Reviewerly/exHarmony huggingface dataset:
Research on Online Leakage Assessment

Leakage assessment is the most common approach applied for assessing side-channel information leakage and validating the effectiveness of side-channel countermeasures. Established evaluation approaches are usually based on Test Vector Leakage Assessment (TVLA) that deployed in a divide and conquer flow with offline computations, which causes two apparent shortcomings in required memory and time. In this paper, a lightweight framework of online leakage assessment is proposed. The problems were analyzed and the evaluation approach was further validated with a Field Programmable Gate Array (FPGA). The experimental results show that it can implement online processing on newly collected data, and instantly stop to give the result when detecting credible leakage. The online leakage assessment can significantly economize on memory and time. It has good performance when there is limited memory or real-time evaluations are needed.
Organizational and legal mechanisms of education export in the Russian Federation

The article is meant to analyze the legal basis and organizational models of educational services export in the Russian Federation. The authors believe that the sale of educational services to foreign legal entities and individuals can become one of the alternatives to commodity exports and an important source of income for educational institutions and the country’s budget. The regulatory framework for developing the export of educational services in the Russian Federation, despite some of its shortcomings, allows to eff ectively promote the interests of Russian universities both in Russia and abroad, but not all universities are ready for such activities. Within the framework of the Federal project "Development of the export potential of Russian education", fi ve models of educational services export by Russian universities were proposed. However, according to the authors, introduction of bilingual bachelor’s, master’s and postgraduate programs, creation of a unifi ed register of accredited providers of Russian educational export services, development of a list of export education courses and standards for their teaching could also contribute to the increase in export indicators.
Improving Robustness and Defense with Signal Augmentation for Modulation Identification

Abstract Based on the Convolutional Neural Network, we consider finding an automatic recognition method of signal modulation. Electronic warfare (EW) is one of the most important forms of war, and the outcome of the war depends largely on it . Fast, accurate, secure and continuous communication is increasingly important, and the identification of signal modulation is one of the key links to ensure communication. The traditional recognition method is artificial recognition, which is inefficient and error-prone. In this paper, aiming at several common modulation signals, the automatic recognition system of modulation signals based on Convolu-tional Neural Network is designed by analyzing mathematical models and important parameters, then upgrade and improve the feasibility and effectiveness of the project.
Phase retrieving using a single-shot by projection of a colored pattern

The optical profilometry techniques have become more and more used in dynamic 3-D shape measurement. In particular, there are two problems to overcome. One is to recover the phase from a single fringe. The second problem is the phase recovering from a pattern avoiding that the harmonics introduced by the projector. In this work, we analyzed two kinds of techniques: off-axis and slightly off-axis interferometry algorithms for phase retrieval. The Hilbert transform is implemented in both cases. Validation experiments are presented.
New Constraint-Driven Mission Construct for Small Satellites and Constrained Missions

Small satellites, though increasingly prevalent in the industry today, tend to not follow traditional development paths, technically or programmatically. In contrast to traditional requirements-driven missions, where meeting requirements is the primary metric of success, small satellite missions tend to be constraints-driven, where mission scope is tradable to maintain cost and schedule constraints. There has not been a concise, fundamental outline of how small satellites - or more broadly, constraints-driven missions - are developed. This paper attempts to create that outline and define an alternative path from traditional requirements-driven (e.g. Class A - D) development frameworks. Stated another way, traditional practices are often not conducted on small satellite missions or constraints-driven missions simply “because it's a smallsat.” This paper attempts to better define the reasoning behind that philosophy, and how to apply it more methodically. The paper defines constraints-driven missions and how they are different from requirements-driven missions. The assumptions going into constrained missions are outlined along with details pertaining to why some programs need to be (or implicitly are) constrained. Then the overall construct for how to organize a constraints-driven mission is described and outlined. The construct is based on Agile software development concepts but is also derived from multiple programs and missions (big and small) that have gone through similar practices to achieve success. Fundamentally, the paper argues that for constrained missions, scope and requirements are tradable attributes. The method for understanding the requirements / constraints balance, recognizing divergence from that balance, identifying methods to address alterations, and then establishment of a new balance is outlined. Finally, an example of how this architecture can be implemented is provided.
Drum rhythm spaces: From polyphonic similarity to generative maps

This paper reports on the design and evaluation of drum rhythm spaces as interactive bi-dimensional maps used for the visualisation, retrieval and generation of drum patterns. We carry out two experiments exploring human processing of polyphonic drum patterns concluding with a list of descriptors that significantly influence similarity sensations. These features are used to build spaces based on drum pattern collections, where patterns are organised by similarity, modelled according to human perception. A drum-interpolation algorithm is introduced (and evaluated) to enhance rhythm space functionality by means of patterns that bound it, converting a discrete space to a continuous generative one.
Image Super-Resolution Reconstruction Algorithm Based on Improved GAN

Image super-resolution reconstruction (ISR) technology based on deep learning has become a hot topic in the field of machine vision because it can reconstruct low-resolution image into high-resolution image, which has been extensive used in many research field such as remote sensing imaging, medical image processing, video monitoring and other fields. However, the conventional deep learning method is not ideal for image super-resolution reconstruction at high magnification, which result in a fact that there is still a gap between the existing research results and practical applications. The paper proposed an ISR algorithm based on improved GAN. Wasserstein distance is introduced to gauge the distance between the real distribution and the generated distribution. The network generator uses the depth residual network, the network discriminator uses the feedforward neural network, the optimization goal is set to minimize the perceptual loss, and the algorithm is applied to the public data set for testing. We can clearly find from test results that compared with other algorithms, the improved GAN can recover the high-frequency details in the low-resolution image with immediate effect, and has better perception effect of human eyes.
“Triple-Alls” Distribution Network Information Model and Platform Design

In recent years, large-scale new energy sources have been connected to the power distribution network, and the ubiquitous power IoT sensing technology has developed rapidly. At the same time, the increase in energy consumption has placed higher requirements on the consumption of new energy, which has led to the “Triple-Alls” (All access, All sensing, All consumption) requirements of the distribution network. To this end, this paper conducts a series of studies on the “Triple-Alls” distribution network: First, it analyzes the characteristics and key technologies of the “Triple-Alls” distribution network by comparing with traditional distribution networks, and then establishes energy storage, photovoltaic, and wind power. Information model of “Triple-Alls” distribution network. Then, a platform design scheme meeting the requirements is constructed. Finally, the application functions of “All access”, “All sensing” and “All consumption” in the distribution network are explained in detail.
A Neural Network Model Compression Approach Based on Deep Feature Map Transfer

Neural network is widely used in computer vision. However, with the continuous expansion of the application field, high-precision large parameter neural network model is difficult to deploy on small equipment with limited resources. In order to obtain a small but efficient network, the soft output of the teacher network was used to train students through the teacher-student structure. A new method of neural network model compression based on deep feature map transfer (DFMT) is proposed in this paper, which uses visual system characteristics adequately. A small decoder is designed in the network to generate a deep feature map from the features extracted by the network, and the feature map is used to transfer knowledge. In addition, cosine similarity is used as the evaluation index of knowledge transfer. A smaller model with better precision can be obtained by the proposed method. Experiments on benchmark datasets prove the validity and advancement of the proposed approach.
Two-Stage Method For Effective User-Guided Video Segmentation

Video segmentation is a complicated task and an active area of research. Many applications need user interaction to obtain desirable segmentation outcome. In this work, we describe a two-stage method to obtain desirable video segmentation within practical execution time, such that it can be verified and modified seamlessly by a user. In the first stage, we use a block-based clustering technique to reduce the complexity of the task. In the second stage, graph-based processing is used to exploit the spatiotemporal dependencies within video frames. User interaction in the form of selection of a few pixels to be labeled as RoI or non-RoI is used to initialize the graph in this stage. Experiments show that our method is effective in producing desirable video segmentation and is user-friendly.
The Criteria, Challenges, and the Back-Propagation Method

In this chapter, we describe our task of symbol spatialization, list the criteria and challenges. We show that despite of the magic power, back-propagation method will not be the right tool to fulfill the criteria.
On Improving the Learning of Long-Term historical Information for Tasks with Partial Observability

Reinforcement learning (RL) has been recognized as the powerful tool to handle many real-work tasks of decision making, data mining and, information retrieval. Many well-developed RL algorithms have been developed, however tasks involved with partially observable environment, e.g, POMDPs (Partially Observable Markov Decision Processes) are still very challenging. Recent attempts to address this issue is to memorize the long-term historical information by using deep neural networks. And the common strategy is to leverage the recurrent networks, e.g., Long Short-Term Memory(LSTM), to retain/encode the historical information to estimate the true state of environments, given the partial observability. However, when confronted with rather long history dependent problems and irregular data sampling, the conventional LSTM is ill-suited for the problem and difficult to be trained due to the well-known gradient vanishing and the inadequacy of capturing long-term history. In this paper, we propose to utilize Phased LSTM to solve the POMDP tasks, which introduces an additional time gate to periodically update the memory cell, helping the neural framework to 1) maintain the information of the long-term, 2) and propagate the gradient better to facilitate the training of reinforcement learning model with recurrent structure. To further adapt to reinforcement learning and boost the performance, we also propose a Self-Phased LSTM with incorporating a periodic gate, which is able to generate a dynamic periodic gate to adjust automatically for more tasks, especially the notorious ones with sparse rewards. Our experimental results verify the effectiveness of leveraging on such Phased LSTM and Self-Phased LSTM for POMDP tasks.
Code Smell Detection Based on Multi-dimensional Software Data and Complex Networks

Code smell is the product of improper design and operation, which may be introduced in many situations. It will cause serious problems for further software development and maintenance. Currently, most code smell detection methods detect through a single type of software data. There are restrictions on detecting code smells with complex definitions and characteristics. In this paper, an approach of applying multi-dimensional software data is proposed. A complex network was built through structural data and historical version data, and code smell instances were determined by searching the network. We designed two smells detection strategies were designed and evaluated them in four open source projects. The results demonstrate that the proposed method has 23% and 15% higher F-measures on Shotgun Surgery and Parallel Inheritance Hierarchy than the existing mainstream detection ways. The code smell detection based on multi-dimensional software data and complex network is effective, and this method of processing multidimensional software data is also applicable for data-driven software research.
A Study on Behaviour of Neural Gas on Images

Neural gas basically a artificial neural network, motivated through self organizing map. The neural gas approach former successfully applied previously to clustering, image processing, and pattern recognition to extract the feature values and to find & identify unique pattern for the input database. In this research work five subjects of images are considered like animal, building, cloud, flower & vehicle, to find whether the neural gas is able to distinguish the pattern of images between them. Different types of parameters are used to extract the feature value of images using neural gas. The parameters are epochs, delta, iteration, alpha0 (initial value), alphaf (final value), lambda0 (initial value) & lambdaf (final value). Maximum and minimum difference values on these parameters are used as the feature values. These feature values are used to distinguish the images to each other. Their observed results on the basis of feature values are represented in the form of graphs, which yields good results.
Pixel Prediction-Based Image Steganography Using Crow Search Algorithm-Based Deep Belief Network Approach

Securing the confidentiality of patient information using the image steganography process has gained more attention in the research community. However, embedding the patient information is a major task in the steganography process due to the complexity in identifying the pixel features. Thus, an effective Crow Search Algorithm-based deep belief network (CSA-DBN) is proposed for embedding the information in the medical image. Initially, the appropriate pixels and the features, like pixel coverage, wavelet energy, edge information, and texture features, such as local binary pattern (LBP) and local directional pattern (LDP), are extracted from each pixel. The proposed CSA-DBN utilizes the feature vector and identifies the suitable pixels used for embedding. The patient information is embedded into the image by using the embedding strength and the DWT coefficient. Finally, the embedded information is extracted using the DWT coefficient. The analysis of the proposed CSA-DBN approach is done based on the performance metrics, such as correlation coefficient, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) that acquired the average values as 0.9471, 24.836[Formula: see text]dB, and 0.4916 in the presence of salt and pepper noise and 0.9741, 57.832[Formula: see text]dB, and 0.9766 in the absence of noise.
Solar Irradiance Prediction using meteorological data by ensemble models

High power quality electrical power grids can be deployed after accurate solar irradiance prediction at any particular location so as to minimize the physical expenses. Number of machine learning models can be applied to endogenous and exogenous datasets. Gathering historical irradiance data(endogenous) is complex task whereas meteorological data(exogenous) is easily and widely available on Internet. This paper proposed ensemble learning models based solar irradiance prediction scheme for NASA generated meteorological data and implemented experimental results in terms of RMSE,Accuracy, MAE and MSE to compare 2 ensemble classifiers and regressors models for short-term prediction. The results demonstrated that the proposed models are more accurate over existing machine learning and neural network algorithms for solar irradiance prediction.
Camera Array Calibration Using a Simple Checkerboard Pattern

Traditional camera calibration method usually considers a single camera or dual cameras. As the growing development of imaging technology, multi camera and camera array are used in more and more applications. In this paper, a calibration method for camera array is proposed. The intrinsic and extrinsic parameters for an individual camera are obtained using single camera calibration method. Then the relationship of the extrinsic parameters for different camera are calculated using the same pattern observation. Only a checkboard is needed in the proposed calibration method, so that it is easy for implement. Calibration results and synthetic aperture imaging result using the calibration results are presented to show the effectiveness of the proposed method.
Optical Flow Estimation Using a Non-Local Convolutional Network

Convolutional neural network(CNN) models for optical flow estimation based on coarse-to-fine method are usually difficult to obtain accurate estimates of large displacement motions in the rough layer, so that the estimation error will be passed to the final estimation result. This article proposes an effective convolutional neural network model for optical flow estimation called NTFlow. NTFlow uses a non-local convolutional layer to obtain the correlation of the full feature map, and constrains the estimate of the larger error in the loss function. Experiment results show that our network can get accurate estimation results on public data sets, and the proposed loss function is very robust.
A Universal Data Compression Scheme based on the AIVF Coding Techniques

In the entropy coding, AIVF (almost instantaneous variable-to-fixed length) codes using multiple parsing trees can attain a better compression rate than the Tunstall code, which attains the best compression rate in the class of VF codes with a single parsing tree. Furthermore, the multiple parsing trees of an AIVF code can be multiplexed into a single parsing tree. In this paper, we propose a new universal data compression code based on the techniques of the AIVF code. The proposed universal code can also be considered as an improvement of the LZW code (Welch code). We explain how the AIVF coding techniques can be applied to universal coding by growing dynamically a single parsing tree, and we evaluate the compression rate of the proposed universal code theoretically and using several corpora.
Visual Tracking via Locality-constrained Affine Subspace Coding

The dictionary plays an important role in the visual tracking whose sample is encoded by a dictionary. However, the dictionary atom is a representative point (visual word) in the previous tracking algorithms. Representative point neglects the geometric manifold structure which the point surrounded by immediately, which leads to limited ability of characterizing the distribution of features on the data manifold. In this paper, we propose a novel visual tracking algorithm with Locality-constrained Affine Subspace Coding (LASC) which explicitly explores the geometric structure of the manifold where these representative points lie. Unlike other encoding methods, LASC formulates the dictionary atom as the subspace attached to the representative point. To further improve the discrimination and diversity of the dictionary, we introduce the weighted spatial information to the representative point. We conduct experiments on challenging benchmarks and the experimental results demonstrate the effectiveness of our method.
Rank Preserving Code-based Signature

We propose a rank metric code-based signature scheme constructed via the Schnorr approach. We define a new problem in rank metric coding theory, namely the Rank Vector Decomposition problem and analyze its solving complexity. The hardness of our signature scheme is based on the Rank Syndrome Decoding problem, Rank Support Basis Decomposition problem and Rank Vector Decomposition problem. We also give detailed analysis for the structural security of our signature scheme. Then, we provide parameters for our constructed signature scheme and compare our scheme with other existing secure rank metric signature schemes. Our signature scheme requires only public key size of 443 bytes and signature size of 4.03 kilobytes for 128-bit security level.
Grid neural network to solve voice recognition problems

To solve many complex scientific problems, grid computing is used by many researchers for utilizing and sharing large-scale resources which is increasing expeditiously, in the distributed heterogeneous system. Computational grid, Data grid and Service grid are the three categories in which grid can be classified. Grid computing is also known as a type of parallel and distributed computing system, which allows sharing, collecting and selection of geographically distributed resources depending on their component such as performance, availability, capability, user QoS requirement, and cost during run time. Job scheduling plays very important role in grid computing system, which is one of the key research area in grid computing. This paper proposed a new scheduling algorithm called Feed forward neural network in grid computing system (FFNNGC), which is used to train the network. The training of the network is done until the network converges to a minimum error solution. To verify the proposed scheduling algorithm performance, a grid computing network is used which is based on Globus Toolkit 4. The proposed method is applied in real life problems such as vowels classification or voice recognition problems. Experiments were performed under these real life problems and the results indicated that the proposed method is helpful in such types of problems.