Reviewerly/exHarmony huggingface dataset:
Evaluation of Web Applications based on UX Parameters

&lt;span lang="EN-US"&gt;The objective of evaluating User Experience (UX) in this era of technology is to enhance the user satisfaction. Earlier applications were built with the aim of reducing the work of users. But with the evolution of the technology, the emergence of new gadgets and new trends in the information technology, the applications had to be more user-centric. The primary objective of this research is to evaluate the user experience of web applications based on different UX parameters using different techniques and given a rating. Each of these ratings are combined to determine the overall rating of UX for the web application. Also, the secondary objective of this research is to provide suggestions or recommendations based on the ratings to improve the UX of the web applications. An experimental study was conducted and the results show a significant improvement. Areas of further enhancements have also been identified and presented.&lt;/span&gt;
Developments on Baby Strollers over the Last Decade

It has come to attention that the interest of baby stroller design is still encouraged and popular among worldwide researches and consumers. A visual history and timeline of invented baby strollers from 1973 to today’s baby strollers was drafted. Moreover, a ten years review on multiple designs of baby strollers was done ranging from the year 2008 to 2018. Based on the review, there are many possible ways to design a baby stroller which its sole purpose is to transport a baby in a much easier method. Through this review, it is noticeable that baby stroller inventors are implementing additional functions like rocking mechanism, diaper changing table, and others. The use of electronic devices in strollers is slowly implemented as the years increases, although there always potential ideas for improvements. Graphs and tables on baby strollers by their popularity and brand was drawn with online shopping websites as a resource to recognize consumer’s interest. Besides that, potential improvements and ideas on the design and fabrication of baby strollers were suggested to fit in with the impending society and future generation.
Chau–Wang–Wong17 scheme is experimentally more feasible than the six-state scheme

Recently, Chau et al. (Phys Rev A 95:022311, 2017) reported a quantum key distribution (QKD) scheme using four-dimensional qudits. Surprisingly, as a function of the bit error rate of the raw key, the secret key rate of this scheme is equal to that of the (qubit-based) six-state scheme under one-way classical communication using ideal apparatus in the limit of arbitrarily long raw key length. Here, we explain why this is the case in spite of the fact that these two schemes are not linearly related to each other. More importantly, we find that in terms of the four-dimensional dit error rate of the raw key, the Chau et al.’s scheme can tolerate up to 21.6% using one-way classical communications, which is better than the Sheridan and Scarani’s scheme (Phys Rev A 82:030301(R), 2010). In addition, we argue the experimental advantages of the Chau et al. implementation over the standard six-state scheme and report a corresponding proof-of-principle experiment using passive basis selection with decoy states. We also compare our experiment with the recent high secret key rate implementation of the Sheridan and Scarani’s scheme by Islam et al. (Sci Adv 3:e1701491, 2017).
Implementasi Algoritma Naïve Bayes Pada Data Set Hepatitis Menggunakan Rapid Miner

One of the diseases anticipated by doctors is hepatitis. This is because if the patient is not detected from the beginning having hepatitis, then the disease will develop into liver cancer. It can be seen that cancer is one of the deadliest diseases in the world that there are no drugs used for healing. By utilizing this increasingly developing science, researchers try to predict or classify whether a patient has suffered from hepatitis sickness based on the results of tests that have been undertaken before. One data mining technique can be used to predict Hepatitis and the method used is Naive Bayes. The data used is sourced from the UCI Repository with the web address https://archive.ics.uci.edu/ml/datasets/Hepatitis . The amount of data available is 155 data with 123 patients with Life hepatitis and 32 patients with Die hepatitis. The attributes contained in this hepatitis dataset are: Age, Sex, Steroids, Antivirals, Fatigue, Malaise, Anorexia, Big Liver, Liver Firm, Spleen Palpable, Spiders, Ascites, Varices, Bilirubin, Alk Phosphate, Sgot, Albumin, Protime, Histology , and Class (predictive result attribute). From the results of the research that has been done, it can be concluded that the Naive Bayes method includes an accurate algorithm to predict because the results of accuracy using Rapid Miner show more than 50% which is equal to 76.77%. With the highest Precision Class results of 98.88% for "Life" predictions, and Class Recall of 96.88% for "Die" Predictions.
A robust authorship attribution on big period

Authorship attribution is a task to identify the writer of unknown text and categorize it to known writer. Writing style of each author is distinct and can be used for the discrimination. There are different parameters responsible for rectifying such changes. When the writing samples collected for an author when it belongs to small period, it can participate efficiently for identification of unknown sample. In this paper author identification problem considered where writing sample is not available on the same time period. Such evidences collected over long period of time. And character n-gram, word n-gram and pos n-gram features used to build the model. As they are contributing towards style of writer in terms of content as well as statistic characteristic of writing style. We applied support vector machine algorithm for classification. Effective results and outcome came out from the experiments. While discriminating among multiple authors, corpus selection and construction were the most tedious task which was implemented effectively. It is observed that accuracy varied on feature type. Word and character n-gram have shown good accuracy than PoS n-gram.
SC-NER: A Sequence-to-Sequence Model with Sentence Classification for Named Entity Recognition

Named Entity Recognition (NER) is a basic task in Natural Language Processing (NLP). Recently, the sequence-to-sequence (seq2seq) model has been widely used in NLP task. Different from the general NLP task, 60% sentences in the NER task do not contain entities. Traditional seq2seq method cannot address this issue effectively. To solve the aforementioned problem, we propose a novel seq2seq model, named SC-NER, for NER task. We construct a classifier between the encoder and decoder. In particular, the classifier's input is the last hidden state of the encoder. Moreover, we present the restricted beam search to improve the performance of the proposed SC-NER. To evaluate our proposed model, we construct the patent documents corpus in the communications field, and conduct experiments on it. Experimental results show that our SC-NER model achieves better performance than other baseline methods.
Writer Identification Based on Arabic Handwriting Recognition by using Speed Up Robust Feature and K- Nearest Neighbor Classification

In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. This paper proposes method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor classification (KNN) to enhance the writer's identification accuracy. After feature extraction, it can be cluster by K-means algorithm to standardize the number of features. The feature extraction and feature clustering called to gather Bag of Word (BOW); it converts arbitrary number of image feature to uniform length feature vector. The proposed method experimented using (IFN/ENIT) database. The recognition rate of experiment result is (96.666).
Grassmann Manifold Based State Analysis Method of Traffic Surveillance Video

For a contemporary intelligent transport system, congestion state analysis of traffic surveillance video (TSV) is one of the most crucial and intricate research topics because of the rapid development of transportation systems, the sustained growth of surveillance facilities on road, which lead to massive traffic flow data, and the inherent characteristics of our analysis target. Traditional methods on feature extractions are usually operated on Euclidean space in general, which are not accurate for high-dimensional TSV data analysis. This paper proposes a Grassmann manifold based neural network model to analysis TSV data , by mapping the video data from high dimensional Euclidean space to Grassmann manifold space, and considering the inner relation among adjacent cameras. The accuracy of the traffic congestion is improved, compared with several traditional methods. Experimental results are conducted to validate the accuracy of our method and to investigate the effects of different factors on performance.
Alternative Methods of Solving Biasedness in Chi – Square Contingency Table

Some methods of solving the biasedness in Chi–square Contingency table statistic were considered. Phi Coefficient, Contingency Coefficient and Cramer’s V tools were employed to solve the biasedness in the use of Chi–square test. Our results show that any of Phi coefficient, Contingency coefficient or Cramer’s V can be used to describe the association between two variables if the data matrix is 2 x 2. Contingency Coefficient was recommended as a good statistic when the matrix dimension is the same while the Cramer’s V is most adequate when the data matrix differs.
Automated and Decentralized Framework for Internet of Things Systems Using Blockchain and Smart Contracts

This paper describes a framework for IoT systems that use blockchain technology and smart contracts. Such systems ensure the storage of information in a decentralized and secure database and can perform transactions between nodes automatically. The architecture of the system is like a LoRaWAN architecture in which the gateway nodes form a blockchain subsystem that runs command scripts for data filtering. The paper focuses on implementing gateways using a powerful microcontroller ADuCRF101 (with integrated ARM Cortex M3 and RF Transceiver) and evaluating performance as computing time and energy consumption.
Social Roles, Interactions and Community Sustainability in Social Q&amp;A Sites: A Resource-based Perspective

Online tech support communities have become valuable channels for users to seek and provide solutions to specific problems.From the resource exchange perspective, the sustainability of a social system is contingent upon the size of its members as well as their communication activities.To further extend the resource-based model, the current research identifies a variety of social roles in a large tech support Q&A forum and examines longitudinal changes in the community's structure based on the identification.Moreover, this study also investigates the relationship between the community's functionality and its traffic.Results suggest that the proportion of unsolved questions negatively impacts the number of future incoming questions and the outcome of a given question is not only dependent on users' interactions within the discussion, but also on the community activities preceding the question.These observations can help community managers to improve system design and task allocation.
Diagnosis of causes for high railway traffic based on Bayesian network

To intuitively analyze and diagnose why high railway traffic often occurs, this paper builds a Bayesian network on a rail transit system, for instance, the Shanghai Metro L1, to explore it from different angles such as holidays, mega-events, bad weather and sudden accidents.The fuzzy set theory is also integrated to quantify the conditional probability of some events using the fuzzy languages.It turns out that the Bayesian network constructed can well infer the probability of massive passenger traffic, and diagnose the dominant factors that may cause it.Hereby, the rail transit management authorities can take proper measures against it to reduce the risks the high rail traffic may impose.
Is Waze joking? Perceived Irrationality dynamics in user-robot interactions.

Artificial intelligence, as well as the use of smarter and smarter systems are gradually pervading our everyday life.While AI-embedded systems are increasingly becoming 'human-like', individuals tend to fictionize interpersonal relationships with them.We conceptualize the notion of Perceived Irrationality as the discrepancy between an individual's expectations and his/her perceptions towards a smart system's recommendation.We then develop a conceptual model that aims at better understanding the inner mechanisms that govern perceived irrationality.This research opens up a vast uncharted research territory that proposes to adopt a long-term relational lens towards the study of humans / smart systems interactions.
Achieving Flatness: Honeywords Generation Method for Passwords based on user behaviours

Honeywords (decoy passwords) have been proposed to detect attacks against hashed password databases.For each user account, the original password is stored with many honeywords in order to thwart any adversary.The honeywords are selected deliberately such that a cyber-attacker who steals a file of hashed passwords cannot be sure, if it is the real password or a honeyword for any account.Moreover, entering with a honeyword to login will trigger an alarm notifying the administrator about a password file breach.At the expense of increasing the storage requirement by 24 times, the authors introduce a simple and effective solution to the detection of password file disclosure events.In this study, we scrutinise the honeyword system and highlight possible weak points.Also, we suggest an alternative approach that selects the honeywords from existing user information, a generic password list, dictionary attack, and by shuffling the characters.Four sets of honeywords are added to the system that resembles the real passwords, thereby achieving an extremely flat honeywords generation method.To measure the human behaviours in relation to trying to crack the password, a testbed engaged with by 820 people was created to determine the appropriate words for the traditional and proposed methods.The results show that under the new method it is harder to obtain any indication of the real password (high flatness) when compared with traditional approaches and the probability of choosing the real password is 1/k, where k = number of honeywords plus the real password.
Optimally Efficient Secure Scalar Product With Applications in Cloud Computing

Secure computation of scalar product is of considerable importance due to its central role in many practical computation scenarios with privacy and security requirements. This paper includes new results about the secure two-party scalar product. Specifically, a perfectly secure and universally composable two-party split scalar product (SSP) protocol is proposed in the preprocessing model. In addition to full security, the proposed SSP protocol enjoys the advantage of optimal efficiency. To show the optimality of this SSP protocol, information theoretic lower bounds on the amount of communicated data in a secure two-party computation in the preprocessing model are derived. These bounds are not limited to SSP functionality but apply to a large class of two-party functionalities. A part of this paper is devoted to applications of the proposed SSP protocol in secure cloud computing. Specifically, based on this protocol, a cloud-assisted privacy-preserving profile-matching scheme and a secure remote health monitoring scheme are proposed. Both of the solutions are highly efficient and significantly improve the previous work.
Syntactic composition and selectional preferences in Hindi Light Verb Constructions

Previous work on light verb constructions (e.g. chorii kar ‘theft do; steal’) in Hindi describes their syntactic formation via co-predication (Ahmed et al., 2012, Butt, 2014). This implies that both noun and light verb contribute their arguments, and these overlapping argument structures must be composed in the syntax. In this paper, we present a co-predication analysis using Tree-Adjoining Grammar, which models syntactic composition and semantic selectional preferences without transformations (deletion or argument identification). The analysis has two key components (i) an underspecified category for the nominal and (ii) combinatorial constraints on the noun and light verb to specify selectional preferences. The former has the advantage of syntactic composition without argument identification and the latter prevents over-generalization, while recognizing the semantic contribution of both predicates. This work additionally accounts for the agreement facts for the Hindi LVC.
Divergence Based Non-Negative Matrix Factorization for top-N Recommendations

Personalized top-N recommendation algorithms are among the most effective techniques providing customized suggestions in information retrieval applications.Most of the current methods construct personalized recommendations based on various loss functions such as pairwise ranking loss and point-wise recovery loss.In this paper, we propose a personalized top-N recommendation method based on non-negative matrix factorization with divergence as a point-wise ranking loss function.Our method finds the latent factors from the existing data to improve recommendation predictions.We formulate the learning problem with regularized divergence as a constrained non-convex minimization problem and develop a projected gradient descent optimization algorithm to solve the divergence problem.We evaluate our approach using six personal recommendation task related datasets by employing root mean squared error (RMSE) and hit rate (HR).Our experimental results demonstrate improved RMSE and HR for most of the datasets.
Total Technology Space Map as a Digital Platform

A strand of recent studies utilized complete patent databases and classification systems to construct large network maps of patent technology classes, which might approximate the total technology space.It has been argued that such maps are useful for competitive intelligence analysis, technology road mapping, innovation decision support, and so on in the literature.In this paper, we illustrate the InnoGPS system to integrate such a map with various map-based visual analytic functions for technology navigation, positioning, neighborhood exploration, path finding and information retrieval.These analytics are either descriptive, predictive or prescriptive.During the process of developing InnoGPS, we have conceived a wide spectrum of other potential applications of the total technology space map for consumers, business, education and so on.These possibilities together with the difficulty to construct an accurate technology space representation suggest the strategic value to develop the total technology space map as a digital platform for any applications to discover, manage or represent any data, information and knowledge related to technologies, and to nurture an ecosystem of developers and users.
Price Prediction for Agricultural Commodities in Bandung Regency Based on Functional Link Neural Network and Artifical Bee Colony Algorithms

In Indonesia, fluctuating agricultural commodity prices often impacts society negatively.In this study, farmers in Bandung Regency, West Java, Indonesia, were chosen as a case study.Fluctuating agricultural commodity prices can lead to farmers suffering losses due to the sale price being smaller or equal to the cost of planting.Price is influenced by crop productivity, while planting productivity is strongly influenced by weather.A system is developed in this study to predict the price of agricultural commodities based on price, productivity and weather history using a Functional Link Neural Network (FLNN) algorithm optimized with the Artificial Bee Colony (ABC) algorithm.The price prediction results can be used as recommendations for farmers as to whether they should plant or not.In addition, the prediction results are compared to the Artificial Neural Network (ANN) algorithm with Backpropagation algorithm as the learning algorithm.From the experimental result, the best Mean Absolute Percentage Error (MAPE) value was obtained with FLNN-ABC: 7.68% for the predicted price of chili and 10.59% for the predicted price of onion.
Efficient Arnold and Singular Value Decomposition based Chaotic Image Encryption

This paper proposes an efficient image encryption that is based on Arnold transform (AT) and the Singular value decomposition (SVD).The proposed method employs AT on a plain image to transpose all image pixels in the positions, then a diffusion process is applied to the resulted encrypted image via SVD decomposing into three segments.The decryption process aims to derive the plain image from the cipher image.Matlab simulation experiments are done to examine the suggested method.The achieved results show the superiority of the suggested approach with respect to encryption quality.
A STUDY OF INTRODUCTION TO COLLEGE ENGLISH TEACHERS� BELIEFS IN THEIR TEACHING ROLES

The study investigated Introduction to College Englishs teachers beliefs about their roles as teachers. The participants of the study were thirteen teachers. It used their reflection papers as the source of data and secondary data, as the reflections were not originally written for the purpose of this study. The study used thematic analysis of the secondary data and it produced four themes or categories regarding the teacher participants beliefs about their roles. First, they believed they should design various activities that promoted learning. Secondly, they needed to monitor their students' progress and conduct appropriate assessments. They also believed that they should always have alternative plans whenever their original plans were not successful, and the last, they believed that they needed to show learners their attention and care. Based on the results of the study and the limitations, some suggestions for future studies are also presented.DOI: doi.org/10.24071/ijiet.2019.030103
Using the mixed methods research to model the hotel performance measurement in Egypt: An example from a hotel chain

measurement in Egypt: An example from a hotel chain chain
Methods and approaches of Complexity Theory and Fuzzy Logic for intensity of university research in terms of Creative Work to be estimated

This article describes the main approaches to research in terms of creative labor regulation and time study. Definition of a creative labor and brain-work product as a subject of regulation and work intensity estimation was developed. The method for creative brain-work intensity estimation was based on complexity theory, which enables the evaluation of the complexity of creative brain-work results and time expenditure. Because of the appropriateness of linguistic expert assessments while labor intensive creative work is carried out, the methods and approaches of fuzzy logic were suggested to assess the Competence Complexity of the creative work product. Based on statistical and expert models, a regression was built to estimate time expenditure dependence of the creative brain-work expected outcomes on Competence Complexity. The resulting dependency allows the estimation of the time spent on the research project based on the estimation of the expected project results.