Reviewerly/exHarmony huggingface dataset:
Properties and approximate <i>p</i>-value calculation of the Cramer test

Two-sample tests are probably the most commonly used tests in statistics. These tests generally address one aspect of the samples' distribution, such as mean or variance. When the null hypothesis is that two distributions are equal, the Anderson–Darling (AD) test, which is developed from the Cramer–von Mises (CvM) test, is generally employed. Unfortunately, we find that the AD test often fails to identify true differences when the differences are complex: they are not only in terms of mean, variance and/or skewness but also in terms of multi-modality. In such cases, we find that Cramer test, a modification of the CvM test, performs well. However, the adaptation of the Cramer test in routine analysis is hindered by the fact that the mean, variance and skewness of the test statistic are not available, which resulted in the problem of calculating the associated p-value. For this purpose, we propose a new method for obtaining a p-value by approximating the distribution of the test statistic by a generalized Pareto distribution. By approximating the distribution in this way, the calculation of the p-value is much faster than e.g. bootstrap method, especially for large n. We have observed that this approximation enables the Cramer test to have proper control of type-I error. A simulation study indicates that the Cramer test is as powerful as other tests in simple cases and more powerful in more complicated cases.
Representing Multicloud Security and Privacy Policies and Detecting Potential Problems

As more organizations adopt cloud computing, they are increasingly moving towards a mixture of public, private, and hybrid cloud services and infrastructure. These organizations turn to multicloud, which involves the use of two or more public clouds, to avoid vendor lock-in, overcome latency, mitigate risks, and control costs. The use of multicloud does have some advantages, such as flexibility and redundancy, but comes with some management, security, and privacy challenges as well. To overcome some of the security challenges, organizations would have to capture and analyze security and privacy policies across multiple clouds to ensure the policies are free from errors and enforce them at runtime independent of the cloud provider. In this paper, we present CERBERUS, a framework for representing multicloud security and privacy policies and detecting potential problems in the policies. CERBERUS adopts an object-oriented approach and consists of an ontology and notation, policies, guidelines and rules, and a tool for capturing and detecting policy errors. Using CERBERUS, policies can be analyzed for potential problems, including policy conflicts, inconsistencies, ambiguities, and incompleteness. An application of CERBERUS shows that it indeed helps discover policy errors, that would otherwise go undetected, or in many cases would be detected a posteriori at runtime.
Comparison of search algorithms in Javanese-Indonesian dictionary application

This study aims to compare the performance of Boyer-Moore, Knuth morris pratt, and Horspool algorithms in searching for the meaning of words in the Java-Indonesian dictionary search application in terms of accuracy and processing time. Performance Testing is used to test the performance of algorithm implementations in applications. The test results show that the Boyer Moore and Knuth Morris Pratt algorithms have an accuracy rate of 100%, and the Horspool algorithm 85.3%. While the processing time, Knuth Morris Pratt algorithm has the highest average speed level of 25ms, Horspool 39.9 ms, while the average speed of the Boyer Moore algorithm is 44.2 ms. While the complexity test results, the Boyer Moore algorithm has an overall number of n 26n 2 , Knuth Morris Pratt and Horspool 20n 2 each.
Application Analysis of Big Data in Library Management and Service

Books are the ladder of human progress, while the library is the ocean of books and the storage place of knowledge. There are many kinds of books in the library, and its management and service have always been a big problem. Along with the technological changes in the information age, big data technology has shined in many fields, including library management and services. Applying big data technology to libraries can make information management of books, facilitate book inquiry, sorting and recycling, and improve work efficiency and service level.
Memory Efficient Training using Lookup-Table-based Quantization for Neural Network

Modern neural networks require a tremendous number of parameters, which causes unaffordable requirements of memory and computation resources for embedded systems. To tackle this issue, we propose a LUT-based training method in this paper. The proposed method consists of two components: cluster swap and factorization. Cluster swap is an extension to the quantization process in Deep Compression that overcomes its drawback of unstable training by reassigning each parameter to the proximate cluster. Factorization is a computation trick to reduce the computational cost of neural networks. The experimental results show that the proposed method can decrease memory usage for forward and backward processes to 22.2% and 60.0%,respectively, and reduce the number of multiplications to 11.7%, with 1.41% accuracy loss.
Coefficient of Variation based Decision Tree Classifier for Face Recognition with Invariant Moments

A biometric facial recognition system helps to identify individuals based on their distinctive physiological facial features. Moreover, the biometric identifiers have been more capable and consistent than knowledge or token based techniques in recognizing individuals. The main concept of our work is to develop a facial recognition approach which increases accuracy of classification and identification using a class of decision trees based on coefficient of variation gain as splitting criteria. The body of the paper discusses about the Coefficient of Variation based decision tree classifier (CVDT) using 7 Hu invariant moments, Mean, S.D and CV as feature measures. After discretization and extracting reduct feature set, the proposed criterion classification accuracy was found to be 91%. For the sake of illustration, the proposed system is defined over a random sample of 10 frontal faces chosen from bio id data base.
Advancing clinical cohort selection with genomics analysis on a distributed platform

The affordability of next-generation genomic sequencing and the improvement of medical data management have contributed largely to the evolution of biological analysis from both a clinical and research perspective. Precision medicine is a response to these advancements that places individuals into better-defined subsets based on shared clinical and genetic features. The identification of personalized diagnosis and treatment options is dependent on the ability to draw insights from large-scale, multi-modal analysis of biomedical datasets. Driven by a real use case, we premise that platforms that support precision medicine analysis should maintain data in their optimal data stores, should support distributed storage and query mechanisms, and should scale as more samples are added to the system. We extended a genomics-based columnar data store, GenomicsDB, for ease of use within a distributed analytics platform for clinical and genomic data integration, known as the ODA framework. The framework supports interaction from an i2b2 plugin as well as a notebook environment. We show that the ODA framework exhibits worst-case linear scaling for array size (storage), import time (data construction), and query time for an increasing number of samples. We go on to show worst-case linear time for both import of clinical data and aggregate query execution time within a distributed environment. This work highlights the integration of a distributed genomic database with a distributed compute environment to support scalable and efficient precision medicine queries from a HIPAA-compliant, cohort system in a real-world setting. The ODA framework is currently deployed in production to support precision medicine exploration and analysis from clinicians and researchers at UCLA David Geffen School of Medicine.
Improving the hate speech analysis through dimensionality reduction approach

One of the key challenges during automatic hate speech analysis on the social media is the separation of hate speech from other parts of the offensive language. Normally hate speech analysis or in case of lexical dictionary approach the classifier may produce low precision because they classify the message as hate speech by recognizing some of the well known specific terms. This is the reason traditional algorithms many times fail to recognize the classes properly. This work is based on crowd-sourced hate speech lexicon which can collect tweets containing hate speech keywords as well as dimensionality reduction is also applied to perform classification and this shows how the implemented approach performs better than existing algorithms. This work has classified the tweets into three categories first one is hate speech, second one is offensive language, and the last one is neutral. We train a multi-class classifier to recognize these various classifications. This work has implemented the classification algorithm i.e. logistic regression through dimensionality reduction approach with an accuracy of 83% which is better than other existing algorithms like Naïve Bayes with 71.33% and SVM with 80.56%.
Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Abstract This study critically reviews the IaaS clouds development on revenue maximization since 2012 to answer these research queries; (i) What are the main influential factors towards revenue maximization in the cloud market? (ii) What are the main challenges and resistance towards revenue maximization in cloud computing? and (iii) What are the possible solutions and potentials to these hurdles in cloud computing? The data was analyzed and the influencing factors of revenue maximization were classified into seven distinct categories, that is, the performance of the services, service level agreement and penalties management, resources scalability, resources utilization and scheduling, customers' satisfaction, cost, and pricing management, as well as advertisement and auction. These parameters are investigated in detail and new dynamics for researchers in the field of the cloud are discovered. These studies are compared against each other for the seven distinct categories and solutions are proposed for the clouds' obstacles to revenue maximization. Furthermore, in the light of the findings and revenue maximization categories, the main limitations, challenges, true potential, and new directions towards revenue maximization are explored.
Characterisation of Uranium Mining and Tailings Pond Areas by Integrated Remote Sensing, Geophysical, Geological and Hydrogeological Methods

Remote sensing, geophysical, geological and hydrogeological methods were used to characterise the aquifer of a proposed uranium mining and tailings pond area in India. Examination of lithologs show uranium occurrence at the Eparchaean unconformity at a depth of about 50 to 60 m below the surface. Resistivity studies showed four distinct zones- top soil, highly weathered, moderately weathered/fractured rock and massive rock. Wells tap groundwater from the weathered and fractured zones. Highly weathered nature of the intrusives, dykes and faults do not allow them to act as barriers to groundwater flow. Rainfall is the major source of groundwater recharge apart from irrigation return flow. Recharge to unconfined aquifers is primarily by downward seepage of rainfall through the unsaturated zone. Groundwater flows towards southeast. Pumping tests resulted in hydraulic conductivity of this aquifer ranging from 5.4 to 19.3 m/d. Infiltration rates were obtained at 1.5 and 2.3 m/d for clayey residual unconsolidated materials and 3.7 and 5.2 m/d for sandy residual unconsolidated materials. Secondary data were used to verify the results of field tests and investigations. Results from this study were also successfully used in radionuclide and geochemical modelling of the effects of leaching from the tailings pond on groundwater quality and its impact on human health. This detailed study provides a wider understanding of the sub-surface characteristics of the proposed uranium mining and tailings pond area which offers useful information for future research and development activities.
The Group Action on the Finite Projective Planes of Orders 29, 31, 32, 37

The goal of this research is to study the group effects on a projective plane PG (2,q), when is not a prime, and then describe the geometry of these orbits by Singer group for values of q=29,31,32,37 . Also, to establish three dimensional codes and arcs and study the properties of subsets in a projective plane of order q.
Towards Understanding Adaptation Latency in Self-adaptive Systems

An important feature of service-based and cloud-based systems is their ability to perform self-adaptation. Through self-adaptation, such systems can automatically react to changes and thus ensure the continued satisfaction of their functional and non-functional requirements. Self-adaptation may take non-negligible time (which we term adaptation latency), and during this period the self-adaptive system may exhibit degraded performance or other negative impact. Hence, it is important to understand how long self-adaptations take and what influences the adaptation latency. However, we are not aware of a systematic study of this question in the literature. This paper is a first step in this direction. We present (i) a model of adaptation latency that breaks it down into four components and (ii) a preliminary survey, limited to one conference series and to service-based and cloud-based systems, to analyze information about adaptation latency in the available literature on self-adaptive systems. According to the findings from this preliminary survey, although some components of the adaptation latency are studied in some publications, the whole adaptation delay is seldom considered.
Efficient multiquality super‐resolution using a deep convolutional neural network for an FPGA implementation

Abstract We propose an efficient deep convolutional neural network for a super‐resolution which is capable of multiple‐quality input, by analyzing the input quality and choosing appropriate features automatically. To implement the network in an FPGA and an ASIC, we employ a network trimming technique to compress the neural network.
Towards Optimal VLAD for Visual Recognition

The Vector of Locally Aggregated Descriptor (VLAD) has achieved great success in a broad range of computer vision tasks and shows its great effectiveness in diverse computer vision tasks. However, it remains unclear about the mathematical foundation of the VLAD and the theoretical connection to basic Algorithms is a theoretical contribution to the practical use of VLAD. And to achieve optimal VLAD for visual recognition, we provide a new formulation of VLAD from a new perspective of match kernels to connect with existing important encoding methods, and we have made a thorough investigation of the roles and effects of the two operations widely used in local feature encoding. What's more, our comprehensive study on VLAD will not only enable to improve the performance but also offer a guide for state-of-the-art algorithms based on local features.
Robust Tracking for Motion Blur Based on Correlation Filter

Correlation filter methods show good performance in robustness, accuracy and speed in visual tracking. In this paper, we propose a new algorithm based on Kernelized Correlation Filter (KCF) to effectively improve performance on motion blur. We adopt a simple and effective image deblurring method based on dark channel prior, combined with correlation filter to form a new Deblurring Correlation Filter (DECF) tracking algorithm. Extensive experimental results on OTB2015 show that our tracker demonstrates the excellent accuracy on tracking with motion blur.
A Proposed Arabic Text and Text Image Classification Technique Using a URL Address

Sentiment analysis is examined within natural language processing. It is instrumental in finding the sentiment (feeling) or opinion (idea) hidden within a text. This research focuses on finding sentiments in a “text image” and then classifying them whether they are desirable or not. These phrases and words refer to the perspectives of people about anything they think about it, such as services, products, governments, and social media events. In this study, the optical character recognition (OCR) algorithm was used, which is considered as a classification procedure of visual patterns that appear in the form of a digital image. Moreover, the Naïve Bayes machine learning algorithm was employed to classify these texts. These two algorithms form a hybrid system that supports our needs, especially in this day of technological advances and frequent use of websites and sharing of text images through the internet. Finally, the new vision in this work involves dealing with Arabic language texts that are transformed into images, which are extracted from a URL address and then classified into desirable and undesirable content.
Decentralized Linear Quadratic Systems With Major and Minor Agents and Non-Gaussian Noise

A decentralized linear quadratic system with a major agent and a collection of minor agents is considered. The major agent affects the minor agents, but not vice versa. The state of the major agent is observed by all agents. In addition, the minor agents have a noisy observation of their local state. The noise process is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> assumed to be Gaussian. The structures of the optimal strategy and the best linear strategy are characterized. It is shown that the major agent's optimal control action is a linear function of the major agent's minimum mean-squared error (MMSE) estimate of the system state while the minor agent's optimal control action is a linear function of the major agent's MMSE estimate of the system state and a “correction term” that depends on the difference of the minor agent's MMSE estimate of its local state and the major agent's MMSE estimate of the minor agent's local state. Since the noise is non-Gaussian, the minor agent's MMSE estimate is a nonlinear function of its observation. It is shown that replacing the minor agent's MMSE estimate with its linear least mean square estimate gives the best linear control strategy. The results are proved using a direct method based on conditional independence, common-information-based splitting of state and control actions, and simplifying the per-step cost based on conditional independence, orthogonality principle, and completion of squares.
Detection of Intoxicated Person using Thermal Infrared Images

The proposed method helps in the identification of an intoxicated person from thermal images. A significant change in the body temperature and irregular blood flow after consumption of alcohol generate some textural changes along the areas of eyes, face, hands, and ears of a person. Textural changes help in identifying temperature distribution along the different regions of the face and other parts of the body. Different changes in facial blood veins can be observed as well. For this paper, Non Sub-sampled Contourlet Transform (NSCT) is used to extract the texture features from thermal images of eye, face, hand, and ear. An unsupervised feature selection technique is utilized for feature optimization which is followed by classification using Support Vector Machine. Extensive experiments prove the efficiency of the proposed work.
Entanglement dynamics in dissipative photonic Mott insulators

We theoretically investigate the entanglement dynamics in photonic Mott insulators in the presence of particle losses and dephasing. We explore two configurations where entanglement is generated following the injection or extraction of a photon in the central site of a chain of cavity resonators. We study the entanglement negativity of two-site reduced density matrices as a function of time and inter-site distance. Our findings show that in spite of particle losses the quantum entanglement propagation exhibits a ballistic character with propagation speeds related to the differerent quasiparticles that are involved in the dynamics, namely photonic doublons and holons respectively. Our analysis reveals that photon dissipation has a strikingly asymmetric behavior in the two configurations with a much more dramatic role on the holon entanglement propagation than for the doublon case.
A Fast Optimization Algorithm for Nearest Neighbor Architecture Based on Quantum Weight

Optimization based on QC (quantum cost) for quantum circuits and the LNN (Linear Nearest Neighbor) Architecture are the bases for mapping quantum logic circuits into physical systems. The correlation between the underlying optimization and the upper layer of quantum logic design is an important issue that needs to be emphasized during the mapping process. In order to improve the efficiency and robustness for the above problems, a fast optimization algorithm for LNN architecture based on quantum weight is proposed. Based on quantum priority, a wire sequence initialization method is designed. Then the neighboring arrangement rule of the NCV gate library circuit and the optimization algorithm are given. The experimental results show that the proposed strategies can effectively reduce the QC and the number of inserted SWAP gates. The average optimization rate reaches 12.3% compared to the corresponding results.
Implementation of Biological Sources of Energy in the System of "Smart Clothes"

The article is devoted to problems of powering embedded devices of "smart clothes".The growth rates of interest in bioenergy energy sources and their variety in the world are shown.The authors analysed the technical and energy indicators of the human body as an energy source for various types of portable devices.The technical characteristics of possible energy converters, which are suitable for this application, are described.Schematic solutions for powering portable devices based on ready solutions are proposed.
Encryption Video Using NTRU and Chaotic Algorithms

The rapid development of most multimedia technologies led to the creation and transmission of much of this type of data in various fields, including medical, military and commercial, which contain sensitive information that can only be accessed by the competent authorities. Because of this, privacy has become vital. Many encryption algorithms have been used to provide security for video transmission. In this paper, NTRU and chaotic algorithms are used for video encryption, which includes sensitive information. The system is designed to encode AVI, WMV, MOV, MP4 and other video file formats. This type of video is encrypted using the NTRU algorithm and decrypted using chaotic algorithm to ensure a high level of strength and security, thus speeding up the encryption process and returning the video to its original location without losing any data. Visual Basic .net 2015 was used to implement this system.
On the New Challenges Faced by Library Book Acquisition in the Age of “Internet +”

This study analyzes the dilemma faced by the library industry in China in the field of book interviews from the perspective of the “Internet +” era. The process of the book interview is cumbersome and rigid, and the channel resources for book interviews are relatively fixed. In response to these problems, we seek solutions for “Internet +” thinking, discuss cloud technology, effective reader interaction mechanism, and streamline the use of workflow in book interviews, which provides a useful reference for improving the quality and efficiency of library procurement.