Reviewerly/exHarmony huggingface dataset:
Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations

Soft biometrics provide information about the individual but without the distinctiveness and permanence able to discriminate between any two individuals. Since the gaze represents one of the most investigated human traits, works evaluating the feasibility of considering it as a possible additional soft biometric trait have been recently appeared in the literature. Unfortunately, there is a lack of systematic studies on clinically approved stimuli to provide evidence of the correlation between exploratory paths and individual identities in “natural” scenarios (without calibration, imposed constraints, wearable tools). To overcome these drawbacks, this paper analyzes gaze patterns by using a computer vision based pipeline in order to prove the correlation between visual exploration and user identity. This correlation is robustly computed in a free exploration scenario, not biased by wearable devices nor constrained to a prior personalized calibration. Provided stimuli have been designed by clinical experts and then they allow better analysis of human exploration behaviors. In addition, the paper introduces a novel public dataset that provides, for the first time, images framing the faces of the involved subjects instead of only their gaze tracks.
Bahasa Indonesia language program impact analysis in the Polytechnic University of the Philippines Manila: a basis for the bahasa Indonesia’s inclusion in the ab English language studies’ and ab literary and cultural studies’ curriculum

The present study aims to know the impact of the Bahasa Indonesia (BIPA) training on enabling the macro skills among the 144 respondents from the Polytechnic University of the Philippines in Sta. Mesa, Manila as well as the perceived benefits of including the language as one of the foreign language courses in the respondents’ curriculum. The researchers used the descriptive research design. The respondents were selected using purposive sampling and the data were gathered through a survey form with 9 questions. Through the grand mean, it was found out that powerpoint presentations were impactful in enabling them to perform reading tasks. Writing paragraphs were impactful for their writing tasks while Listening to lecture and doing dialogues were impactful for enabling them to perform listening and speaking tasks. Opportunity to work in other South East Asian Countries emerged as the mostly agreed benefit of including Bahasa Indonesia in the curriculum.
SKETCH TO IMAGE GENERATION USING GENERATIVE ADVERSARIAL NETWORK

A Generative Adversarial Network is primarily based on the competition between the two neural networks, namely the generator and the discriminator. This technique is utilized to develop a system that produces realistic images based on the black and white sketch that is provided by the user. Sketches made by novice artists can be converted to colorful images with this system. Each time the user uploads the same sketch, slight changes in the color perspective can be achieved. The model has been trained and tested for the generation of shoes and handbags with an epoch range of 100 for training the model. Since it is a versatile network, given any dataset it can generate images accordingly. The model takes 50 seconds on an average to generate an image from a sketch. The quality of the images has been tested based on Peak Signal to Noise Ratio and Structural Similarity Index. This paper proposes an idea of including Super Resolution Convolutional Neural Network (SRCNN) to the deployed model. The model is presented with the help of a Graphical User Interface (GUI).
Analisis Keberhasilan Penerapan E-Learning di Fakultas Teknik Universitas Riau

E-learning is a learning system that uses information systems and internet and intranet technology. E-learning is one of the learning tools in the era of the technological revolution 4.0 which is one of the programs of the Ministry of Higher Education Research. Higher education institutions in Indonesia have used e-learning in learning activities. This study aims to evaluate, and analyze the factors that influence the successful use of e-learning in the Faculty of Engineering. Respondent in this Research consisted of 330 people consisting of students and lecturers of the Faculty of Engineering who use e-learning. Sampling is done by accidental sampling method. The model used in the study is the successful model of Delone and McLean using descriptive analysis and Structural Equation Models (SEM) analysis using SMARTPLS 2.0. The results showed the high level of success in using e-learning at Engineering Faculty at Riau University in semantic succes level, then technical success level, and effectiveness success level. Factors that influencing the Engineering Faculty of Riau University are information quality and service quality. Keywords: delon and mclean, e-learning, SEM, Faculty of Engineering University of Riau
PENINGKATAN KETERAMPILAN MENULIS CERPEN MENGGUNAKAN TEKNIK TERBIMBING DENGAN MEDIA CERPEN SISWA KELAS IX A SMP-IT AR RAHMAH PACITAN

The purposes of this article is to explain the process and the improvement ability to write a short story using guided exercises at IX A SMP IT Ar Rahmah Pacitan student. This type of research finished by using descriptive method trough four stages in each cycle ie planning, action, observation and reflection. The research data in the form of short stories written tests, interviews, observations and field notes sheet student. Based on the results of the study concluded that the precycle average is 46,11% of the students qualified nearly enough, in cycle 1 is sufficiently increased to 65,29% in cycle 2 and the average value of student learning to write short stories increased to 89,61% with exelent qualifications.
Integrity Issue on Grab Drivers' Fraud Phenomenon

Recent development of technology undeniably and rapidly goes into orbit. Internet technology turns out to be a media to simplify anyone's work. Along with its developments and the service industry revolutions, a new breakthrough has emerged, namely smartphone app-based ride-hailing services. This eliminates the problems existing in conventional services, such as the difficulty of finding rates, uncertain rates and passenger safety guarantee. This study aims to describe the fraud committed by the Grab drivers and its impact for the drivers and Grab, and how to overcome it. This study uses qualitative research with a phenomenological approach. The object of this research is Grab drivers in Malang. The data used are primary and secondary data obtained through interviews, Application screenshots, and news items. The results of this study indicate that there are some frauds committed by Grab drivers such as (1) fictitious orders, (2) account sales, (3) account leases. To anticipate it, Grab has initiated a program namely "Grab Lawan Opik". One of Grabs internal controls is the selfie verification used to reduce account sales and leases.
PENGARUH PENGGUNAAN MULTIMEDIA PEMBELAJARAN DAN MOTIVASI TERHADAP PRESTASI BELAJAR BAHASA INGGRIS DI SMA 10 PALEMBANG

The influence the use of multimedia learn and motivation achievement of students in learning English lesson in school district 10 Palembang. Guided by Waspodo and H.Lin Yan Syah. In the process of achieving the goal of learning, the use of multimedia motivation to learn is also a very big role on the interpretation of student learning. Due to the learning motivation in students , thus increasing learning achievement is also high on student achievement. The purpose of the study is to investigate and examine the effect of multimedia learning and motivation against students in achievement learning English lesson in school in school district 10 Palembang. The study also wanted to know and test whether the two exogenous variables from the above that the dominant influence on student achievement in English language subject in school district 10 Palembang. Causal type of research is the research that aims to explain the influence of several variables. The object of the study were students and a sample set a of 60 samples. By using random sampling model analysis was used to prove the hypothesis that use of the Path Analysis Testing (path analysis) with AMOS, USING Structural Equation Model (SEM). SEM results with the value of CR (Critical Ratio) of-0 082 for multimedia on learning achievement and 8595 for Achievement Motivation Learning to learn, with CR Values &gt; 1671 CR means of two values which are able to explain and a strong influence on the variable endogenous variable is the motivation to learn. Where multimedia effect on motivation to learn is casually with the CR 3297
Efficient Multi-User and Multi-Keyword Ranked Search Scheme for Encrypted Cloud Data

With the improvement of cloud storage, greater data users are willing to outsource their data to cloud offerings.For privacy issues, sensitive data must be encrypted before outsourcing.There are numerous searchable encryption schemes to ensure statistics availability.However, the present search schemes pay little attention to the performance of facts users' queries, specifically for the multi-user situation.To allow the cloud servers to perform a search without understanding any statistics, to assemble a novel search and efficient technique based totally on the encrypted cloud statistics the usage of the "ECC cryptography scheme".To obtain an efficient search, for every statistics person, a tree-based totally index encrypted with an additive order and secure characteristic is constructed.In order to rank the search outcomes, the proposed method utilize and model the relevance ratings of facts documents and advise a ``Iterative Deepening Depth-First Search'' (ID-DFS) set of rules to attain the ranked results.To perform a keyword-primarily based query, the complete records set needs to be decrypted despite the fact that the matching end result set may be very small.It poses insufferable query latency and incurs unacceptable computational overhead.Finally, the proposed approach confirms the security and performance of the proposed scheme through complete theoretical evaluation and big experiments with real dataset.
Calibration and Evaluation of Outlier Detection with Generated Data

Outlier detection is an essential part of data science --- an area with increasing relevance in a plethora of domains. While there already exist numerous approaches for the detection of outliers, some significant challenges remain relevant. Two prominent such challenges are that outliers are rare and not precisely defined. They both have serious consequences, especially on the calibration and evaluation of detection methods. This thesis is concerned with a possible way of dealing with these challenges: the generation of outliers. It discusses existing techniques for generating outliers but specifically also their use in tackling the mentioned challenges. In the literature, the topic of outlier generation seems to have only little general structure so far --- despite that many techniques were already proposed. Thus, the first contribution of this thesis is a unified and crisp description of the state-of-the-art in outlier generation and their usages. Given the variety of characteristics of the generated outliers and the variety of methods designed for the detection of real outliers, it becomes apparent that a comparison of detection performance should be more distinctive than state-of-the-art comparisons are. Such a distinctive comparison is tackled in the second central contribution of this thesis: a general process for the distinctive evaluation of outlier detection methods with generated data. The process developed in this thesis uses entirely artificial data in which the inliers are realistic representations of some real-world data and the outliers deviations from these inliers with specific characteristics. The realness of the inliers allows the generalization of performance evaluations to many other data domains. The carefully designed generation techniques for outliers allow insights on the effect of the characteristics of outliers. So-called hidden outliers represent a special type of outliers: they also depend on a set of selections of data attributes, i.e., a set of subspaces. Hidden outliers are only detectable in a particular set of subspaces. In the subspaces they are hidden from, they are not detectable. For outlier detection methods that make use of subspaces, hidden outliers are a blind-spot: if they hide from the subspaces, searched for outliers. Thus, hidden outliers are exciting to study, for the evaluation of detection methods that use subspaces in particular. The third central contribution of this thesis is a technique for the generation of hidden outliers. An analysis of the characteristics of such instances is featured as well. First, the concept of hidden outliers is broached theoretical for this analysis. Then the developed technique is also used to validate the theoretical findings in more realistic contexts. For example, to show that hidden outliers could appear in many real-world data sets. All in all, this dissertation gives the field of outlier generation needed structure and shows their usefulness in tackling prominent challenges of the outlier detection problem.
Measuring Performance of Blockchain Enabled IoT over Edge Computing System

In today's era there has been a huge demand in the automation space where most of the human tasks could be operated remotely. IoT (Internet enabled devices) makes it possible with the usage of sensors and gateways. The success of mission critical systems depends upon how quickly such devices respond. Edge computing can boost up the responsiveness of these devices by offloading certain computational tasks closer to these devices. Merging these technologies with blockchain makes this distributed system more secure and reliable. Our paper focuses on these technologies, their working and how well they complement each other when integrated. The main focus is to shed some light on the key metrics for measuring responsiveness of the system, the challenges encountered while performance testing such applications and to provide the possible solutions for the same. We have referred published literatures around these topics and collated insights under the hood. The main purpose of this paper is to maximize the ROI made on the testing activities of such complicated integrated system.
Perbandingan Penyimpangan Prinsip Kesopanan Budaya India dan Perancis Dalam Film The Hundred-Foot Journey

This study aims to investigate the comparison of the deviations of the principle of politeness in Indian and French culture in a film called The Hundred-Foot Journey. Here the author uses Leech's theory of politeness principles which consists of maxims: (1) maxim of wisdom, (2) maxim of generosity, (3) maxim of praise, (4) maxim of humility, (5) maxim of compatibility, and (6) maxim of sympathy. Of these 2 cultures, only 3 maxims deviations were found consisting of: (1) deviations of wisdom maxims, (2) deviations of humility maxims, and (3) deviations of the maxims of agreement. The most common deviations in the principle of politeness in these two different cultures are maxim of agreement. The culture that often violates the maxim of agreement is Indian culture, which commits 16 violations, while France has 11 violations.
Dual Objective Task Scheduling Algorithm in Cloud Environment

Cloud computing takes into account to permit the sharing of resources like networks, servers, storage, applications, and services to achieve access of these resources from any computer in the world through the Internet.The task scheduling algorithms are of NP-hard in nature.The importance of task scheduling is to map the tasks with the appropriate resources for execution.Makespan is the difference in time from start to end of the scheduling.Load balancing is the sharing of workload among the resources.The objective of any scheduling algorithm is to reduce the makespan with proper utilization of resources.In this paper a new algorithm (DOTS) is developed with the realistic dual-objective criteria that minimize the makespan and balance the load across the resources.The distribution of tasks among the resources is also evaluated using the coefficient of variation.The dual objectives are transformed into a single score using weighted sum method.The results clearly indicate that the proposed DOTS technique performs well when compared with seven other scheduling algorithms.
Avanzar en la transformación educativa tras la pandemia del coronavirus

The coronavirus pandemic, that has affected almost every country on the planet, has mean an unprecedented shake-up on a global scale in the world of education. Has shown that we are facing a change of era, the possibility of the greatest leap or educational transformation of the century. This work proposes key points to approach the educational transformation and carry it out.
Dimensiones del Ecosistema Digital Universitario: Validación del Instrumento «University Digital Ecosystem» (UN-DIGECO)

The purpose of the study was to design and validate the UN-DIGECO (University Digital Ecosystem) instrument, aimed at learning about the use that undergraduates make of social media and/or digital applications. The content and comprehension validation was carried out in three phases: construction, refining and psychometric validation, based on confirmatory factor analysis, supported by the initial theoretical construct. The items, designed by experts, are in line with indicators assigned to six theoretically defined dimensions: Learning, Social Interaction, Content Creation, Online Consumption, Leisure, and Recreational. Own indicators were integrated together with others adapted from similar published instruments. The preliminary version of the questionnaire was tested by a group of 25 students, some items were refined and redefined to facilitate their understanding. The pilot version included 44 items and involved 484 students from Spanish and Colombian universities. The authors analysed the latent structure of the items by applying exploratory factor analysis and, after examining the composition of the factor structure of the indicators, excluded the non-relevant ones. The results confirmed the existence of these six dimensions, which include 37 items, with a Cronbach's alpha greater than 0.8. The instrument and its components show appropriate psychometric properties of validity and reliability, being applicable to other contexts. The originality of the designed questionnaire lies in the structuring of the information, establishing and integrating six dimensions related to the use university students make of SM.
Twitter Sentiment Analysis with Diabetic Drugs Using Machine Learning Techniques with Glowworm Swarm Optimization Algorithm

Twitter is a Social Media that distributes opinions and sentiments of the people in day today life to friends and general public.Many people use twitter to communicate their side effects/benefits of diabetic medicines.Other people in turn seek these posts to gain feedback regarding their own Adverse Drug Reactions(ADR).Opinion mining of twitter data is an area that has experienced enormous growth in the last decade.For this purpose various Machine Learning(ML) Techniques and tools have been created.In this paper ML techniques were used in opinion analysis for processing information about ADR on taking diabetic drugs-Metformin(generic and brand name).The aim of this paper is to identify the optimal ML algorithm.Glowworm Swarm Optimization(GSO) is used to derive the optimal feature selection and is combined with various classification algorithms namely Naïve Bayes(NB), K-Nearest Neighbor(KNN) and Support Vector Machine(SVM).The Experimental result shows GSO+SVM combination proved maximum accuracy of 94%.
THAIWRITTENNET: THAI HANDWRITTEN SCRIPT RECOGNITION USING DEEP NEURAL NETWORKS

Thai is a non-tonal language usage for 70 million speakers in Thailand.A variety of Thai handwrit-ten styles has been a challenge in handwriting recognition.In this paper, we propose a novel "ThaiWrittenNet" based on Convolutional Neural Network (ConvNet or CNN) with a cutout to identify the handwritten recognitions.Deep Belief Network (DBN) is also combined with Con-vNet to reduce network complexity.From the results, ThaiWrittenNet outperforms the flat Con-vNet and other handcrafted features with traditional machine learning algorithms.It appears that DBN helps ConvNet to improve the accuracy of Thai-handwritten recognition.
Analysis of Attribute Reduction Effectiveness on The Naive Bayes Classifier Method

Abstract Naïve Bayes is a prediction method that contains a simple probabilistic based on the application of the Bayes theorem (Bayes rule) with the assumption that the dependence is substantial. Classification is a technique in data mining to form a model from a predetermined data set. Data mining techniques are the choices that can overcome in solving this problem. Therefore, researchers make a comparison of Naïve Bayes by modifying using PCA. Improving the comparative performance of the Naive Bayes method by weighting using Principal Component Analysis (PCA) and Naive Bayes was carried out in this study. Research conducted on the classification model of Naïve Bayes (PCA + Naïve Bayes) using the Iris dataset that simplified into two attributes, four classes and 147 instances with an accuracy rate of 97.78% with a classification error rate of 2.22%. Meanwhile, the Conventional Naïve Bayes classification model uses four attributes with four classes from the Iris dataset, which has an accuracy rate of 95.45% with a classification error rate of 4.55%. Based on the test results of the classification model, it can be concluded that the success of the PCA can be used as a reference to improve the accuracy performance of the Naïve Bayes classification model.
Narrative Objects in Virtual Reality

This chapter questions what differentiates interactive narratives in virtual reality, proposing spatial storytelling as a dimension natural to the medium. We analyze Offland, a project which consisted in the creation of a fictional world and a virtual reality experience, to explore this medium’s immersion and storytelling. The lessons taken from user testing help understanding how goals, exploration, linearity and interactivity relate to the fictional immersion and experience. From there we look at the structure of space and narratives as constructed by virtual objects with narrative value. It is clear that interactive storytelling is based on modularity (Miller in Digital storytelling : a creator’s guide to interactive entertainment. CRC Press 2014; Murray in Hamlet on the holodeck: the future of narrative in cyberspace. The Mit Press, Cambridge, Massachusetts 2017), and when looking at the spaces, events and action points, objects are what sustain these modules. Objects can be essential to the intended narrative, can add secondary value to the experience or can be decorative, giving spatial context and immersion. Through details an object can indicate a much larger fictional world and allow immersion in it (Pacat in Fantasy worldbuilding: the power of detail. WQ. writers in their landscapes. Queensland Writers Centre 2017). Objects can engage in the narrative, through indicating different atmospheres, pacing, tension and goals. With interactivity, the user can create or alter virtual objects, adding their own contribution to the narrative. Objects connect the physical and the virtual, being that they can act as a visual interface or a controller, to interact with the virtual environment. The use of objects is natural form of interaction and narrative, as it presents virtual reality as we are used to, from the mental schemas built from our natural lives in physical reality.