Reviewerly/exHarmony huggingface dataset:
Different Realism Designs of 2D Virtual Agents and i ts' Arousal Effect on Students’ Emotions in Learning

This research was aimed to analyse students’ emotions caused by the character’s realism design in the dimension of arousal and its’ effect on emotions in learning. Thus, 2D female virtual agents with four different realism appearance namely realistic, semi-realistic, stylized and cartoon-like were designed and developed. Thus, quasi experimental design was used to answer the research questions derived from this study. The data obtained from the experiment was analysed using ANOVA, post hoc and bootstrap mediation analysis. A number of 600 Electrical Engineering students were chosen from seven polytechnics in Malaysia as respondents in the experiment. From the research it was found that cartoon-like design obtained the lowest mean score arousal followed by stylized, realistic and semi-realistic designs. However, cartoon-like design scored highest mean score for emotions in learning followed by semi-realistic, realistic and stylized designs. Therefore, cartoon-like agent is the best agent among others in inducing positive emotions in learning. In addition, the mediation analysis shows that arousal has mediating effects on relation between different realism designs and emotions in learning. In conclusion, this study recommends cartoon-like agent as the best suitable design for 2D virtual agent followed by semi-realistic, realistic and stylized designs subsequently. Finally, the findings of this study can be a useful guideline for multimedia designers in determining the ideal 2D virtual agent appearance to elicit maximum impact of it in promoting positive emotions in multimedia learning environment.
Designing a Simulation Platform for Generation of Synthetic Videos for Human Activity Recognition

The field of human activity recognition from video data has recently made great strides. However, the large amount of labelled data needed to train activity recognition models remains a common bottleneck. This paper describes the design of a novel simulation platform to procedurally generate synthetic videos of household activities, which randomizes portions of the virtual scene like camera position, human model, and interaction motion to introduce video variation. We describe our system design, evaluation methodology, and discuss experimental results.
Flowcharting

Programming and coding are just as much about thinking and planning as it is about sitting in front of a keyboard. Often, a developer needs to plot out in their mind or on paper how a program is going to work. This can make the coding process much easier, especially if you are working together with others on a project.
AN Efficient Ant Colony Optimization Algorithm for Resource Provisioning in Cloud

Paper Several Ant Colony Optimization (ACO) techniques for Cloud resources management are considered by many researchers. ACO techniques in existence still need some improvements for effective resource management and planning with the heterogeneous and voluminous services offered. Hence, an optimized hybrid scheme that combined deterministic characteristics for exploiting ACO search process is proposed. Spanning Tree (ST) algorithm was chosen in the hybridization that obtained a faster convergence speed, minimized makespan time and throughput that ensured resource utilization in least time and cost. Extensive experiments were conducted in cloudsim simulator provided an efficient result compared to other ACO techniques as it significantly improves performance.
Lost-Min Voting Strategies for Speeding up Multi-SVMs

Support vector machines (SVMs) possess good accuracy in big data classification.However, the computational cost in both training and testing stages is a critical issue.The authors recently proposed a twophase sequential minimal optimization to largely reduce the training cost (tested with 3186-70,000-sample datasets).The authors now focus on speeding up the testing speed of SVMs for multi-class classification.A lost-min strategy is proposed to accelerate the voting algorithm used in multi-SVMs.The number of the used binary classifiers is reduced from an order of to (nearly to ).The proposed lost-min voting strategy was tested with DNA dataset (bioinformatics), Usps datasets (handwritten digits), Letter dataset (English alphabet) and Satimage dataset (satellite imagery of Earth).The time complexity for all of the datasets approaches to algorithm and the accuracy is remained at the same time.
Viewing the Rings of a Tree: Minimum Distortion Embeddings into Trees

We describe a (1 + ε) approximation algorithm for finding the minimum distortion embedding of an npoint metric space, (X, d X ), into a tree with vertex set X.The running time of our algorithm isparameterized with respect to the spread of X, denoted by ∆, the minimum possible distortion for embedding X into any tree, denoted by δ opt , and the doubling dimension of X, denoted by λ.Hence we obtain a PTAS, provided δ opt is a constant and X is a finite doubling metric space with polynomially bounded spread, for example, a point set with polynomially bounded spread in constant dimensional Euclidean space.Our algorithm implies a constant factor approximation with the same running time when Steiner vertices are allowed.Moreover, we describe a similar (1 + ε) approximation algorithm for finding a tree spanner of (X, d X ) that minimizes the maximum stretch.The running time of our algorithm stays the same, except that δ opt must be interpreted as the minimum stretch of any spanning tree of X.Finally, we generalize our tree spanner algorithm to a (1 + ε) approximation algorithm for computing a minimum stretch tree spanner of a weighted graph, where the running time is parameterized with respect to the maximum degree, in addition to the other parameters above.In particular, we obtain a PTAS for computing minimum stretch tree spanners of weighted graphs, with polynomially bounded spread, constant doubling dimension, and constant maximum degree, when a tree spanner with constant stretch exists.
Error Analysis Speaking on Foreign Speakers: Case Study of Brazilian Youtuber

Bahasa Indonesia both orally and in writing that have rules of order in order to be used effectively. Foreign speakers are part of the Indonesian language learners because Indonesian becomes a foreign language for the learner. In the learning process found various errors. The purpose of this research is to explain and describe Indonesian language by foreign speaker, youtuber from Brazil. This research uses descriptive qualitative method with approach. The data source used video from youtube. Technique of data completion using note note. Then, the data is compiled by mistake. The results of the study indicate the existence of language errors at the level of phonology and morphology.
Employees’ Perception towards Effectiveness of Induction Program

The induction program in an organization becomes effective only if it fulfills the objectives and minimizes the gap between employees’ perception to that of organization’s goals. Previous research studies conclude that, employees’ effectiveness has a significant effect on job competence, which in turn emphasizes on the induction programs. Hence, there is a need to understand the synergy between employees’ perception towards induction program and the effectiveness of the same. Keeping in view, the researcher conducted an empirical study in a renowned IT Enabled Services firm in Chennai, having branches in USA, UK and Australia. The primary aim of the study is to understand the perception of employees’ towards the effectiveness of induction program conducted in their organization. The hypotheses have been formulated to find the existence of relationships between dependent variables. The study administered a structured questionnaire to collect primary data, comprising of 120 samples. The collected data has been analyzed using descriptive research method. The study concludes that the induction program is an integral part in building organization-employee relationship in accordance with job competence
MEChain: A Multi-layer Blockchain Structure with Hierarchical Consensus for Secure EHR System

Although Electronic Health Record (EHR) systems are widely used in health care organisations, they still face security and privacy problems. Blockchain is considered a promising technology that could help overcome many of these problems. However, efforts to incorporate blockchain into EHR systems so far have shown heavy overhead, unsuitable software architectural structures and inefficient consensus protocols. We present ME Chain, a multi-layer blockchain structure, which aims to solve the adoption, storage and consensus problems when implementing blockchain in EHR systems. The multi-layer structure is better suited for the operational hierarchy in health organisations and is supported by the following novel features: (i) a consensus implementation optimised for the multi-layer operations, which solves the byzantine failure in both layers, hence guarantees consistency, it also reduces request confirmation latency and overhead and provides every layer with the ability to correct the faults, (ii) a data synchronisation method for some nodes that does not receive the correct information or simply get offline to catch up with the system, data verification and retrieve mechanisms to protect data integrity. Experiments are conducted to evaluate ME Chain in terms of security, performance and storage cost. The results show that ME Chain can establish a secure EHR with high performance and acceptable storage cost.
A hybrid approach based on transfer and ensemble learning for improving performances of deep learning models on small datasets

The need for high-volume data is one of the challenging requirements of the deep learning methods, and it makes it harder to apply deep learning algorithms to domains in which the data sources are limited, in other words, small.These domains may vary from medical diagnosis to satellite imaging.The performances of the deep learning methods on small datasets can be improved by the approaches such as data augmentation, ensembling, and transfer learning.In this study, we propose a new approach that utilizes transfer learning and ensemble methods to increase the accuracy rates of convolutional neural networks for classification tasks on small data sets.To this end, we generate different-sized sub-networks by fragmenting an existing large pre-trained network then gather those networks to form an ensemble.For ensemble scoring, we also suggest two new methods.Conducted experiments with the proposed technique, on a randomly sampled Cifar10 small subset dataset, reveals promising results.
H uman D evelopment I ndex Forecasting using Exponentially Weighted Moving Average

Human Development Index (HDI) is an indicator to see the ruling government achievement, especially in the human development aspect. It was introduced by United Nations Development Programme (UNDP) in their 1990 report and integrated three basic dimensions of human development, i.e., the ability to lead a long and healthy life, to acquire knowledge, and to achieve a decent standard of living. Indonesia itself was ranked 113 out of 188 UNDP countries and ranked fifth among ASEAN countries for HDI value based on UNDP Human Development Re-port 2016: Human Development for Everyone. In this study, we are trying to forecast Indonesia’s HDI using Exponentially Weighted Moving Average (EWMA) method. EWMA is a popular and widely used method to smooth out random fluctuations. Based on the experiments conducted in 34 provinces in Indonesia, EWMA has been successfully applied to forecast the human development index in Indonesia. It has an average score of MSE and MAPE values at 1.002279153 for MSE and 1.374754513% for MAPE.
Challenges in Face Detection and Recognition techniques

Face Recognition System is popular topic in the biometric world .This system provide Features to detect the person’s face and identify on basis of existing records in database .The aim of this study is to described how to show various facial features of an image. Face Recognition system, based on Biometric AI, uniquely finds out a person by analyzing the person's facial textures and shape. In this paper, our aim is to study various face detect and recognition techniques such as Harr Like Feature Algorithm resulting to retort criminality and public crisis. Also, some facial recognition approaches PCA and LDA have been discussed in the research paper for abstracting the image information.
A multidimensional service template for data analysis in highway domain

In highway domain, business analyses are always multidimensional on massive data for the traffic monitor and control. It is tedious to develop data analysis jobs from scratch and is hardly to consider comprehensive factors from requirements. In this paper, we propose a domain specific service template on massive toll data in highway domain. Based on the service template, abundant multidimensional analysis jobs as services can be built and managed flexibly. In a practical project, our method proves the feasibility and advantages by exhaustive experiments and case studies.
Simulation of grasping attitude control of robotic arm based on synovial error feedback

In order to improve the attitude stabilisation of robotic arms during grasping, a method of grasping attitude control of the robotic arm based on synovial error feedback is proposed. Based on it, the attitude parameter information of the robotic arm is measured and fused by using multi-sensor fusion information acquisition method to construct a constraint parameter model and control objective function for grasping attitude control of the robotic arm. The experimental results show that the proposed method has high control precision in the attitude control of the robot arm, and can accurately track the position information of the object to be captured in real time, realise zero error capture, and the parameter tracking performance is good, which improves the grip on the robot arm. Taking the control quality of the attitude, the overall has an application advantage.
Energy-Based Modeling and Simulation Framework Based on Deep Learning

In recent years, neural networks have made remarkable progress in various fields. In particular, a method called Hamiltonian neural network has been developed to construct a model for data that is obtained by observing a phenomenon considered to follow classical mechanics, while preserving the physical properties of the phenomenon. The method has been extended in various ways; however, no discrete-time model that preserves energy conservation and dissipation laws has been proposed. In this study, we propose such a model by combining a method called the discrete gradient method with deep learning. In general, research on deep learning involves not only building models, but also making predictions using the models, which in this case corresponds to physical simulation. Therefore, the proposed framework can be used not only for modeling but also for simulation.
Privacy-preservation in data pre-processing for web usage mining

Many online systems offer recommendation of varied aspects to web users to provide enough utility and the right kind of user preference data. Most of these recommendations arise out of well-processed underlying data from data mining procedures. The data mining procedures tend to expose seemingly private data item values to unintended analysts, especially that of demographic data. In this paper, the research study proposes a privacy-preserving framework architecture that enforces privacy on the data item values of web log and demographic data of users. The privacy issues are addressed by introducing a privacy-preserving database that stores a privacy commitment tuple; composed of the data item value and the privacy-preservation property values. This methodology approach proposes to offer merits in ensuring the protection of web users' privacy information during data mining tasks. Additionally, the approach offers different levels of protection; where on the user-centric level, users' personal privacy preferences are enabled, as part of subscribing to the laid down privacy policy on the web system. On the other hand, general privacy protection is enforced at the system-level by service providers (SPs).
Soil discharging mechanism for seafloor drilling robot

This work introduces a soil discharging mechanism that involves water jetting to further the development of a seafloor robotic explorer that can excavate and collect samples of rare earth elements.The excavation depth of previously reported drilling robots has been limited to 430 mm; the discharging outlet became buried in the ground, making it difficult to discharge the drilled soil.
Measuring Citizen Satisfaction with Public Services Delivery: Evidence from Kuwait Public Sector

The objectives of this study are to examine the degrees of public satisfaction with the quality of public services provided by the ministry of public work in Kuwait.As well as to identify the most important service quality indicators, that determine citizen satisfaction.We would like to emphasize that the ministry of work is just an example represents the public sector where all government organizations work under the same laws and regulations.This study administered a survey to meet these goals, where we designed, revised, and launched a structured questionnaire among 500 citizens who have been receiving services from the ministry of work.Of which only 439 questionnaires were received in completed forms, with response rate close to 87.8 %.Descriptive statistics, expletory factor analysis, correlation analysis, and structural equation modeling statistical techniques were employed to identify the most influential quality indicators on citizen's satisfaction with the government services.The results indicated that satisfaction with development and maintaining of infrastructure represents 69% of the sample, development and maintenance road network makes 76%, employee courtesy and hospitality accounts for 63%, rules and regulations explains 60%, and satisfaction with ease of procedures signifies 62%.Moreover, the path analysis statistical technique revealed that government rules and regulation has the most impact on overall satisfaction among other quality indicators 45%, while employee's hospitality and courtesy has 38% effect, infrastructure has 20% effect, road development has 9% effect, and finally, respondents have negative (-12)% impact on overall satisfaction.
Evolutionary Method o f Detecting Intrusions u sing Different Population Dynamics

Chaos theory plays a vital role in any evolutionary based algorithms for avoiding the local optima and to improve the convergence speed. Various researchers have used different methods to increase the detection rate and to speed up the convergence. Some researchers have used evolutionary algorithms for the same purpose and has proved that the application of those algorithms provide good results. Most of the researchers have used population sizes which remains constant throughout the evolution. It has been seen that small population size may result in premature convergence and large population size requires more computation time to find a solution. In this paper, a novel application of different population dynamics to the genetic programming (GP) algorithm has been applied to manage the population size. The main focus was to improve the accuracy of the normal GP algorithm by varying the population sizes at each generation. The experiments were conducted on the standard GP algorithm using static and dynamic population sizes. Different population dynamics has been used to check the effectiveness of the proposed algorithm. The results obtained has shown that dynamic population size gives better results compared to static population size and also solves the problem of local optima.
Relationship of Leadership and Organizational Climate Indispensable Element to Facilitate Employee Engagement

Purpose – Present study is a descriptive and analytical in nature aiming to bring out the relationship of leadership and organizational climate on employee engagement of the employees working in IT companies of South India. Design/Methodology/Approach- This study adopts descriptive and analytical approach it, starts with clear variables which are derived from theory and based on these variables. The data for the study was collected through the structured questionnaire. The sample for the present study was collected from the IT companies working in South India. A total 785 questionnaires were returned by the respondents, this indicating a response rate of 59 %. The questionnaire uses five point Likert type response format is used for all the measures ranging from strongly agree to strongly disagree. SPSS 21 and Microsoft excel are the statistical packages and Independent correlation and regression are the tools used in the present study Findings- A significant relationship was found between leadership and organizational climate with employee engagement of the employees working in IT companies of South India. It further shows that when there is a conducive working environment prevailing in the organization the employees feel a sense of conducive climate which makes them feel good about the organization and motivates them to work. Further, along with climate transformational leadership style facilitates in grooming the employees according to organizations vision. Hence, it’s observed that these factors facilitate in enhancing employee engagement. Employees with skills and potentials are more preferring transformational leadership style. Employees with potentials always aspire to grow along with the organization. Hence, leadership style plays a vital role in engaging the employees. Originality/value- This study provides insightful findings in understanding the role organizational competencies like leadership and organizational climate play in facilitating employee engagement. This study also shows the importance of the demographic variables with employee’s engagement. In spite of the limitations of the research, the study contributed to the international human resources management by exploring the South Indian IT company’s context. The theoretical framework proposes the relationship between employee engagement, organizational climate and leadership style. The study has found that organizational climate and transformational leadership is correlated with employee engagement and serves as the important indicator for engaging employees.
Energy-based features for Kannada handwritten digit recognition

In this paper, Kannada handwritten digit recognition system is proposed based on energy features. Ground truth datasets are not available to test the performance of proposed features. Hence, own dataset of Kannada handwritten digits are collected from schools, colleges, business persons and professionals. The digital images are pre-processed using morphological opening operation for removing the noise and bilinear operation is used for normalisation. The normalised image is divided into 16 blocks, and then wavelet filters were applied for each of the 16 blocks and computed the standard deviation for each of them. In this process, a total of 64 standard deviation of the wavelet coefficients are generated of which 48 coefficients are selected as potential features. The average recognition accuracy of 94.80% is achieved using nearest neighbour classifier. The proposed algorithm is free from skew and thinning and it is novelty of the paper.