Reviewerly/exHarmony huggingface dataset:
The STViewer, a Visual Method with Sentiment Analysis

In this paper, we propose a new visualization method called STViewer, which can visualize a large number of social media text based on a certain topic. STViewer can not only perceive the sentiment polarity of the whole text, show the opinions with high frequency of occurrence, but also preserve the semantic structure of the unstructured text. This helps users gain an overview of the key concepts and opinions of a large-size text rapidly and visually. Our method also provides users with some interactions to know more about his interested details. The method combines the design ideas of word cloud and word tree, which presents the popular opinions with large font and use some links to show the structures between words, and overcomes the limitations of both the two. STViewer is implemented as a light-weight application runs in the browser. Additionally, we propose a sentence sentiment analysis algorithm which is suitable for light-weight application and has good performance.
XCS-CR for handling input, output, and reward noise

To briefly represent a dataset, it is crucial to find common attributes among the data. Extended learning classifier system (XCS) finds common attributes of multiple data and acquires generalized rules that match multiple data. In real-world problems, it may be challenging to find common attributes due to noise in the data and the inability of XCS to acquire the generalized rules. Considering a classification problem as an example, noise may be included at each input, output, as well as in the evaluation of the output. To tackle this problem, our previous work proposed XCSs that acquire appropriately generalized rules, specifically for a problem with one of the three mentioned type of noises added. In real-world problems, it is difficult to identify the type of noise in advance, which requires an XCS to cope with multiple types of noise. For this issue, this paper proposes an XCS that can handle any noise on the input, output, and evaluation of the output, and aims at investigating the effectiveness of the proposed XCS in the multiplexer problems including any of the three types of noise.
Selection among Bernoulli populations in comparison with a standard

In this article we consider the problem of comparing several Bernoulli populations in order to identify the population producing the largest success probability that is also larger than a given standard. We propose a fixed sample size procedure for which we develop the probability of a correct selection and the least favorable configuration. We also propose a curtailed version of the fixed sample size procedure and show that the curtailed procedure reaches the same probability of a correct selection as the fixed sample size procedure, while using fewer observations. We provide tables to implement the procedures and illustrate them via an example. We use simulations to estimate the savings by the curtailment procedure.
Detection of Sarcasm from Consumer Sentiments on Social Media About Luxury Brands

Social media sites act as a platform for customers to express their opinions/sentiments on brands and products. The opinion of the customers in social media in case of luxury brands plays a great role in improving the sales by building a better brand strategy. Most of the existing analysis used by the luxury brand industry ignores the importance of sarcasm analysis. A common type of sarcasm that is given in the form of opinion is positive sentiments, which contain a negative meaning. This paper studies the scope of Lexicon based approach, K-means and Naïve Bayes for analyzing the sarcastic opinion and analyzing the impact of these algorithms in recognition of sarcasm, which has a negative context for analyzing the luxury, brand data.
Assistance application of people with cognitive disabilities in tasks for their social inclusion

People with intellectual disabilities receive education in special centers, however, many of these centers do not have the current technological means to strengthen the teaching methodology and support them within the labor field. The present research work has as its proposal the development of a tool for Android smartphones designed to help people with cognitive disabilities in tasks for their social inclusion using Beacon technology to locate the positioning of the user and evaluate its functioning within a center educational. The result of the evaluation was that the application called Tk-Helper managed to reduce the time, errors, help and assistance in the tasks of a certain activity that each user performed.
Human Computer Interaction in Education

The Artificial Intelligence Assisted Learning application has been created to help Mathematics students at primary school level and assist their teachers. The application generates classwork and homework worksheets based on each student’s ability level, corrects the worksheet, and gives immediate feedback to both the student and teacher. A crucial Human Computer Interaction concept implemented in the AIAL system is usability since the target audience is primary students and teachers who may have a basic proficiency in IT skills. Through the use of clearly partitioned sections, for classwork, homework, results and trophies, and intuitive controls, the application is easy to use and has a very low learning curve. Gamification and reinforcement elements were included in this application to further increase student engagement and enjoyment levels. The AIAL application can also be used for continuous assessment purposes. In the testing phase, it was noted that students enjoyed using the app.
Subjective and Objective QoE Measurement for H.265/HEVC Video Streaming over LTE

Mobile video traffic is the fastest growing segment of mobile data driven by proliferation of devices with larger screens and higher resolution, as well as increased network performance achieved through long term evolution (LTE) deployments. Emerging video formats and applications, such as H.265/high efficiency video coding (HEVC) will increase the video traffic consumption while improving the user quality of experience (QoE). Although QoE is affected by many influence factors (IFs), this paper focuses on media-related system IFs and their impact on subjective and objective QoE metrics for H.265/HEVC video streaming. The aim is to examine the impact of media-related system IFs on QoE for video streaming and compare the results of subjective and objective QoE measurements. Results obtained from experimental study are used to analyze the relationship between subjective and objective QoE metrics for H.265/HEVC video streaming.
Semantic Topic Discovery for Lecture Video

With more and more lecture, videos are available on the Internet, on-line learning and e-learning are getting increasing concerns because of many advantages such as high degree of interactivity. The semantic content discovery for lecture video is a key problem. In this paper, we propose a Multi-modal LDA model, which discovers the semantic topics of lecture videos by considering audio and visual information. Specifically, the speaking content and the information of presentation slides are extracted from the lecture videos. With the proposed inference and learning algorithm, the semantic topics of the video can be discovered. The experimental results show that the proposed method can effectively discover the meaningful semantic characters of the lecture videos.
Spread of Anycast and GSLB

Content Delivery Networks (CDN) are regarded as important internet infrastructure that supports business on the Internet. Network latency has been identified as an important metric to improve the quality of service (QoS) of CDNs. Given the limitations to response times because of the geographic distance between servers and end-users, CDN providers have developed architectures such as Global Server Load Balancing (GSLB) and IP Anycast to realize fast responses. This study investigates how GSLB and IP Anycast contribute towards improving QoS from the users' perspective. User traffic in the campus network was analyzed and it was found that: 1) 88% of the traffic reaps the benefits of well operated internet services, i.e., they have a Round-Trip Time (RTT) of less than 100 milliseconds. 2) Although GSLB is still the primary architecture for realizing fast responses, IP Anycast supports 5.8% of the traffic.
Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm

In this paper we show that the negative sample distance covariance function is a quasi-concave set function of samples of random variables that are not statistically independent. We use these properties to propose greedy algorithms to combinatorially optimize some diversity (low statistical dependence) promoting functions of distance covariance. Our greedy algorithm obtains all the inclusion-minimal maximizers of this diversity promoting objective. Inclusion-minimal maximizers are multiple solution sets of globally optimal maximizers that are not a proper subset of any other maximizing set in the solution set. We present results upon applying this approach to obtain diverse features (covariates/variables/predictors) in a feature selection setting for regression (or classification) problems. We also combine our diverse feature selection algorithm with a distance covariance based relevant feature selection algorithm of Kong et al. (2015) to produce subsets of covariates that are both relevant yet ordered in non-increasing levels of diversity of these subsets.
Stacked Hourglass Network Joint with Salient Region Attention Refinement for Face Alignment

Localizing facial landmarks is a fundamental step in facial image analysis. However, the problem continues to be challenging in condition of large variations caused by pose disparity, illumination, expression and occlusion. In this paper, we propose a coarse-to-fine framework which joints stacked hourglass network and salient region attention refinement for robust face alignment. To achieve this, we firstly develop a multi-scale region learning module (MSL) to analyze the structure and texture information at different facial region and extract strong discriminative deep feature. Then we employ a novel convolutional neural network named stacked hourglass network (SHN) for heatmap regression and initial facial landmarks prediction. Moreover, we present a salient region attention module (SRA) to extract precise feature based on the heatmap regression, and the filtered feature is used for landmarks refinement. The extensive experimental results on two public datasets, including 300W and COFW, confirm the validity of our model.
Implementing Cybersecurity into the Wisconsin K-12 Classroom

Cybersecurity is a field that has seen its workforce demands rising steadily throughout the past decade. Although the Wisconsin Department of Administration has been actively encouraging collaboration efforts between the public and private sectors and promoting cybersecurity as a promising career path, the demand for cybersecurity professionals continues to be greater than the supply, which is a trend noticed also nationwide. The state of Wisconsin is facing several challenges in attempting to promote cybersecurity including limited security curricula resources, lack of programs and other initiatives that promote security principles, and lack of awareness of cybersecurity risks. In this paper, we discuss the major challenges Wisconsin is facing towards establishing proper cyber hygiene for the general population and growing the cybersecurity work force. In addition, we suggest ways to overcome or lessen the effect of the identified issues.
Kernel Generalized Canonical Correlation and a New Feature Fusion Strategy

When processing face images, we usually use the data analysis tools to find the underlying relations in data. As an extraction method based on two groups of features, Canonical Correlation Analysis (CCA) can better reveal the inner relations between two variates. Recently, CCA has been widely used in multiple feature fusion and extraction and it has gained a series of achievements. The basic idea of CCA is: firstly, establish correlation criterion function between two groups of features; then extract the correlation feature of each data; finally, obtain the combined correlation feature, which is used in image classification. In practice, we can also add the category information of training samples by improving the discriminant criterion function. Furthermore, by exploiting corresponding kernel function, the original data can also be projected into higher dimensional spaces to reduce the gaps between the heterogeneous spaces. In this paper, we focus on the Generalized CCA(GCCA) and Kernel CCA(KCCA) and then introduce the idea of kernel GCCA(KGCCA). We solve the problem by Lagrangian multiplier method and we also propose a new feature fusion strategy (FFS). Finally, experiments on Yale and ORL face datasets demonstrate the performance of our proposed method KGCCA and the new FFS.
Facial Landmark Detection based on Cascade Neural Network

Abstract A three-stage cascade convolutional neural network is proposed in this paper to learn the facial features. In order to improve the robustness and accuracy of facial landmark detection, several parallel sub-networks are involved in each stage of network. The convolutional neural network performs global advanced feature extraction for the entire facial region in the preceding stage, so as to obtain more accurate positioning. Since the first-stage input covers the whole image, the global facial information can be effectively used to weaken or even avoid the detection errors caused by occlusion, large posture changes, extreme illumination and etc. During facial detection, the first-stage network will make initial prediction of facial landmarks, and then get the facial area narrowed down and accurate according to the prediction results; the second-stage network will take the narrowed-down facial area above as the input, the three parallel convolution networks are used to classify the facial landmarks for quadratic estimates, so as to improve the accuracy of prediction results based on the results of first-stage network. The input of the third-stage network is limited to the small neighborhood of the second-stage prediction results, which can make the prediction results more accurate. The experimental data shows that our method has certain advantages in detection accuracy and reliability.
Information Security Awareness of School Administrators

The aim of this study is to determine the information security awareness levels of school administrators. The sample of the quantitative study conducted in the scanning model was composed of 174 school administrators selected by the disproportionate element sampling method among the school administrators working in the high schools in the city of Elazlğ. The Information Security Awareness Scale was used as the data collection tool. The mean, percentage, and frequency techniques, correlation analysis and t-test were used to data analysis. According to the results, school administrators mostly connect to the internet at school and use the internet mostly to do research. Information security awareness level of school administrators is generally above the medium level and the highest level of awareness is the dimension of social media usage. It has been revealed that there is a moderate, negative, and significant relationship between the school administrators' age and their information security awareness level. The awareness level of male school administrators is higher than females in terms of information security. At the end of the study, various suggestions were made about increasing the awareness of school administrators about information security.
An Intelligent Test Paper Generation Method to Solve Semantic Similarity Problem

In order to solve the problem of semantic duplication in the Intelligent Test Paper Generation Method, Genetic Particle Swarm Optimization Algorithm was used to search multi groups of test papers that conform to the constraints of the test. Then the idea of density entropy was used to screen the test papers, so that test papers will cover more questions uniformly. After that, analyze the semantic similarity of the test papers that use the TextRank algorithm to extract the key words of test questions, and use knowledge-keywords weighted VSM model to calculate the semantic similarity of test questions. Eliminate the high repetition rate of the test papers and avoid the repetition of the inspection points. The experimental results show that the algorithm can solve the semantic similarity problem in intelligent test paper generation of mass item bank.
Designing Early Testing Course Curricula with Activities Matching the V-Model Phases

This work addresses the gap between software engineering process terminology in formal education, and the practical skills relevant to testing related work. The V-model is a commonly referenced description of how the software engineering processes are tied to the different software testing levels. It is used in software engineering education to illustrate which type of testing work should be carried out during a certain development stage. However, the V-model is mainly conceptual and tied to the steps in the Waterfall model, leaving the students with little knowledge about what is actually done. To solve this problem, we propose an approach to map the V-Model development phases and testing levels with corresponding, actual testing techniques. We then evaluate the approach by designing the weekly topics, learning goals and testing activities for a 7 week introductory course on the basics software testing and quality assurance. Based on the course outcomes and recent literature, we discuss the strengths and weaknesses of the proposed curriculum.
Paper Document Authentication Using Print-Scan Resistant Image Hashing and Public-Key Cryptography

Identity documents, such as passports, visa stickers, national identity cards and educational institutions’ identity cards etc., are used for personal identity verification by different government and academic organizations. These printed domain documents can be counterfeited by deploying different forgery techniques. This work suggests authenticity verification of printed documents and their sources using digital signature based on print-scan resistant image hashing and public-key cryptography. We present application of print-scan resistant image hashing based on wave atom transform (WAT) for document authentication. Image hash is calculated by extracting robust image features in WAT domain. Hash value of the person’s original image is encrypted with the private key of trusted authorities to form a digital signature which is encoded in a QR code printed on the document. Digital signature, extracted from a QR code, is decrypted with the public key of trusted authorities for identity verification, thus provides offline verification of printed documents without the need of online network access or database.
Word Spotting Based on Bispace Similarity for Visual Information Retrieval in Handwritten Document Images

Retrieving information from a huge collection of ancient handwritten documents is important for indexing, interpreting, browsing, and searching documents in various domains. Word spotting approaches are widely used in this context but have several limitations related to the complex properties of handwriting. These can appear at several steps: interest point detection, description, and matching. This article proposes a new word spotting approach for the word retrieval in handwritten document, which mainly leverages the properties of image gradients for visual features detection and description. The proposed approach is based on the combination of spatial relationships with textural information to design a more accurate matching. The experimental results of the proposed approach demonstrate a higher performance over the Jeremy Bentham dataset, evaluated following the recent benchmarks of ICDAR 2015 Competition on Keyword Spotting for Handwritten Documents.
An Improved Method for Calibration Between a 2D Lidar and a Camera Based on Point-Line Correspondences

Abstract The paper depicts an improved method for calibration parameters, which is a 2D homography, using point-line correspondences. The correspondences are the edge lines of the board provided by camera and the intersection points obtained by lidar. Particularly, a data preprocessing method is put forward and exploited to dramatically reduce the lidar’s measurement error. Afterwards, we use a triangular board to establish constraints. At least 8 correspondences are employed to compute the initial value, and the final result is optimized by L-M algorithm. Massive experimental results show that proposed method has about 1 pixel improvement, compared with the previous methods.
A Novel Chaotic Northern Bald Ibis Optimization Algorithm for Solving Different Cluster Problems [ICCICC18 #155]

This article proposes a new optimal data clustering method for finding optimal clusters of data by incorporating chaotic maps into the standard NOA. NOA, a newly developed optimization technique, has been shown to be efficient in generating optimal results with lowest solution cost. The incorporation of chaotic maps into metaheuristics enables algorithms to diversify the solution space into two phases: explore and exploit more. To make the NOA more efficient and avoid premature convergence, chaotic maps are incorporated in this work, termed as CNOAs. Ten different chaotic maps are incorporated individually into standard NOA for testing the optimization performance. The CNOA is first benchmarked on 23 standard functions. Secondly, testing was done on the numerical complexity of the new clustering method which utilizes CNOA, by solving 10 UCI data cluster problems and 4 web document cluster problems. The comparisons have been made with the help of obtaining statistical and graphical results. The superiority of the proposed optimal clustering algorithm is evident from the simulations and comparisons.
Through the Limits of Newspeak: an Analysis of the Vector Representation of Words in George Orwell’s 1984

The era of fake news, media manipulation and information wars has been beneficent to the lasting fame and continuous acclaim of George Orwell's 1984. The novel, published in 1949, influences to the present day the terminology of various political and social analysts through the use of its fictional language called “Newspeak”. The question arises - can the inner connections of the concepts present in Orwell's 1984, when analysing the text on a semantic level of the words in their contextual environment, be used to further the understanding of the inner-workings of the novel's language itself? More specifically, the aim of this paper is to examine whether a reader without knowledge of the subject of a fictional work of art, exemplified by Orwell's 1984, could gain deeper comprehension of the text just from analysing word vector representations, without the use of external resources and only through an overview of the established similarities on the semantic level of words in a given text. In fact, word vector representations, as a form of word embeddings in a vector space model, are a machine learning technique sometimes applied in natural language processing, which attempts to identify semantically similar words.