Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition Using Extreme Learning Machine

Facial expression recognition is one of the technological capabilities in identifying a face image to follow up on research conducted by psychologists. The recognition of facial expressions is very important to know the emotions of someone who is experiencing it. In this study two datasets were used, namely the FER2013 and CK + datasets. The FER2013 dataset and CK+ are datasets designed to identify facial expressions. At the feature extraction stage, it uses the Histogram of Oriented Gradient (HOG) feature dan Local Binary Pattern (LBP) feature. Whereas in the classification stage, the Extreme Learning Machine (ELM) classifier is used. The greatest accuracy by using HOG feature is 63.86% for the FER2013 dataset and 99.79% for the CK + dataset with sigmoid as an activation function. And the greatest accuracy by using LBP feature is 55.11 % for the FER2013 dataset and 98.72% for the CK + dataset with RBF as an activation function.
Sentiment Analysis of Posts and Comments in the Accounts of Russian Politicians on the Social Network

Russian politicians are increasingly using social networks publishing a lot of texts. One of the important issues in the context of the analysis of political online communication is the choice of negative and positive topics in publications as well as the reaction of the audience. In order to analyze the main patterns of this process we have collected the data from the social network Vkontakte. Our sample covers the period from 1 January, 2017 to 25 April, 2019, in total 46293 posts and 2197063 comments in 23 politician's accounts. To build the classifier we used two text corpora: Rubtsova's corpus and RuSentiment corpus. The algorithm of sentiment analysis was implemented on the basis of bidirectional recurrent neural network. Using Rubtsova's corpus we provided the accuracy of 91% and using RuSentiment we provided the accuracy of 84% (accuracy is calculated as the proportion of correctly identified cases from the test sample). We found that the markup of data significantly differs when different corpora were used. The most adequate results in the analysis of posts and comments, in our opinion, are obtained by using an ensemble of models based on the both corpora. As a result of classification, we identified a number of patterns. Thus, the number of likes and views of posts is higher for the posts classified as positive, and the number of reposts is higher for the posts classified as negative. We also found that the number of comments is higher for the posts with a negative sentiment, and the average sentiment of comments on positive posts is more positive than the average sentiment of comments on negative posts.
On privacy preserving data release of linear dynamic networks

Distributed data sharing in dynamic networks is ubiquitous. It raises the concern that the private information of dynamic networks could be leaked when data receivers are malicious or communication channels are insecure. In this paper, we propose to intentionally perturb the inputs and outputs of a linear dynamic system to protect the privacy of target initial states and inputs from released outputs. We formulate the problem of perturbation design as an optimization problem which minimizes the cost caused by the added perturbations while maintaining system controllability and ensuring the privacy. We analyze the computational complexity of the formulated optimization problem. To minimize the ℓ0 and ℓ2 norms of the added perturbations, we derive their convex relaxations which can be efficiently solved. The efficacy of the proposed techniques is verified by a case study on a heating, ventilation, and air conditioning system.
Summary of web crawler technology research

Abstract With the continuous development of network information technology, there is a large amount of unstructured data called big data on the network. Human resources to collect information laborious, so web crawler technology came into being. This paper explores the basic principle and characteristics of web crawler and the classification of current popular crawler, introduces the key technology of crawler, compares two search strategies and the current application of crawler. Finally, the future research direction of web crawler is introduced.
Digital Literacy

Digital literacy is a widely used concept that has become pluralistic, often avoiding a clear definition of what digital literacy actually means. However, acquiring digital literacy is still a global issue. Given the vast potential of digital technology as it grows and evolves, it is essential to understand what the concept of digital literacy is and how it is applied in today's context. The aim of the study was twofold: (a) to analyse the recent literature available in the EBSCO database regarding definitions of digital literacy; and (b) to provide an overview of the concept of digital literacy and related skills. Using a systematic literature review, 45 academic papers were selected, of which 17 articles were analysed after further evaluation. Qualitative analyses were carried out to better comprehend the range of digital literacy definitions, as offered by authors.
Hadoop-based Dark Web Threat Intelligence Analysis Framework

With the development of network services and people's privacy requirements continue to increase. On the basis of providing anonymous user communication, it is necessary to protect the anonymity of the server. At the same time, there are many threatening crime messages in the dark network. However, many scholars lack the ability or expertise to conduct research on dark-net threat intelligence. Therefore, this paper designs a framework based on Hadoop is hidden threat intelligence. The framework uses HDFS as the underlying storage system to build a HBase-based distributed database to store and manage threat intelligence information. According to the heterogeneous type of the forum, the web crawler is used to collect data through the anonymous TOR tool. The framework is used to identify the characteristics of key dark network criminal networks, which is the basis for the later dark network research.
SWAN

In this pictorial, we unfold and reflect on the design process behind the creation of a research product - SWAN. SWAN is an augmented spoon that encourages people to pay more attention to their food and urges them to eat mindfully. With SWAN, our aim is to address the increasing tensions between the lucrative appeal of screen-based media and ideologies of mindful eating. We present a descriptive account on how we brought SWAN into being. In attending to key design decisions across our design process, we unveil ideas and challenges in creating a domestic research product to support everyday mindful eating.
Entanglement and photon statistics of two dipole–dipole coupled superconducting qubits with Kerr-like nonlinearities

The engineering of Kerr and time-dependent coupling interactions is of great attention for treating quantum information in quantum systems and for investigating the collective behavior of large numbers of interacting particles in a cavity-qubit network. In this manuscript, we investigate the time evolution of the entanglement and some nonclassical properties of two superconducting qubits interacting with a single-mode field in the presence of a Kerr-like medium and dipole–dipole interaction without and with time-dependent coupling effect. We show that a slight alteration in the interaction, detuning, and Kerr parameters might cause a change in the entanglement of subsystem states during the evolution. By taking into account the influence of the different physical parameters, we show the statistical distributions produced in the photons of the single mode field through the calculation of the Mandel's parameter. Finally, we find that the time-dependent Mandel's parameter not only provide the statistical properties of the field, but also include the information of quantum entanglement for the subsystem states.
Data Cleaning for Personal Credit Scoring by Utilizing Social Media Data: An Empirical Study

With the accumulation of data on personal behavior and the development of machine learning models and algorithms, it is becoming possible to use social media data for personal credit scoring. In this article, we use the systematic sampling method to obtain Douban's social media data. Because there are many abnormal users in these data, they are “real but false data” for personal credit evaluation. In order to better carry out personal credit scoring, we propose three criteria, power exponents of time interval distribution of individual user $\gamma _i$γi, user activity $A_i$Ai, and the ratio of out-degree and in-degree $R_i$Ri of user $i$i, which are used to systematically clean the data. And then, we used the logistic regression method to score the individual credits of users before and after data cleaning, and found that the rank order of personal credit scoring has changed significantly. This change is largely attributed to the changes of network structure after data cleaning. We believe that our work is very important to use the social media data to establish a credible personal credit evaluation system to reduce the credit risk of the current Internet financial industry.
Leveraging Ordinal Regression With Soft Labels For 3d Head Pose Estimation From Point Sets

Head pose estimation from depth image is a challenging problem, considering its large pose variations, severer occlusions, and low quality of depth data. In contrast to existing approaches that take 2D depth image as input, we propose a novel deep regression architecture called Head PointNet, which consumes 3D point sets derived from a depth image describing the visible surface of a head. To cope with the non-stationary property of pose variation process, the network is facilitated with an ordinal regression module that incorporates metric penalties into ground truth label representation. The soft label representation encodes inter-class and intra-class information contained in the class labels simultaneously, and guides the network to learn discriminative features. Experiments on two challenging datasets, namely the Biwi Head Pose Dataset and Pandora Dataset, show that our proposed method outperforms state-of-the-art approaches.
Image dehazing based on a transmission fusion strategy by automatic image matting

Most dehazing methods fail to estimate satisfactory transmission simultaneously in both normal and bright regions. To estimate more accurate transmission for these two kinds of regions, we propose a transmission fusion strategy based on automatic image matting for image dehazing. We first extract the mean and variance of a local patch around each pixel, and propose a binary classification method with the mean and variance of each patch to coarsely segment an input image into a binary map of normal and bright regions. Then we smooth and quantize the binary map to automatically generate a trimap of ternary values. Thus we can avoid the difficulty in manually labeling trimaps. Both the image and the trimap are input into a Bayesian matting method for soft segmentation of normal and bright regions to produce an alpha map. The dark channel prior (DCP) is adopted to extract a transmission map for normal regions, while an improved atmospheric veil correction (AVC) method is proposed to generate another transmission map for bright regions. Finally, we propose to use the alpha map to fuzzily fuse the two transmission maps for final image dehazing. Experimental results show that our method significantly outperforms existing methods.
Cyber security threats, challenges and defence mechanisms in cloud computing

With the advent of computers and their widespread use, cloud computing has been identified as one of the major emerging components of computer technology. The benefits of cloud computing, in the form of processing power and computing resources connected via the internet, have not only bolstered business and personal operations of users but have also led to severe security and privacy threats that require adaptation into cloud computing systems. In light of this, the present study explores the various threats to cloud computing, in addition to outlining defence mechanisms against these threats. It was found that there is a major threat concerning data breaches because of the lack of management understanding of the use of cloud computing services and their defence mechanisms. Furthermore, there can be an abuse of cloud computing services that, in turn, affect not only sensitive data pertaining to the organisation but also the personal identity and information of the user.
Handling Imbalance Issue in Hate Speech Classification using Sampling-based Methods

In a text classification problem, imbalance nature in dataset oftentimes has been disregarded even though it might significantly have an impact on the results of the classification model performance. This issue also occurred in the hate speech detection where most collected datasets are highly unbalanced. Among state-of-the-art methods that deal with classifying disparity data, the sampling-based technique is the most effective approach in classifying an imbalanced data. In this paper, four resampling methods include Random Oversampling (ROS), Synthetic Minority Technique (SMOTE), Adaptive Synthetic (ADASYN) and Random Undersampling (RUS) are used as an answer to the inequality of class distribution in a hate speech dataset. With three basic machine learning classifiers i.e. Support Vector Machine, Logistic Regression and Naïve Bayes, the evaluation results show that the oversampling approach improves the accuracy and the overall performance of three classifiers. Among all resampling techniques and machine learning algorithms, Logistic Regression enforced by ROS performed the best with an overall accuracy of 91 percent and F1-Score of 0.95.
Knowledge Discovery and Recommendation With Linear Mixed Model

We give a concise tutorial on knowledge discovery with linear mixed model in movie recommendation.The versatility of mixed effects model is well explained.Commonly used methods for parameter estimation, confidence interval estimate and evaluation criteria for model selection are briefly reviewed.Mixed effects models produce sound inference based on a series of rigorous analysis.In particular, we analyze millions of movie rating data with LME4 R package and find solid evidences for a general social behavior: the young tend to be more censorious than senior people when evaluating the same object.Such a social behavior phenomenon can be used in recommender systems and business data analysis.
Quadrotor Trajectory Tracking Control using Non-Linear Model Predictive Control with ROS Implementation

This paper presents a Model Predictive Controller for trajectory tracking control of the quadrotor using the ACADO Toolkit on Matlab/Simulink. Model Predictive Control (MPC) prediction feature and ability to obtain optimal control action yields an accurate trajectory tracking performance. The controller is applied to a quadrotor system. The mathematical model was derived using Newton's and Euler's laws. Simulations for the trajectory tracking test was done for evaluating the trajectory tracking performance. Afterwards, an interface between RotorS Gazebo Simulator and Simulink was implemented using the Robot Operating System (ROS) for validation of the controller's performance. The paper presents the results of both simulations under disturbances to determine the suitability and validity of the proposed control algorithm.
Loci of 3-periodics in an Elliptic Billiard: Why so many ellipses?

A triangle center such as the incenter, barycenter, etc., is specified by a function thrice- and cyclically applied on sidelengths and/or angles. Consider the 1d family of 3-periodics in the elliptic billiard, and the loci of its triangle centers. Some will sweep ellipses, and others higher-degree algebraic curves. We propose two rigorous methods to prove if the locus of a given center is an ellipse: one based on computer algebra, and another based on an algebro-geometric method. We also prove that if the triangle center function is rational on sidelengths, the locus is algebraic
Artificial Intelligence Prediction and Decision Evaluation Model Based on Deep Learning

Under the active promotion of major countries in the world, the integration of AI and many fields has continued to deepen, and a series of new technologies, new formats and new models have emerged. The application of artificial intelligence algorithm for machine learning, to help government departments and institutions predict information related to the future, to provide decision-making advice. Based on the relevant data of the talent market in A city, this paper analyzes the supply and demand situation of local talents. The data is processed using wavelet threshold denoising and a NAR neural network model is established for prediction. Taking employment satisfaction, Chinese student employment rate and average salary as indicators, combined with GM (1,1) forecasting model to predict employment situation. The SOM neural network model is established based on the number of applicants, academic requirements, the number of students admitted, and the fast-growing industry in the A city. Finally, the paper compares the characteristics of the above-mentioned indicators in A city with other cities in China, and matches other cities with the same characteristics as the city A, and builds an "intelligent forecasting and decision-making evaluation model" to help government departments and related institutions predict future urban development.
GREG: A Global Level Relation Extraction with Knowledge Graph Embedding

In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level ‘local’ relation’s to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model’s modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.
Evaluating E-marketplace Mobile Application Based on People At the Center of Mobile Application Development (PACMAD) Usability Model

The existence of e-marketplace on mobile platform facilitates transactions anywhere and anytime, making e-commerce in Indonesia grow rapidly. Competitive pressure on e-marketplace demands continuous improvement. Usability is an important aspect of e-commerce because today's consumers are not only satisfied with the mobile app that simply allow them to shop, the experience must also be pleasant. This study evaluates the usability of the three most popular mobile applications in Indonesia: Tokopedia, Bukalapak, and Shopee. The evaluation based on the People at the Center of Mobile Application Development (PACMAD) usability model developed by emphasizing limitations and characteristics of mobile devices. The usability evaluation includes seven aspects: effectiveness, efficiency, learnability, memorability, errors, satisfaction, and cognitive load. The results are Bukalapak was leading in almost all aspects: effectiveness, efficiency, learnability, memorability, errors, and cognitive load, followed by Tokopedia and Shopee. Bukalapak was excel because not only the interactions were simple and easy but also it worked well when the connection was interrupted. However, it turns out various promotions given by Shopee affected user perceptions about the usability of the application, resulted that Shopee was preferably and had the highest score of satisfaction.
Fostering Secondary Students' STEM Career Awareness through IoT Hands-On Educational Activities: Experiences and Lessons Learned

This article reports on a research case study about raising the motivation of young boys and girls (14-16 years old) toward science, technology, engineering, and math (STEM) education to increase their prospects of choosing a relevant career. Using the Internet of Things (IoT) at the core of a design-based methodology, multiple implementations of IoT-oriented educational scenarios (ESs) were realized in different educational contexts. More than 150 secondary students and nine teachers, in seven different settings, were involved in an integrated educational framework with hands-on activities and teamwork. Using a concrete evaluation methodology exploiting both quantitative and qualitative data collected before, during, and after the interventions, we analyze the students' and teachers' experience to understand critical parameters and provide directions for successful ESs. Our findings show that the IoTbased ES realizations were successful overall and enhanced STEM career awareness and skills, with boys showing greater appreciation than girls. Different ES timeframes resulted in different conclusions, while proper organization and preparation in all aspects involved in the realization are critical.
Smart Hotels and Technological Applications

Technological developments and demographic changes have started to encourage the hotel businesses to use automation systems and adapt the concept of smart hotel. Smart hotels use state-of-the-art technology to provide customers with different and more technology-oriented experiences. A robot works as receptionist, concierge, bellboy, and housekeeper in such hotels. These applications are observed to minimize service failures and save on costs. For instance, chatbots created in smart hotels by means of artificial intelligence respond to customers' problems and demands quickly and accurately. Virtual reality applications used in the marketing of smart hotels make it possible for guests to take a virtual tour of the cultural heritage sites of the destinations. Internet of things (IoT) is another technology that is often used in smart hotels to offer customers a more comfortable holiday and get instant data. Likewise, in smart hotels, self-service software technology is often used and enables customers to perform many tasks such as check-in / out, getting hotel room keys, etc.
Impact of Argument Type and Concerns in Argumentation with a Chatbot

Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. They could possibly be deployed in tasks such as persuasion for behaviour change (e.g. persuading people to eat more fruit, to take regular exercise, etc.). However, to achieve this, there is a need to develop methods for acquiring appropriate arguments and counterargument that reflect both sides of the discussion. For instance, to persuade someone to do regular exercise, the chatbot needs to know counterarguments that the user might have for not doing exercise. To address this need, we present methods for acquiring arguments and counterarguments, and importantly, meta-level information that can be useful for deciding when arguments can be used during an argumentation dialogue. We evaluate these methods in studies with participants and show how harnessing these methods in a chatbot can make it more persuasive.