Reviewerly/exHarmony huggingface dataset:
Density-aware Stratified Sampling for Visualizing Large Volume Geo-Spatial Data

Sampling is a popular approach in big data visualization, however, current sampling approaches don't work well when visualization type is scatter plot, and are even worse in supporting keyword search queries. In this paper, we present an approach of density-aware stratified sampling, it first probing the density of record in different areas of the visualization, then taking the density data to guide the stratified sampling. We conducted an extensively user study to show the efficiency and efficacy of our approach, the experiment shows that our approach can provide very close scatter plots of keyword search queries of a 200 million record dataset within 0.2 second, and the construction time is only 1/4 of an alternative method.
Research on Design of Intelligent Commercial Super System Based on Internet of Things

With the development of commodity supermarket and modern enterprise management, how to better realize the convenient shopping and freight management, which provide the perfect shopping experience for users, has become an urgent problem of super managers. This paper designed an intelligence supermarket management system based on the Internet of things and established the corresponding solution. The management system is divided into four functional modules, which respectively intelligent storage management, intelligent shopping cart, virtual shopping, background management. The flow chart and the realization process were given. Finally, the intelligent storage management function including environmental monitoring, freight management, security monitoring and the navigation shopping function were simulated by FS_RFID internet of things experiment box of a company in Qingdao. It will support the introduction and promotion of the internet of things.
An Online Verifiable Rating System Based on Unverified Rating Output (URTO)

Ratings online are often listed alongside product recommendations, but to date, limited attention has been paid as to how credible these ratings present to end-users. For lack of an effective rating verification scheme, a cheater could fabricate some high ratings and the recommended product usually does not match the corresponding ratings. To make ratings verifiable, we encapsulate each rating into a specific structure whose core is unverified rating output (URTO) that we propose. The URTO is designed based on unspent transaction output (UTXO) in Bitcoin. On this foundation, we propose the online verifiable rating system. Our system can make ratings verifiable and the evaluated validation and mining time of a rating by the whole network peers demonstrate the high reliability of our system.
An Adaptive Adjustment Algorithm of the Parameters in Alarm Association Rule Mining

With the rapid development of communication technology, communication networks are playing an increasingly important role in people's lives. Effective management of increasingly complex networks can improve the efficiency and stability of network operations. Fault management is one of the important functions of network management. The analysis of the alarms generated in the network can dig into the underlying rules to provide useful information for fault management. However, the existing alarm association rule mining algorithms often have the problem of parameter rigidity. In this paper, a two-level windows based alarm transaction extracting algorithm is proposed to solve the problem of low efficiency when using fixed size windows. Then this paper proposes an experience extraction method based on alarm priority in deep Q network (DQN), which can calculate the sampling probability according to the importance of alarm when the memory unit enters the queue. Aiming at the rare item problem caused by the fixed support threshold in association rule mining, the improved DQN is used to dynamically adjust the minimum support in rule mining algorithm. Experimental results show that the algorithm proposed in this paper can effectively improve the efficiency of alarm transaction extraction and the accuracy of alarm association rules mining.
Event Causation and Force Dynamics in Argument Structure Constructions

This paper extends Croft’s theory of argument realization to include nominals that denote events, instead of participants. Events are represented as force dynamic interactions between participants and their subevents. That is, each participant is associated with its own subevent; participants are related to each other through force dynamic interactions as part of a causal chain (Croft 2012). There is extensive cross-linguistic evidence that a participant’s place in the causal chain determines its argument realization. In this paper, we tested three hypotheses about the argument realization of event nominals against English sentences from VerbNet. First, we define an event nominal as any nominal that refers to an event, regardless of whether it is morpho-syntactically derived from a verb. Our three hypotheses are (i) event nominals correspond to participant subevents, (ii) event nominals follow the same argument realization rules as their associated participant, and (iii) when both a participant and its subevent are realized as arguments, the subevent (i.e., the event nominal) is construed as subsequent to its participant in the causal chain. We find support for these hypotheses in the VerbNet data. In addition, we discuss more complex cases of argument realization with event nominals.
Compression Algorithm Based on Irregular Sequence

The paper introduces a new lossless, highly robust compression algorithm that similar with LZW algorithm, yet the algorithm discards dictionary processing and uses irregular sequences with massive, random information instead. Then the paper found the ineffectiveness of the algorithm due to limited computing ability of hardware and made a few improvements to the algorithm. The algorithm is recommended to be applied in interplanetary communications between a high-compute-ability device and a low-compute-ability receiving device, whose signal would be easily interfered by cosmos rays.
A Study on the Influence of Personal Characteristics of Youth Employment on the Preparation for Turnover: Focused on the adjustment effect of experience in failing to get jobs

Purpose: In this study, we would like to confirm that the transfer of young workers may be a means of enhancing their internal satisfaction, not to get a better job, by setting work-related characteristics that are highly relevant to job-related factors.Research design, data, and methodology: In this study, preparation for turnover was set as dependent variables to identify factors related to the turnover of young people, and the type of business, employment type, debt status, job satisfaction, job difficulty compared to education level, job difficulty, job degree, job major agreement, debt status, and other demographic social characteristics were selected as independent variables.Results: The characteristics related to personal criteria in job-seeking process were significant in the form of business, employment type, job satisfaction, work difficulty compared to the level of education, work difficulty compared to the level of technology, job major matching, and debt status.Conclusions: This study confirmed that young people's turnover may not simply be a means to get a better job, but to increase satisfaction in the internal aspects of their jobs, and that for young people, a job is an important development process that represents their identity and needs to be approached from a life-cycle perspective.
Distributed Adaptive State Estimation and Tracking Scheme for Nonlinear Systems using Active Passive Sensor Networks

This paper proposes a novel adaptive neural network (NN) based distributed state estimation scheme for a heterogeneous sensor network (HSN), to estimate the state vector of an unknown nonlinear process/target by using sensed output when the target input remains unknown. The active nodes in the HSN can sense the target output based on the detection range. By using a connected graph, the active nodes will communicate their estimated state vector from their adaptive NN observer to other passive nodes in the neighborhood that cannot sense the target, so that they can estimate the target state vector. Next, a subset of nodes in the HSN, referred to as the mobile nodes, track the moving target by using their estimated state information and a state feedback controller. For the communication topology considered, it is shown that the distributed state estimation, the NN observer weight estimation, and the tracking errors are uniformly ultimately bounded. Simulation results verify the theoretical claims.
A Comparative Study of Popular CNN Topologies Used for Imagenet Classification

Deep Learning is a relatively modern area that is a very important key in various fields such as computer vision with a trend of rapid exponential growth so that data are increasing. Since the introduction of AlexNet, the evolution of image analysis, recognition, and classification have become increasingly rapid and capable of replacing conventional algorithms used in vision tasks. This study focuses on the evolution (depth, width, multiple paths) presented in deep CNN architectures that are trained on the ImageNET database. In addition, an analysis of different characteristics of existing topologies is detailed in order to extract the various strategies used to obtain better performance.
A Study of Big Data Processing for Sentiments Analysis

Data processing has become an important field in today's big data-dominated world. The data has been generating at a tremendous pace from different sources. There has been a change in the nature of data from batch-data to streaming-data, and consequently, data processing methodologies have also changed. Traditional SQL is no longer capable of dealing with this big data. This chapter describes the nature of data and various tools, techniques, and technologies to handle this big data. The chapter also describes the need of shifting big data on to cloud and the challenges in big data processing in the cloud, the migration from data processing to data analytics, tools used in data analytics, and the issues and challenges in data processing and analytics. Then the chapter touches an important application area of streaming data, sentiment analysis, and tries to explore it through some test case demonstrations and results.
Understanding Blockchain

In this chapter, the authors have detailed the need of blockchain technology along with its case studies in different domains. The literature survey is described that describes how blockchain technology is rising. Further, a number of domains where blockchain technology can be applied along with its case studies have been discussed. In addition, the authors have considered the various use cases with their recent issues and how these issues can be resolved using the blockchain technology by proposing some new ideas. A proposed security framework in certain applications using blockchain technology is presented. Finally, the chapter is concluded with future directions.
Study on Data Valuation Oriented to Identification of Scientific and Technological Frontier

The open source scientific and technological data show a sharp increase with the emergence of the Internet, many of which are available directly from open network channels. These data are characterized by large scale, diverse forms, difficult to evaluate quality and complex computing. The really valuable information, for instance, scientific and technological frontier, may be covered by abundant invalid information if there is no effective method available. One important problem to be solved urgently is how to collect and process data from massive data. Therefore, data processing oriented to identification of scientific and technological frontier is proposed in this paper.Take papers and patent data for example, the experimental results show that the evaluation proposed in this paper is effective to some extent.
Application of deep learning and image feature retrieval in E-commerce transaction and customer management

The huge amount of digital image data in e-commerce transactions brings serious problems to the rapid retrieval and storage of images. Image hashing technology can convert image data of arbitrary resolution into a binary code sequence of tens or hundreds of bits through a hash function. In view of this, based on the image content characteristics, this study improved the traditional hash function and proposed a hash method based on bilateral random projection. At the same time, the projection vectors are acquired in the low-rank sparse decomposition process of the image data matrix, and the projection vectors are group orthogonalized. In addition, this study designed contrast test to carry out research and analysis on the effectiveness of the algorithm. The results show that the proposed algorithm works well and can be applied to practice and can provide theoretical reference for subsequent related research.
Cooperative Avoidance Control with Relative Velocity Information and Collision Sector Functions for Car-like Robots

This paper presents a closed-form feedback control design to guarantee collision-free maneuvering for four-wheel car-like robots. Relative velocity information and collision sector functions between the host robot and its neighbors are introduced to adjust the gain of avoidance controllers in real-time, such that controllers create smoother maneuvers and thus further improving the dynamic performance of the system. Meanwhile, the velocity-based switching functions are designed to make the avoidance control terms only active when robots and/or obstacles move towards each other, which effectively reduces unnecessary dispersal and energy consumption in collision conflicts.
Application of Employment Quality Analysis Model Based on Professional Competence System

Based on the idea of international engineering certification, an index system of professional competence for talent cultivation is constructed. From the perspective of data mining, the employment prediction models based on Stochastic Forest and BP neural network are established to classify and predict the employment trend of students. Furthermore, the two models are compared, validated and analyzed. In this paper, an analytic hierarchy process model based on BP neural network is established, in which, the contribution degree of the classification characteristics of each professional competence index is calculated. Through the analysis of the results, the influence degree of each index on the employment quality is identified to provide data support for teaching reform and personnel training of the universities, and ultimately improve the employment quality.
Basic Artifact Conservation and Lab Management

While remedial artifact conservation is the province of specialist conservators, it is useful for archaeologists to be aware of the fundamental principles of artifact conservation. This chapter introduces the factors that affect the preservation of materials, the proper ways to handle artifacts, and the environmental conditions that are important for designing storage and curatorial systems that protect the integrity of the artifacts and their documentation. It briefly reviews basic artifact cleaning and refitting of pottery and concludes with a section on conservation and curatorial ethics.
Electronic Voting Application Powered by Blockchain Technology

India is the largest democracy in the world, and in spite of that, it faces various challenges on a daily basis that hinder its growth like corruption and human rights violations. One of the ugliest phases of corruption and political mayhem is visible during the election process where no stone is kept unturned in order to gain power. However, it is the common citizen who suffers most in terms of clarity as well as security when it comes to his/her vote. Blockchain can play a very important role in ensuring that the voters registering their votes are legit and the counting of votes is not manipulated in any way. It is also needed in today's times where the world is available to people in their smart phones to also give them the opportunity to register their votes hassle free via their smart phones without having to worry about the system getting hacked. Therefore, in this chapter, the proposed layout will be based on a smart contract, using Ethereum software to create an e-voting app. In this chapter, the authors have proposed a secure e-voting framework through blockchain mechanism.
Online distributed optimization and stabilization of regularization paths

We consider optimization problems with regularization and propose a provably correct algorithm to further optimize the regularization parameter. A regularization parameter controls the relative importance of the primary objective term versus the regularization term. The proposed algorithm combines the generalized ADMM algorithm with a controls algorithm called extremum seeking. We show how the proposed algorithm is parallelizable and thus amenable to problems with large dimension, data privacy considerations, and/or resource constrained sensor networks. We validate the proposed algorithm by using it to train a housing price model where the regularization term induces clustering of the model parameters within neighbourhoods.
Development of a Holistic Prototype Hadoop System for Big Data Handling

In the upcoming smart world where billions of sensors, actuators, communication, and networking devices are expected to be connected with each other, these devices will be of various types. The volume of the data expected to be generated will be huge and due to the heterogeneity of the connected devices, there will be various types of data as well as the rate of data generation will be variable. This leads to a challenge of storage and processing these “Big data”. In this paper, development of a Hadoop prototype system is presented. The system is developed using HDFS and MapReduce techniques. Red Hat Linux, Windows 10 with Oracle VM VirtualBox are used as the platform to develop this system.
An Approach to Developing a LitRPG Psychology Teaching Guide in Undergraduate Education

The given article deals with aspects of studying psychology in the modern world and reasons for developing a teaching guide (course) with Literary RPG elements. The article focuses on a connection between psychology and literature as sources of psychological knowledge. The article defines components of the teaching model which embodies psychological cognition methods with elements typical for LitRPG game mechanics: plot, quest solving, immersion, character’s role in the learning game environment, based on the realia of the target discipline.
A Security Review of Event-Based Application Function and Service Component Architecture

The term service component is derived from SCA (service component architecture) for event based distributed system design. Although service component pattern offers composite application development and support application reusability functionality. However, security in event based communication in components interaction model mostly discussed on upper layer in SCA while developing service oriented component application logic. This layer is called application business process logic layer, which produces the application's rendering logic, having being authenticated from ACL.The need for such a comprehensive security review is required in this field that could possibly elaborate the issues in composite application and Event based attack in service component architecture model. The paper achieves this target by analysing, reviewing the security issues, modelling techniques in service component application functionality, while application components, that produces, consume, and processing events.
Local-to-Global Semantic Supervised Learning for Image Captioning

Image captioning is a challenging problem owing to the complexity of image content and the diverse ways of describing the content in natural language. Although current methods have made substantial progress in terms of objective metrics (such as BLEU, METEOR, ROUGE-L and CIDEr), there still exist some problems. Specifically, most of these methods are trained to maximize the log-likelihood or objective metrics. As a result, these methods often generate rigid and semantically incomplete captions. In this paper, we develop a new model that aims to generate captions conforming to human evaluation. The core idea is to use local-to-global semantic supervised learning by introducing the two-level optimization objective functions. At the word level, we match each word to the image regions using the local attention objective function; at the sentence level, we align the entire sentence and the image using the global semantic objective function. Experimentally, we compare the proposed model with current methods on MSCOCO dataset. We show that either local attention supervision or global semantic supervision is the necessary component for the success of our model through ablation studies. Furthermore, combining these two supervision objective functions achieves state-of-the-art performance in terms of both standard evaluation metrics and human judgment.
Self-Service Analysis Tools Based on Open Sharing

Scientific data is a very important scientific asset and strategic resource, and it has become an irreplaceable key content in modern science and technology infrastructure. The open sharing of scientific data has received much attention and attention from the academic community, but in fact there is still resistance to wider dissemination and sharing. Based on the above background, the purpose of this article is to research on self-service analysis tools based on open sharing, and to transform the traditional individual self-service data analysis methods into active and efficient information sharing analysis tools through data integration of related information. Its core purpose is to provide open and shared self-service data analysis information and recommend shared resources on the platform with the best matching conditions. This article elaborates on the model from four aspects: overall goals, construction principles, infrastructure, and operation of the model. Finally, based on the investigation and analysis of the existing open sharing self-service analysis tools and their data sharing problems, and compared with the big data technology-driven data open sharing model, suggestions are made for the improvement of the existing model.
Fraudulent Face Image Detection

Due to the growing advancements in technology, many software applications are being developed to modify and edit images. Such software can be used to alter images. Nowadays, an altered image is so realistic that it becomes too difficult for a person to identify whether the image is fake or real. Such software applications can be used to alter the image of a person’s face also. So, it becomes very difficult to identify whether the image of the face is real or not. Our proposed system is used to identify whether the image of a face is fake or real. The proposed system makes use of machine learning. The system makes use of a convolution neural network and support vector classifier. Both these machine learning models are trained using real as well as fake images. Both these trained models will take an image as an input and will determine whether the image is fake or real.