the major of Mining Engineering under the background of intelligent mining were proposed.
SEACHER AND ANALYSIS IN OLYMPIAD PROBLEMS. EVALUATION CRITERIA

Olympiad problems that can be solved both by long monotonous calculations, following a given algorithm, and analytically are analyzed. The possibility of using such tasks in intra-university Olympiads of technical universities is justified.
Text Summarization of Multiple Documents Using Binary Fruit Fly Optimization Algorithm

Due to the availability of huge data on the Internet, it becomes a time-consuming process for the user to find the most relevant text related to their interesting topic. This process can be simplified by text summarization. Initially, a set of multiple documents are preprocessed separately. Later, word frequency and the similarity of the sentences from every preprocessed document are calculated and then most redundant sentences are eliminated. Next, the content from these multiple preprocessed document is merged into a single input document. Finally, the merged document is given as input to the proposed Binary Fruit Fly Optimization Algorithm to generate a text summary. We used the ROUGE system to evaluate the results.
Power Grid Adaptive Security Defense System Based on Artificial Intelligence

With the increasing production safety requirements of national grid companies, grid enterprises should break away from the traditional safety management model and form a more accurate grid adaptation safety management system. The goal of security defense is the safety accident prevention and prediction power grid enterprises, and implementation measures are put forward and eliminate the potential risk of physical, environmental and safety equipment. Security defense plays a key role in predetermining and controlling risks. This paper mainly studies and analyzes the adaptive security defense system of power grid based on the background of artificial intelligence. Firstly, the significance of power grid adaptive security defense system and the influence of artificial intelligence on power grid adaptive security defense system are expounded. Then it will discuss the method of adaptive security defense system for power grid under the background of artificial intelligence. Finally, the application status of the adaptive security defense system for power grid based on artificial intelligence is investigated, questionnaires are issued, and data are collected and processed. The survey results show that the probability of lightning and typhoon to the artificial intelligence defense system causing equipment failure accounts for 37% respectively, which will enable the power grid adaptive security defense system to operate risk prevention in real time. The influence of network information on the adaptive security defense system of power grid based on artificial intelligence will reach the peak with the perfection of information data and defense system, and then decline.
Dendrogeochemistry and soil geochemistry applied to exploration for alkalic Cu-Au porphyry mineralization under cover at the Racecourse prospect, NSW, Australia

ABSTRACT The Racecourse Cu-Au porphyry prospect is found within the Macquarie Arc of the Lachlan Fold Belt, in the Lachlan Transverse Zone, a cross-arc structure hosting significant world-class mineralization, including the Cadia and Northparks districts. Several geochemical and geophysical surveys of the prospect have been complimented by a total of 19,819 m of drilling, with only four holes reaching a depth greater than 300 m. Positive lithogeochemistry (fertility indices, comparisons with the Cadia and Northparks systems) subtle alteration, and mineralized intercepts indicate heightened mineral potential, yet the prospect has lacked a comprehensive geochemical survey outlining the extent of the mineralized target at surface. Soil samples and Monterey pine (Pinus radiata) tree cores were collected above and distal to mineralization intercepted by prior drilling in order to outline the ore deposit footprint and test the viability of dendrogeochemistry as an exploration tool for porphyry Cu mineralization. Ultimately, this study documented the spatial extent of the Racecourse target and identified potential areas for additional Cu mineralization. Soil samples were separated with the &amp;lt;250 μm size fraction analyzed and show distinct anomalous populations of Au, Cu, Mo, Pb, and Zn above prior drilling. Tree cores were collected by increment borer and tree rings of the Pinus radiata were counted and measured, with an age interval of 2003–2008 exhibiting the least ring-width variability chosen to chemically analyze. Selected intervals were digested and analyzed and have elevated Cu, Mo, and Zn in an area that overlaps a previously drilled soil anomaly, whereas an anomaly in the southwest of the survey area documents a Cu, Pb anomaly corresponding to localized faulting and tertiary basalt subcrop. Tree roots are directly tapping chemical variability at depth, aided via metal mobilization through faulted fluid conduits. Lead isotope ratios from the Pinus radiata identify distinct groups of lead spatially associated with discrete metal anomalies of varied lithological ages. At the Racecourse target, anomalous Pinus radiata samples yield a similar isotopic signature to the faulted southwestern anomaly, potentially linking the source of these two metal anomalies. When these results are integrated with the current understanding of the mineralized body, geochemical media suggest that mineralization may continue down-plunge at depth.
Learning Effective Discriminative Features with Differentiable Magnet Loss

Neural network optimization relies on the ability of the loss function to learn highly discriminative features. In recent years, Softmax loss has been widely used to train neural network models in various tasks. In order to further enhance the discriminative power of the learned features, Center loss is introduced as an auxiliary function to aid Softmax loss jointly reduce the intra-class variances. In this paper, we propose a novel loss called Differentiable Magnet loss (DML), which can optimize neural nets independently of Softmax loss without joint supervision. This loss offers a more definite convergence target for each class, which not only allows the sample to be close to the homogeneous (intra-class) center but also to stay away from all heterogeneous (inter-class) centers in the feature embedding space. Extensive experimental results demonstrate the superiority of DML in a variety of classification and clustering tasks. Specifically, the 2-D visualization of the learned embedding features by t-SNE effectively proves that our proposed new loss can learn better discriminative representations.
Cartoon and texture decomposition for color image in opponent color space

The Meyer model has been successfully applied to decompose cartoon component and texture component for the gray scale image, where the total variation (TV) norm and the G-norm are respectively modeled to capture the cartoon component and the texture component in an energy minimization method. In this paper, we extend this model to the color image in the opponent color space, which is closer to human perception than the RGB space. It is important to extend the TV norm and the G-norm correspondingly because the color image is viewed as a vector-valued vector. We introduce the definition of the L1 norm and L∞ norm for the vector-valued vector and accordingly define the TV norm and the G-norm for the color image. In order to handle the numerical difficulty caused by the non-differentiability of the TV norm and G-norm, the dual formulations are used to represent these norm. Then the decomposition problem is reformulated into a minimax problem. A first-order primal-dual algorithm is readily applied to compute the saddle point of the minimax problem. Numerical results are shown the performance of the proposed model.
Differentiation of “expertise” and “competency” concepts in education

Scientists of various fields such as sociology, psychology, pedagogy and other study the “expertise” and “competency” concepts, but despite a large number of published works on this issue, there are gaps in the study of the structure of these words, which makes it difficult to clearly separate these concepts. I.A. Zimnaya, L.Yu. Zueva, E.P. Nepochatykh, V.I. Tityushin, E.V. Kharitonova, M.A. Kholodnaya, A.V. Khutorsky and other researchers considered the normative legal acts and scientific views on the “expertise” and “competency” concepts to identify their similarities and differences, as well as to reveal the vision of scientists and practitioners on the classification of competency and expertise. The basis for the study of the “expertise” and “competency” terms served as domestic and foreign scientific sources. The methods for the research were: analysis and generalization of educational literature on the topic of research, analysis and generalization of scientific views. The directions of interpretation of competency were investigated: psychological, linguistic, psychological and pedagogical; in addition, several classifications of competencies were analyzed (socio-personal, general professional, economic, organizational and managerial, special, key, general subject and subject). Since the concept of competency is capacious and multifaceted we also consider its types (professional, social and communicative). Based on concepts analysis, it is concluded that “competency” is the ability of an individual to apply practical skills in a certain area expected by the educational process, and “expertise” is the results of the competencies acquired during training.
Whole Slide Image Classification and Segmentation using Deep Learning

Whole slide imaging is now being used across the world in pathology labs for an accurate diagnosis of biopsy specimens. However, due to the large size of these images, an automatic deep learning-based method is highly desirable for diagnosing. Herein, we propose a two-step methodology for the classification and segmentation of whole-slide image (WSI). First, the patches are extracted from the image and fed into deep learning based techniques like U-Net with its corresponding mask for the accurate segmentation. Further, the cancerous patches are trained for the classification task. During inference, the predicted segmented mask are evaluated in the classification model. Our experimental results demonstrated that the proposed methodology can be used for accurate segmentation and classification.
DiterGraph: Toward I/O-Efficient Incremental Computation over Large Graphs with Billion Edges

The growing demand for iterative computation over large-scale graphs has attracted a lot of enthusiasm. Distributed-disk systems can accommodate the high-level scalability requirement as graphs grow in size, but the computation is greatly expensive due to a large number of communications and a high frequency of random data-accesses. Alleviating the two limiting factors pose great challenges for graph partitioning, disk-oriented data management and the iterative mechanism. This paper derives insights from the natural locality of raw graphs and then proposes a lightweight partitioning algorithm GPNL with the goal of balancing load and accelerating communication. Accordingly, a hybrid index RC-Index is proposed to improve the I/O-efficiency by reducing disk-accesses for graph data and message data. We also introduce an across-iteration mechanism (AIM) based on the extended BSP model, and then design two policies AIMP and AIMC to prune the message scale and accelerate the message-spreading respectively. Comprehensive experiments versus the state-of-the-art solutions demonstrate significant performance gains over a broad spectrum of real-world and synthetic graphs with up to billion edges.
Two new methods for facial expression recognition using Convolutional Neural Networks

Abstract In this research, we propose two novel methods for facial expression recognition to improve the accuracy of recognition. The first of our novel approach is to add the Batch Normalization (BN) layer to the CNN model, and the second of the novel approach is to preprocess the image before image training, such as rotating image, cropping the image and adding Gaussian noise in the picture, especially it is beneficial for unbalanced classifications. Our model consists of 3 CNN layers, 3 BN layers, three average-pooling layers, and three fully-connected layers; our model has a satisfying performance on the prediction category after adopting the two methods mentioned above. Our CNN model is trained and tested with Kaggle facial expression recognition challenge databases. The implemented system can automatically recognize seven expressions in real-time: anger, disgust, fear, happiness, neutral, sadness, and sur-prise. The experimental results demonstrate the effectiveness of our proposed approach.
Dual Network Based Complementary Learning System for Continual Learning

Catastrophic forgetting is a well-known problem when it comes to training neural networks in the continual learning setting. The main focus of research has been on training a single network when addressing the problem. In our work, we explore a dual network approach. We propose a brain-inspired complementary dual network model for continual learning that comprises a fast learner and a slow consolidator. The fast learner first adapts to a new task seen only once, and the slow consolidator distills the new task information using knowledge distillation from the fast learner. The two networks are trained in an alternate manner. To consolidate the learning of a new task with the learning of past tasks, we employ a small memory of each task for replay during the training of the slow consolidator. In addition, we incorporate a context-based gating mechanism on the slow consolidator, and empirically prove its positive impact on the performance of the proposed model. We show the improved results of the proposed model on several classification datasets.
Design of Fast Image Recognition Accelerator Based on Convolutional Neural Network

Due to the limited resources of the Internet of things terminal, the speed of image recognition is difficult to meet the application requirements. A design method of a fast image recognition accelerator based on a convolutional neural network (CNN) is proposed. A pipeline processing scheme combining software and hardware is designed. The operation strategy of the parallel image block, parallel input channel, and parallel output channel is adopted. Based on this strategy, a model of terminal resources and recognition time is established. By solving the model, the optimal number of image partition blocks and convolution parallel parameters are obtained. The experimental results show that the computational performance of the proposed accelerator is improved from 8.86 GOPs to 12.26 GOPs, which effectively improves the speed of image recognition.