Reviewerly/exHarmony huggingface dataset:
Refined Management of College Financial Budget Based on Cloud Computing Technology

With the rapid development of higher education in China, the number of school affairs departments has increased, and management issues have followed one after another. The content of financial budget management has become increasingly complex and increasingly difficult. In this development background, if scientific financial budget management methods are not adopted, the entire budget management will be in a very chaotic state, which will seriously affect the normal operation of funds, let alone optimize the resource allocation of universities. It is not difficult to imagine that the traditional extensive budget management method can not keep up with the times. Without the introduction of the first-pass management model, it will not be conducive to the improvement of the efficiency and effectiveness of financial accounting in colleges and universities. The purpose of this paper is to study the fine management of college financial budget based on cloud computing technology. This paper uses cloud computing as the method, and based on the relevant realistic background and theory, a specific university in Nanchang City, Jiangxi Province is the specific research object, from the establishment of refined management concepts, the establishment of scientific budget management goals, and information environment elements As a basic method to realize the refinement of financial budget, on this basis, an optimization path of refined management is proposed to improve the efficiency of college financial work. The research results show that, compared with the traditional management mode, the refined management of college financial budget proposed in this paper can reduce a lot of time cost, manpower investment, and can effectively manage college financial budget.
Observation and Analysis of the Electrical Characteristic and Precipitation Particle Distribution of a Thunderstorm Process in Yun-Gui Plateau Region

Using X band dual line polarization radar data, atmospheric electric field data and VLF/LF three-dimensional lightning localization data, the electrical characteristic of a thunderstorm process occurring in the platform of the Yunnan-Guizhou Plateau were analyzed in detail, and the precipitation particles in the cloud were inverted based on the fuzzy logic hydrometeor classification(FHC). The results show that: (1) The thunderstorm was tripole charge structure in the development and maturity stage, and there was a certain range of positive charge region at the bottom of thunderstorm cloud. The height of the positive cloud flash was between 1.66˜24.7 km, and the height of the negative cloud flash was between 1.0˜17.12km.The cloud flash mainly occured between the lower positive charge region and the middle negative charge region,.The vertical distribution of the thunderstorm charge structure can be understood by the height distribution of the cloud flash radiation source polarity. (2) In the development and maturity stage of thunderstorm, there were some differences in the number and distribution height of different particles in thunderstorm cloud. The evolution characteristic of graupel particles and ice crystals were consistent with the development and maturity stages of thunderstorm. Combined with atmospheric electric field and lightning location data, the evolution of graupel particle and ice crystal particle was closely related to the electrification of thunderstorm. (3) It can be observed from the vertical distribution characteristic of different particles that the strength of the positive charge region in the lower part of the thunderstorm cloud was most likely determined by the number of graupel particle. The graupel-ice charging mechanism can better interpret the formation of the tripole charge structure of thunderstorm. (4) The cloud flash source information detected by the VLF./LF three-dimensional lightning location system has a certain indication on the general distribution of the thunderstorm cloud charge region. It is necessary to dig deep into the cloud flash information in the future to provide support for the thunderstorm charge structure research
Data-driven acceleration of first-principles saddle point and local minimum search based on scalable Gaussian processes

The potential energy surface (PES), the mapping from high-dimensional space of atomic positions to the free energy level of a material system, is important for building structure–property relationships at the atomistic level. The PES can be constructed by calculating the system energy with first-principles approaches such as density functional theory (DFT). Searching for local minima, saddle points, and minimum energy paths (MEPs) on the PES is the essential task in functional materials design. But it is very challenging because of the curse of dimensionality and the numerical approximation errors involved in constructing the PES. In this chapter, a first-principles local minima and saddle points searching method based on a scalable Gaussian process (GP), called GP-DFT, is described. The searching algorithm is performed based on the DFT calculation and a concurrent searching method for MEPs assisted by the GP surrogate model that approximates the PES. The concurrent searching method is able to locate multiple MEPs on the PES simultaneously. The surrogate model is composed of multiple local GPs, where each corresponds to a subset of observations in the complete dataset. The potential energy is then predicted using a weighted linear average scheme of the posterior means for each local GP. The scalable GP scheme is developed to alleviate the computational bottleneck of the classical GP method. The uncertainty associated with the potential energy prediction is quantified through the posterior variance of the surrogate model. Two computational materials examples of studying hydrogen embrittlement in Fe and FeTi systems are used to demonstrate the scalability and efficiency of the proposed searching algorithm.
The Application of Intelligent Classroom in College English Teaching in the Era of “Internet +”

In the actual teaching process, the application of “Internet +” technology, through the construction of corresponding smart classroom, to continuously improve the overall level of College English education, has a very positive significance for improving the professional ability and professional quality of students. “Internet + Intelligent Teaching” breaks the limitation of traditional teaching environment, makes college English teaching face new development space, brings more innovative characteristics and teaching innovation possibilities. Teachers must constantly learn and improve their own cultural literacy teaching skills, so that they can strongly stimulate students’ innovation consciousness ability, have a comprehensive understanding of students’ spontaneous innovation potential, better stimulate students to generate innovative thinking, and help students to develop the habit of innovative thinking. Taking this as the core, this paper explores the innovative teaching mode of “Internet + Intelligent Teaching” in College English.
Adaptive Denoising of Images using Single Layer Neural Network and Normalized Signed Regressor LMS Algorithm

Denoising of images is very important aspect during image enhancement process. In this paper, a novel image denoising model based on adaptive threshold estimation using functional link artificial neural network (FLANN) is proposed. The proposed FLANN structure is trained by Normalised Signed Regressor LMS Algorithm (NSRA). The denoising model uses a second-order difference operator and median filtering that work on only degraded pixels. The method is tested with different test images under various noise ratios that the performance of proposed technique provides a significant improvement over other existing methods.
Motion Vectors Estimation for Video Encoding Based on the Macroblock Signatures

To solve the problems of motion compensation during video encoding, algorithms are used that minimize the prediction error, based on either the sum of the absolute differences of pixels or frequency values after the Hadamar transform. The paper considers an alternative algorithm for finding the best approximation of the reference macroblock to the estimated one. For the search, the evaluated macroblock is presented in the form of a unique signature. To build a unique signature, the original macroblock of the video image is divided into microblocks. For each microblock, the coordinates of the center of mass are found. Depending on the required accuracy of the estimation of the motion vectors, the coordinates of the centers of mass are determined with an accuracy of a pixel, a half of pixel or a quarter of pixel. The set of coordinates of the positions of the microblocks mass centers defines an unique macroblock signature. As a criterion for the quality of the approximation of the estimated macroblock, the reference set is similar to the signatures: the mutual shift of the positions of the mass centers of the microblocks is minimized. The proposed algorithm is well vectorized and may be used for hierarchical estimation of motion vectors.
Gaussian Mixtures Match and Fusion Algorithms for Multi-Sensor Multi-Target Tracking

The results of single sensor Gaussian Mixture PHD Tracker (GM-PHDT) will deteriorate when tracking targets in dense clutter and low detection rate environments. In this work, we propose three multi-sensor multi-target GM-PHD fusion algorithms. For the proposed algorithms, we obtain the multi-sensor posterior GMs by fusing the matched GMs from multiple sensors. Specifically, the GM matching and fusing methods are proposed to fullfill the applicable GM-PHD algorithms. The simulation results demonstrated the efficiency of the proposed algorithms
Analysis of Classroom Teaching Status Based on Target Detection Model

The research and evaluation of classroom teaching quality has always been an important concern of education. Traditional classroom teaching quality evaluation mainly depends on human subjective judgment.It is time-consuming, laborious and inaccurate to some extent.The rapid development of the Internet has brought new progress to the wisdom classroom. This paper aims at the classroom teacher's teaching action recognition,we use the deep convolutional neural network to build the target detection model, classifies the behavioral actions of the classroom teaching.It is more objectively for judging the teaching quality and more convenient for making appropriate improvements. The model consists of an input layer, a two-layer convolutional layer, a two-layer pooling layer, a Batch Normalization layer, and an output layer, which can effectively extract data features and achieve better classification and recognition effects.
A Research on Information Hiding Algorithm Based on Frequency Blocks of DCT Coefficients

There are two embedded methods of computer information hiding, the time-domain method and the frequency-domain method, and the time-domain method has a large amount of information hiding but poor robustness, while the frequency-domain method has a small amount of information hiding but strong resistance to attack. The DCT information hiding technique used in this paper is a common frequency-domain information hiding technique, which is widely used because of its strong robustness. In the process of information embedding, how to choose the embedding position of information determines the hiding amount and robustness of information. Based on the DCT domain information hiding technology and MATLAB 7.0 as the experimental platform, this paper carried out experiments on the low, medium and high frequency coefficients of the image DCT domain, proposed the strategy of how to select the information hiding location, and implemented the corresponding embedding and extraction algorithms. The experimental results showed that the algorithm in this paper had certain anti-aggression.
Using SDP to Parameterize Universal Kernel Functions

We propose a new class of universal kernel functions which admit a linear parametrization using positive semidefinite matrices. We refer to kernels of this class as Tessellated Kernels (TKs) due to the observation that if applied to kernel-based learning algorithms, the resulting discriminants are defined by continuous piecewise-polynomial functions with hyper-rectangular domains whose vertices are determined by the training data. The number of parameters used to define these TKs is determined by the length of an associated monomial basis. However, even for a single monomial basis function the TKs are universal in the sense that the resulting discriminants occupy a hypothesis space which is dense in L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . This implies that the use of TKs for learning the kernel (aka kernel learning) can obviate the need for Gaussian kernels and associated problem of selecting bandwidth - a conclusion verified through extensive numerical testing on soft margin Support Vector Machine (SVM) problems. Furthermore, our results show that when the ratio of the number of training data to features is high, the proposed method will significantly outperform other algorithms for learning the kernel. Finally, TKs can be integrated efficiently with existing Multiple Kernel Learning (MKL) algorithms such as SimpleMKL.
Learning Domain-Specific Grammars from a Small Number of Examples

In this chapter we investigate the problem of grammar learning from a perspective that diverges from previous approaches. These prevailing approaches to learning grammars usually attempt to infer a grammar directly from example corpora without any additional information. This either requires a large training set or suffers from bad accuracy. We instead view learning grammars as a problem of grammar restriction or subgrammar extraction. We start from a large-scale grammar (called a resource grammar) and a small number of example sentences, and find a subgrammar that still covers all the examples. To accomplish this, we formulate the problem as a constraint satisfaction problem, and use a constraint solver to find the optimal grammar. We created experiments with English, Finnish, German, Swedish, and Spanish, which show that 10–20 examples are often sufficient to learn an interesting grammar for a specific application. We also present two extensions to this basic method: we include negative examples and allow rules to be merged. The resulting grammars can more precisely cover specific linguistic phenomena. Our method, together with the extensions, can be used to provide a grammar learning system for specific applications. This system is easy-to-use, human-centric, and can be used by non-syntacticians. Based on this grammar learning method, we can build applications for computer-assisted language learning and interlingual communication, which rely heavily on the knowledge of language and domain experts who often lack the competence to develop required grammars themselves.
Research on Trusted Authentication Model and Mechanism of Data Fusion

Firstly, this paper analyses the technical foundation of single sign-on solution of unified authentication platform, and analyses the advantages and disadvantages of each solution. Secondly, from the point of view of software engineering, such as function requirement, performance requirement, development mode, architecture scheme, technology development framework and system configuration environment of the unified authentication platform, the unified authentication platform is analyzed and designed, and the database design and system design framework of the system are put forward according to the system requirements. Thirdly, the idea and technology of unified authentication platform based on JA-SIG CAS are discussed, and the design and implementation of each module of unified authentication platform based on JA-SIG CAS are analyzed, which has been applied in ship cluster platform.
Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.
Fighting the “blackheart airports”: internal policing in the Chinese censorship circumvention ecosystem

The “Great Firewall of China” (GFW) has become a familiar trope in information security circles. China is comparatively shut off from the rest of the Internet, with extensive censorship and blocking of websites, especially those hosted outside China. The Chinese public use a variety of methods to circumvent the extensive online censorship practised by the Chinese state. In scaling the “Great Firewall”, these ordinary users of the Internet now make use of so-called “airport services” which allow tunnelling through to censored websites. This has resulted in the emergence of both an illicit ecosystem of censorship circumvention providers and a population of scammers who set up fake or unreliable services to con these would-be everyday Internet freedom enthusiasts. We have discovered, through scraping the public chat channels used by “airport” providers, that this anticensorship ecosystem is beginning to develop highly organised methods of self-policing, making co-ordinated use of technicques associated with malicious cybercrime to force these scammers offline and promote a healthy market. While so-called “booter” services - websites where users can purchase Denial of Service attacks for a small fee - have generally been used for malicious purposes, amusement, protest, or extortion, our research suggests that in the Chinese censorship circumvention market they are being used for internal policing.
The Crown Jewels of Quantum Algorithms

This chapter provides detailed descriptions of the most intellectually valued algorithms in quantum computing, including Peter Shor’s factoring algorithmQuantum algorithms Shor’s factoring algorithm and Lov Grover search algorithm, among others. An attempt is made to explain the subtle aspects of the algorithms and why such algorithms are valued.
Quantum Networks Based on Single Photons

Quantum networks rely on the transfer of quantum information between stationary quantum nodes. Physically the nodes are connected by single photons. In a first part of this chapter we address different components of a quantum network. We start with a discussion of semiconductor photon sources with an emission wavelength near 900 nm. In order to make them suitable for fiber-networks a conversion to the telecom band is required. We describe how such converters can be realized with the help of nonlinear optics. Next we address photon storage devices as crucial components of quantum repeaters, which are necessary to establish quantum key distribution (QKD) over long distances. We concentrate on the approach of room-temperature gas cells filled with alkali atoms and outline first promising result. In a second part we address a special QKD protocol, the so-called time-frequency (TF) protocol. It can mostly be realized with off-the-shelf components and its encoding of quantum bits in frequency and time suggests a straightforward way to utilize multiplexing. We analyze the TF-protocol numerically before we report on an actual free-space link over 100 m as a testbed for a quantum network in a realistic environment.
Java Session Language

Since the limited capability of single one, cloud services need to be assembled to form business process, which crate for users demands. However, to realize service composition is a time-consuming task which requires users know both the business logic and techniques of service composition. Its main reason is that current solution lacks a suitable language which can implement cloud service and easy to use. Regarding this issue, we put forward JSL, a new dialect language for integrating services. Because interaction is the basis of service composition, JSL apply session to depict the operation of interaction, then express complex composition further. JSL also use functions operators to shield the prone-to-error concurrent programming. Moreover, we designed model-driven framework to support JSL Simulation experiences demonstrates the efficiency of JSL since JSL and its framework can speed up the development and decrease the concurrent mistakes.
Blockchain Auditing Technology for Inter-provincial Generation Right Trading

Abstract This paper proposed an auditing method for medium and long-term inter-provincial generation right trading based on blockchain technology, ensuring that market participants’ private data and trade secrets cannot be tampered, and the transaction process can be traced back. In this way, trading institutions and regulatory authorities can obtain credible data. The method can provide a two-way book to trace the flow of energy and financial assets, provide data verification services to confirm the integrity and validity of the data, and it can provide data auditing service to identify contents that have been tampered. This paper took the generation right trading between the East and the West of China as an example, a blockchain testing environment was established to verify the feasibility of the auditing method for generation right trading. This paper offered an auditing solution for registration, declarations, clearing, and settlement of data involved in inter-provincial generation right trading.
Supplement Products Data Extraction and Classification Using Web Mining

Currently, many product sellers like to advertise their supplement products on web. However, there are some ads showing messages to deceive consumers. This work presents a system to extraction supplement products advertisement data from web and classifies the illegal ads that show misleading properties. Therefore, we proposed a method to automatic search and extract ads text from multiple websites using defined supplements keywords. Then, the extracted ads texts were preprocessed by word segmentation, stop words eliminate methods, and classified by the misleadingness words database that be prohibited by the Food and Drug Administration of Thailand. All illegal classified ads would be computed TF-IDF vectors and stored in an illegal reference database. However, some illegal ads avoided to use the prohibited words that they can be classified as legal. Therefore, they would be re-classified by measuring the similarity with all ads in the reference database. The experimental results show that the proposed system can detect forbidden ads with an accuracy of 0.775.
A Regulatory Framework for Cryptocurrency

The paper examines cryptocurrency in the crypto-finance ecosystem and identifies areas in which regulatory intervention is required. Part I focuses on the system and processes of cryptocurrency and identifies the features of this ‘decentralised’ system and the legal and regulatory implications. Part II discusses the perceived benefits and the rise of cryptocurrency to determine whether those benefits fit with regulatory objectives and contribute to the rise of cryptocurrency. Part III discusses the fall of cryptocurrency and some associated factors, particularly a lack of governance. Part IV discusses the need for regulation and governance.