Reviewerly/exHarmony huggingface dataset:
Determining User Requirements for a Mobile Class Record Application: Towards a Usable Technology for Teachers

This paper measures the user requirements for the proposed mobile class record application. Specifically, this paper presents the distribution and need level of the functionalities of a mobile class record application as perceived by the respondents. The r espondents are 122 teacher educators in Central Visayas, Philippines. The study utilized a survey method. The result shows that majority of the teacher educators said that they highly needed the intended functionalities. The result also indicates that all suggested features are highly needed. The result implies that classroom information management, attendance monitoring, test scores and grades calculation, a dictionary of teacher education terms, task management, address book, groups, and photos management are imperative and necessary.  The study concludes that all the identified functionalities are required for the overall quality of the proposed application. There is a high degree of satisfaction of the stated needs in the proposed mobile class record.
Analysis of the ASTER Image Topographic Correction Method in Complex Mountain Areas

Abstract The phenomenon of “anisotropic reflection” caused by gradient and aspect changes in complex mountain areas has large disturbance on the spectral information of ground feature in remote sensing images, which will further affect the inversion of surface information based on the spectral characteristics of ground feature. Topographic correction is an effective means to weaken the topographical effect and improve the inversion accuracy of spectral reflectance of remote sensing images. Ten topographic correction methods, such as Teillet regression, VECA, Cosine, Cosine-B, Cosine-C, Cosine-T, C, SCS, SCS+C and Minnaert, were used to conduct topographic correction of ASTER remote sensing images in the complex mountain areas of Qulong, and visual effects, statistical results and spectral changes of ground features were used to quantitatively evaluate the correction results. The results show that the topographic effect in the study area is weakened to different degrees. Teillet-regression correction model has the best correction effect and the topographic effect is weakened to the maximum extent. VECA, cosine-c, and Minnaert calibration models followed. Teillet-regression model can be used as topographic correction model in complex mountain areas.
Farmer the Entrepreneur—An Android-Based Solution for Agriculture End Services

Rapid growth of mobile telephony and the introduction of mobile-enabled information services to provide ways to improve information dissemination to the knowledge-intensive agriculture sector and also help to overcome information asymmetry existing among the group of farmers. The agriculture role in the society and economy of various countries is above the per capita income. The enormous amount of population survives on their little source of farming for their living and the state of being comfortable. All the farmers overlook unusual challenges frequently because of the absence of approach to knowledge about prices and trades.
Super-Resolution Image Generation from Enlarged Image Based on Interpolation Technique

This research proposed a Super-Resolution Image Generation (SRG) from enlarged image based on bicubic interpolation technique in order to reconstructs a higher-resolution image. This technique uses neighboring pixels to calculate a value for adjusting an appropriate coefficient of a new pixel that given higher resolution. SRG technique is developed based on popular pixel estimation called Bicubic technique which widely used for image resolution adjustment. The performance is evaluated based on PSNR and SSIM measurement, the results showed better overall reconstruction quality in terms of resolution and sharpness.
Multivariate Microaggregation with Fixed Group Size Based on the Travelling Salesman Problem

Maya López, Armando Solanas, AgustiDue to the growing use of IoT and 5G technologies, data are collected at an unprecedented pace. These data are used to improve decision-making processes. However, they could endanger individuals privacy, which is protected by international regulations. In this article, we propose a privacy-preserving microaggregation technique, inspired by the Travelling Salesman Problem, to protect individuals privacy through k-anonymity. We recall the basics on microaggregation and the TSP and, we describe the algorithm behind our approach. Also, we report experiments with real benchmark data sets showing that our approach outperforms current methods for low cardinality values.
An Identification of Software Requirement for Web Based Judging System

Abstract The paper aims to identify the requirement for the judging system. The requirement identification of the system involves two phases; requirement gathering and requirement analysis. Several activities are done in each of the phases and the result is discussed. This study translated the requirement gathered from the interview and document analysis into several diagrams; use case diagram, activity diagram and entity relationship diagram.
Recommendation System using Keyword Extraction Based on User Profile

: In this paper, we proposed PRPRS (Personalized Research Paper Recommendation System) that designed expansively and implemented a User Profile-based algorithm for extracting keyword by keyword extraction and keyword inference. If the papers don't have keyword section, we consider the title and text as an argument of keyword and execute the algorithm. Then, we create the possible combination from each word of title. We extract the combinations presented in the main text among the longest word combinations which include the same words. If the number of extracted combinations is more than the standard number, we used that combination as keyword. Otherwise, we refer the main text and extract combination as much as standard in order of high Term-Frequency. Whenever collected research papers by topic are selected, a renewal of User Profile increases the frequency of each Domain, Topic and keyword. Each ratio of occurrence is recalculated and reflected on User Profile. PRPRS calculates the similarity between given topic and collected papers by using Cosine Similarity which is used to recommend initial paper for each topic in Information retrieval. We measured satisfaction and accuracy for each system-recommended paper to test and evaluated performances of the suggested system. Finally PRPRS represents high level of satisfaction and accuracy.
Interval prediction of photovoltaic power generation based on cloud theory

Abstract With the increasing integration of photovoltaic power into the power system, the reliable photovoltaic power generation prediction is of significant to the security and economics of the power system operation. However, the prediction deviation is unavoidable, therefore this paper presents an interval prediction model for the photovoltaic power generation based on cloud theory. Based on the error analysis of the photovoltaic power prediction, the training samples can be selected to establish the cloud model for each small power generation bin. In this way, the predictive cloud distributions for different predictive power generation can be obtained to generate the prediction intervals at each time slot. To take the photovoltaic power plant in German as example, the proposed model is validated. The results show that the proposed model outperforms the other benchmark method. And the calculation process of the proposed model is simple and short in computation time. The analysis results are expected to be used in the field of power grid dispatching and decision making.
An improved Performance of Segmentation Evaluation Based on Feature Extraction using Kinect Sensors

Abstract Kinect sensor suggestions new viewpoints for the advance and application of inexpensive, portable and easy-to-use indication less motion capture skill. The goal of this work is to estimate accuracy of the Kinect cameras for full body motion investigation. This study developed an application that of using multiple depth and RGB Kinect sensors for that reasonable system that prepared with multi-depth of sensing was used in this work. Additional application confirmed the Kinect camera validity the evaluated of postural control and different images of biomedical for segmentation skin lesions. In this work, multi-depth assessment and segmentation are conjointly addressed using RGB input image under Median filter with post-processing. Compared with our algorithm outputs an organized-to-use highly suitable for creating 3D Kinect sensors with pre and post-processing steps. The multi-depth extracted image features have higher measurement and accuracy. The results are dealing out the depth and RGB picture with segmentation evaluation depend on feature extraction technique to enhance accuracy.
Ontological Approach for the Conceptual Development of Products

Abstract In order to improve the structuring process of a product a company often refer to adopt different solutions for correlating the structure of the newly product and its components with other complementary factors. In essence, these factors target the market, suppliers, solutions of manufacturing, terms for delivery, price, quality, etc. All these factors must be taken into considerations in order to make an optimal decision during the conceptual or detailed design process. These factors are collected preliminary and introduced into the proposed ontology. Despite other approaches, the method proposed into this paper uses also some limitations to determine the right decision-making act even for unqualified people and some other knowledge’s incorporated into the ontology.
Evaluating distribution and variation of strains near eyes in blinking using digital image correlation

Abstract Background Strain is occurred due to deformation such as the change of facial expression and is expected as a new evaluation approach to know the condition of skins applied to cosmetics. In this study, a noncontact in‐site method by using digital image correlation for measuring the strain on the skin surface near the eyes where deformation always occurs by blinking is proposed. In this method, skin microrelief and irregularities are utilized for the measurement. Materials and Methods Images on the skin surface below the eyes of the four female subjects are pictured during a blink using high‐speed cameras. The principal strains as well as the principal directions are obtained using digital image correlation. The reference updating algorism is used when the movement of the skin surface due to blink is large, and the eyelashes elimination algorism is used when the long eyelashes hide their skin surface in an image. The strain distributions in the local area below the right eye and the time histories of the strain near the eye under several skin conditions are evaluated for each subject. Results The strain can be obtained for each subject under each condition and the different strain distributions and time histories under different condition are visualized. Conclusion The individual behavior of the subject and the different condition of the skin can be captured by the proposed method.
Feature Selection Method Based on Correlation Tree

Machine learning is one of techniques adapted to detect intrusion for cyber security. One of importance techniques to find anomaly is classification. But classification with huge dataset has the resources and time consumption. Feature selection is choice to reduce the data dimension to improve processing performance. In this paper, we introduce the new feature selection method that selects some fields of data set using position of each feature in correlation tree. Then, the result from the correlation tree feature selection of KDDCUP’99 data set are compared with two feature selection technique, correlation of coefficient (CC-type) and BFS by using three reference classifier, Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB).
Parametric and Nonparametric Classification for Minimizing Misclassification Errors

Parametric classification fits the parametric model to the training data and interpolates to classify the test data, whereas nonparametric methods like regression tree and classification trees use different techniques to determine classification. The classification process can be of two types: supervised and unsupervised. In supervised classification, training data are used to design the classifier. Bayes’s rule, nearest neighboring rule, and perceptron rules are few widely used supervised classification rules. For unlabeled data, the process of classification is called clustering or unsupervised classification. This paper proposes a wrapper-based approach for pattern classification to minimize the error factor. Techniques, such as Bayes’s classification, K-NN classifier, and NN classifier, are used to classify the patterns using linearly separable, linearly nonseparable, and Gaussian sample dataset. These methods classify the data in two stages: training stage and prediction stage. In this paper, we will be using parametric and nonparametric decision-making algorithm as we know the statistical and geometric properties of the patterns under study.
A Wavelet-Based Adaptive Finite Element Method for the Stokes Problems

In this work, we present the mathematical formulation of the new adaptive multiresolution method for the Stokes problems of highly viscous materials arising in computational geodynamics. The method is based on particle-in-cell approach—the Stokes system is solved on a static Eulerian finite element grid and material properties are carried in space by Lagrangian material points. The Eulerian grid is adapted using the wavelet-based adaptation algorithm. Both bilinear (Q1P0, Q1Q1) and biquadratic (Q2P-1) mixed approximations for the Stokes system are supported. The proposed method is illustrated for a number of linear and nonlinear two-dimensional benchmark problems of geophysical relevance. The results of the adaptive numerical simulations using the proposed method are in an excellent agreement with those obtained on non-adaptive grids and with analytical solutions, while computational requirements are few orders of magnitude less compared to the non-adaptive simulations in terms of both time and memory usage.
Full Reference Image Quality Assessment Algorithm based on Haar Wavelet and Edge Perceptual Similarity

Abstract The research on image quality assessment (IQA) has been become a hot topic in image processing. Many studies show that HVS edge information plays crucial role when human perceive the quality of an image. The proposed metric is called HEPSI. The method from HaarPSI metric is combined with edge structural similarity and a contrast map is added for pooling the structural similarity map, Validation is taken by comparing HEPSI with the well-known state-of-the-art IQA metrics: PSNR, SSIM, MSSIM, FSIM and HaarPSI over the LIVE database. Experiment shows that HEPSI achieved better performance than other 5 IQA metrics.
Pengaruh Penggunaan Media Pop Up Book Terhadap Kemampuan Membaca Anak di TK Insan Cita Kec. Masamba Kab. Luwu Utara

Abstract . The problem of this research were: (1) how is the description of the implementation media pop up book in TK Insan Cita Kelurahan Bone Kecamatan Masamba Kabupaten Luwu Utara? (2) how is the description of children’s linguistic intellegence before and after using the media pop up book in TK Insan Cita Kelurahan Bone Kecamatan Masamba Kabupaten Luwu Utara (3) is there an influence of the use of pop up book media on chilndren’s linguistic intellegence in TK Insan Cita Kelurahan Bone Kecamatan Masamba Kabupaten Luwu Utara? The aims of this research were (1) to find out the description of using the media pop up book implementation in TK Insan Cita Kelurahan Bone Kecamatan Masamba Kabupaten Luwu Utara; (2) the description og children’s linguistic intelegence befor and after using the media pop up book implementation in in TK Insan Cita Kelurahan Bone Kecamatan Masamba Kabupaten Luwu Utara; (3) to know influence of the use of pop up book media on chindren. The kind of this research is pre eksperimental designe. The population are 21 and the sample is 11. The result showed a comparison between the value of Thitung < Ttabel using a signicance value  of 05, the conclusion, value of 0,05 that means Ho was rejected. This means learning methods using using pop up media has an influence on aspect of development epecially aspek of language development Keywords : Pop up book, linguistics
Couplets Translation from English to Hindi Language

We are a part of that society which has different-different cultures, languages, rituals, etc. There are some sentences which we used to say someone at particular situation or to motivate someone which we called as couplets. A couplet is a pair of consecutive lines of rhythm in poetry. Couplet usually consists of two consecutive lines that poem has the same rhythm. Couplets are balanced in length and word segmentation, in a manner that matching words in the two consecutive sentences match each other by observing certain limitation on semantic, syntactic, and lexical connection. In this research, authors have tried to translate few couplets from English to Hindi language and this kind of task comes under the umbrella of natural language processing. In continuation of the same, authors have used a statistical machine translation (SMT) approach for producing good results (of English to Hindi translation of couplets).
A Novel Evolution-Based Recommendation System

Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Prior MF-based methods adapt the overall rating to make the recommendation by extracting latent factors from users and items. However, in real applications, people's preferences usually vary with time; the traditional MF-based methods could not properly capture the change of users' interests. In this paper, by incorporating the recurrent neural network (RNN) into MF, we develop a novel recommendation system, M-RNN-F, to effectively describe the preference evolution of users over time. A learning model is proposed to capture the evolution pattern and predict the user preference in the future. The experimental results show that M-RNN-F performs better than other state-of-the-art recommendation algorithms. In addition, we conduct the experiments on real world dataset to demonstrate the practicability.
Distance-Shape Signature Duo for Determination of Human Emotion

Previous three chapters contain three feature descriptors individually distance signature, shape signature, and texture signature. In course of Distance-Shape (D-S) signature, respective stability indices and statistical measures supplement the signature features with a view to enhance the performance of the task of facial expression classification. Incorporation of these supplementary features is duly justified through extensive study and analyses of results obtained thereon.
Data Securing of Patients in Cloud Computing Using A Combination of SHA256 and MD5

Patient data archiving in a medical institution is a relatively critical asset. If the data are managed by irresponsible parties, it will be risky both for the patients and the institutions. Therefore, data securing of patients are required to prevent negative impacts. The method in this study uses a prototype cycle, in making patient registration applications online. The technique used in this study is a combination of MD5 and SHA256 to be optimized for the data sent from database to the users, so that the encryption results in concise filing and timing.
Delay Impacts on EEG-Based Determination of the Human Visual Interface QoE for Virtual and Augmented Realities

The emergence of 5G services includes those requiring extremely low-latency for command and control application scenarios in the low millisecond range. With a significant human-machine interaction component, the Tactile Internet will require that the experiences based on the human visual interface can be dynamically adjusted within similar time frames. In this paper, we evaluate the impact that different delays currently attainable with commercial hardware would have on predicting the Quality of Experience (QoE) with immersive images. Specifically, we employ electroencephalography (EEG) data to predict how future subjects would determine the media quality in a Passive Human In-the-Loop (PHIL) scenario. This initial extension of our prior work focuses specifically on the delay in the gathering and processing of data and presents a first foray of bringing the passive human-in-the-loop QoE adjustment approach to the Tactile Internet. We find that there is limited value in increasing the delays of two different approaches to predicting the QoE. We additionally note that current approaches to predicting a user's QoE based on other users' EEG patterns exhibit only limited prediction accuracy.
Learning Latest Private-Cluster-State to Improve the Performance of Sample-Based Cluster Scheduling

Sample based cluster scheduling is considered promising for its high-scalability and low-latency. Its major limitation, on the other hand, is its very limited view of cluster resource state. The limitation confines both its decision precision and the support towards many important scheduling features. There have been several approaches to solve this limitation, yet these works are mostly high-cost solutions that use either extra communication or system component to collect more resource information, which damage the scalability and latency of sample based cluster scheduling. In this paper, we propose L-PCS, a novel learning-based approach based on latest private-cluster-state to generate a relatively accurate knowledge of global cluster state. L-PCS gathers and learns process data of schedulers and predicts a more precise approximation of real-time cluster state for each scheduler. It is a dynamic model updated through time for time-validity. The results predicted by trained model serve as references when schedulers make scheduling decisions. Experiment shows that comparing to sample based schedulers without such learning mechanism, L-PCS improves mean absolute error by 2 × to 3 × and gang scheduling results show a maximum increase of 10.1% to 25.09%.
Learning Gaussian Processes with Bayesian Posterior Optimization

Gaussian processes (GPs) are often used as prior distributions in non-parametric Bayesian methods due to their numerical and analytical tractability. GP priors are specified by choosing a covariance function (along with its hyperparameters), a choice that is not only challenging in practice, but also has a profound impact on performance. This issue is typically overcome using hierarchical models, i.e., by learning a distribution over covariance functions/hyperparameters that defines a mixture of GPs. Yet, since choosing priors for hyperparameters can be challenging, maximum likelihood is often used instead to obtain point estimates. This approach, however, involves solving a non-convex optimization problem and is thus prone to overfitting. To address these issues, this work proposes a hybrid Bayesian-optimization solution in which the hyperparameters posterior distribution is obtained not using Bayes rule, but as the solution of a mathematical program. Explicitly, we obtain the hyperparameter distribution that minimizes a risk measure induced by the GP mixture. Previous knowledge, including properties such as sparsity and maximum entropy, is incorporated through (possibly non-convex) penalties instead of a prior. We prove that despite its infinite dimensionality and potential non-convexity, this problem can be solved exactly using duality and stochastic optimization.