Reviewerly/exHarmony huggingface dataset:
Online Feature Selection for Low-overhead Learning in Networked Systems

Data-driven functions for operation and management require measurements and readings from distributed data sources for model training and prediction. While the number of candidate data sources can be very large, research has shown that it is often possible to reduce the number of data sources significantly while still allowing for accurate prediction. Consequently, there is potential to lower communication and computing resources needed to continuously extract, collect, and process this data. We demonstrate the operation of a novel online algorithm called OSFS, which sequentially processes the collected data and reduces the number of data sources for training prediction models. OSFS builds on two main ideas, namely (1) ranking the available data sources using (unsupervised) feature selection algorithms and (2) identifying stable feature sets that include only the top features. The demonstration shows the search space exploration, the iterative selection of feature sets, and the evaluation of the stability of these sets. The demonstration uses measurements collected from a KTH testbed, and the predictions relate to end-to-end KPIs for network services.
A Novel Optimization Algorithm Leveraging a Three-Dimensional Approach of Periscopic, Pheromonic and Fractal Search

This paper focuses on a new algorithm for solving optimization problems using the nature of food search behaviour of caterpillars. The paper describes how the periscopic, pheromonic and fractal search properties analogous to the caterpillars, can aid in designing a new optimization algorithm. The performance characteristics of the new method is compared using 26 standard test functions and the area under the curve of the fitness evaluations is used to validate and compare the proposed algorithms against existing related works. The proposed algorithm is found to be efficient when compared with the existing methods. The proposed algorithm is then tested on a real world problem to remove signal noise from eye gaze data, effectively.
Comparative Analysis of the Quality of Teaching Students of the Institute of Informatics, Electronics and Robotics of Kabardino-Balkarian State University at Remote and Traditional Format

The article analyzes the progress of students of the Institute of Informatics, Electronics and Robotics using control charts on an alternative basis. Disciplines that are well adapted to remote technologies and disciplines to which such technologies are difficult to apply are identified. In addition, there were discovered disciplines in which low academic performance is of a systemic nature. Based on the findings, recommendations to improve the quality of education at the institute were made.
Photovoltaic Power Prediction System Followable to Gaps of Weather Forecasts

For independent power source operating facilities, which can only be equipped with small-scale photovoltaic power generation system and batteries, local and short-term power generation forecasting is required. In this article, using commercial weather forecasts for multiple locations and multiple times, we propose an LSTM(Long Short-Term Memory)-based photovoltaic power predicting method that can follow the position gap and time gap of weather forecasts. We confirmed that our method can perform high prediction accuracy using weather forecast and field observation data.
Research on Tone Perception Model Based on E-prime

With the development of many disciplines and the rise of emerging disciplines, interdisciplinary disciplines have gradually been sought after by scholars, of which computer is a very important part. So far, computer science has conducted interdisciplinary research with psychology, linguistics, cognitive science, physics, statistics and other disciplines. It provides scientific and efficient means and methods for the research and development of other disciplines. This article mainly combines linguistics, psychology and computer science to study the mental model of people's perception of tones through computer technology.
Simple optoelectronic chaotic generator: computer simulation and practical realization

In this paper, the circuit of a non-autonomous generator that realizes chaotic behavior is presented. This oscillator-circuit contains four resistors, one capacitor, one inductor, two diodes, one operational amplifier, one bipolar voltage source and one sinusoidal source. All nominal of components are shown. The proposed circuit was modeled by utilizing NI’s MultiSim software environment. The system’s behavior was investigated through numerical simulations, by using wellknown tools of nonlinear theory, such as phase portrait, chaotic attractor and time distributions of two chaotic systemvariables. Spectral analysis are also presented.
Understanding Musical Instants

Abstract We may typically experience music as continuous streams of sound and associated body motion, yet we may also perceive music as sequences of more discontinuous events, or as strings of chunks with multimodal sensations of sound and body motion, chunks that can be called ‘sound-motion objects’. The focus in this chapter is on how such sound-motion objects emerge at intermittent points in time called ‘musical instants’, and how musical instants are necessary in order to perceive salient features in music such as of timbre, pitch, texture, contour, and overall stylistic and affective features. The emergence of musical instants is also understood as based on the combined constraints of musical instruments, sound-producing body motion, and music perception, also suggesting that understanding musical instants may have practical applications in making music.
Deep Convolutional Neural Network to Handle Saturation in Image Deblurring

Deep neural network techniques are being developed to extract sharp edges from fuzzy photos for kernel estimation. The formulation is based on conventional models, but it has limits and is likely to fail when the blurred images contain a large number of outliers, such as saturated regions. For deblurring photos, several neural networks have been constructed that do not address outliers, while non-neural network algorithms have been designed to manage saturation alone. Few smoothing techniques, edge enhancing filters technique are used as an image pre-processing stage before the deconvolution process, but they don't manage saturated pixels, resulting in ringing artifacts and poor deblurring outcomes. In this work, a masking technique is introduced at pre-processing stage to handle saturation effectively in prior to Deep Convolutional Neural Network for image deblurring. The results show the reduced ringing artifacts and considerable improvements with the performance of existing deep deblur networks.
Optimize the Residual Signal Transmission Based on Moving Vector Classification for Video Coding

Figuring interpolated images is an exemplary issue in picture and video preparation. It is an essential advancement in various applications, such as frame rate conversion, temporal upsampling for producing moderate movement video, and picture transforming, just as virtual view blend. Standard ways to deal with figuring interpolated frames in a video grouping require precise pixel correspondences between pictures. Customary answers for picture interjection initially register correspondences (for the most part utilizing optical stream or stereo techniques), trailed by correspondence-based picture distorting. Because of characteristic ambiguities in figuring such correspondences, most strategies are vigorously subject to computationally costly worldwide improvement and require extensive parameter tuning. In this proposed technique, a new complication motion with a low vector dispensation procedure at the end side is suggested for motion-compensated video vector frame interpolation or frame rate up-conversion. Generally, it is possible to detect the problems of broken edges and distorted arrangement difficulties in a frame interpolation through orderly refining motion vectors on various block sizes. Investigational consequences show that this proposed organization’s visual superiority is improved and is also rugged, even in video sequences containing fast motions and difficult sections.
Real-Time Multi-Person Action Recognition with a Neural Compute Stick

Deep learning has successfully boosted a performance of action recognition and inspired model developments for it. Specifically, 3D convolutional neural network (CNN) which best fits the purpose of vision-based action recognition has been applied to the task in various forms. In this paper, comprehensive research process for practical multi-person action recognition is presented. We perform various experiments using 3D CNN considering both performance and time efficiency. Distinguished from previous studies, we consider a performance on an embedded platform which consists of an embedded computer, ZED2 camera and a neural compute stick. The neural compute stick has its own memory and can be utilized asynchronously. This is a pioneer work proposing a multi-person action recognition framework using a neural compute stick. Step-by-step experiments verify a validity of the model configuration and the proposed framework.
Determination of Parameters Balancing between Security and Search Performance on Searchable Encryption

In the searchable encryption, searching encrypted data is slow, compared to searching non-encrypted data. As a solution, there is an acceleration method that discloses a small portion of a hash value of keyword to be searched. The disclosure bit(s) allows the cloud to narrow down the search space and reduce the amount of computation. The more bits are disclosed, the faster the search speed becomes. However, repeated disclosure of part of hashed keyword in queries leads to reveal the frequency distribution of plaintext keywords. The longer the disclosure bits are, the more accurate revealed distribution becomes. In this paper, we propose a method to determine the disclosure bit length by using minimum entropy and k-anonymity. Furthermore, scalability of the proposed method is enhanced by employing distributed databases. To show that the proposed method achieves both high acceleration effect and security, we show an evaluation result of the proposed method applied to the conventional full-text search system using searchable encryption. Our evaluation result shows that the proposed method reduces the search time of the system by up to 97.2% when the number of stored document files is 1,000. While a database in this evaluation is designed so that disclosure bits satisfy at least 31-anonymity by using the proposed method, we observe that the database actually satisfies 2,598-anonymity.
Interpretability Analysis Method Based on Path Decomposition for Cell-based Neural Architecture Search

Neural Architecture Search (NAS) has become an emerging hot research problem in the field of artificial intelligence recently. The cell-based neural architecture search method has flexible search space conversion and good performance, which has become one of the mainstream search methods of NAS. But current cell-based neural architecture search strategies offer little insight into its interpretability (what kind of architectures the NAS algorithms are learning and why they learn these specific architectures). We proposed an Interpretability Analysis Method Based on Path Decomposition (IAM-PD) that combines the cell similarity representation based on path decomposition and neural network architecture evaluation perspectives. More importantly, our method affords interpretability by discovering useful architecture features and their corresponding impact on the architecture performance. Indeed, we demonstrate empirically that our model is capable of identifying useful motifs which can guide the generation of new architectures. Finally, the experiment results on the two search spaces of NAS-Bench-101 and NAS-Bench-201 show that the similarity metric obtained by IAM-PD has better interpretation than the existing methods.
BP Neural Network Computer Network Information Security Risks and Solutions

Guided by the national information security risk assessment guide and referring to the security control idea of NIST sp800-30, this paper constructs a set of risk assessment index system, and discusses the principle of risk assessment by using the improved BP neural network method. The result of practice shows that BP artificial neural network is an effective and feasible method for risk assessment.
New Artificial Neural Network Models for Bio Medical Image Compression

This article presents an image compression method using feed-forward back-propagation neural networks (NNs). Marked progress has been made in the area of image compression in the last decade. Image compression removing redundant information in image data is a solution for storage and data transmission problems for huge amounts of data. NNs offer the potential for providing a novel solution to the problem of image compression by its ability to generate an internal data representation. A comparison among various feed-forward back-propagation training algorithms was presented with different compression ratios and different block sizes. The learning methods, the Levenberg Marquardt (LM) algorithm and the Gradient Descent (GD) have been used to perform the training of the network architecture and finally, the performance is evaluated in terms of MSE and PSNR using medical images. The decompressed results obtained using these two algorithms are computed in terms of PSNR and MSE along with performance plots and regression plots from which it can be observed that the LM algorithm gives more accurate results than the GD algorithm.
INTERPRETATION OF THE SCIENTIFIC PICTURE OF THE WORLD IN THE CONTEXT OF KNOWLEDGE DEVELOPMENT

The scientific picture of the world enters into constant interaction with cognitive experience. And changes in the structure of informational space change behavioral patterns and criteria of influence of environment on individuals are determined. The purpose of this work is to review individual branches of scientific knowledge as a component of the scientific picture of the world and to identify characteristic figures of scientific research of the XV-XVI centuries as an epoch of "flight" of thought. The descriptive method of the investigated phenomenon is used in the work. The purpose of the research is to form a reliable and verified knowledge of the scientific picture of the world of the stated period, to identify the value potential of the representatives of different branches of knowledge, because it is impossible to relieve the phenomenon of current science and set the prospects for its further development without analysis and interpretation of the sources from the previous centuries. The scientific picture of the mentioned period shows the discordance of ideas and the scale of their theoretical and practical implementation (from philosophy to art). The author highlights the most characteristic features of the scientific picture, which contributed to the appearance of new lines of research, a new necklace base on which the scientific thinking of the next centuries was built.
Speed Up Light Field Synthesis from Stereo Images

In this paper, we focus on the speedup of a learning-based light field synthesis pipeline. The pipeline involves a disparity estimation neural network and a light field blending component. The former achieves high speed performance through the use of feature extraction and multi-stage disparity refinement, while the latter warps and merges coarse light fields generated from the left and right disparity maps in a novel and efficient way. The pipeline can produce a full light field in less than 1/10 of a second, while retaining fairly reasonable image quality. The model itself has a very low parameter count, which is ideal for devices with limited computational power.
A Novel Approach to Dynamic Pricing for Cloud Computing Through Price Band Prediction

Cloud computing emerges as a boon to business enterprises that offers increased productivity, economic efficiency and least operational and maintenance costs. The cost for the services offered is variable and dependent on market trends. The pricing model for cloud services are pay-per-use or subscription based as required circumstantially. With more and more provisions created for growing demands of resources, the pricing models are metering the requirements and are constantly moderating to provide optimal prices for services yet keeping best revenue schemes. To deliver business value to customers with QoS considerations within limits of infrastructure that they have, a price-wise categorization is required. In addition to this, the operational cost as well as infrastructure cost for cloud providers will differ insignificantly based-on whether one consumer or multiple consumers are serviced. The subscription model to service provisioning by the provider end is fueled up with discounts, price-cuts, offers and benefits to lure the customers thereby maximizing their resource utilization implicitly. Therefore, in order to grab the opportunity created by this competitive environment, we propose a dynamic pricing model for cloud brokers. In the existing cases of unexpected costs due to resource constraints going towards higher extremities, our model evaluates a price band for the customer’s transparency in cost and optimizes it. The benefits of the proposed pricing model is two fold. It provides the assurance about the maximum price that will be charged to the consumer while enjoying the existing benefits of dynamic pricing model; the price range calculation is an estimation done on the basis of resources requested, price history, current price, and risk premium are the decisive factors for our estimation rule. A fair proposition of resource allocation with maximal resource utilization, due returns for investments and an effective cost offering for the services is the aim of our work.
Human Motion Assessment on Mobile Devices

Human motion analysis is being performed in various places using various devices. However, the complexity of the analysis algorithms required by high-performance devices makes it difficult to run these algorithms anytime and anywhere. We have successfully ported a pose estimation model to mobile devices and applied various post-processing algorithms. Our analysis algorithm, which uses the joint coordinates of the pose estimation model, has both fast calculation speed and versatility. Our lightweight pose models and motion analysis algorithms allow users to analyze fitness actions and receive instant feedback in real time using their smartphones. Our motion assessment system is applied to real mobile devices and tested in the real world. Based on empirical research on real environments, our motion assessment system is shown to be applicable to various fields.
Learning from Noisy Labeled Data via Sharpen Prediction Loss and Re-Correction

Deep learning has achieved excellent results in many applications with a large number of high-quality annotation datasets. However, it is difficult to obtain numerous high-quality labeled samples, since it may generate noisy labeled data during manually annotating. Deep neural network with noisy labeled data leads to overfitting a nd greatly affects t he performance. In order to overcome the issue, a deep model with sharpen prediction loss and re-correction is proposed for learning from noisy labeled data, which aims to modify the loss distributions of noise samples and clean samples, and eliminate noise samples by unsupervised clustering. In the proposed framework, the deep model is warmed up through several epochs, sharpen prediction loss is proposed to effectively measure the sample loss, and recorrection is utilized to separate noise and clean samples as well as predict the final l abels. Experimental results on two datasets with noisy labeled data demonstrate that our proposed model yields superior classification accuracies.
Research on Chinese Legal Document Reading Comprehension Based on Pre-Training Model

We research how to read and understand Chinese legal documents. At first, we analyze the difficulties of reading comprehension of Chinese legal documents. Data imbalance exists seriously among span extraction query, yes/no query and unanswerable query, that is span extraction queries account for more than 80%. The reading comprehension of Chinese legal documents is a typical long text reading problem. Then we propose a framework for reading and understanding Chinese legal documents. Based on the Bert pre-training model, the framework performs fine-tine for Chinese legal documents, adopts a variety of deep learning models, and uses data enhancement and ensemble strategy to solve reading comprehension of Chinese legal documents. Finally, we test the framework with real legal documents, and the macro average F value can reach 82.773.
Feature Projection and SVR-Based Model Update for Object Tracking

Siamese network-based object tracking algorithms have recently gained popularity due to continuous improvement in tracking accuracy and robustness. However, these trackers are often limited by inefficient initialization and unstable online update. The model drift accumulates as more frames are processed. To address the above issues, this paper introduced a method that combines both deep learning and conventional regression algorithms (SVR, Support Vector Regression). Specifically, we propose a feature projection algorithm, which can effectively reduce the dimension of the features extracted from deep neural networks and improve the distinguishability of them at the same time. To make the features more robust for SVR training, we further propose two feature aggregation methods at both the channel level and the spatial level. We update the SVR model online instead of the deep neural network to make the tracking process more robust. Extensive experiments on four challenging benchmarks indicate that our proposed tracker is superior to baseline methods both qualitatively and quantitatively.
Handwritten Signature Spoofing With Conditional Generative Adversarial Nets

Generative adversarial networks have been a highly focused research topic in computer vision, especially in image synthesis and image-to-image translation. There are a lot of variations in generative nets, and different GANs are suitable for different applications. In this chapter, the authors investigated conditional generative adversarial networks to generate fake images, such as handwritten signatures. The authors demonstrated an implementation of conditional generative adversarial networks, which can generate fake handwritten signatures according to a condition vector tailored by humans.
Research on fine tuning parameter insulator identification based on YOLOV4 algorithm

Abstract Aiming at the problem that the training network time of YOLOV4 algorithm is too long due to the large data set of aerial insulator images, a method based on YOLOV4 algorithm is proposed to shorten the training time by fine-tuning parameters without affecting the positioning detection accuracy. Based on the development of UBANTU virtual machine, through CUDA and CUDNN environment configuration, and through the detection and verification of insulator aerial photo data set, the feasibility of accurate positioning of insulators under the condition of fine tuning parameters of YOLOV4 algorithm is successfully proved.