Reviewerly/exHarmony huggingface dataset:
Natural Scene Statistics And CNN Based Parallel Network For Image Quality Assessment

Image Quality Assessment (IQA) tasks have increasing importance in today’s world due to the widespread use of imaging devices and social media. Statistical studies proved that naturalness measures are good discriminators for evaluating image quality. Convolutional neural networks (CNN) based IQA models gained popularity in recent years due to their enhanced performance. In this article, we present a no-reference image quality assessment method that integrates natural image statistics (NSS) with CNN. The proposed approach extracts NSS features from the image, integrates them with the CNN features to predict the quality score. Our experimental results show that the performance of the proposed method is competitive against the existing methods of image quality assessment. Cross database testing on Live in the Wild (LIVE-itW) and Smartphone Photography Attribute and Quality (SPAQ) databases shows excellent generalization.
Management aspects of blended learning implementation in an educational organization: the main problems and ways to solve them

This paper examines the management tasks that the administration of an educational organization has to solve when introducing blended learning technology. Particular attention is paid to the creation of material and technical conditions, taking into account the specifics of blended learning models implementation, the organizational and regulatory components of this process in conditions of a lack of financial resources (in particular, centralized access to the resources of the school media center). The approaches to content creation for information and educational environment are analyzed, taking into account their variability and educational organization capabilities, the analysis of management decisions is carried out. Particular attention is paid to the preparation of subjects of the educational process for the implementation of blended learning. Considering the issues of improving the qualifications of teaching staff, a comprehensive analysis of the problem and the associated need for a systematic approach to its solution are noted; variability of managerial decision making; advantages of in-house training and the creation of temporary creative teams are noted. The paper reveals the problems of preparing students for blended learning, gives recommendations for extracurricular activities organization aimed at students digital competence development in accordance with the requirements of the Federal State Educational Standard. In conclusion, it is noted that there is a need for a systematic and planned nature of measures to introduce blended learning and monitor the achievement of the set goals, which will require the development of a system of criteria and indicators of the attainability of the tasks.
Technologies of embedded project-based learning for undergraduate engineering students

Abstract The purpose of the research is to develop a technology for incorporating project work in undergraduate engineering student training. A pedagogical experiment was conducted in the regional universities of Russia, the Kyrgyz Republic, and Uzbekistan in 2017-2021 among second-, third- and fourth-year engineering students. 126 students and 14 educators took part in it. To achieve the research goal, the authors used project-based learning, the authors’ technique for changing the content of the subject, and the questionnaire method. The authors designed the surveys in the Google forms service and processed the results with MS Excel. According to the experiment results, it can be argued that the successful implementation of project work in training is available through six techniques determined from the ratio of the time spent on face-to-face and independent study on the subject or learning and extracurricular activities. All the participants of the pedagogical experiment evaluated the techniques positively.
Turing Machine based Syllable Splitter

Nowadays, children, teens, and almost everyone around us tend to receive abundant and frequent advice regarding the usefulness of syllabification. Not only does it improve pronunciation, but it also makes it easier for us to read unfamiliar words in chunks of syllables rather than reading them all at once. Within this paper, we have designed, implemented, and presented a Turing machine-based syllable splitter. A Turing machine forms the theoretical basis for all modern computers and can be used to solve universal problems. On the other hand, a syllable splitter is used to hyphenate words into their corresponding syllables. We have proposed our work by illustrating the various states of the Turing machine, along with the rules it abides by, its machine specifications, and transition function. In addition to this, we have implemented a Graphical User Interface to stimulate our Turing machine to analyze our results better.
Enhanced Back Projection Network Based Stereo Image Super-Resolution Considering Parallax Attention

Recent years have witnessed great advances in stereo image super-resolution (SR). However, the existing methods only consider the horizontal parallax when capturing the stereo correspondence, which is insufficient because the vertical parallax inevitably exists in stereo image pairs. To address this problem, we propose an enhanced back projection stereo SR network (EBPSSRnet) to make full use of the complementary information in stereo images for more accurate SR results. Specifically, we propose a relaxed parallax attention module (rePAM) to handle different stereo images with vertical and horizontal parallax. Then, an enhanced back projection block (EBPB) is developed to extract discriminative features for capturing the stereo correspondence and consolidate the best representation for reconstruction. Extensive experiments show that the proposed method achieves state-of-the-art performance on the Flickr1024, Middlebury, KITTI2012 and KITTI2015 datasets.
Learning To Disentangle Representations For Rain Streak Removal

Rain streak removal is an important task in real-world vision applications since rain streaks in the air largely threaten the performance of visual analytics. When training conventional models, it is common to remove rain streaks without defining explicitly biased information. Although it is often regarded that learned representation is effective in capturing informativeness, it is still likely to indicate incongruent information to spoil removal task while learning biased representation. To handle this issue, we employ an information-theoretic concept to define disentangled representation which is divided into shared and biased characteristics respectively. Our key idea is to remove biased feature representations from a set of co-occurrence features while preserving details using mutual information. We achieve this by proposing a novel stage-wise training strategy that captures a more discriminative and pure factor that preserves details. Specifically, we utilize an adversarial objective that explicitly defines each representation to enforce disentanglement. Extensive computational experiments on three benchmark datasets show the superiority of our proposed model.
On Communication for Distributed Babai Point Computation

We present a communication-efficient distributed protocol for computing the Babai point, an approximate nearest point for a random vector <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbf{X}}\in \mathbb {R}^{n}$ </tex-math></inline-formula> in a given lattice. We show that the protocol is optimal in the sense that it minimizes the sum rate when the components of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {X}$ </tex-math></inline-formula> are mutually independent. We then investigate the error probability, i.e. the probability that the Babai point does not coincide with the nearest lattice point, motivated by the fact that for some cases, a distributed algorithm for finding the Babai point is sufficient for finding the nearest lattice point itself. Two different probability models for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {X}$ </tex-math></inline-formula> are considered—uniform and Gaussian. For the uniform model, in dimensions two and three, the error probability is seen to grow with the packing density, and we demonstrate that the densest lattice in dimension two presents the worst error probability. For higher dimensions, we develop probabilistic concentration bounds as well as bounds based on geometric arguments for the error probability. The probabilistic bounds lead to the conclusion that for lattices which generate suitably thin coverings of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {R}^{n}$ </tex-math></inline-formula> (which includes lattices that meet Rogers’ bound on the covering radius), the error probability goes to unity as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> grows. Probabilistic and geometric bounds are also used to estimate the error probability under the uniform model for various lattices including the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A_{n}$ </tex-math></inline-formula> family and the Leech lattice, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Lambda _{24}$ </tex-math></inline-formula> . On the other hand, for the Gaussian model, the error probability goes to zero as the lattice dimension tends to infinity, provided the noise variance is sufficiently small.
Design of Three-Dimensional Gesture Recognition and Motion Tracking Human-Computer Intelligent Interaction System based on PAJ7620

Abstract With the increasing degree of informatization and intelligence in modern society, human-computer interaction technology has become one of the commonly used scientific and technological achievements. “Gesture” can be used as inputing image information, and make more intuitive and convenient for perception of user interface. To a certain extent, it can complete the intelligent interaction function between human and computer. The design is a human-computer intelligent interaction system for 3D gesture recognition and motion tracking based on gesture sensor PAJ7620. STM32 chip is used as the main control unit to recognize accurately, analyze and process different gestures collected by the gesture sensor, and control the OLED screen to display the gesture information. It can further drive the carrier to take corresponding actions and realize the intelligent human-computer interaction recognition of gestures. The experimental test results show that the recognition rate is lose to 100% when the distance is within 10cm.
Enhanced models in deep image steganography

Seeking to take advantage of the innovations brought by machine learning, a field in a continuous movement and development, the present paper aims to enhance a practice that has been used since ancient times: steganography. Thereby, we targeted the implementation of a system aimed to hide image-type messages with the aid of deep neural networks. We followed a baseline model designed according to the recommendations stated into the state-of-the-art section. Then, we progressively developed three new models, each adding a new improvement on top of the previous one.
ICT dynamics for gender inclusive intermediary education: minimum poverty and inequality thresholds in developing countries

This study examines linkages between information and communication technology (ICT) dynamics, inequality and poverty in order to establish critical masses of poverty and inequality that should not be exceeded in order for ICT dynamics to promote gender inclusive education in 57 developing countries for the period 2012-2016. Poverty is measured with the poverty headcount ratio at national poverty lines (% of the population) while inequality is proxied by the Gini coefficient, the Atkinson index and the Palma ratio. The ICT dynamics are measured with ‘internet access in school’, ‘virtual social network’, ‘personal computers’ ‘mobile phone penetration’, ‘internet penetration’ and ‘fixed broadband subscriptions’. The empirical evidence is based on interactive Generalized Method of Moments estimators from which thresholds are computed contingent on the validity of tested hypotheses. First, the Gini coefficient should not exceed 0.5618 in order for ‘internet access in school’ to positively affect inclusive education. Second, the poverty headcount ratio at national poverty lines (% of the population) should remain below 33.6842% in order for ‘internet access in school’ to favorably influence inclusive education. Third, the Palma ratio should not exceed 3.3766 in order for internet penetration to favorably affect inclusive education. Fourth, for personal computers to increase inclusive education, the Gini coefficient, Palma ratio and poverty headcount (% of the population) should not exceed 0.4781, 3.5294 and 17.7272, respectively. The study confirms the significant role technological deepening plays in advancing inclusive education by means of policies that reduce poverty and income inequality, with potentially wider applicability to other developing economies. The study has provided poverty and inequality levels that should not be exceeded in order for personal computers, internet penetration and ‘internet access in school’ to promote gender inclusive education.
Time for Truth: Forensic Analysis of NTFS Timestamps

Timeline forgery a widely employed technique in computer anti-forensics. Numerous freely available and easy-to-use tampering tools make it difficult for forensic scientists to collect legally valid evidence and reconstruct a credible timeline. At the same time, the large number of possible file operations performed by a genuine user can result in a wide variety of timestamp patterns that pose a challenge when reconstructing a chain of events, especially since application-specific discrepancies are often disregarded.
Statistical Quantification of Differential Privacy: A Local Approach

In this work, we introduce a new approach for statistical quantification of differential privacy in a black box setting. We present estimators and confidence intervals for the optimal privacy parameter of a randomized algorithm A, as well as other key variables (such as the “data-centric privacy level”). Our estimators are based on a local characterization of privacy and in contrast to the related literature avoid the process of “event selection” - a major obstacle to privacy validation. This makes our methods easy to implement and user-friendly. We show fast convergence rates of the estimators and asymptotic validity of the confidence intervals. An experimental study of various algorithms confirms the efficacy of our approach.
Disentangled Representation Learning For Deep MR To CT Synthesis Using Unpaired Data

Many different methods have been proposed for generation of synthetic CT (sCT) from MR images. Most of these methods depend on paired-wise aligned MR and CT training images of the same patient, which are difficult to obtain. In this paper, we propose a novel disentangled representation learning method for MR to CT synthesis using unpaired data. Specifically, we first embed images onto two spaces: a modality-invariant geometry space capturing the shared anatomical information across different imaging domains, and a modality-specific appearance space. From the embedding, a sCT image can be synthesized from a MR image by taking the encoded geometry features from the MR image and an appearance vector sampled from the appearance space of a CT image. To handle the challenging of distinguishing cortical bone from air in MR images, where both of them have low intensity values, we propose a novel Geometry Similarity Module (GSM) to take the context information into consideration. Experimental results demonstrated that our approach achieved better or equivalent results than the state-of-the-art.
A Genetic Algorithm for Quantum Circuit Generation in OpenQASM

In this paper, we present the results of our research in quantum circuit program generation using genetic algorithm for the Open Quantum Assembly Language (OpenQASM). The generated circuit program from our genetic algorithm is formulated based on the set of quantum gates documented on the official IBM Quantum Experience OpenQASM site to make them 100% compatible with Qiskit 0.23.1 (December 2020), so this makes the generated programs to be usable for any future quantum hardware that follows the circuit model for universal quantum computation, which Qiskit is based upon. The genetic algorithm is programmed to interact directly with Qiskit Aer to perform fitness evaluation of the population so that the resultant programs are always error free and readily available to be deployed in various applications in the future.
Architectural framework for Multi sensor Data fusion and validation in IoT Based system

The IoT Based system is expected to be a tipping point for many applications such as Aircraft, Medical diagnosis, Autonomous systems, Military and defense services. Over the past few years, IoT based applications are witnessing the results of multiple sensors, which plays a vital role in decision making. The proposed work suggests the layered architectural framework and guidelines for modeling multi sensor-based applications. The application that involves multiple meshed IoT systems generates voluminous data that needs to be handled in the right way to make the correct decision. The conventional multiple data fusion methods are application-specific. The primary objective of our work is to narrate the handling of a larger number of data generated from multiple sensors and addressing the problems in data fusion for sensitive IoT based applications. In addition to that comparative analysis of various sensor fusion and validation, techniques are surveyed.
Error Compensation Heatmap Decoding for Human Pose Estimation

As a fundamental component of heatmap-based human pose estimation methods, heatmap decoding is to transform heatmaps into joint coordinates. We found that previous heatmap decoding methods generally ignored the effect of systematic errors introduced by the resolution increaseing operations in the network decoder. This work fills the gap by taking the systematic errors in heatmap decoding into consideration. We proposed a fast method to reduces the systematic and random errors in one shot by error compensation. The proposed method outperforms the previous best method on the COCO and the MPII datasets while being over 2 times faster. Extensive experiments with different networks, resolutions, metrics and datasets have proved the rationality of the proposed idea.
SMGAN: A self-modulated generative adversarial network for single image dehazing

Single image dehazing has become a key prerequisite for most high-level computer vision tasks since haze severely degrades the input images. Traditional prior-based methods dehaze images by some assumptions concluded from haze-free images, which recover high-quality results but always cause some halos or color distortion. Recently, many methods have been using convolutional neural networks to learn the haze-relevant features and then retrieve the original images. These learning-based methods achieve better performance in synthetic scenes but can hardly restore a clear image with discriminative texture when applied to real-world images, mainly because these networks are trained on synthetic datasets. To solve these problems, a self-modulated generative adversarial network for single image dehazing named SMGAN is proposed. The SMGAN inputs prior-dehazed images into a parameter-shared encoder to produce some latent information of these dehazed images. During the hazy image decoding process, the latent information is sent to self-modulated batch normalization layers, which makes the network fit in real haze removal. Moreover, consider that there are some over-enhanced regions in the guidance images, and a refine module is proposed to alleviate the negative information. The proposed SMGAN combines the advantages of prior-based methods and learning-based methods, which provides superior performance compared with the state-of-the-art methods on both synthetic and real-word datasets.
SPACESeg: Automated Detection of Bed Junction Morphologies Indicating Signs of Life in Ediacaran Period

With Perseverance out looking for life on Mars, we identify the need to equip ourselves with automated techniques for remote assessment of geological information. The first step in this translational research is studying early signs of life on Earth. More specifically, we examine the Ediacaran sedimentological record of the Flinders region in Australia, whose unique bed ripple junction morphologies have been determined as the definite indicators of early life on Earth. We propose an automated technique, SPACESeg, that robustly detects the artifact-clouded, miniature ripple structures from cross-sectional views of the Ediacaran rocks. We demonstrate the efficacy of SPACESeg in precisely extracting the desired structures with high accuracy, outperforming many techniques. We also establish the robustness of this technique as it extracts desired biosignatures from drastically varying image conditions, even when the ripples comprise of < 1 % of the image around significant artifacts. We provide quantitative and qualitative analysis and compare our method against many unsupervised rule-based and supervised deep learning methods, outperforming them all.
Cost Efficient Sensor Positions Determination For Human Activity Recognition

Human activity recognition(HAR) is one of the most active topics in the field of ubiquitous computing. Multi-sensor based HAR has attracted extensive interest because of its high recognition accuracy. Correspondingly, the number of body sensors in terms of hardware cost, the overload on communications, the storage and the computational complexity will be very high. In this paper, we propose a novel approach which can optimize cost-efficient sensors positions to save all the costs while maintaining high recognition performance. The tradeoff among the sensor positions, the target category, and the redundancy is considered. We also propose a data set D that contains acceleration sensor data for seventeen positions of the human body by simulating the worker actions in a factory assembly lines. The experimental results show that only six sensors can maintain high activity recognition accuracy out of seventeen sensors by using the proposed method.