Reviewerly/exHarmony huggingface dataset:
Abelian group factorization from cyclic and quasi-cyclic codes

In this paper, we use the algebraic structures of cyclic codes and algorithmic techniques to find factorizations of abelian groups from cyclic codes. We construct specific subclasses of quasi-cyclic codes and provide the conditions with which we obtain a normalized factorization of certain abelian groups. The factorization, in both cases, is constituted by two sets, one corresponding to the cyclic code and the other corresponding to the words that represent all possible error polynomials of the cyclic code besides the zero vector.
Radar-based Noncontact Human Activity Classification Using Genetic Programming

This paper analyzes the structure of the feature space of radar data collected from real subjects either still or in motion and provides dimensionality reduction and modeling through genetic programming. Three movement classes: sedentary and still, sedentary with movements, and walking contained in the returns obtained from a single channel continuous wave phase-modulated radar are considered. Unsupervised methods are used for finding the intrinsic dimensionality of the space of the original features and nonlinear mappings are used to obtain lower dimensional representations. The classification results for the original and the reduced dimension data are similar, thus, indicating the redundancy of the eliminated features. The white-box models obtained through genetic programming is then compared with the conventional black-box models obtained through supervised classification using random trees, extreme learning machines and multilayer perceptron. For this problem, the explicit white-box models obtained with genetic programming produced equal or better classification accuracies than those obtained with black-box approaches. In addition to explainability, the genetic programming models found have the additional advantage of involving only a few relevant predictors, exhibiting good feature selection capabilities.
Unprovability of Leakage-Resilient Cryptography Beyond the Information-Theoretic Limit

In recent years, leakage-resilient cryptography—the design of cryptographic protocols resilient to bounded leakage of honest players’ secrets—has received significant attention. A major limitation of known provably-secure constructions (based on polynomial hardness assumptions) is that they require the secrets to have sufficient actual (i.e., information-theoretic), as opposed to comptutational, min-entropy even after the leakage. In this work, we present barriers to provably-secure constructions beyond the “information-theoretic barrier”: Assume the existence of collision-resistant hash functions. Then, no $$\mathcal{NP}$$ search problem with $$(2^{n^{\epsilon }})$$ -bounded number of witnesses can be proven (even worst-case) hard in the presence of $$O(n^{\epsilon })$$ bits of computationally-efficient leakage of the witness, using a black-box reduction to any O(1)-round assumption. In particular, this implies that $$O(n^{\epsilon })$$ -leakage resilient injective one-way functions, and more generally, one-way functions with at most $$2^{n^{\epsilon }}$$ pre-images, cannot be based on any “standard” complexity assumption using a black-box reduction.
Recognition of Off-line Handwritten Uyghur Words Using Bayesian Networks with Grapheme Nodes

This study proposes a new algorithm that constructs a word Bayesian network (BN) framework with grapheme nodes to recognize off-line handwritten Uyghur words. First, we build an Uyghur script grapheme library according to the rules and morphological structure of Uyghur. The library includes main grapheme, affix grapheme, and dot grapheme categories. Second, word images are segmented into grapheme sequences by subjecting the individual strokes to extraction, segmentation, and clustering operations. Then we design specific feature extractors and classifiers for specific graphemes to detect and identify small differences between similar words. Finally, we construct a hierarchical matching model for graphemes, conjoined segments, and words using a discrete BN. The BN infers word categories from grapheme features, calculates the confidence of inference, and integrates the grapheme recognition information and word-formation prior information to obtain the final word recognition results. A word recognition rate of 91.65% is obtained during experiments conducted with a database consisting of 12,500 samples and a total of 58 trained grapheme categories. These results indicate that the proposed algorithm not only provides a high word recognition rate by effectively avoiding character over-segmentation errors, but also employs a small and fully predictable number of training categories, which facilitates strong expansibility.
Non-Gaussian Noise Reduction in Measurement Signal Processing

The currently available computational power of machines allows for the modelling and analysis of nonlinear processes measured in the presence of non-Gaussian disturbances. This work gives an overview of methods useful for the analysis and reduction of the noise that can be met when using modern sensors. In order to obtain reliable estimates of the measured values, the noise reduction method should be chosen according to the type of process being measured (linear or nonlinear) and the characteristics of the noise (Gaussian or non-Gaussian). We focused on filters belonging to the Kalman family i.e.: original Kalman filter, extended Kalman filter, unscented Kalman filter, particle filter and ensemble Kalman filter. The key ideas behind the design of these filters were explained and their theoretical properties were described. Importantly, recommendations were made regarding their applicability in various types of measuring systems.
On The Role Of Execution Order In Hybrid Evolutionary Algorithms

Many real-world problems can be formulated as the optimization of a continuous function. Furthermore, these problems are becoming increasingly more complex every year, reaching, or even exceeding, the thousand of variables. Evolutionary Algorithms have been traditionally successful at solving this kind of problems, due to their good balance in terms of solution quality and computation time. However, the aforementioned growth in the size of the problems requires of novel approaches to deal with the increased complexity of larger solutions spaces. Hybrid evolutionary algorithms are a powerful alternative in these scenarios as they are able to combine the strengths of multiple search methods to solve more complex problems. These hybrid approaches normally do not pay attention to the execution order of their components, being the most frequent strategy to always run them in a predefined sequence. In this contribution we study the role of execution order in hybrid evolutionary algorithms within the context of the multiple offspring sampling framework, one of the best algorithms in large-scale global optimization. As shown in the experimentation, a proper execution order policy can boost the performance of MOS to improve the results of other state-of-the-art algorithms.
A Comparison of Speech-to-Speech Neural Network Methodologies for Digit Pronunciation

In this work, the classical problem of digit recognition and pronunciation from an audio source in Spanish is revisited and compared with that of directly teaching a deep neural network to pronounce the corresponding digit. While the first approach roughly corresponds to that of most current speech processing methodologies that intend to identify and reconstruct phonetic units, before performing any task from reproducing to translation, the second approach is rarely found in the literature despite the fact that it is clearly more biologically inspired than the first one. Advantages and disadvantages of both methodologies are discussed based on the obtained results.
Distribution of quantum Fisher information in spin channel

Towards the statistical distinguishability about parameters encoded in the bipartite quantum state, we mainly investigate the distribution and transformation of quantum Fisher information over composite systems in spin channel. The relationship between the global quantum Fisher information and the sum of the locally accessible quantum Fisher information are studied, which can be subadditivity, additivity, or superadditivity. The transfer among them under the unitary operation is illustrated. We find that the quantum Fisher information can not only flow between the reduced systems, but also between the different types of distribution.
Salient object segmentation for image composition: A case study of group dinner photo

The rocketing number of photos shared on social media leads to the increasing demand for photo editing. We here focus on a specific scenario - group dinner photo and tackle two user-demanding problems - to add a person or replace the tabletop. The target objects are determined according to the saliency detection results. We developed a novel application to solve these problems. With our system, non-professional users can accomplish semantic editing within a few seconds, including inserting human and tidying up tabletops. Our system contributes to the state-of-the-art by (1) efficiently selecting the saliency area by its semantic meaning, (2) accurately compositing the salient content with the target image, based on the contextual knowledge. The context refers to the key factors, including occlusion and artifacts during the composition. The feedback from users shows that the authenticity of inserting human is satisfying. A comparative study shows that our system can more efficiently produce pictures with comparable quality as those edited by professional editing software in the tidying up tabletops work.
Optimal Filtering for Two-Dimensional Nonlinear System with Stochastic Parameter Matrix in Measurement

This paper is concerned with the recursive minimum-variance filtering problem for a two-dimensional(2-D) system with known statistical characteristics of stochastic nonlinearity in state equation and stochastic parameter matrix in measurement equation. A two-step recursive filtering is proposed according to the innovation information. The obtained filtering is proved to be unbiased by applying mathematical induction and to minimize the error variance of state estimation at every step. The gain matrices of the optimal filter are designed by utilizing the complete-square method. Moreover, An algorithm for the optimal 2-D filtering is given to implement the computation.
A Novel Copy-Move Image Forgery Detection Method Using 8-Connected Region Growing Technique

With the recent advancement of digital communication and Internet technology, there exist so many applications surrounded in our daily life where digital images play a vital role. Manipulation of digital images becomes a serious authentication threat in today’s world due to the availability of high-resolution cameras, powerful computer, and above all advanced image editing tools. Forgers are advancing themselves day by day with more sophisticated forgery techniques, so researchers must come up with more updated and advanced forgery detection techniques. Copy-move forgery or region duplication is a well-known image tampering technique where one part of an image is copied and pasted in another location of the same image in a way undetectable by human eye. A novel copy-move forgery detection technique is presented in this paper. The detection starts from a seed block and grows by finding match in an eight-connected region. Experimental results show that the proposed method has higher accuracy ratio and less performance time than the state-of-the-art techniques in the field of block-based image forgery detection.
Collaborative Estimation of State and Guidance Parameter for Interceptor Based on Variational Bayesian Technique

In this paper, we study the state estimation problem with an unknown guidance parameter of the interceptor, where the guidance parameter is modeled by normal-gamma distribution. To solve the problem, we propose a variational Bayesian (VB) based collaborative estimation algorithm for state and parameters, where the joint posterior distribution (JPD) of state and guidance parameter is approximated by a free-form distribution. The proposed algorithm can be divided into two-stage iterative steps: in variational Bayesian expectation (VB-E) step, with guidance parameter fixed, the cubature Kalman filter (CKF) is employed to realize state estimation. In variational Bayesian maximum (VB-M) step, the statistical characteristics of the guidance parameter are then deduced with state fixed. The state and the guidance parameter can be effectively estimated by performing VB-E and VB-M steps recursively. Finally, we illustrate the effectiveness of the proposed algorithm by a collaborative estimation problem in the two-dimensional aerial engagement scenario.
A Distance-Preserving Matrix Sketch

Visualizing very large matrices involves many formidable problems. Various popular solutions to these problems involve sampling, clustering, projection, or feature selection to reduce the size and complexity of the original task. An important aspect of these methods is how to preserve relative distances between points in the higher-dimensional space after reducing rows and columns to fit in a lower dimensional space. This aspect is important because conclusions based on faulty visual reasoning can be harmful. Judging dissimilar points as similar or similar points as dissimilar on the basis of a visualization can lead to false conclusions. To ameliorate this bias and to make visualizations of very large datasets feasible, we introduce two new algorithms that respectively select a subset of rows and columns of a rectangular matrix. This selection is designed to preserve relative distances as closely as possible. We compare our matrix sketch to more traditional alternatives on a variety of artificial and real datasets.
An Heuristic Algorithm to Calculate the Minimum Amount of Anesthesia Using MRI

In this work, we introduce a heuristic segmentation algorithm for the detection and subsequent segmentation of the volume of cerebrospinal fluid (CSF) that allows from MRI to calculate the CSF due to the importance of this represents to calculate the amount of anesthesia to be used in patients.
A decision system for computational authors profiling: From machine learning to deep learning

Summary In this study, we tackle the problem of author profiling. The aim of the proposed approach is to determine the author's age and gender. Once the user connects to the company website, this company collects the available data about him (which is usually very limited). Then, the user receives a service recommendation according to his gender and age. Thus, a context‐specific decision‐making system based on these limited data is required to produce an efficient classification. Such a decision system allows companies to promote their marketing. To obtain the best categorization, machine learning (ML) and deep learning (DL) techniques have been applied in the literature. In this article, we apply both classical ML techniques and recently developed DL techniques. More precisely, we adopt the gated recurrent unit model. Our experiments show that our findings are positively comparable with the best state‐of‐the‐art methods.
Evaluating GPU Performance for Deep Learning Workloads in Virtualized Environment

Deep Learning (DL) is the fastest growing high performance data center class workload today. Deep learning algorithms render themselves well to taking advantage of GPU parallelism, therefore GPGPU acceleration is a mainstay of the DL computing infrastructure. In this paper we evaluate virtualized GPU performance based on training of state-of-the art deep learning models. We find that there is a correlation between the amount of I/O traffic generated in the deep learning training workload and the efficiency of GPGPU performance in virtualized environments. We show that one can achieve high efficiency when using GPGPUs in virtualized and networkattached multi-GPU environments to perform highly computeintensive workloads.
Research on chasing algorithm based on motion control of cutting machine

In order to improve the cutting requirement of high speed and high precision on the production line, a cutting algorithm based on the motion control of the cutting machine was designed. In this paper, we take the mechanical structure and process flow of the cutting machine as the research object, the process flow of the cutting machine is abstracted, the mathematical model is established, and the motion control chase and shear algorithm of the cutting machine is designed and the relevant parameters are obtained. The three-axis coordinated motion problem of the feed axis, the tracking axis and the cutting axis is solved. A five-order polynomial optimization is used for the cam curve to solve the rigid impact problem of the catton and jitter of the equipment. At present, the system has been put into use in the production line of the enterprise, which solves many production problems and greatly improves the production efficiency of the enterprise.
Using Least-Significant Bit and Random Pixel Encoding with Encryption for Image Steganography

Steganography is the process of hiding data inside audio, video, and image sources using various encoding methods. This paper presents a novel steganography algorithm that hides files in images using a combination of strong symmetric AES encryption, random pixel selection, and encoding in least-significant bits. The algorithm is implemented in Python 3 and made publicly available as part of a steganography tool, BPStegano. An evaluation using five steganalysis techniques shows that the new algorithm is effective and secure.
Survey on VANET and Various Applications of Internet of Things

The user of the cloud storage can store an enormous amount of the data without any worries of the local maintenances of hardware and software. The user outsources the data and takes security control in order to maintain the reliability of the data. The data deposited in the cloud server is under frequent audit to the user to check the correctness of the service provider using wireless networks. The user can service a third-party checker to do the security audit on behalf of them for managing time constraints. The confidentiality of the data outsourced is preserved as of the third-party checker also. The system should be more efficient build for securing the data from various vulnerabilities. The proposed audit mechanism satisfies the user by providing integrity of the data stored simultaneously effectively and efficiently using VANET.
Modeling, Analysis and Simulation on Cloud Optimization Algorithm

A new cloud optimization algorithm, which is mainly motivated by several swarm optimization methods and elastic computing in cloud computation, mainly tackles with the complicated optimization problem in few computational time. Some concepts of cloud optimization are provided to better illustrate the corresponding algorithm, while the velocity and position’s equations of cloud optimization algorithm, which are closely referred to as the topology, the delay time and key parameters, are also mathematically given and inspired by the existing swarm optimization methods and those improved methods. More importantly, the main steps of cloud optimization are also introduced to optimize the parameters. Numerical results, which are programmed by Java Language and executed in Spring Boot, show that cloud optimization including several PSO algorithms achieves good performance and has few computational time.
Effective Compression of Digital Images Using SPIHT Coding with Selective Decomposition Bands

Wavelet-based image coding techniques are emerged as a standard with the inception of embedded zerotree wavelet coding about two decades ago. Since then a number of variations are presented in the literature, the concept of wavelet-based image coding is to apply coding scheme on wavelet decomposed image data, while the conventional coding schemes are applied directly on spatial domain of image data. The wavelet domain of image has four bands LL, LH, HL, and HH. The LL band constitutes the major part in reconstruction while the HH band the minor part. The coding based on embedded zerotree wavelet or set partitioning in hierarchical tree considers the magnitude of wavelet decomposed image data. When the coefficients are significant, they have to be coded; otherwise, they need not be coded. Now the proposal of the paper is that, as HH band constitutes a minor role in reconstruction, the band is supposed to be ignored, making all the coefficients in that band insignificant expecting a significant improvement in compression ratio and a little decrement in PSNR. The simulation results prove the same. In addition to HH band, HL and LH bands are also ignored to obtain higher CRs.
Advanced Key Management System (AKMS) for Security in Public Clouds

Cloud computing has become an emerging model of information technology industry as it can be accessed anywhere in the world on a pay-per-use basis. However, one major problem it is facing in today’s challenging world is the security issues. The various existing key generation and management systems are suffering with the security issues. Along with the security feature, the data sharing is also more important issue to be studied. Considering these two parameters, it is necessary to work on the secure data sharing in public clouds. For that purpose, we have proposed an advanced key management system (AKMS) which can perform identity-based encryption. Also, the proposed AKMS type encryption provides high security, scalability, and flexibility in the public clouds.
Research on 3D Virtual Fitting System Based on Unity3d

With the development of augmented reality technology, the concept of virtual fitting has been proposed gradually. However, due to the limitations of various software and hardware conditions, the development of virtual fitting is relatively slow. In order to solve the two key problems of three-dimensional dynamic fitting and man-machine interaction, a 3D virtual fitting system based on Unity3d is proposed in this paper. The system studies creation of 3D clothing model, somatosensory interaction technology and recognition of hand semantics to solved the three-dimensional dynamic the fitting problem. At the same time, a somatosensory interaction devices Kinect is combined with the Unity3d engine to realize dynamic tracking of human skeleton data and human motion capture, which breaks through barriers of hardware and software. Finally, the system presents the fitting effect of "clothing deforms properly as the body moves".
Content-Based Image Retrieval Using Statistical Color Occurrence Feature on Multiresolution Dataset

In modern life, the increasing use of different image-taking devices made image acquisition no longer a difficult task. To access a huge quantity of images having different resolutions stored in the dataset, the images must be kept in an organized manner. Content-Based Image Retrieval (CBIR) is an application of image retrieval problem, that is searching for a digital image from image dataset. Then term “content” in the context refers to some features that can be derived from the image itself. Color features are one of the important content of image which plays a vital role in image retrieval. Existing color features concentrate the only occurrence of pixel values or the correlation between pixel values. This paper proposed a new color feature which combines information about color shade percentage, color pixel occurrence percentage, pixel having maximum and minimum occurrence altogether. At the same time, proposed feature vector has a significantly reduced length which reduce computational cost of the retrieval system. This new feature is applied to one computer-generated image dataset (NITW-7500) and it’s translated and multiresolution version (using bilinear interpolation) and one standard natural image dataset (Corel-1K). Performance is improved in all variations of computer-generated images.