Reviewerly/exHarmony huggingface dataset:
Digital Ticket Booking and Checking Using Aadhaar Card or Fingerprint and Android Application

In the current system, we can get ticket both over the counter and online, but often we do not get ticket due to black-marketing and also it generates the paper ticket with the carbon printing. And in ticket checking process, there is lots of manual work to take place for maintaining passenger's records which is tedious to manage. To deal with these problems, this paper gives the solution by regulating ticket booking process using Aadhaar card no. or fingerprint which will reduce the use of carbon printed paper tickets and paper waste. We are developing an android application that will help the ticket checker to check the ticket and keep records effectively.
Oil crisis: the political economy of digital data. Conclusion of the special issue

It is a truism to say that we live in a digital era. Advances in digital technologies in recent decades have changed personal, political, and social practices. Digital tools – and with it, new capabilities for the generation, storage, analysis, and exploitation of data – have also had profound effects on the political economy, including the financialisation of our economy, movements of data, money, and companies across national borders. At the same time, economic, political and social factors shape what and how digital data are generated, how they are stored and used, and for whose benefits. This concluding chapter to the Special Issue on the Political Economy of Digital Data gives an overview of the contributions` main findings and suggestions.
Comparative Analysis of Different Loss Functions for Deep Face Recognition

Face Recognition has been one the fastest emerging field in the last ten years. Convolutional neural network (CNN) or Deep convolutional neural network (DCNN) have significantly developed the extraordinary state-of-the-art solution for Face Recognition. This promising development results from the enhanced learning and representation of the discriminative features. The learning depends extensively on the loss function employed in the model. The loss function plays a vital role in the training of CNN and its job is to evaluate the performance of the model, i.e. bad performance results in a huge loss and vice versa. The gradients of this loss function are further used in the back propagation of errors which in turn enables the model to improve its learning from the given data. The objective of this paper is to have a comparative analysis of different loss functions available for the Deep Face Recognition.
A Novel Indoor Human Fall Detection Method Based on an End-to-End Neural Network and Bagged Tree Classifier

In this paper, a novel indoor human fall detection method based on an end-to-end neural network and bagged tree classifier is proposed. This method is different from the conventional subjective feature extraction based fall detection algorithm, which combines the advantages of above-mentioned two algorithms and converts the fall detection problem into target tracking, features extraction and posture recognition problem. Firstly, a lightweight neural network is utilized to extract informative features from the video in the initial tracking process. Secondly, the extracted features are processed by discriminant correlation filter to get the correlation information of two adjacent frames which is used in the following process of inverse fast Fourier transformation to obtain the response map. The position of the predicted target is then updated by getting the maximum value of the response map. Furthermore, the position predicted in the previous frame is cropped and expanded as the input in the prediction of the next frame to achieve online tracking and feature updating. Finally, the updated features are used as the input for training bagged tree classifier. After that, posture recognition task is carried out by using the trained bagged tree classifier. The experimental results demonstrate that the proposed method performs well and achieves reasonably accuracy.
Real-time Communication Measurement on Web Services in the Fingerprint Machine

The problem of fingerprint-based attendance management has been overcome in previous studies. Web Service Implementation (WS) has successfully handled fingerprint machine communication with a web-based monitoring system with a different platform. However, it is not yet known the size of the data transfer rate between the fingerprint machine and the monitoring system. The primary purpose of this research is to test and measure data transfer speed and memory usage. Experiments in this study were carried out by connecting five fingerprint machines with a web-based monitoring system. Node.js is installed on the server-side that is connected to the web service and database. The experiment is repeated; the data transfer speed and memory usage are recorded in the table and averages are calculated. The experimental results in the study showed that after 50 trials it was known that the average time of data transfer was 14.53 ms and the average memory usage was 17.42 Mb. So that communication between fingerprint machines and web-based monitoring systems can be categorized as real-time communication.
An Anonymous Attribute-based Access Control System Supporting Access Structure Update

It is quite common nowadays for clients to outsource their personal data to a cloud service provider. However, it causes some new challenges in the area of data confidentiality and access control. Attribute-based encryption is a promising solution for providing confidentiality and fine-grained access control in a cloud-based cryptographic system. Moreover, in some cases, to preserve the privacy of clients and data, applying hidden access structures is required. Also, a data owner should be able to update his defined access structure at any time when he is online or not. As in several real-world application scenarios like e-health systems, the anonymity of recipients, and the possibility of updating access structures are two necessary requirements. In this paper, for the first time, we propose an attribute-based access control scheme with hidden access structures enabling the cloud to update access structures on expiry dates defined by a data owner.
Camlearn as Photography Mobile Learning Application and its Effects on Academic Achievement

Learning photography in vocational high school always encounters problems related to the availability of adequate camera and studio facilities. These problems make the material acceptance by students is reduced. The percentage of theoretical material is greater than practicum material, whereas this subject focuses on students' skills to use photographic equipment well. As a solution to this problem, Camlearn was developed to reduce the gap between learning in schools and industry needs through the use of mobile-based photography learning media. This research uses an experimental and descriptive approach. Experimental research is used to see the difference between experimental class learning outcomes using Camlearn as a learning medium and a control class that utilizes Edmodo-assisted blended learning models as a learning management system. Descriptive research to determine the level of Camlearn functionality as a valid learning media. Data collection tools consist of test instruments (pretest and posttest) and non-test instruments in the form of a questionnaire application Camlearn functionality. The results showed that mobile learning had a significant influence on students' academic achievement as indicated by differences in the learning outcomes of class control and experimental class students. Camlearn also received valid assessments and was suitable for use as learning media. Based on these findings, schools are expected to utilize mobile learning technology to improve the quality of learning.
Comparative Study Using Neural Networks Techniques for Credit Card Fraud Detection

Credit cards have become a necessity in the virtual world for digitized and paperless transactions. Every day, millions of credit card transactions occur, but these transactions are subject to various types of fraud. Numerous works have proposed techniques developed to analyze, detect and prevent credit card fraud. In recent years, several studies have used machine-learning techniques to find solutions to this problem. In this article, we conducting a comparative study of techniques based on neural networks, applied to the same data set. Our goal is to offer a complete analysis to choose the best credit card fraud detection techniques.
Smart meters: the gate to behind-the-meter?

The gate to the services behind-the-meter lies at the meter stage. Smart meters provide real-time consumption data that can be used by other smart devices, appliances, and applications to improve the efficiency of energy use. This chapter examines the role that smart meters can play in promoting the consumer/prosumer/prosumager engagement and asset aggregation behind the meter. A sound design of smart-meter rollout and a proactive use of the data made available henceforth may either promote or hinder the development of services behind the meter. As the consumer becomes engaged and possibly evolves in prosumer or prosumager, a legally valid, close to the real-time reading of energy flows to and from the meter is required.
Design of Fire Emergency Command System Based on Cloud Platform

With the developments of communication network, wireless sensor network and multi-network integration technologies, in the fire emergency command information construction, a large number of monitoring, control and real-time information generated data will form a considerable data set, the fire emergency command system. The data collection and data processing of the architecture poses even greater challenges. In order to meet the challenges of the massive data storage technology, through the analysis of the source and characteristics of the massive data of fire emergency management, the design of the fire emergency command system based on the cloud platform; we use soft switch technology, container virtualization and cluster technology to build a variety of functions Distributed, soft, and easy to manage distributed soft switch cluster. The application scheme of sensor network and positioning technology in fire emergency command is discussed. Finally, an intelligent command and control system that integrates multiple data such as communication, drone, sensor and control terminal is established to realize "a map of situation and deal with a game of chess".
A Website Source Evaluation Algorithm Based on Comprehensive Feature Analysis

Traditional web page sorting algorithms can only find the single web page that is the most relevant to keywords, but can not find the relevant website information source. For tackling the problem, we Propose a website information source evaluation algorithm based on comprehensive feature analysis. This algorithm first obtains multiple web pages corresponding to keywords through Baidu and other search engines, then obtains the contents of corresponding website information sources through crawler program and extracts the features, and finally obtains the sorting results of information sources of relevant websites by calculating relevancy combining BM25 algorithm and cosine distance. At the same time, combined with the implicit feedback behavior of users' browsing time, the sorting results could be dynamically adjusted to make the search results personalized. Experiment results show that this approach could make full use of web features, and improve the quality of web source evaluation algorithm by combining the semantic information of web content.
Retrieving Surface Solar Irradiance from Himawari-8: a Physics Based Approach and its Validation

This paper presents a physics-based method to estimate all components of surface solar irradiance (SSI) from Himawari-8 imagery. Cloud parameters were retrieved from satellite data, and SSI calculated using radiative transfer through look-up-tables. Detailed validation using 3 years of high quality 1-minute surface measurements at 11 stations across Australia consistently showed low bias (≤ 5 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) across timescales for all SSI components, and rapidly reducing RMSE with increasing averaging period. Issues in validation using in-situ measurements will be discussed. We will also present an accurate analytical model for the calculation of circumsolar radiance applicable to clear and cloudy sky conditions.
The Applications of Artificial Intelligence and the Abilities of Supporting in Learning Japanese Language

Machines have long been created by humans to serve many needs in life. In the length of history of technology development, they gradually improved, along with the amount and type of assigned and more diverse assigned tasks. However, the application level of the machines only stops at simple things, only requires strength and accuracy. In the areas that require flexibility, the ability to analyze situations, human intelligence, and artificial intelligence is the brain that originates the wisdom of machines. The potential benefits they bring seem impossible to limit, and the new way to utilize artificial intelligence does not stop appearing. This paper mainly deals with the issues surrounding the application of artificial intelligence to life and in approaching a rather complex language like Japanese. The application of artificial intelligence to Japanese language teaching and learning will be of great significance, helping both learners and teachers achieve certain goals in today's modern society. The paper is completed through documents, surveys and confirms the indispensable importance of artificial intelligence in the field of education.
Experimental Study of the Spectral and Angular Solar Irradiance

We developed an experimental set-up and procedure to measure spectral and angular solar irradiance to accurately predict and optimize solar power plant performance. The set-up is comprised of a fiber coupled spectrometer mounted to a rotational stage which allows for 360° light capture. The lightweight and flexible design enables irradiance measurements at any location. Measurements were taken in Enschede, Netherlands and in Phoenix, AZ, USA. We find a strong dependence of spectro-angular irradiance on the location, surroundings and cloud coverage which needs to be considered when modelling and optimizing location dependent solar power plant output.
Raindrop Removal and Background Information Recovery in Coastal Wave Video Imagery using Generative Adversarial Networks

In this paper, we propose a video enhancement method using generative adversarial networks to remove raindrops and restore the background information on the removed region in the coastal wave video imagery distorted by raindrops during rainfall.Two experimental models are implemented: Pix2Pix network widely used for image-to-image translation and Attentive GAN, which is currently performing well for raindrop removal on a single images.The models are trained with a public dataset of paired natural images with and without raindrops and the trained models are evaluated their performance of raindrop removal and background information recovery of rainwater distortion of coastal wave video imagery.In order to improve the performance, we have acquired paired video dataset with and without raindrops at the real coast and conducted transfer learning to the pretrained models with those new dataset.The performance of fine-tuned models is improved by comparing the results from pretrained models.The performance is evaluated using the peak signal-to-noise ratio and structural similarity index and the finetuned Pix2Pix network by transfer learning shows the best performance to reconstruct distorted coastal wave video imagery by raindrops.
Research on Chinese New Word Discovery Algorithm Based on Mutual Information

For the existing Chinese new word discovery method, some fixed expressions that are not phrases or non-new words will be recognized as new words. Aiming at this problem, a new word discovery algorithm based on the principle of similarity judgment is proposed. The principle of similarity judgment is that if the context of a candidate substring is similar to the context of another expression, the probability of it becoming a new word will increase. The new algorithm is based on the unsupervised new word discovery algorithm filtered by statistical information such as mutual information, information entropy, and word frequency. The similarity and mutual information are combined into similarity enhancement mutual information, which is used as an indicator to measure internal integration. Finally, experiments show that the algorithm of similarity enhancement mutual information can effectively identify new words in the industry with high precision.
Automated Requirements Extraction and Product Configuration Verification for Software Product Line

The success of a software product line (SPL) development heavily relies on the management of features. Feature management includes the identification of domain-specific features, analyzing the features to check various types of feature anomalies and finally verifying the customized product configuration from the features. While features are the most important and critical part of SPL, identifying features from users’ requirement description is considered to be one of the difficult tasks. In addition to this, if users receive an immediate feedback regarding the validity of the target product just after providing their requirements, the product development time can be improved significantly. This chapter presents an automated approach where a customized product configuration, which is derived from the extracted features from product description, is validated giving immediate feedback to users. First, product description is collected from users, and then, machine learning techniques are applied to examine the text and identify the domain of the product, and domain-specific features are then extracted. From the extracted feature, a product configuration is derived and validated by using a developed tool. A set of previously identified rules are used to check the validity of a product configuration, and the presented tool follows those rules. As a result, the clients can get an instant feedback about the validity of their expected product and can update their requirement if required. This step can significantly improve the quality as well as the time-to-market of the product by reducing the effect of late modification of the initial requirement. Experimental result shows comparable accuracies are achieved from the applied machine learning techniques.
Quantum evolution speed in a double-layer environment

We investigate the dynamics of a two-level system in the presence of an overall environment composed of two layers. The first layer is just one single-mode cavity that decays to a memoryless reservoir while the second layer is two coupled single-mode cavities that decay to memoryless or memory-keeping reservoirs. We show that, in the weak-coupling regime between the qubit and the first-layer environment, the speed of evolution for the system can be controlled to a speeded-up or speeded-down process by manipulating the coupling strength in a double-layer environment. In particular, we find that multiple dynamics transitions from a not-speeded-up evolution to speeded-up evolution can be realized for the system by increasing the coupling strength in the second-layer environment. These results hold independently of the nature of the reservoirs in the second-layer environment.
Using Normal/Abnormal Video Sequence Categorization to Efficient Facial Expression Recognition in the Wild

The facial expression recognition in real-world conditions, with a large variety of illumination, pose, resolution, and occlusions, is a very challenging task. The majority of the literature approaches, which deal with these challenges, do not take into account the variation of the quality of the different videos. Unlike these approaches, this paper suggests treating the video sequences according to their quality. Using Isolation Forests (IF) algorithm, the video sequences are categorized into two categories: normal videos that visibly express clear illumination and frontal pose of face, and abnormal videos that present poor illumination, different poses of face, occulted face. Two independent facial expression classifiers for the normal and abnormal videos are built using Random Forests (RF) algorithm. The experiments have demonstrated that processing independently normal and abnormal videos can be used to improve the efficiency of the facial expression recognition in the Wild.
e-Vent : Support System for Event Registration

Registration, ticketing, and valid certificates are a series of work that must be done by event organizer. The registration, ticketing, and certificate issuance stages are often carried out using conventional or manual methods using stationery, print, and paper media. The conventional method is often an obstacle that causes the program to run less smoothly. This problem can be overcome by applying e-registration technology and QR Code as a substitute for tickets and e-certificates. Cellular technology such as smartphone can be the right solution for event organizers to overcome these obstacles. The application of an event registration system (e-Vent) that combines e-registration technology and QR Code as validation of registration and certificates is an effective and easy solution in implementing the stages of procuring an event. With E-Registration, data on participants who have registered will be stored in the database and will be displayed using the QR Code as the entry ticket sent to the participant's email. With this, QR Code readers can access data quickly to validate event participant data. The process of filling out the registration form can be done anywhere and anytime and the registration verification process can be done more quickly and accurately through this e-Vent system. The e-Vent system is designed using the FAST method and uses CodeIgniter as a development framework for web admins and Android Studio as a tool for developing QR Code reader validator applications.
Analyzing Security Protocol Web Implementations Based on Model Extraction With Applied PI Calculus

Analyzing security protocol web implementations is a crucial part of web security. Based on the model extraction technology, this paper first defines SubJavaScript and SubPython languages, and then establishes mapping models from SubPython and SubJavaScript to Applied PI Calculus respectively, after that, develops the semi-automatic model extraction tools SubPython2PV and SubJavaScript2PV to analyze the four widely used security protocol web implementations. The experiment shows that the four typical security protocol web implications have confidentiality, but lack of authentication.