Reviewerly/exHarmony huggingface dataset:
A Classification Model for Thai Statement Sentiments by Deep Learning Techniques

At present, many organizations realized the importance of sentiment analysis for consumer reviews. The positive and negative comments can help to evaluate the user satisfaction of products and services to control and improve their qualities. In addition, the deep learning techniques are very interesting methods for current researches in the data mining field. Therefore, this research studied on the deep learning techniques to analyzed user reviews and comments in Thai Language from the TripAdvisor website. To begin with, user comments in four categories: hotels, restaurants, tourist attractions, and airlines were collected and tested on the combination of two basic deep learning technique that are convolutional neural network and long-short term memory. All user comments were divided into individual statements to classify into three groups: positive feelings, negative feelings, non-expressed feelings or neutrality. The research results found that the best classification model is the combination of three convolutional neural networks with 32, 64, and 128 filters, respectively, and the kernel size of 2 equal to the three components. Moreover, the performance of the proposed classification model was evaluated by accuracy, precision, and recall values which were higher than 80% in positive and negative groups, including F1 score about 0.8.
Variational correctness-by-construction

Nowadays, the requirements for software and therefore also the required complexity is increasing steadily. Consequently, various techniques to handle the growing demand for software variants in one specific domain are used. These techniques often rely on variable code structures to implement a whole product family more efficiently. Variational software is also increasingly used for safety-critical systems, which need to be verified to guarantee their functionality in-field. However, usual verification techniques can not directly be applied to the variable code structures of most techniques. In this paper, we propose variational correctness-by-construction as a methodology to implement variational software extending the correctness-by-construction approach. Correctness-by-construction is an incremental approach to create and verify programs using small tractable refinement steps guided by a specification following the design-by-contract paradigm. Our contribution is threefold. First, we extend the list of refinement rules to enable variability in programs developed with correctness-by-construction. Second, we motivate the need for contract composition of refined method contracts and illustrate how this can be achieved. Third, we implement variational correctness-by-construction in a tool called VarCorC. We successfully conducted two case studies showing the applicability of VarCorC and were able to assess reduced verification costs compared to post-hoc verification as well.
Fuzzy Questions for Relational Systems

Databases are becoming more and more inevitable for websites and applications that handle large amounts of data, such as bank accounts (games, social networks, videos, etc.). However, they are unable to respond to human-type questions that are generally imprecise, uncertain and vague, and therefore need to be appropriately addressed. Therefore, fuzzy interrogation systems have become indispensable to represent and manage these data, and particularly facilitate the interrogation to a non-expert user. Fuzzy logic provides a powerful tool to take into account various aspects of fuzzy information. In this paper, we present a short study of fuzzy querying relational databases. We start by the design and operation of a fuzzy system and present the different types of architectures of fuzzy question database systems. Lastly, we introduce a comparison of the most pertinent features in fuzzy query systems of databases.
Cost‐driven software migration: An experience report

Abstract Software migration projects are often bound either by time or cost or by both. If the project is bound by both time and cost, the user must sacrifice something else, usually the quality. The migration strategy depends on how the project is bound. Most migration projects are bound by time. The new system must be in operation by a given date, no matter what it costs. The project described here—a state employee payroll system—is bound by cost. It must remain within the budget, no matter how long it takes. The original costs were estimated based on the code size and the productivity measured in previous migration projects using three different approaches: conversion, redevelopment, and reimplementation. The conversion approach would have been the cheapest, but it had already been tried and failed. The redevelopment approach was considered to be out of the question due to the high costs. Thus, reimplementation remained as the only alternative. The costs of this approach were estimated using three different estimation methods and approved by the state government. The project has been in progress for 4 years, and until now, the estimated costs and actual costs are in the same order of magnitude: the costs have remained within budget. In fact, the costs are less than what was estimated with some methods. As this particular project is not bound by time, it is a good example of continuous migration.
Quantifying Human Activity Using Head Tracking

This research focuses on quantifying human activity for healthcare applications. Distance traveled, average velocity, and postures are the primary focus. A Microsoft Kinect V2 camera was used to collect data for analysis. By tracking the human head, position relative to the camera can be determined. Accuracy of at least 87% was achieved for distance traveled and average velocity measurements. For posture detection, accuracy of at least 80% was achieved.
Mobile devices use in analyzing the engineering students attitude towards programming by using a fuzzy logic technique

Abstract The aim of this study is to use mobile devices in the determination of engineering students' attitudes towards programming by using a fuzzy logic technique. First of all, a mobile game that is played by engineering students is developed to make learning programming more enjoyable. After that, the proposed fuzzy logic‐based attitude determination system which runs on mobile devices comes into play. Student answers and gives points between 1 and 5 to the survey questions which are presented by the developed mobile application. These points are first evaluated in the fuzzification step by using membership functions and then the fuzzied input is given to the rule base step. To get crisp output value, fuzzied output is defuzzified at the last step of the fuzzy logic‐based system. Hence the attitude of the student towards programming is inferenced. The developed system is carried out with 100 first‐grade students of the software engineering department. Frequency, mean, standard deviation, normality, t test, and analysis of variance (ANOVA) analyses are performed with the obtained data. Results show that the proposed fuzzy logic‐based system performs much better than the classical approach. As a result of Article Reliability Analysis of the Attitude Scale Towards Mobile Learning, the scale is found highly reliable. A significant difference is found in favor of fuzzy logic‐based attitude score among classical logic‐based attitude scores as a result of the paired‐samples t test. The results of t test and ANOVA tests according to gender, mother, and father education levels are found not statistically significant.
A Comparative Study on Interactive Digital Classrooms: Edmodo and Google

The word digital denotes the technology. In education world, teaching learning has become more interactive due to technology which is supported through strategic use of information and communication technologies (ICT). This paper focuses on the two tools which have been used massively. Hence in this paper, we will compare two common digital classrooms: Edmodo and Google classroom. This paper gives the analysis of usage of two technologies based on submission of assignment i.e lab work, quiz, poll, usage etc.The objective is to investigate the students and teachers preference between Edmodo and Google classroom. The study is been done by analysing the 30 students sample data. The survey questions were designed using ATE-B(Assignment, Time allotted, Expert group discussion and Badges ) pedagogical model to support the comparative study between Google and Edmodo.
Deep Learning for Natural Image Reconstruction from Electrocorticography Signals

Several recent studies proposed various methods for reconstructing natural images from human functional magnetic resonance imaging (fMRI) data. However, few studies have proposed reconstruction methods for electrophysiolgical brain activities such as electroencephalography (EEG) and electrocorticography (ECoG). To investigate whether natural images can be reconstructed from electrophysiological brain activities, we conducted a large-scale experiment on natural image reconstruction from ECoG signals using deep learning. We first recorded ECoG signals from two macaque monkeys while presenting diverse natural images. Then, we trained several deep learning models for reconstructing presented images from ECoG signals. Comparing reconstruction models, we find that models trained with an adversarial loss produced reconstructions that contain visible features in presented images. Furthermore, our results with downsampled ECoG signals show the importance of rich temporal dynamics in ECoG signals for image reconstruction. Our results indicate the possibility of reconstructing diverse natural images from electrophysiological brain activities using deep learning.
A Probabilistic Distance Clustering Algorithm Using Gaussian and Student-t Multivariate Density Distributions

A new dissimilarity measure for cluster analysis is presented and used in the context of probabilistic distance (PD) clustering. The basic assumption of PD-clustering is that for each unit, the product between the probability of the unit belonging to a cluster and the distance between the unit and the cluster is constant. This constant is a measure of the classifiability of the point, and the sum of the constant over units is called joint distance function (JDF). The parameters that minimize the JDF maximize the classifiability of the units. The new dissimilarity measure is based on the use of symmetric density functions and allows the method to find clusters characterized by different variances and correlation among variables. The multivariate Gaussian and the multivariate Student-t distributions have been used, outperforming classical PD clustering, and its variation PD clustering adjusted for cluster size, on simulated and real datasets.
ReInCre: Enhancing Collaborative Filtering Recommendations by Incorporating User Rating Credibility

We present ReInCre (Demo video available at https://youtu.be/MyFczz7Vefo ) as a solution demo for incorporating user rating credibility in Collaborative Filtering (CF) approach to enhance the recommendation performance. The credibility values of users are calculated according to their rating behavior and they are utilized in discovering the neighbors (Code available at https://github.com/NaimeRanjbarKermany/Cred ). To the best of our knowledge, it is the first work to incorporate the rating credibility of users in a CF recommendation. Our approach works as a powerful add-on to existing CF-based recommender systems in order to optimize the neighborhood. Experiments are conducted on the real-world dataset from Yahoo! Movies. Comparing with the baselines, the experimental results show that our proposed method significantly improves the quality of recommendation in terms of precision and $$F_1$$-measure. In particular, the standard deviation of the errors between the prediction values and the real ratings becomes much smaller by incorporating credibility measurements of the users.
A Robust DWT-Based Compressed Domain Video Watermarking Technique

To achieve robustness and imperceptibility, an adaptive compressed domain blind video watermarking method based on Discrete Wavelet Transform (DWT) is proposed in this research. In this technique, multiple binary images derived from a single watermark image are first embedded in a video sequence. The spatial spread spectrum watermark is directly incorporated in the compressed bit streams by modifying the four sets of discrete wavelet coefficients. Comprehensive simulation experiments demonstrate that the developed approach is efficient and also robust against spatial attacks such as scaling and frame averaging, noise attacks such as Gaussian and salt pepper noise, and temporal attacks like frame dropping and shifting. Moreover, the proposed approach can also withstand against rotation attacks of arbitrary angle.
Basic Security Challenges in Cloud Computing

With the advancement in information technology, everyone moves towards pleasure, ease, and luxury. From which the most advanced and usable field is Cloud Computing which uses the internet and gives access to storing the data to the user. It has many characteristics like elasticity, extensibility, smartness and lower cost, which causes companies to address the clients' requirements. In spite of this, associations are delayed in accommodating it because of security issues and difficulties related with it. The development of cloud is affected by the security. This paper gives ongoing advances in cloud computing and highlights on some challenges, security issues, and solutions. It also incorporates the basics of cloud computing to make itself contained so that pursuers can have a reasonable comprehension of the subject and cloud computing security.
Privacy Preserving Cyberbullying Prevention with AI Methods in 5G Networks

Children and teenagers that have been a victim of bullying can possibly suffer its psychological effects for a lifetime. With the increase of online social media, cyberbullying incidents have been increased as well. In this paper we discuss how we can detect cyberbullying with AI techniques, using term frequency-inverse document frequency. We label messages as benign or bully. We want our method of cyberbullying detection to be privacy-preserving, such that the subscribers' benign messages should not be revealed to the operator. Moreover, the operator labels subscribers as normal, bully and victim. The operator utilizes policy control in 5G networks, to protect victims of cyberbullying from harmful traffic.
Semantic Segmentation With Oblique Convolution for Object Detection

With the rapid development of artificial intelligence, object detection is playing an important role in the field of computer vision. Instead of anchors, we use pixel classification inspired by Semantic Segmentation to get the local extreme points of the four boundaries of an object and then the boundary positions. We calculate the possibility whether every pixel in the image is the extreme point by hourglass network. With the introduction of the mask mechanism and oblique convolution, the network has achieved better results. The experiment result shows that: it achieves an AP of 37.7% on the MS COCO dataset while costing less than 3 seconds on mobile.
A Statistical Comparative Study on Image Reconstruction and Clustering With Novel VAE Cost Function

Deep clustering achieves unprecedented levels of accuracy with unsupervised feature extraction on rich datasets where the joint statistics of the latent space is learned via highly nonlinear compression.This paper has two separate contributions to this field.First, we conduct an extensive and first-of-its-kind empirical study on the statistical relationship between the clustering accuracy and image reconstruction quality of a state-of-the-art deep clustering topology in the form of a convolutional variational autoencoder (VAE) with a K-means back end.We change the latent variable z at the bottleneck of the network to create different latent dimensions and explore how clustering performance metrics and reconstruction metrics are statistically related.Secondly, based on our data-driven statistical findings, we also propose a novel cost function for the VAE which includes the structural similarity index measure to jointly optimize image quality and latent statistics for improved clustering.The preliminary results show significant increases in clustering accuracy of as much as 10.76% on two popular benchmark datasets.The TensorFlow implementation for the experimental framework can be found here: https://github.com/alla15747/IEEE-Comparitive-Study-VAE-Paper-(Pythoncode will be available at the time of publication).
General Temporally Biased Sampling Schemes for Online Model Management

To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporally biased sampling schemes that weight recent data most heavily, with inclusion probabilities for a given data item decaying over time according to a specified “decay function.” We then periodically retrain the models on the current sample. This approach speeds up the training process relative to training on all of the data. Moreover, time-biasing lets the models adapt to recent changes in the data while—unlike in a sliding-window approach—still keeping some old data to ensure robustness in the face of temporary fluctuations and periodicities in the data values. In addition, the sampling-based approach allows existing analytic algorithms for static data to be applied to dynamic streaming data essentially without change. We provide and analyze both a simple sampling scheme (Targeted-Size Time-Biased Sampling (T-TBS)) that probabilistically maintains a target sample size and a novel reservoir-based scheme (Reservoir-Based Time-Biased Sampling (R-TBS)) that is the first to provide both control over the decay rate and a guaranteed upper bound on the sample size. If the decay function is exponential, then control over the decay rate is complete, and R-TBS maximizes both expected sample size and sample-size stability. For general decay functions, the actual item inclusion probabilities can be made arbitrarily close to the nominal probabilities, and we provide a scheme that allows a tradeoff between sample footprint and sample-size stability. R-TBS rests on the notion of a “fractional sample” and allows for data arrival rates that are unknown and time varying (unlike T-TBS). The R-TBS and T-TBS schemes are of independent interest, extending the known set of unequal-probability sampling schemes. We discuss distributed implementation strategies; experiments in Spark illuminate the performance and scalability of the algorithms, and show that our approach can increase machine learning robustness in the face of evolving data.
New <sup>40</sup>Ar/<sup>39</sup>Ar Data of Gold Mineralization in the Ailaoshan Gold Belt, Yunnan Province, China

Acta Geologica Sinica - English EditionVolume 94, Issue 1 p. 210-211 Research Advance New 40Ar/39Ar Data of Gold Mineralization in the Ailaoshan Gold Belt, Yunnan Province, China Zongyao YANG, Zongyao YANG Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610059 ChinaSearch for more papers by this authorGuyue HU, Corresponding Author Guyue HU wanghuguyue@126.com MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, CAGS, Beijing, ChinaCorresponding author. E-mail: wanghuguyue@126.comSearch for more papers by this authorXiaoyan ZHAO, Xiaoyan ZHAO Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610059 ChinaSearch for more papers by this author Zongyao YANG, Zongyao YANG Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610059 ChinaSearch for more papers by this authorGuyue HU, Corresponding Author Guyue HU wanghuguyue@126.com MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, CAGS, Beijing, ChinaCorresponding author. E-mail: wanghuguyue@126.comSearch for more papers by this authorXiaoyan ZHAO, Xiaoyan ZHAO Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610059 ChinaSearch for more papers by this author First published: 06 February 2020 https://doi.org/10.1111/1755-6724.14415Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume94, Issue1Special Issue: Devoted to the 36th International Geological Congress, India, 2020February 2020Pages 210-211 RelatedInformation
Impulse Noise Detection and Removal Method Based on Modified Weighted Median

Impulse noise generally occurs because of bit errors in progression of image acquisition and transmission. It is well known that median filtering method is an impulse noise removal method. Lots of modified median filters have been proposed in the last decades to improve the methods for noise suppression and detail preservation, which have their own deficiencies while identifying and restoring noise pixels. In this article, after deeply analyzing the reasons, such as decreased noise detection and noise removal accuracy that forms the basis of the deficiencies, this article proposes a modified weighted median filter method for color images corrupted by salt-and-pepper noise. In this method, a pixel is classified into either “noise free pixel” or “noise pixel” by checking the center pixel in the current filtering window with the extreme values (0 or 255) for an 8-bit image using noise detection step. Directional differences and the number of “good” pixels in the current filtering window modify the detected noise pixels. Simulation effects on considered test images reveal the proposed method to be improved over state-of-the-art de-noising methods in terms of PSNR and SSIM with pictorial comparative analysis.
On the Interdependence and Importance of Meteorological Variables for Photovoltaic Output Power Estimation

While the large-scale deployment of Photovoltaic (PV) systems plays an important role in limiting global warming, the variability of PV output power poses challenges in grid management. Typically, the PV output power is dependent on various meteorological parameters at the PV site. In this paper, we analyse the interdependence of different meteorological variables and show their importance for PV output power estimation. Using Principal Component Analysis (PCA), we identify the primary meteorological variables for PV output power estimation. The numerical evaluation is performed using 3 years long of 9 meteorological variables data and PV output power data of 10 distinct rooftop PV systems, located in the city of Utrecht, the Netherlands. Simulation results show the interdependence between the meteorological variables and demonstrate that relative humidity, visibility, temperature and cloud cover are the most important variables for estimating PV output power in Utrecht.