Reviewerly/exHarmony huggingface dataset:
An Intelligent Recommendation Mobile Application Privacy Risk Evaluation Method Based on Optimized SVM

In recent years, intelligent recommendation (IR) technique has been widely utilized in mobile applications (App), which promotes the development of mobile internet and improves the user experience. However, to provide the service related to users' personalized taste, the mass privacy information of user needs to be collected by IR Apps. This makes IR Apps in front of various security risks, especially the privacy security risk. It is of great significance in both theory and practice to make a privacy risk evaluation on IR Apps. In this work, an approach for IR App privacy risk evaluation based an optimized SVM classifier from a hierarchical privacy security risk factor Set is proposed, which reveals the changing relationship between risk factors and risk levels, and also gives a way to solve the nonlinearity, high-dimensional, and multi-sample problems in the IR App privacy risk evaluation work. In this evaluation model, first we give out a hierarchical privacy security risk factor set oriented the hole data life cycle by comprehensive analysis. Then a feature selection method based on MRMD algorithm is applied to calculate the correlation between features and risk levels, and the factors have a strong correlation with risk level evaluation are selected to constitute a feature vector, which reduce the redundancy. Finally, the extracted features are given input the evaluation model based on an optimized SVM classifier to calculate the risk level. The experiment results show the improvement and a certain generalization in optimized model. This work also enriches the quantitative method in risk evaluation, and can provide quantitative and visual decision support for application providers to improve the privacy protection and for government to keep abreast of development of application security in practice.
Color instance segmentation and classification of cervix images

Instance segmentation is the task of assigning a label to each pixel in the image, treating objects of same class separately. On the other hand, classification assigns a label to the whole image. In this paper, we are comparing both methods on a data-set of different cervix types. It was required to detect which cervix type out of 3 categories the image holds. For classification using Convolutional neural network, the region of interest is segmented before starting to train the network. However, in instance segmentation, the input is the full image.Instance segmentation happened to outperform the classification pipeline on this data-set with accuracy of 62% vs 55% for the latter approach.
CloudSCAN: A Smart Content Aggregation and Navigation Technique on Cloud

Smart Content is similar to Semantic web which relates the content to context and various services from various domains to single service on web. We require a service that generate and aggregate other services and components according to the contents from Cloud-of-Things (CoT). This work finds out mixed contents present on cloud but not being able to discover due to the diversity of content. The proposed work is a technique to search for the desired content on the cloud, whose content is aggregated from various IoT devices. This technique saves time for searching content in each file also saves time of tagging a file or a collection of files on cloud.
The Inference Graph of Cybersecurity Rules

The concept that cybersecurity architecture is the discovery, definition and validation of rules is introduced. The new concept of inference graphs for illustrating the relationship between cybersecurity rules is defined. Three increasingly complex examples of inference graphs for systems needing cybersecurity architecture are presented, including the detailed proofs which form the basis of these inference graphs, in some cases. The software which has been developed to support the development and use of cybersecurity inference graphs is described including details of the public server where it can be used. It is shown that cybersecurity inference graphs can significantly contribute to development of, and validation of cybersecurity and also that rigorous validation of cybersecurity is not necessarily as difficult as previously thought.
Schaalvergroting in het syntactische alternantieonderzoek

In this paper, we revisit earlier analyses of the distribution of er ‘there’ in adjunct-initial sentences to demonstrate the merits of computational upscaling in syntactic variation research. Contrary to previous studies, in which major semantic and pragmatic predictors (viz. adjunct type, adjunct concreteness, and verb specificity) had to be coded manually, the present study operationalizes these predictors on the basis of distributional analysis: instead of hand-coding for specific semantic classes, we determine the semantic class of the adjunct, verb, and subject automatically by clustering the lexemes in those slots on the basis of their ‘semantic passport’ (as established on the basis of their distributional behaviour in a reference corpus). These clusters are subsequently interpreted as proxies for semantic classes. In addition, the pragmatic factor ‘subject predictability’ is operationalized automatically on the basis of collocational attraction measures, as well as distributional similarity between the other slots and the subject. We demonstrate that the distribution of er can be modelled equally successfully with the automated approach as in manual annotation-based studies. Crucially, the new method replicates our earlier findings that the Netherlandic data are easier to model than the Belgian data, and that lexical collocations play a bigger role in the Netherlandic than in the Belgian data. On a methodological level, the proposed automatization opens up a window of opportunities. Most important is its scalability: it allows for a larger gamut of alternations that can be investigated in one study, and for much larger datasets to represent each alternation.
Life in the groove: Re-visiting the common sense of sound reproduction

In this paper, we describe our project of "Life in the Groove," an attempt to re-examine the material and historical basis of sound reproduction.Through this paper, we would like to argue that the realities are more complex than the oft-told tales.
Partial Secret Sharing Schemes

The following standard relaxations of perfect security for secret sharing schemes (SSSs) exist in the literature: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quasi-perfect</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">almost-perfect</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">statistical</i> . Understanding the power of these relaxations on the efficiency of SSSs, measured via a parameter called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information ratio</i> , is a long-standing open problem. In this article, we introduce and study an extremely relaxed security notion, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partial security</i> , for which it is only required that any qualified set gains strictly more information about the secret than any unqualified one. To get a meaningful efficiency measure, we normalize the (standard) information ratio of such schemes by an appropriate parameter and refer to the new measure as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partial information ratio</i> . We present three main results in this paper. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , we prove that partial and perfect information ratios coincide for the class of linear SSSs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , we prove that for the general (i.e., non-linear) class of SSSs, partial and statistical information ratios are equal. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Third</i> , we show that partial and almost-perfect information ratios do not coincide for the class of mixed-linear schemes (i.e., schemes constructed by combining linear schemes with different underlying finite fields). We also use the notion of partial secret sharing to strengthen and unify the previous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decomposition</i> theorems for constructing SSSs.
A hybrid optimization approach using Evolutionary Computing and Map Reduce Architecture

Big data and its application are very popular now a day because due to technological advancement in the field of Information technology the amount of data generation rate is too high. Data Mining & analysis is done for making better decision with respect to the data generated from different sources. Decision making for the route in the traffic environment is also a problem of Big Data because it creates a huge amount of data so we need to optimize or seprate the data with respect to various criteria, for this we need to know about the optimization algorithms. Evolutionary Computing is a branch of Computer science which works upon the concept of Darwinian evolution and the evolutionary computing algorithms are used to find the optimal solution. The review of optimization algorithm is presented in this paper. For the optimization process we have selected Ant Colony optimization algorithm to find the best route and implemented the algorithm using the concept of Map reduce architecture for parallel processing.
On the sparse multi-scale solution of the delay differential equations by an efficient algorithm

An efficient algorithm based on wavelet Galerkin method is proposed for solving the time-varying delay systems. According to the useful properties of Alpert’s multiwavelets such as interpolation, orthonormality, flexible vanishing moments and sparsity, a time-varying system is reduced to the sparse linear system of algebraic equations. The results illustrate the error will not be greater than a certain amount while the number of nonzero coefficients is reduced by selecting the appropriate threshold. Therefore the computational cost is reduced using thresholding. The analysis of convergence has been investigated and the accuracy and efficiency of the proposed method are illustrated by several examples.
Comparison of Flight-Planning Algorithms in View of Certification Requirements

In this paper, a comparison of flight path planning algorithms is presented to solve three-dimensional planning problem for a typical flight path scenario for UAV indoor and outdoor applications with oversight into general aviation and paid transport. The algorithms consider the performance of the navigation sensors and expected departure and arrival procedures which use the existing navigation means (VOR, NDB, ILS, GPS). The cruise situation is simplified to GPS navigation and obstacle avoidance. We choose to analyze the selected algorithms from the point of view of the certification issues according to the existing HW and SW requirements on determinism and time consumption. The analysis is made from the point of view of DO-178 standard. We describe the Artificial Intelligence phenomena and discuss the determinism of the currently used algorithms for flight-path panning. Within our work we focus on and summarize advantages and disadvantages of Breadth First Search, A*, Iterative Deepening A*, Theta*, and RRT* algorithms. Their reasoning process and path selection methodology with perspective of aerospace requirements are evaluated. Our main focus will be on the randomization element and uncertainty of these algorithms. We will also describe selected evaluation parameters required by FAA and EASA Technical Standard Order (TSO) documents on electronic systems and what are the conflicts between these requirements and the natural principle of the existing path-planning algorithms.
Learning Citizenship in Practice with SchoolVote System

This paper aims to present SchoolVote system that is a participatory innovation of Blockchain e-Voting system for schools in Thailand. SchoolVote is developed as a digital tool for Thai youth to learn citizenship in practice. Participatory innovation approach is employed into the system as a key approach for developing the system due to the approach emphasized on user participation similar to the basic principle of both the system and democracy. Blockchain Technology, which is a shared database storage technology, is a main technology for the system because its key function of data integrity validation is properly performed voting. The system has implemented for student council election activity in a school in Thailand.
Analysis of student preparedness levels in facing flood disasters for development of high school physics digital books (e-books) with flood theme

Abstract West Sumatra is a potentially large flood area, where the city of Padang is one of the cities in West Sumatra that has a number of sub-districts with high flood vulnerability. To reduce the risk of flooding that occurs in Padang required a good level of preparedness from the community in facing flood disasters. Preparedness faces the problem of flooding can be improved through education in schools. The aim of this research to describe the extent to which students’ preparedness in facing flood disasters and the results of research analysis will be a reference in the development of the High School Physics e-book with flood theme. The method used in the research is descriptive method with collecting data in the form of student questionnaires consist of five indicators of flood disaster preparedness. The results of the analysis show that the level of preparedness of students in facing floods is relatively low. These results indicate that the development of the high school Physics e-book flood theme needs to be done.
GAN-based End-to-End Unsupervised Image Registration for RGB-Infrared Image

Image registration is a pre-processing step used in various computer vision applications. This paper presents an unsupervised image registration for a given pair of RGB and infrared images, with the RGB image used as the reference for infrared. This method exploits the GAN architecture with a spatial transformer module, to synthesize the transformed image using an unsupervised loss criterion. This loss used for error backpropagation taken as a linear combination of adversarial loss; Mean Squared Error (MSE) loss between the input RGB image and image synthesized by the generator; KL divergence loss between the IR image and the synthesized image; and another MSE loss estimated using features maps extracted from pretrained VGG-16. The adversarial loss forces the generator to output an IR like image, with the input IR image and the generated IR image labelled as real and fake respectively. The other three losses are backpropagated through the generator network to learn the transformation as well as to preserve the structure and resolution of the generated image. This unsupervised learning process is stopped after a specified number of iterations based on a validation set. A supervised method has also been developed for comparison with the presented method. The SSIM and PSNR values estimated between predicted registered image and its ground truth has been used as evaluation criteria. The unsupervised method has scored 0.8351±0.06 and 35.2723±0.68 for SSIM and PSNR respectively, while supervised scored as 0.7620±0.08 and 15.8978±2.21.
Towards Analyzing the Sentiments in the Fields of Automobiles and Real-Estates with Specific Focus on Arabic Online Reviews

The importance of performing sentiment analysis is noticed in various fields, such as politics, education, marketing, and so forth. However, in the Arabic language domain, there are limited studies that focus on analyzing the sentiments in the text in comparison to the English language. There is a lack of available annotated Arabic datasets covering specific domains (such as real-estates and automobiles) and containing data written in both modern standard Arabic (MSA) and the Gulf Cooperation Council (GCC) dialect. Furthermore, the limited and inadequate adoption of natural language processing and machine learning techniques is noteworthy in the current sentiment analysis contributions targeting the Arabic language. Therefore, the gap could be bridged by creating real-estates and automobiles datasets. Moreover, customizing, enhancing, and applying suitable natural language processing techniques and machine learning algorithms to analyze the sentiments in these datasets will also contribute to filling the current gap. Performing these steps will benefit the people interested in analyzing the sentiments related to real-estates and automobiles, and will add a new scope to the Arabic sentiment analysis field. Future researchers in this field could also be benefited by using the datasets that will be freely available. The aforementioned factors encouraged the researcher to conduct this research in order to fill the current gap in this area.
Study of robust language identification techniques for future smart cities

In the development of smart cities, mobile and speech technologies may play a significant role. Moreover, multilingual speech recognition systems, speech-to-speech translation, interactive voice assistive systems, and other speech systems may offer better sophistication to smart cities. Many multilingual speech systems use language identification (LID) systems as an ancillary technology. In development of efficient smart cities, the speech systems need to be robust to noise and mobile environments. This work aims toward developing an LID system that can robustly operate in clean and noisy conditions. The three major approaches explored in developing LID systems to improve the performance in varying background conditions are front-end speech enhancement methods, a front-end vowel region-based robust feature extraction method, and a curriculum learning method. In this work, deep neural networks and deep neural networks with attention architectures are explored to develop LID systems using an AP17-OLR database. The performance of LID systems is studied at various signal-to-noise ratio (SNR) levels using white and vehicular noises from a NOISEX dataset. The performance of LID systems is reported in terms of the equal error rate. It is observed from the experiments that multi-SNR-based curriculum learning models can generalize the LID performance in noisy environments.
Genre-Based Indian Viewer Movie Reviews—A Sentiment Analysis Classification of Text and Emoticons with a Supervised Machine Learning Approach

Indian movie reviews, sentiments play an important role in knowing the feedback of a movie comment that will identify whether the movie is good, bad or average. This research paper collects dataset of online movie review comments from a popular movie booking website BookMyShow for purely research purpose where the dataset of the movie reviews are based on the five genres such as: Animation, Comedy, Action, Romance, and Epics for a varied age group of Kids, Teens, Young adults, Adults and Family. This research study analyses live movie review comments of five movies to classify the sentiment of each movie review comment, emoticons and rating stars to calculate an overall polarity percentage. In our experimental approach, we have taken the unstructured data of movie reviews from a popular booking website BookMyShow using a Python script called Grabber.py which is later converted into structured form with a defined bag of words. Further, it goes for data preprocessing by extracting the corpus using syntactic feature selection method and using phrase patterns for positive and negative sentiment orientation, a supervised learning approach is applied on the movie comments for text processing and emoticons with SVM classification and Naïve Bayes classifier for classifying the polarity of the movie review comments.
Gap-Filling Techniques for Solar Radiation Data and Their Role in Solar Resource Assessment

Some of the highest solar insolation regions of the world are in the arid and semi-arid zones. These zones have seen large-scale deployment of solar power plants during the previous decade. Data quality analysis of solar radiation information from various sources is significant in this context. Semi-arid zones have large number of clear-sky days per year but are also subjected to other micro-climactic events. All the three components of solar radiation change during these events. The gaps in the solar radiation data sets need to be filled appropriately to obtain a continuous time series that can be used for solar resource assessment and forecasting. A review of the gap-filling techniques and their application to a semi-arid zone solar radiation data set is presented in this work. Further, it is pointed out that various cloud conditions need to be considered while gap filling for a better assessment of bankability of a solar power project.
Algorithms for elliptic curves

We introduce in this paper the algorithmic aspect of elliptic curves together with their applications. We also recall one of the promising application in the field of zero knowledge proofs with concrete implementations.
Scalable Large Margin Gaussian Process Classification

We introduce a new Large Margin Gaussian Process (LMGP) model by formulating a pseudo-likelihood for a generalised multi-class hinge loss. We derive a highly scalable training objective for the proposed model using variational-inference and inducing point approximation. Additionally, we consider the joint learning of LMGP-DNN which combines the proposed model with traditional Deep Learning methods to enable learning for unstructured data. We demonstrate the effectiveness of the Large Margin GP with respect to both training time and accuracy in an extensive classification experiment consisting of 68 structured and two unstructured data sets. Finally, we highlight the key capability and usefulness of our model in yielding prediction uncertainty for classification by demonstrating its effectiveness in the tasks of large-scale active learning and detection of adversarial images.
Understanding features on evolutionary policy optimizations

We analyze two deep reinforcement learning algorithms, gradient-based policy optimization and evolutionary one, by a number of visualization techniques and supplement experiments. As such techniques, filter visualization and saliency map are used to examine whether meaningful features properly extracted in the two algorithms. In addition to visual analysis, some experiments are devised to enhance the validity of the analysis. We observed that an evolutionary policy optimization tends to make use of the prior knowledge and learn the prior action distribution of the policy by a powerful exploration ability, which a gradient-based algorithm cannot do easily.
Research on Semantic Segmentation of Portraits Based on Improved Deeplabv3 +

Abstract At present, semantic segmentation is one of the hottest research directions in the field of computer vision, and the Deeplab series is one of the best neural network models in semantic segmentation. Based on the original Deeplab-v3 + neural network model, this paper makes a series of improvements to make it perform better on semantic segmentation of portraits: 1. Add channel attention module to speed up the convergence speed of model training and improve segmentation accuracy. 2. Improve the original AtrousSpatial Pyramid Pooling (ASPP), adjust the receptive field of the network, and improve the segmentation accuracy. Finally, this paper compares the segmentation performance of different semantic segmentation network models on human-parsing dataset and proves that the proposed neural network structure has better segmentation effect.