Reviewerly/exHarmony huggingface dataset:
An Object IQA Framework Based on Saliency Map Similarity

Objective image quality assessment (IQA) aims to establish a mathematical model on computer to simulate human perception for image to evaluate the quality of it. This paper analyzes the image characteristics for IQA, from which gradient, spectrum, and color are selected as indexes to establish a novel IQA framework. The gradient similarity, spectral similarity and color similarity are fully utilized, and the similarity calculation results are weighted by the spectral saliency map to obtain the final evaluation result. The experiments were conducted on both TID2008 and TID2013 dataset respectively. Through comparison, it is found that the evaluation algorithm proposed in this article can not only ensure the robustness of the evaluation result, but also perform a lower algorithm complexity.
Extended Adjacent Local Binary Pattern for Texture Based Image Retrieval

Abstract In this paper, a novel local texture descriptor named Extended Adjacent Local Binary Pattern (EALBP) for images is proposed. In this method, each image is subdivided into local regions of size 4*4. For each local region, 13-bits of binary value are generated by comparing the adjacent neighborhood pixel in three phases. In the first phase, the inner 2x2 region from the local region is considered and each pixel is compared with the adjacent pixel which results in 4 bits. In the second phase, the outer region from the local region is considered and the diagonal pixels present in the 4 directions such as 45°, 135°, 225° and 315° are considered. The pixels values present in each direction are compared with the adjacent neighborhood pixels in the outer region and this comparison results in 4 bits. In the third phase, the pixels except the diagonal pixel values in the outer region are compared against each other and this result in 4 bit binary value. Finally, the mean of the local region is compared with the mean of the input image which results in one bit binary value. The 13-bit binary value of each local region is thus formed and the above steps are repeated for the entire image. The experimental results show that the proposed method could characterize the texture property of the image efficiently when compared with the existing methods on two databases.
Single-photon generation of entangled triplet state in an atomic spin dimer

Abstract We show that the entanglement between two atomic spins, coupled via XY interaction, can be achieved with the aid of a single photon in a controllable manner. Assuming a constant spin-photon coupling, two distinct cases are considered — excitation with a constant and with a linear time-dependent photon frequency. We demonstrate that these problems reduce to the study of the well known Jaynes-Cummings and Jaynes-Cummings – Landau-Zener models, respectively, where the two-level system is formed by the zero-momentum mode states of the dimer. Owing to the exact solubility of both models, by tuning the relevant parameters, one can design and coherently control the dynamics of the excitation process. For instance, one can adiabatically switch from an atomic spin system initialized in a state with both spins down to the maximally entangled triplet state.
A Case Study on Experiment Site Selection for PV Energy Generation Forecast

Renewable energy from solar and wind energy systems is highly dynamic depending on different meteorological conditions. This paper focuses on developing a timing-intensive meteorological data management system (MDMS) for selection of a suitable experiment site of PV energy power forecasts. Intuitively, the factors of affecting renewable-energy power forecasts include solar radiation, wind speed, wind direction, temperature, and humidity. In practice, it needs to create meteorological records for every 5-10 minute collected from multiple data sources as a basis of renewable power forecasts using deep learning networks (DLNs). To meet evolving mission needs of project, the MDMS design incorporates MVC architecture and Laravel scheduling tool to software development that allows developers effectively perform program maintenance. Finally, a better experiment site of PV energy forecasts is decided using the MDMS by comparing the prediction errors of between power generation output vs. solar irradiation of each inverter in each PV experiment site.
A User-Side POIs Mobile Recommender System

Recommending pertinent Place Of Interests (POIs) is a desirable feature for mobile users, and which is generally served by for-profit proprietary platforms, such as Yelp, TripAdvisor, etc. However, the siloed design of these platforms raises today several issues about privacy, user data portability, and algorithm transparency. To address these issues, we propose a decoupled recommender system (RS) architecture. The idea consists of externalizing the sensitive features, such as the users’ preferences and the underlying RS algorithm, from the service’s application. Hence, the proposed RS operates as an interchangeable third-party service. We conducted several experiments to evaluate the impact of the decentralization, and we were able to improve the performances by relying on Linked Open Data (LOD) and on an appropriate similarity measure.
Beyond Rectangle Boundingbox: Visual Tracking Using Characteristic Points

Visual Tracking plays a key role in computer vision application and artificial intelligent research. The main representation of tracking results come to rectangle boundingbox leading to inaccurate performance, which may not meet the requirements of computer vision application nowadays. We are more likely to obtain the pose estimation of tracking objects in order to achieve more complex computer vision mission including behavior detection and video analysis. While, rectangle boundingbox representation mostly dominates the output model when describing the target appearance in existing DNN-based trackers, leading to precision. We introduce the characteristic points(ChaPoints) to represent the both feature extraction and output model in order to accomplish pose estimation during training and tracking procedure. To this end, we build the mapping method between our proposed characteristic points and rectangle boundingbox. The multi-branches Siamese networks can further matching the candidates and the templates for final tracking results. We extensively prove the effectiveness of the proposed method through the ablation studies of the tracking benchmark, including OTB-2015 and UAV123.
Multi-level display for digital power grid based on homeomorphic transformation and hierarchical graph model

The popularization and application of three-dimensional digital technologies in power grids and the development of digital visualization technologies provide possibilities for visualized operations of power grid. The research of combining the digital achievements of power grid engineering and visualization technology of digital power grid multi-level display can effectively provide power grid visualization support and solve the actual work needs for the operation and maintenance. In this paper, a graph model based on homeomorphic transformation of the power grid topology is proposed to simplify and stratify the network structure of the grid. Subsequently, the contents and forms presented in the digital grid under three different levels of macro, meso and micro are studied from both topological and information hierarchy, respectively. Next, this paper classifies the key nodes of the power grid based on the graph theory, from which the primitive graph model is progressively simplified to form three abstraction level topological models: primitive, skeleton and geographic. The reverse application of these three topological models can gradually realize the hierarchical display of digital power grid. Finally, Jiangsu Power Grid was taken as the application object to demonstrate the prototype of multi-level display for digital power grid.
Address-Based Signature

In most applications using digital signatures, public keys are used to verify the signatures. The public keys are authenticated by a trusted certificate authority. However, this chain of trust is fragile in the real world. Systems like blockchain and eXpressive Internet Architecture (XIA) proposed to use the hash of a public key as an address. Then signatures are verified against addresses. In this paper, we first formalize the notion of address-based signatures to analyze the security of these address-based systems. We give a strong model which considers the security of multiple addresses, with respect to attackers who even know the randomness used by the system developers. The formalization of security model is important to understand the security of real world system. We propose an efficient and secure construction of address-based signature, which can overcome the existing problems of address-based ECDSA and Schnorr signatures. Then we give two generic constructions of address-based signatures. We find out that these solutions are either not efficient enough (in terms of signature size and address size) or not secure enough.
Research on Image Deblurring Method of Transmission line for Unmanned Aerial Vehicle Inspection based on Scale Cyclic Network

In the process of UAV inspection, jitter often occurs, which leads to motion blur of camera when shooting images. Image blur will cause image content distortion, and even lead to the loss of edge information of target objects. The lack of image detail information will make the subsequent image processing extremely difficult and reduce the accuracy of image target detection, which hinders the popularization and practicability of UAV inspection technology. In this paper, the generation mechanism of image motion blur is studied, and a scale cycle network based image deblurring method is proposed. In this method, the image deblurring is divided into three sub problems, and three scale networks are used to realize image deblurring step by step, and LSTM network is used to realize sharing and continuous optimization of network parameters of different scales. Experimental results show that the proposed method can effectively remove image blur and suppress image ringing effect. Compare to the traditional methods, more complex motion blur problems can be solved.
Evolution of Bitcoin Addresses from Security Perspectives

Since Satoshi Nakamoto launched bitcoin in 2009, there have been many improvements in bitcoin addresses that are required to fulfill newer requirements. These improvements have made the learning curve steeper for a regular user. Many readers do not know the real motivation why certain features are implemented or otherwise. In this paper, the evolution of bitcoin addresses is clearly described so that users can better understand the progress made in bitcoin. This paper also explains the motivation for some features' motivation and why these features were implemented in a certain way.
An improved data integrity validation model for cloud storage

Data integrity validation is considered to be an important tool to solve the problem that cloud subscribers cannot accurately know whether there are non-subjective changes in the data they upload to cloud servers. In this paper, a data integrity verification model based on dynamic successor tree index structure, Bloom filter and Merkle tree is proposed. The block labels generated according to the features of the dynamic successor tree index structure can sense whether changes have been made to the user's data, while the Merkle tree can track the cha * nged data blocks, enabling the user to effectively verify the integrity of the data stored in the cloud server and provide more effective protection for data.
Device position estimation for Multi-tablet virtual display system

Several studies have proposed composing a virtual single display using the displays of several devices and acquiring the relative position information of each device from images taken by a camera. In these studies, each device 's position information is acquired using markers and image features. However, the marker method obstructs the content displayed on the device, and the image feature method cannot acquire position information when the same content is displayed on multiple devices. Therefore, we will propose and evaluate a method of acquiring device position information by using digital watermark-like patterns.
Sub-band identification of speech signals word fragments according to given sample

Abstract The article considers the problem of the speech signals fragments, generated when pronouncing a certain word form, emphasis in the recordings, which is of the interest from the position of the speech information exchange analysis solvable applied problem. In this case, it is assumed that initially the class of the desired word form is given by the fragment available in the real speech signal recording. Therefore, it is natural to term the problem under consideration as precedent identification. The relevance of the methods and algorithms development for automatic precedent identification of voice recordings fragments in such situation is determined by the breadth of their possible applications, for example, in information and analytical security systems. The variability of the speech signals fragments properties, even generated when pronouncing the same word form by the same person, and the need to learn from one precedent in determining the critical areas of decisive functions are the main factors that determine the complexity of solving this problem. It is shown in this paper that sub-band analysis is an adequate basis for solving the problem under consideration, and an original mathematical apparatus was developed for its implementation. On the basis of original sub-band representations, the decision procedures for identification of speech signals recordings fragments, including the selection of pauses between word fragments, were developed. In particular, training procedures were proposed for one precedent while maintaining its original sub-band properties.
Using cloud storage to support reminiscence

We consider the problem of people having trouble retrieving their important reminiscence, it's may get buries in the countless folders. In this study, we present the future of sorting out important reminiscence data using cloud services. Based on our respondents we find out a lot of people having trouble with cloud storage for keeping their reminiscence. To solve this problem we are trying to find the most suitable cloud computing model to support reminiscence and create a cloud services application with the chosen cloud model and the QR algorithm. The result shows that this application is feasible to be made and tried by customers and this app supported by the SaaS model which has a low price and it is easily used for reminiscence purposes because users can upload and easily lookup for the data again.
Cybersecurity Counter Unmanned Aircraft Systems (C-UAS) and Artificial Intelligence (AI)

The risk of successful terrorist attacks on US Air Defense Systems by small unmanned aircraft system (sUAS)/UAS is growing because of increased commercial capabilities and accessibility of advanced small drones. Commercial and military UAS are integrally linked to payload, navigation, communications, and control linkages from ground, air, or satellite. UAS are being designed with increasingly advanced artificial intelligence (AI) and automation capabilities that can be both beneficial and harmful. The increased automation and AI capabilities can be used to complete dull, dangerous, and dirty missions in all weather conditions and are capable of longer flying times and endurance at more effective altitudes. The cyber risk analyst’s mission when flying a UAS against hostile forces is to determine the risk, threats, vulnerabilities, impact, and CMs that may apply in an attack/defense scenario. In the arena of cybersecurity and information, much depends on the correct evaluation of risk potentials.
Research on Distributed Storage Data Consistency Control Method Based on Pattern Recognition

The traditional consistency control method of distributed storage data, the recognition ability of the selected algorithm is poor, which leads to the low consistency control index. According to the relay node, the implementation control network is deployed; the consistency algorithm of distributed storage data is designed according to the consistency constraint rules; and the consistency of distributed storage data is controlled by constructing a control model based on pattern recognition. Experimental results: compared with traditional methods, this research method has stronger recognition ability and higher consistency control index. It can be seen that pattern recognition can effectively classify data and meet the requirements of this research.
Crack Detection Method of Shadow Asphalt Pavement based on Cuckoo Search

The daily detection of shadow asphalt pavement cracks, there are multiple sets of zero points in the asphalt pavement image, which causes the detection process to take too long. Therefore, a method of shadow asphalt pavement crack detection based on cuckoo search is studied. According to the three-dimensional laser data acquisition system, the asphalt pavement crack characteristics are extracted. The zero pixel of the adjacent image area is processed by median filter. The cuckoo search algorithm is used to complete the crack search, and the crack skeleton is extracted to complete the crack detection of the shadow asphalt pavement. A section of asphalt pavement data collected by wireless laser in a certain area is used as experimental data. Three different asphalt crack samples are prepared. Two traditional crack detection methods and the crack detection methods studied in the article are used to conduct experiments. The results show that the proposed crack detection The method requires the shortest detection time.
Incremental Sensitivity Analysis for Kernelized Models

Despite their superior accuracy to simpler linear models, kernelized models can be prohibitively expensive in applications where the training set changes frequently, since training them is computationally intensive. We provide bounds for the changes in a kernelized model when its training set has changed, as well as bounds on the prediction of the new hypothetical model on new data. Our bounds support any kernelized model with \(L_2\) regularization and convex, differentiable loss. The bounds can be computed incrementally as the data changes, much faster than re-computing the model. We apply our bounds to three applications: active learning, leave-one-out cross-validation, and online learning in the presence of concept drifts. We demonstrate empirically that the bounds are tight, and that the proposed algorithms can reduce costly model re-computations by up to 10 times, without harming accuracy.
A Road Marking Detection System Using Partial Template Matching and Region Estimation by Deep Neural Network

We improve the performance of a road marking detection system by incorporating You Only Look Once (YOLO) into the processing for vehicle location estimation in autonomous driving technology. The conventional detection method uses a template matching process based on luminance values to detect road marking. However, there are some markings that cannot be detected by this method due to halation by sunlight or strong blurred of road markings. In contrast, the proposed method uses YOLO to search for areas where road marking exists and restricts the area of adaptation for template matching. Owing to this area restriction, the proposed method can prevent the occurrence of false detection, lower the detection threshold for template matching, and reduce the number of previously undetected road markings. In addition, the search area for template matching is restricted, which also can improve the processing speed. Experimental results show that the proposed method is able to reduce the number of undetected road markings compared to the conventional method while keeping the number of false detections to zero. The accuracy of the system was improved by 0.013 and the processing speed was increased by 4.6 FPS compared to the previous method.
Effect of Head-Mounted Augmented Reality Devices on Electric Utility Manhole Workers: Neck Muscle Activity and Eye Blink Rate

Two head-mounted augmented reality (AR) systems, Microsoft HoloLens and RealWear HMT-1, were tested to determine their effect on blink rate and muscle activity of the neck and shoulder muscles of electric utility manhole workers. The task of splicing a cable was performed under three conditions: HoloLens, HMT-1, and No AR (normal). Surface electromyography (sEMG) of the right and left sternocleidomastoid, splenius, semispinalis capitis, and upper trapezius muscles were measured on 13 manhole workers, and a small camera recorded blink rate of the right eye. Results revealed, in general, no significant differences in 50 th and 90 th percentile sEMG between the three conditions. There was no difference in blink rate between the HMT-1 and No AR, but the HoloLens blink rate was 7.8 to 11 blinks/min lower than the HMT-1 for two of the three tasks. A decrease in blink rate of these magnitudes may indicate risk of eye strain to manhole workers who use an OST AR device without appropriate rest breaks. Head-mounted AR devices deployed for underground utility workers warrant further study.
Minerals of the Old Reliable and Childs-Aldwinkle Mines, Copper Creek, Pinal County, Arizona

Figure 1. Surface workings of the Old Reliable mine in the 1940s. Note the outcrop of the breccia pipe in the upper right. Photo courtesy of the Arizona Geological Survey photo archive. Figure 2. L...
Quantization coefficients for uniform distributions on the boundaries of regular polygons

In this paper, we give a general formula to determine the quantization coefficients for uniform distributions defined on the boundaries of different regular m-sided polygons inscribed in a circle. The result shows that the quantization coefficient for the uniform distribution on the boundary of a regular m-sided polygon inscribed in a circle is an increasing function of m, and approaches to the quantization coefficient for the uniform distribution on the circle as m tends to infinity.
A Low Power Consumption and High Reliability Distributed Cold Storage System

In the era of the intelligent Internet, data is exploding and growing in geometric quantities. The storage of these data has become a hot issue in the current storage field. According to statistics, only a small part of these massive data is frequently accessed, and most of them will gradually become cold after being generated. The traditional data storage method solves the problem of large-scale data storage, but there are still problems of high energy consumption and high cost in cold data storage scenarios. This paper optimizes the storage scenario of the cold data, and then uses the server to control the power switch of hard disk to hibernate the hard disk where the data accessed at a low frequency, thereby reducing the overall power consumption. At the same time, in order to ensure the reliability of the data, we add scanning module and file reconstruction module, design and implement a low-power and highly reliable distributed cold storage system.
The Faculty Building about Voice Teachers of Pop Songs in Southwest University, China

A Study of the Undergraduate Curriculum for Voice Major Program in Popular Songs in Selected Normal Universities in China."The objectives are to improve the construction of voice teachers in the pop singing direction about undergraduate stage in Southwest University.The study employs the qualitative research methods and data from the fieldwork