Reviewerly/exHarmony huggingface dataset:
Compounded Layer-Prior Unrolling: A Unified Transmission-Based Image Enhancement Framework

Improving the quality of images degraded by various transmission media has important practical significance. Such enhancement tasks involve resolving both transmission degradation and residual contamination including imaging noise, color distortion, and occlusions. Existing methods typically develop the priors on natural scenes to resolve ill-posed problems separately. However, the solutions derived from hand-crafted priors may fail on specific regions where a priori assumptions break, and recent data-driven methods highly depend on training data owing to the absence of effective priors. Based on a unified formulation for transmission-based image enhancement tasks, we develop a compounded unrolling framework to generate hybrid image layer propagations. Specifically, as multiple deeply-trained priors are integrated into the iterative propagation scheme, the deep model can recognize specific task properties and data distributions for different applications. Both quantitative and qualitative experiments demonstrate the superior performance of the proposed framework on various transmission-based tasks (haze removal, underwater image enhancement and rain removal).
The covering radius of the Reed–Muller code <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e112" altimg="si8.svg"><mml:mrow><mml:mi>R</mml:mi><mml:mi>M</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>7</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math> is 40

It was proved by J. Schatz that the covering radius of the second order Reed–Muller code RM(2,6) is 18 (Schatz (1981)). However, the covering radius of RM(2,7) has been an open problem for many years. In this paper, we prove that the covering radius of RM(2,7) is 40, which is the same as the covering radius of RM(2,7) in RM(3,7). As a corollary, we also find new upper bounds for the covering radius of RM(2,n), n=8,9,10.
Application of Cloud Computing Information Processing System in Network Education

With the goal of realizing computer hardware resources, software resources, teaching resources and other resource sharing and teacher-student interaction, the cloud computing teaching platform has built a cloud education platform with complete functions, stability, efficiency and security. Based on the application development platform launched by cloud computing, this paper developed the application of college education system. At the same time, this paper expounds the cloud computing theory, the architecture of cloud computing, the structural model of cloud computing, and analyzed the abstract model and overall technical architecture of infrastructure cloud services. In addition, this paper designed and built an infrastructure cloud service platform and implemented a college network education platform based on cloud computing.
Beta Distribution-Based Cross-Entropy for Feature Selection

Analysis of high-dimensional data is a challenge in machine learning and data mining. Feature selection plays an important role in dealing with high-dimensional data for improvement of predictive accuracy, as well as better interpretation of the data. Frequently used evaluation functions for feature selection include resampling methods such as cross-validation, which show an advantage in predictive accuracy. However, these conventional methods are not only computationally expensive, but also tend to be over-optimistic. We propose a novel cross-entropy which is based on beta distribution for feature selection. In beta distribution-based cross-entropy (BetaDCE) for feature selection, the probability density is estimated by beta distribution and the cross-entropy is computed by the expected value of beta distribution, so that the generalization ability can be estimated more precisely than conventional methods where the probability density is learnt from data. Analysis of the generalization ability of BetaDCE revealed that it was a trade-off between bias and variance. The robustness of BetaDCE was demonstrated by experiments on three types of data. In the exclusive or-like (XOR-like) dataset, the false discovery rate of BetaDCE was significantly smaller than that of other methods. For the leukemia dataset, the area under the curve (AUC) of BetaDCE on the test set was 0.93 with only four selected features, which indicated that BetaDCE not only detected the irrelevant and redundant features precisely, but also more accurately predicted the class labels with a smaller number of features than the original method, whose AUC was 0.83 with 50 features. In the metabonomic dataset, the overall AUC of prediction with features selected by BetaDCE was significantly larger than that by the original reported method. Therefore, BetaDCE can be used as a general and efficient framework for feature selection.
Handwritten Recognition based on Hand Gesture Recognition using Deterministic Finite Automata and Fuzzy Logic

Hand Gesture Recognition (HGR) is one of the most interesting branches in computer vision with lots of applications in mobiles, tablets, personal computers, and interactive platforms. The aim of this technology is to efficiently relate facilitate the people's communication with gadgets. Tracking hand movement in order to understand the desired operation can play an important role in the recognition of the hand gesture. In this paper, we propose a new approach for handwritten recognition in the context of hand gesture recognition. Our approach relies on the hand movements by means of its edge information and the movements directions. The next contribution is also minimizing the uncertainty of the handwritten recognition using fuzzy logic and deterministic finite automata (DFA). The uncertainty of the handwritten is due to the similar movements of the hands during writing the characters that we decreased it using our technique. The experimental result on real-world activities shows the success and usefulness of our approach.
m-SAAC: Multi-stage adaptive approximation control to select approximate computing modes for vision applications

The psycho-visual nature of images and iterative nature of processing algorithms make vision and image processing suitable applications for approximate computing. State-of-the-art research in this area examines application resilience to approximation while assuming a uniform distribution for the information source. In this paper, we demonstrate that data-driven analysis can provide better insight into approximation requirements for image processing applications. Furthermore, this analysis is leveraged to design the multi-stage adaptive approximation control (m-SAAC) methodology that can save compute power by utilizing approximate computing, without compromising on image quality. The results demonstrate the efficacy of the proposed methodology for a variety of test cases.
Using an ASG Based Generative Policy to Model Human Rules

Generative policies have recently been researched to provide a method for next generation security policies. They are created using either traditional machine learning techniques or, more recently, inductive learning of answer set programs. The latter method is targeted to the learning of Answer Set Grammars (ASG), a new notion of generative policy model for security policies that has the benefit of transparent explainability of the learned outcomes. This paper proposes a military scenario based on logistical resupply from a military base to coalition forces located in a nearby urban area or city. We describe the scenario and accompanying policy such that the context of the resupply missions (and therefore the policy) changes over time. The set of policies and related changes over time have been manually defined using a set of human created rules to replicate how security policies would currently be created by humans in such scenarios. We show how inductive learning of answer set programs can successfully learn ASG generative policy models that capture the human-driven rules from just example traces and decisions made at different time points and with respect to different contextual situations that can arise during the resupply mission. These results demonstrate the utility of ASG generative policy as a method for modelling human-driven policy rules.
Sentiment Classification and Prediction of Job Interview Performance

Attracting and hiring talented employees is a challenge for companies. The job interview process is a very critical step for both employer and candidate. Having a smooth hiring process in a company will increase future employees' satisfaction. Candidates tend to share their feedback and experience of interviews and company's hiring process with others. Having a negative experience can affect its brand image and reputation as an employer. This will make it hard to attract talented employees. In this research, machine learning and neural network models, such as support vector machines, logistic regression, Naive Bayes, and long short–term memory (LSTM), were trained to predict the candidates' sentiments after a job interview. Each model was trained using several data representations and weighting approaches, such as term binary, term frequency, and term frequency–inverse document frequency (TF–IDF). As a result, training logistic regression with TF–IDF and unigram word representation achieved an F1-measure of 0.814.
A method to classify digital images by means of statistics of a wavelet decomposition

There is a wide variety of methods for the analysis of textures and the extraction of image characteristics based on wavelet decomposition. The objective of this paper is to find a vector of characteristics of each image and to determine a classification among them using principal component analysis. The procedures presented here are for classifying test images. After fragmenting the image, a wavelet decomposition of the fragmented images is performed both in scale and in orientation. To characterize the images, statistics of marginal and joint distributions based on local neighborhoods of spatial, orientation and scale type are used.
Topological characterization of a polyphased gold-bearing vein network

It is challenging to evaluate the connectivity of metal-bearing hydrothermal vein systems, i.e. the degree to which fractures or veins are connected within a network. Moreover, many ancient ore-bearing vein networks have been deformed and metamorphosed, which hinders the reconstruction and quantification of their primary characteristics. Here, we explore the reliability and limitations of the network distribution and topologic (classification of vein terminations into I, Y and X nodes) approaches to study deformed and metamorphosed mineral deposits. We focus on the Cheechoo gold deposit located in the Eeyou Istchee James Bay area of Quebec, Canada (Archean Superior Province). This gold deposit shows a complex hydrothermal and magmatic polyphased gold-bearing vein system hosted in a granodioritic-tonalitic intrusion. The Cheechoo deposit is composed of quartz-feldspar-diopside (Qtz-Fsp-Di) and quartz (Qtz) veins that underwent ductile deformation and amphibolite facies metamorphism. The high-grade metamorphism led to the modification of vein shape, relative position and spacing, making these parameters unreliable for the characterization of the Cheechoo vein system and similar metamorphosed networks. However, the number of veins per length unit and topology are reliable tools to characterize such networks. In the gold-bearing Cheechoo system, the gold mineralization appears to be mainly controlled by (i) the number of Qtz and Qtz-Fsp-Di veins per length unit and by (ii) the connectivity of these networks as shown by the increase of both parameters with the gold grade. Furthermore, we introduce the Y/I ratio as a simple topological parameter to quantify the connectivity of two dimensional (I-Y dominated) systems. This parameter is positively correlated with the number of connections per branch (CB) used to quantify network connectivity, and ultimately represent the development of a fault or fracture zone.
Algorithm for Image Transfer Using Dynamic Chaos

The following topics are dealt with: CAD; production engineering computing; telecommunication traffic; mathematical analysis; design engineering; optimisation; image coding; microsensors; probability; quality of service.
Nonsubsampled contourlet transform and k-means clustering for degraded document image binarization

Binarization is the starting step of document analysis and recognition systems. A binarization method is proposed for a degraded historical document image. The binarization methodology is based on the joint use of nonsubsampled contourlet transform (NSCT) for enhancement and <italic>k</italic>-means clustering for binarization. The input degraded image is decomposed by NSCT for generating coefficients, which are handled through a weighting scheme for highlighting significant features. The resulting reconstructed enhanced image is then binarized by mapping pixels into foreground (text) or background (no text) using <italic>k</italic>-means clustering. Experiments are conducted on document image binarization competition datasets using blind and unblind evaluation protocol. Unblind evaluation is performed on four specific types of degradations, which are stain, ink bleed-through, nonuniform background, and ink intensity variation. The obtained results show the effectiveness of the proposed scheme in terms of objective and subjective evaluations as well as stability with respect to the other well-known methods.
Quantifying the Error of Light Transport Algorithms

Abstract This paper proposes a new methodology for measuring the error of unbiased physically based rendering algorithms. The current state of the art includes mean squared error (MSE) based metrics and visual comparisons of equal‐time renderings of competing algorithms. Neither is satisfying as MSE does not describe behavior and can exhibit significant variance, and visual comparisons are inherently subjective. Our contribution is two‐fold: First, we propose to compute many short renderings instead of a single long run and use the short renderings to estimate MSE expectation and variance as well as per‐pixel standard deviation. An algorithm that achieves good results in most runs, but with occasional outliers is essentially unreliable, which we wish to quantify numerically. We use per‐pixel standard deviation to identify problematic lighting effects of rendering algorithms. The second contribution is the error spectrum ensemble (ESE), a tool for measuring the distribution of error over frequencies. The ESE serves two purposes: It reveals correlation between pixels and can be used to detect outliers, which offset the amount of error substantially.
NNC: A tool based on Grammatical Evolution for data classification and differential equation solving

<h2>Abstract</h2> A genetic programming tool is demonstrated for data classification and differential equation solving. The fundamental element of the method is the well-known technique of Grammatical Evolution. The method constructs artificial neural networks in a C – like programming language in order to learn the input data. The paper presents the programming tool NNC (Neural Network Constructor) as long as a series of experiments.
Constructing and Mining Heterogeneous Information Networks from Massive Text

Real-world data exists largely in the form of unstructured texts. A grand challenge on data mining research is to develop effective and scalable methods that may transform unstructured text into structured knowledge. Based on our vision, it is highly beneficial to transform such text into structured heterogeneous information networks, on which actionable knowledge can be generated based on the user's need. In this tutorial, we provide a comprehensive overview on recent research and development in this direction. First, we introduce a series of effective methods that construct heterogeneous information networks from massive, domain-specific text corpora. Then we discuss methods that mine such text-rich networks based on the user's need. Specifically, we focus on scalable, effective, weakly supervised, language-agnostic methods that work on various kinds of text. We further demonstrate, on real datasets (including news articles, scientific publications, and product reviews), how information networks can be constructed and how they can assist further exploratory analysis.
Implementation of an Attribute-Based Encryption Scheme Based on SM9

In recent years, attribute-based encryption (ABE) has been widely applied in mobile computing, cloud computing, and the Internet of things, for supporting flexible and fine-grained access control of sensitive data. In this paper, we present a novel attribute-based encryption scheme that is based on bilinear pairing over Barreto and Naehrig curves (BN-curves). The identity-based encryption scheme SM9, which is a Chinese commercial cryptographic standard and a forthcoming part of ISO/IEC11770-3, has been used as the fundamental building block, and thus we first introduce SM9 and present our SM9 implementation in details. Subsequently, we propose the design and implementation of the ABE scheme. Moreover, we also develop a hybrid ABE for achieving lower ciphertext expansion rate when the size of access structure or plaintext is large. The performance and energy consumption of the implementation of the proposed ABE and its hybrid version are evaluated with a workstation, a PC, a smart phone, and an embedded device. The experimental results indicated that our schemes work well on various computing platforms. Moreover, the proposed schemes and their implementations would benefit developers in building applications that fulfill the regulatory compliance with the Chinese commercial cryptographic standard since there is no existing ABE scheme compatible with any Chinese cryptographic standard.
Towards trustless prediction-as-a-service

Prediction-as-a-Service is a promising new paradigm that brings the advantages of Software-as-a-Service's business model to the world of prediction APIs. In such a scenario, prediction API providers can leverage a Cloud provider's infrastructure to offer their inference service to the general public without having to worry about infrastructure acquisition and operation costs. Indeed, in the case of prediction APIs, self-hosting costs could be much higher than usual due to the fact that inference models, e.g., deep learning models, need specific hardware (e.g., graphical processing units) for an efficient execution. In such a context, trust is of great importance as the prediction API provider's most valuable asset, i.e., the inference model, is transferred to the Cloud provider. Thus, specific countermeasures should be designed to mitigate the possible attacks. In this paper, we analyze this scenario identifying the peculiar threat models. Then, we present a decentralized blockchain-based system, implemented on top of the popular Tendermint framework, that provides countermeasures to some of the main attacks. Numerical results, obtained executing deep neural network models, demonstrate that the overhead with respect to a centralized approach is negligible if compared with the advantages in terms of prevention of malicious behaviors.
Real-Time Human-Posture Recognition for Human-Drone Interaction Using Monocular Vision

This paper presents a real-time monocular vision solution to human postures recognition for human-drone interaction. The approach achieves a more natural interaction between human and drone. Image regions and joint positions of human bodies in images from a monocular camera mounted on a micro drone are extracted by using a deep neural network. Then, feature vectors of a human body are generated by the relative distance among the joints and classified by a support vector machine (SVM) classifier. The performance of the solution is demonstrated by extensive experiments. Our method obtains an average recognition accuracy of 97.34% on the micro drone and keeps high precision even with a large distance between the human and the micro drone. Furthermore, our method consumes limited computing resources and is suitable for onboard applications of a micro drone.
Design and Analysis on Structure of a Solar Tree for 3 kW Capacity

Nowadays with the growing population, energy demand is increased. Sunlight is often estimated as the only energy profusion and truly free energy resources. Among all the techniques available to utilize the solar energy, the utmost popular and matured technology is the photovoltaic conversion of sunlight into electricity. Even with its advantages, solar PV technology has issues with land requirement (less space), capture efficiency, and public perception. So, this should be taken as an advantage of such an energy which requires a very less amount of space to provide effectively with graceful. In this position, structure of the solar tree is the best option for us. To evade this problem, we can install a solar tree in malice of a number of solar panels which require a very small space. In this project, first different parts of structure of the solar tree which are taken from the literature review are designed using Creo Parametric 2.0 whose weight is 630 kg, which is very high. So, there is a need for design optimization without compromising the strength. Design optimization is done on the various components of the structure of the solar tree, i.e., trunk, base plates, and middle plates, stems, and also gussets in order to reduce the weight and to increase the production of power. Due to this optimization, the weight of the body radically reduced, and finally, it is 373 kg for structural steel. Static structural analysis is done on the model before optimization and after optimization using ANSYS 16.2 software. The results showed that stress is 90.45 MPa and deformation is 5.45, and the factor of safety is more than 10 for the model before optimization and the results of the model after optimization showed that stress and deformation are within limits and the factor of safety is 2.02, i.e., for structural steel, and the weight is reduced. In this work, design of the structure of the solar tree (without panels) is proposed with structural Steel is to calculate the displacement, stress, strains, due to external loads and its self-weight.