Reviewerly/exHarmony huggingface dataset:
Using Word Embeddings and Deep Learning for Supervised Topic Detection in Social Networks

In this paper we show how word embeddings can be used to evaluate semantically the topic detection process in social networks. We propose to create and train a word embeddings with word2vec model to be used for text classification process. Then when the documents are classified, we use a pre-trained word embeddings and two similarity measures for semantic evaluation of the classification process. In particular, we perform experiments with two datasets of Twitter, using both bag-of-words with conventional classification algorithms and word embeddings with deep learning-based classification algorithms. Finally, we perform a benchmark and make some inferences about results.
Smart Rubric-based Systematic Model for Evaluating and Prioritizing Academic Practices to Enhance the Education Outcomes

Recently, the impact of free-market economy, globalization, and knowledge economy has become a challenging and focal to higher educational institutions, which resulted in radical change.Therefore, it became mandatory for the academic programs to prepare highly qualified graduates to meet the new challenges, through the implementation of well-defined academic standards.For this reason, the National Center for Academic Accreditation & Evaluation (NCAAA) in Kingdom of Saudi Arabia (KSA) defined a set of standards to ensure that quality of education in KSA is equivalent to the highest international standards.NCAAA standards contains of good criterions to guide the universities in evaluating their quality performance for improvement and obtain NCAAA accreditation.However, implementing NCAAA standards without supportive systems has been found to be a very complex task due to the existence of a large number of standard criterions, evaluation process occurs according to personal opinions, the lack of quality evaluation expertise, and manual calculation.This, in turn, leads to inaccurate evaluation, develops inaccurate improvement plans, and difficulty in obtaining NCAAA accreditation.Therefore, this paper introduces a systematic model that contain smart-rubrics that has been designed based on NCAAA quality performance evaluation elements supported with algorithms and mathematical models to reduce personal opinions, provide an accurate autoevaluation, and auto-prioritization action plans for NCAAA standards.The proposed model will support academics and administrative by facilitating their NCAAA quality tasks with ease, an authenticate self-assessment, accurate action plans and simplifying accreditation tasks.Finally, the implementation of the model proved to have very efficient and effective results in supporting KSA education institution in accreditation tasks that will lead to enhance the quality of education and to obtain NCAAA accreditation.
Failure-Robot Path Complementation for Robot Swarm Mission Planning

Currently, unmanned vehicles are widely used in different fields of exploration. Due to limited capacities, such as limited power supply, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Multiple unmanned vehicles with well-planned routes are required to minimize an unnecessary consumption of time, distance, and energy waste. The aim of the present study was to develop a multiple-vehicle system that can automatically compile a set of optimum vehicle paths, complement failed assignments, and avoid passing through no-travel zones. A heuristic algorithm was used to obtain an approximate solution within a reasonable timeline. The A* Search algorithm was adopted to determine an alternative path that does not cross the no-travel zone when the distance array was set, and an improved two-phased Tabu search was applied to converge any initial solutions into a feasible solution. A diversification strategy helped identify a global optimal solution rather than a regional one. The final experiments successfully demonstrated a group of three robot cars that were simultaneously dispatched to each of their planned routes; when any car failed during the test, its path was immediately reprogrammed by the monitoring station and passed to the other cars to continue the task until each target point had been visited.
IoT and "Smart" Technology

Presents the introductory editorial for this issue of the publication.
Research on Construction Method of Glue Spreading Quality Database Based on Extreme Learning Machine

In order to ensure the accuracy of real-time online inspection system in machine vision glue spreading and reduce the deviation caused by the uncertainty of offline database establishment, this paper proposes an algorithm based on extreme learning machine to build a glue spreading quality database. Firstly, the robot position coordinates of the offline glue spreading track and the calibration result of the image processing are combined into the training sample and the test sample. Secondly, in the training stage, the "Sigmoid" function is used to construct the excitation function, and the best model is trained to establish a standard glue spreading quality database. In the best model, the test samples should be predicted and validated. The experimental results show that the algorithm can make the training time less than 0.1s, and the measurement accuracy of the calibration result is less than 0.01mm. Finally, the practice verification is carried out in the online inspection system to realize the intellectualization of the glue application.
Code-Switching Detection Using ASR-Generated Language Posteriors

Code-switching (CS) detection refers to the automatic detection of language switches in code-mixed utterances. This task can be achieved by using a CS automatic speech recognition (ASR) system that can handle such language switches. In our previous work, we have investigated the code-switching detection performance of the Frisian-Dutch CS ASR system by using the time alignment of the most likely hypothesis and found that this technique suffers from over-switching due to numerous very short spurious language switches. In this paper, we propose a novel method for CS detection aiming to remedy this shortcoming by using the language posteriors which are the sum of the frame-level posteriors of phones belonging to the same language. The CS ASR-generated language posteriors contain more complete language-specific information on frame level compared to the time alignment of the ASR output. Hence, it is expected to yield more accurate and robust CS detection. The CS detection experiments demonstrate that the proposed language posterior-based approach provides higher detection accuracy than the baseline system in terms of equal error rate. Moreover, a detailed CS detection error analysis reveals that using language posteriors reduces the false alarms and results in more robust CS detection.
Users’ Behavioral Strategies Toward Mobile App Problems: Fight or Flight

In this paper, we identify two distinct behavioral strategies for dealing with problems encountered in the use of mobile apps - fight or flight. In the fight strategy, individuals do not give up using an app when faced with a problem; rather, they experiment with different ways to cope with that problem, whereas the flight strategy refers to the user's decision to uninstall an app when they encounter a problem and/or their intention to use an alternative app. These strategies were identified from an analysis of documents, which forty-two users reported, and can be used to understand how users deal with encountered problems. The participants were asked to use a mobile app of their choice for one week and report the behavioral strategies they utilized to counter problems they experienced. According to the findings obtained from content analysis, the most reported complaints concerned the categories of interface design, functional error, feature request, and feature removal. The participants who complained about functional errors, frustrating features, and slow application speed stopped using the app (flight behavior) whereas those that were dissatisfied with the interface, a missing feature or the content of the app continued to use the app and tried to overcome the problems (fight behavior).
Cloud Security based on the Homomorphic Encryption

Cloud computing provides services rather than products; where it offers many benefits to clients who pay to use hardware and software resources.There are many advantages of using cloud computing such as low cost, easy to maintain, and available resources.The main challenge in the Cloud system is how to obtain a highly secured system against attackers.For this reason, methods were developed to increase the security level in different techniques.This paper aims to review these techniques with their security challenges by presenting the most popular cloud techniques and applications.Homomorphic Encryption method in cloud computing is presented in this paper as a solution to increase the security of the data.By using this method, a client can perform an operation on encrypted data without being decrypted which is the same result as the computation applied to decrypted data.Finally, the reviewed security techniques are discussed with some recommendations that might be used to raise the required security level in such a system.
Exploiting Particle Swarm Optimization to Attune Strong Fuzzy Partitions Based on Cuts

Cut-based strong fuzzy partitions (SFP) are characterized by cuts, i.e. points in the universe of discourse where the non-zero membership degrees of the fuzzy sets in the partition is 0.5.Cuts are useful to identify the most representative regions for the fuzzy sets involved in a SFP but pose loose constraints on the slopes of trapezoidal fuzzy sets.We address the problem of optimizing such slopes in order to maximize the performance of fuzzy rule-based systems while keeping cuts constant.This way, model performance is improved and interpretability is preserved.We use Particle Swarm Optimization to perform optimization and we analyze two different approaches for generating solution spaces.We tested the proposed approach on a number of fuzzy rule-based classifiers designed by DC* (Double Clustering with A*) on synthetic data.For all the considered models, performance is never degraded but improved in many cases, without violating any interpretability constraint.
Digital Technology And The Real Estate Industry

This paper briefly summarises the extent of the technological impact on the real estate industry, in particular, digital technology. As real estate industry is naturally characterized by several disadvantages in comparison to other industry, understanding the impact of digital technology is crucial to the industry on whether this technology will distrust the industry or complement the business process. The emergence of PropTech start-ups has made the industry players to re-evaluate its organisational strategy to align the business process to a possible threat to the present business model. Prior studies suggest that PropTech comes in there waves in which each wave is characterized by the respective period advancement in digital technology. Several studies also indicate that digital technology may not entirely disrupt the industry but do have a significant potential to act as a complement to the industry in improving its productivity and efficiency.
EpaDB: A Database for Development of Pronunciation Assessment Systems

In this paper, we describe the methodology for collecting and annotating a new database designed for conducting research and development on pronunciation assessment.While a significant amount of research has been done in the area of pronunciation assessment, to our knowledge, no database is available for public use for research in the field.Considering this need, we created EpaDB (English Pronunciation by Argentinians Database), which is composed of English phrases read by native Spanish speakers with different levels of English proficiency.The recordings are annotated with ratings of pronunciation quality at phrase-level and detailed phonetic alignments and transcriptions indicating which phones were actually pronounced by the speakers.We present inter-rater agreement, the effect of each phone on overall perceived non-nativeness, and the frequency of specific pronunciation errors.
Mapping of Independent Tasks in the Cloud Computing Environment

Cloud computing is a technology that provides many resources and facility to share data.Due to the concept of open environment in the cloud computing the request or data increases quickly.So this problem can be solved by proper utilization of tasks along with available resources.Task scheduling algorithm plays an immense role in the cloud computing environment in minimizing the time required for completion of the task assigned to the resource available.There are several algorithms introduced to solve the problem of scheduling task of several kinds but all the developed algorithms are task dependent algorithms.The major criteria of the task scheduling algorithm are to optimize resource utilization in the diverse computing environment, so as to minimize makespan and execution time so that the accountability of healthcare industry that uses cloud computing can be enhanced.The proposed algorithm is designed to deal with variable length tasks by taking the advantages of the different heuristic algorithm and ensures optimum task scheduling with various available resources to enhance the quality of the healthcare system.
Prototipe Manajemen Bandwidth pada Jaringan Internet Hotel Harvani dengan Mikrotik RB 750r2

Harvani Hotel has 5 (five) floors, where each floor has 1 (one) Access Point (AP). All Access Points, servers and several Personal Computers (PCs) are connected to the RB 750r2 microtic router which is connected directly to the Internet Service Provider (ISP) so that all users are connected to the internet network. The problem that occurs is the uneven distribution of bandwidth so that the first user connected to the internet will get more bandwidth with the highest internet access speed. This causes interference to other users who feel the speed of internet access is very slow, which results in user inconvenience in using internet access. Therefore, bandwidth management is done in this research uses the action research method, where the prototype is focused on 5 Access Points by using ISP that provide bandwidth services of 2.5 Mbps and shared equally among 5 Access Points. From the testing results using the speedtest known that every AP only get the throughput between 100 - 400 kbps. This is providing that the bandwidth used of every AP does not expand 512 kbps and the bandwidth distribution of every AP has been ordered to remove the existence of user interest.
Cervical Cancer Test Identification Classifier using Decision Tree Method

Cervical cancer is one of the most crucial and important type of cancers which needs to be treated as early as possible.The most difficult task is to detect a cancer at early stage so that necessary cure can be done on time.Several medical research works have been done towards the various medical tests which cervical cancer patients should go for.The four most frequent tests are: hinselmann, schiller, citology, and biopsy.These four tests might be expensive to afford all at a time.This research work focuses on prediction of which test a patient should go first for cervical cancer identification.a real dataset of approx.800 patients of hospital universitario de caracas' in caracas, venezuel is used for the training purpose.Decision tree method is used to build a classifier.
Mining Distributed Databases for Negative Associations from Regular and Frequent Patterns

Most of the transactional data stored in distributed databases, especially when region wise business conducted.Extensive knowledge about the business is hidden in these databases, the discovery of which will give immense power to the decisionmakers.Most of the decisions are taken around frequently occurring patterns and the positive associations that exist among those patterns.Mining a single database can yield the patterns of sales happening at different distributed locations.The regularity of the occurrence of sales pattern is equally as important as frequent patterns.Negative associations also reveal interesting discovery about the business in addition to positive associations and yet time negative associations among the regularly and frequently occurring patterns having a heavy bearing on the Business.It is also important to study the occurrence of the patterns at all the distributed locations and determine the regularity or global nature of the patterns.In this paper, Algorithms presented using which mining of negative association that exist among regular and frequent patterns mined presented considering both local and Global Locations.
A Quantum Key-based Mobile Security Payment Scheme

In view of the safety problem of current mobile security payment encryption schemes, this paper proposed a quantum key-based MSP (mobile security payment) scheme.First, this scheme introduced quantum encryption technology to solve the symmetric key problem between the payment platform server and the commercial supermarket server.Then, the quantum key was safely obtained and the QR (quick response) code was generated by using the proposed quantum key gateway.Finally, the mobile device finished the payment according to the one-time pad encryption scheme.Compared with the existing schemes, the proposed scheme can fully employ the advantages of quantum encryption technology.It can also resist multiple security threats by using the quantum key gateway.Under the real quantum key distribution and quantum key gateway environment, the feasibility and effectiveness of this scheme were proven by experimental results.
Object detection in the police surveillance scenario

Police and various security services use video analysis when investigating criminal activity.One typical scenario is the selection of object in image sequence and search for similar objects in other images.Algorithms supporting this scenario must reconcile several seemingly contradicting factors: training and detection speed, detection reliability and learning from sparse data.In the system that we propose a combined SVM/Cascade detector is used for both speed and detection reliability.In addition, object tracking and background-foreground separation algorithm together with sample synthesis is used to collect rich training data.Experiments show that the system is effective, useful and suitable for selected tasks of police surveillance.
DRAW AND ORDER – MODELESS INTERACTIVE ACQUISITION OF OUTLINES

Abstract. The representation of an object’s outline with polygons or more specific shapes is the first choice for interactive outline acquisition. Usually, an operator has to select appropriate tools to capture an object’s outline according to his or her notion of the shape with its underlying construction principles. We propose a modeless approach guided by geometric reasoning, i.e., the user does not have to select operation modes according to his or her model. To cope with modern input devices such as touch pens, styluses, or fingers, we consider strokes as input in arbitrary order. By approximating theses strokes with straight line segments, we obtain estimated levels of noise accounting for the uncertainty of the acquisition. These straight line segments are the basis for the subsequent hypothesis generation to recognize geometric relations. After identifying sets of consistent and nonredundant constraints, we enforce these constraints in an adjustment process. To demonstrate the usability of the proposed approach, we capture the outlines of buildings and roof areas in true orthophotos of a benchmark data set.
AN ANALYSIS OF STUDENTS’ MOTIVATION IN LEARNING ENGLISH AFTER GIVEN REWARDS AT THE EIGHT GRADE STUDENTS’ OF SMPN 3 RAMBAH

The purpose of this research was to find out the eight grades students’ motivation in learning English after given rewards at the SMPN 3 Rambah. The students’ was low motivation in learning English. This research was design a descriptive qualitative research and there were 2 instruments were used in collecting the data. There are 2 instruments that is questionnaire and interview. The researcher used three classes. There were 60 samples joined in the research. The purpose of giving the questionnaire was to find out the students motivation in learning English after given reward and interview given was purposed to find out the students’ opinion of their English learning. The result of the research in the questionnaire showed that from 60samples of the research, there were 57 students can be classified have strong motivation in learning English after given rewards and there 3 students can be classified have fair motivation. The calculation was taken from the students’ answer in questionnaire and converted to the numeric score on Likert scale. Based on the finding of this research, the questionnaire score and classify it. The researcher found that the average of students questionnaire score was 78,8 and it means that their motivation averagely can be classified Good categories.
KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG

Heart disease is one of the types of deadly diseases whose treatment must be dealt with as soon as possible because it can occur suddenly to the sufferer. Factors of heart disease that are recognized based on the condition of the body of a sufferer need to be known from an early age so that the risk of possible instant attacks can be minimized or can be overcome in various ways such as a healthy lifestyle and regular exercise that can regulate heart health in the body. By looking at the condition of a person's body based on sex, blood pressure, age, whether or not a smoker and some indicators that become a person's characteristics of heart disease are described in a study using the Neural Network and Naïve Bayes algorithm with the aim of comparing the level of accuracy to attributes influential to predict heart disease, so the results of this study can be used as a reference to predict whether a person has heart disease or not.
A Novel Sparse Representation Based Visual Tracking Method for Dynamic Overhead Cranes

Efficient tracking of heavy loads is the overall goal for overhead cranes in the workplace. This article presents a new way to simply and effectively track overhead cranes at higher speeds. A real-time tracking method is presented here to make tracking the overhead crane load not only accurate, but also faster. First, to observe and extract details and information to use in the appearance model, a measurement matrix is constructed here. Second, a sparse representation is also adopted to track dynamic overhead crane features effectively. Finally, a Naïve Bayesian classifier has been used as a binary classification in a compressed domain. Experiments on the VOT2013 benchmark and constructed data Cranes40, the results demonstrate that the presented sparse representation tracking method can successfully track cranes in real-time.
A roadmap for a comparison framework for an adaptable software process improvement framework in small software companies

Poor software quality due to failure to organize development processes using SPI is visible in small software companies, although these companies are significant to industry and world economy; the challenge of quality needs much more attention.The development of different frameworks to sort the development process in SSC continues to leave a lot to be desired.This work leads us to the development of a comparison framework which will ultimately end with an adaptable SPA and SPI framework for SSC.From a SLR to identify the factors affecting SSC and mapping them to the specific processes in which they occur, we analyze the software development environment and identify SPI frameworks that have registered success in similar environments of SSC to help us adopt best practices from which comparison is made to generate requirements for an adaptable SPI framework within context for small companies.