Reviewerly/exHarmony huggingface dataset:
Predictive Analysis of Photovoltaic Power Generation Using Deep Learning

A novel deep learning approach is proposed for the predictive analysis of trends in energy related time series, in particular those relevant to photovoltaic systems. Aim of the proposed approach is to grasp the trend of the time series, namely, if the series goes up, down or keep stable, instead of predicting the future numerical value. The modeling system is based on Long Short-Term Memory networks, which are a type of recurrent neural network able to extract information in samples located very far from the current one. This new approach has been tested in a real-world case study showing good robustness and accuracy.
The Power of Bounds: Answering Approximate Earth Mover's Distance with Parametric Bounds

The Earth Mover's Distance (EMD) is a robust similarity measure between two histograms (e.g., probability distributions). It has been extensively used in a wide range of applications, e.g., multimedia, data mining, computer vision, etc. As EMD is a computationally intensive operation, many efficient lower and upper bound functions of EMD have been developed. However, they provide no guarantee on the error. In this work, we study how to compute approximate EMD value with bounded error. First, we develop a parametric dual bound function for EMD, in order to offer sufficient trade-off points for optimization. After that, we propose an approximation framework that leverages on lower and upper bound functions to compute approximate EMD with error guarantee. Then, we present three solutions to solve our problem. Experimental results on real data demonstrate the efficiency and the effectiveness of our proposed solutions.
Parallel Framework for Data-Intensive Computing with XSEDE

With the increase in data-driven analytics, the demand for high performing computing resources has risen. There are many high-performance computing centers providing cyberinfrastructure (CI) for academic research. However, there exists access barriers in bringing these resources to a broad range of users. Users who are new to data analytics field are not yet equipped to take advantage of the tools offered by CI. In this paper, we propose a framework to lower the access barriers that exist in bringing the high-performance computing resources to users that do not have the training to utilize the capability of CI. The framework uses divide-and-conquer (DC) paradigm for data-intensive computing tasks. It consists of three major components - user interface (UI), parallel scripts generator (PSG) and underlying cyberinfrastructure (CI). The goal of the framework is to provide a user-friendly method for parallelizing data-intensive computing tasks with minimal user intervention. Some of the key design goals are usability, scalability and reproducibility. The users can focus on their problem and leave the parallelization details to the framework.
A Survey of Periodic Binary Nearly Perfect Signals with Lengths N≡1mod4

The binary phase manipulated radio signals, which auto - correlation functions have small side-lobes, are named perfect, as they have a critical role for many types of radio communication systems. With regard to this situation, in the paper we suggest several necessary conditions for existence of periodic binary nearly perfect PM signals with lengths N=1mod4. On their base an exhaustive computer search has been conducted. As a result, several unknown up to now periodic binary nearly perfect signals with lengths N≡1mod4 have been found.
Pre-Training of DNN-Based Speech Synthesis Based on Bidirectional Conversion between Text and Speech

Conventional approaches to statistical parametric speech synthesis use context-dependent hidden Markov models (HMMs) clustered using decision trees to generate speech parameters from linguistic features. However, decision trees are not always appropriate to model complex context dependencies of linguistic features efficiently. An alternative scheme that replaces decision trees with deep neural networks (DNNs) was presented as a possible way to overcome the difficulty. By training the network to represent high-dimensional feedforward dependencies from linguistic features to acoustic features, DNN-based speech synthesis systems convert a text into a speech. To improved the naturalness of the synthesized speech, this paper presents a novel pre-training method for DNN-based statistical parametric speech synthesis systems. In our method, a deep relational model (DRM), which represents a joint probability of two visible variables, is applied to describe the joint distribution of acoustic and linguistic features. As with DNNs, a DRM consists several hidden layers and two visible layers. Although DNNs represent feedforward dependencies from one visible variables (inputs) to other visible variables (outputs), a DRM has an ability to represent the bidirectional dependencies between two visible variables. During the maximum-likelihood (ML) -based training, the model optimizes its parameters (connection weights between two adjacent layers, and biases) of a deep architecture considering the bidirectional conversion between 1) acoustic features given linguistic features, and 2) linguistic features given acoustic features generated from itself. Owing to considering whether the generated acoustic features are recognizable, our method can obtain reasonable parameters for speech synthesis. Experimental results in a speech synthesis task show that pre-trained DNN-based systems using our proposed method outperformed randomly-initialized DNN-based systems, especially when the amount of training data is limited. Additionally, speaker-dependent speech recognition experimental results also show that our method outperformed DNN-based systems, by setting the initial parameters of our method are the same as that in the synthesis experiments.
Impact of Steganography on JPEG File Size

Hiding data in JPEG images is usually performed by modifying quantized DCT coefficients. This will affect the entropy coding and consequently the size of the resulting compressed data. The change in the file size might be used as a feature in steganalysis. In this paper, we investigate the impact of several well-known steganography methods on the size of JPEG file. The experiments show interesting results, where we considered several embedding payloads and quality factors. OutGuess 0.1, OutGuess 0.2, and complementary embedding methods increase the file size, while F5, nsF5, and PQ decrease it. The secure steganography methods J-UNIWARD and SI-UNIWARD almost do not change the file size.
Analysis of Basketball Training Model Optimization Based on Artificial Intelligence and Computer Aided Model

With the rapid development of electronic computer and its software in recent decades, the information of basketball has become more and more obvious. It has become the goal of sports computing to use computer technology to help the athletes to train, to improve their competition level, and to assist the coaches make decision. Existing basketball technical and tactical analysis and statistical software owns several shortcomings such as poor motion control and real-time performance, small database capacity, weak ability of extracting video, low efficiency of game statistics algorithm. It predicts changes of the state inside the system by calculating the probability of state transition, which makes the prediction method own advantage in application and a high practical quality. This paper based on data mining technology, aiming at the characteristics of the analyzed object, selects the appropriate data mining methods and statistical analysis methods to solve the various problems encountered in the basketball match, which can obviously improve the sports level and management level.
Image Denoiser Using Convolutional Neural Network with Deconvolution and Modified Residual Network

Due to improvements in hardware and software performance, deep learning algorithms have been used in many areas and have shown good results. In this paper, we propose a noise reduction framework based on a convolutional neural network (CNN) with deconvolution and a modified residual network (ResNet) to remove image noise. Simulation results show that the proposed algorithm is superior to the conventional noise eliminator in subjective and objective performance analyses.
An Anonymous Credential System with Constant-Size Attribute Proofs for CNF Formulas with Negations

To enhance the user's privacy in electronic ID, anonymous credential systems have been researched. In the anonymous credential system, a trusted issuing organization first issues a certificate certifying the user's attributes to a user. Then, in addition to the possession of the certificate, the user can anonymously prove only the necessary attributes. Previously, an anonymous credential system was proposed, where CNF (Conjunctive Normal Form) formulas on attributes can be proved. The advantage is that the attribute proof in the authentication has the constant size for the number of attributes that the user owns and the size of the proved formula. Thus, various expressive logical relations on attributes can be efficiently verified. However, the previous system has a limitation: The proved CNF formulas cannot include any negation. Therefore, in this paper, we propose an anonymous credential system with constant-size attribute proofs such that the user can prove CNF formulas with negations. For the proposed system, we extend the previous accumulator for the limited CNF formulas to verify CNF formulas with negations.
Adoption of VR influencing AI on 3D objects

The advent of new technologies has made it possible for new areas to be explored in the gaming industry. Virtual Reality has progressed at an exponential rate which has allowed it to be used in many industries. Virtual Reality allows users to be immersed in a highly detailed environment which allows for a more realistic experience. This paper outlines the development of a game on adoption of VR influencing AI on 3D objects, and the integration of virtual reality gaming Oculus and Leap Motion device in the gameplay, The Leap Motion is used to recognize hand gestures while Oculus will serve as the visual medium for the user. The game itself has been designed and developed on the Unity 3D gaming engine. This project endeavors to highlight the importance and diverse application of VR in various industries. The distinctive integration of Oculus, Leap and PC leads to a realistic gaming experience.
Readability, communication and terms of use of software packages

Terms of Use, which is sometimes called Terms and Conditions (T&C), are frequently unread as a result of their complexity and length.In the Information and Communication Technology (ICT) domain, it is also referred to as End User Licensing Agreement (EULA) which acts as a disclaimer between the manufacturer and the end user in the event of any malfunction.Readability formulae were used to objectively measure the comprehensibility of a document in terms of how it effectively achieves its intended purpose.End users rarely read or fully understand what they are agreeing to because of the urgency with which the software is needed.Hence, the need to evaluate the readability of terms of use of software packages to benefit both the Terms of Use of Software Packages to benefit both the end user and programmers.
Combination of Biogas Station and Photovoltaic Power Plant Operation

This article deals with the possibilities of cooperation of selected renewable energy sources in the distribution grid. This is a combination of the operation, or cooperation of, a biogas station with a photovoltaic power plant. The aim of the article is to outline several basic possibilities of cooperation of the above mentioned sources, including the introduction of technical aspects. To conclude, it proposes further solutions to the above problem, including their strengths and weaknesses.
An Approach to Constructing a Taxonomic Tree of Models Cyclic Signals in the Tasks of Developing an Onto-Oriented System for Decisions Supporting of Models Choice

In this paper an approach to the construction of a taxonomic tree of signal models with a cyclic structure is developed in the framework of the theory of cyclic functional relations, which is the first important step in the creation of a unified validated methodology, structural and parametric identification of mathematical and computer models, methods of transformation, analysis and prediction of cyclic signals in modern intellectualized information systems within the framework of deterministic, stochastic, fuzzy and interval approaches. On the basis of the developed approach to the generation of a taxonomic tree model of cyclic structure signals, with the use of machine-interpreted OWL ontology representations, taxonomy of cyclic signal mathematical models in the editor Protégé ontology is constructed, which makes it possible to create an onto-oriented system to support the adoption of model decisions in the field of modeling, analysis and forecasting processes of the cyclic structure, which will significantly increase the efficiency of the development of mathematical support of the corresponding projection information system.
BRICS – Efficient Techniques for Estimating the Farness-Centrality in Parallel

In this paper, we study scalable parallel algorithms for estimating the farness-centrality value of the nodes in a given undirected and connected graph. Our algorithms consider approaches that are more suitable for sparse graphs. To this end, we propose four optimization techniques based on removing redundant nodes, removing identical nodes, removing chain nodes, and making use of decomposition based on the biconnected components of the input graph. We test our techniques on a collection of real-world graphs for the time taken and the average error percentage. We further analyze the applicability of our techniques on various classes of real-world graphs. We suggest why certain techniques work better on certain classes of graphs.
System Development using Face Recognition

Face Recognition is one of the most emerging technology nowadays by which different types of system can be implemented in our regular life. It has a great scope to make our lives easier, and comfortable. This technology is using in various field. In this paper, an experimental approach is introduced to make a system which is based on the technology. The system can detect human face in real time. At the same time it recognize faces. Hence, this work aims to develop a system that detect and recognize individual faces in real time.
An Approach for the Solar Energy Assessment using Weather Medium-Range Forecasting

This paper presents a service that allows an operational assessment of solar energy systems based on the prediction of climate variables on different time scales, daily, monthly, seasonal or annual. The service is a planning tool building upon the Copernicus Climate Change Services, together with solar models and spatial and operational data of photovoltaic facilities. The added value of this service, as opposed to other systems that can be found on the market, is that it has been developed through an effective co-design with end-users, contributing to mutually beneficial collaboration, making possible to sustain their marketability and value. The proposed service is introduced and tested for automating both spatial and operational assessment of a utility-scale photovoltaic (PV) power plant. Experimental results are presented, which show and address the performance of the entire service.
Varieties of Data Languages.

We establish an Eilenberg-type correspondence for data languages, i.e. languages over an infinite alphabet. More precisely, we prove that there is a bijective correspondence between varieties of languages recognized by orbit-finite nominal monoids and pseudovarieties of such monoids. This is the first result of this kind for data languages. Our approach makes use of nominal Stone duality and a recent category theoretic generalization of Birkhoff-type HSP theorems that we instantiate here for the category of nominal sets. In addition, we prove an axiomatic characterization of weak pseudovarieties as those classes of orbit-finite monoids that can be specified by sequences of nominal equations, which provides a nominal version of a classical theorem of Eilenberg and Schutzenberger.
Moving to a World of More Seamless Single Sign-on Access: NISO’s RA21 Recommended Practice

In this Standards Update column, Todd Carpenter, Executive Director of the National Information Standards Organization (NISO), provides a preview of NISO's upcoming RA21 Recommended Practice for single sign-on systems. Carpenter describes previous attempts at single sign-on standards, the essential difficulties in authenticating users, and how RA21 will address these issues while ensuring the privacy of user data.
Object Localization and Segmentation Using Hybrid Features and Fuzzy Classifiers With a Small Training Set from an RGB-D Camera

This paper proposes an object localization and segmentation method based on a small set of training images captured from a Kinect red-green-blue-depth (RGB-D) camera. The method consists of three stages. The first stage localizes candidate objects based on the hybrid color features of cluster-based pixel distribution and color entropy and a new fuzzy classifier (FC). In the second stage, the object candidates are then sent to another FC for filtering by using the color feature of entropies of color geometrical distributions. After the two-stage localization using the color features, the depth measurement from the Kinect is used to segment the shape of the object for final localization and shape segmentation. A histogram-based shape feature is used to filter the candidate objects from the first two stages. Experimental results show that good performance is achieved by using only a small set of training images..
An Improved Algorithm for 3D Reconstruction Integration Based on Stripe Reflection Method

This paper introduces the basic principle of stripe reflection method and proposes an improved algorithm on the traditional Southwell gradient iterative integration algorithm.The algorithm adds a coefficient value with an attenuation factor to the compensation height value and the value of the attenuation factor is changed by the determination of the compensation height threshold.Through computer simulation, the fitting error of the reconstructed surface show that the RMS of the new method is one order of magnitude better than the traditional algorithm and the PV value of the high frequency part is about 1/15 of the traditional algorithm.It is proved that the improved algorithm can effectively improve the convergence and noise resistance of the iterative algorithm.
Using deep learning for calculation detection in coronary artery disease intravascular ultrasound image

Coronary artery calcification is also part of atherosclerosis in cardiovascular systems. In this study, we present an automatics deep learning systems to detect the presence and the absent of calcification in intravascular ultrasound (IVUS) images. The standard practice for radiologists and clinicians to detect calcification are using visual inspection. The proposed system used Convolutional Neural Networks (CNNs), named AlexNet model, with six types of classifiers (Support Vector Machine, Discriminant Analysis, Ensembles, Decision Tree, K-Nearest neighbour and Naïve Bayes). The dataset B from MICCAI challenge 2011 consists of 1643 with calcification absent and 530 images with calcification present is used to demonstrate the effectiveness of our proposed automatic deep learning approach. The performance measures recorded are Accuracy, Sensitivity, Specificity, Positive predictive value and Negative predictive value. The performance is compared to the ground truth provided by the MICCAI challenge 2011. For testing and training, the cross-validation k-fold used are 2, 3, 5 and 10. The accuracy of one is obtained using four classifiers namely Support Vector Machine, Discriminant Analysis, Ensembles and k-Nearest neighbour using a Cartesian coordinate image when taken with k-fold=10. This is followed by Decision Tree classifier with an accuracy of 0.9890 using Polar Reconstructed Warp Coordinate images and Naïve Bayes classifier with an accuracy of 0.8464 when using Cartesian Warp coordinate images.
Unsupervised deep feature representation using adversarial auto-encoder

Feature representation is typically applied to reducing the dimensions of high-dimensional data, so as to accelerate the data processing. However, it is difficult to label the data with high dimension for supervised learning, and make use of the information in high-dimensional form directly, which resulting in extensive discussion about unsupervised feature representation methods. In this paper, an unsupervised adversarial auto-encoder network is studied. This network is a probability model that combines generative adversarial networks and variational auto-encoder to perform variational inference, and aims to generate reconstructed data similar to original data as much as possible. First, the architecture and training strategy of adversarial auto-encoder are presented, we attempt to learn a feature representation for high-dimensional data using adversarial auto-encoder. Then a number of comparative experiments are carried out. The comparisons contain six unsupervised feature representation methods performed on eight different publicly available image data sets. Finally, for evaluating their performance, we employ K-means clustering on the low-dimensional feature learned from each algorithm, and select three evaluation metrics including clustering accuracy, adjusted rand index and normalized mutual information, to provide comparisons. Extensive experiments indicate the effectiveness of the learned feature representation via adversarial auto-encoder on the tested data sets.
A Risk Redistribution Standard for Practical Cryptocurrency Payment

Cryptocurrencies are developed as a decentralized and trustless payment system, in which participants should be able to conduct payments across borders with acceptable latency. However, the fluctuation of the exchange rate between crypto and fiat currencies has raised significant concerns and thwarted the prevalence of cryptocurrency payment adoption. Existing solutions require merchants to liquidate the received cryptocurrency on an exchange platform. To compensate for the exchange rate risk, merchants tend to charge a higher price in cryptocurrencies compare to in fiat currency, which dampens the incentive of customers to choose cryptocurrencies as the means of payment. This paper proposes an architecture bolstered by smart contracts to transfer the risk from the merchants to the cryptocurrency issuer. This narrows the gap between prices denominated in cryptocurrencies and fiat currencies, and thus increases the adoption of cryptocurrencies as a payment method. The Ethereum blockchain is chosen as the experimental environment in this work, yet the architecture can be migrated to other decentralized systems without additional efforts. This work devises a novel ERC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> standard to resolve the payment at a predetermined exchange rate that can be employed by any existing cryptocurrency. Immutable events on the blockchain will be generated upon the issuance and settlement of a payment, which are considered as the receipts for granting rights to the merchants to settle the payment at a regular basis. The architecture demonstrates a notable reduction on the exchange rate risk for the merchants, solving the primary problem of cryptocurrency payment adoptions nowadays.