Reviewerly/exHarmony huggingface dataset:
A Modern Perspective on Cloud Testing Ecosystems

The Cloud testingCloud testing market share is expected to be over 10 billion USD by 2022. Cloud migration for applications has become an attractive phenomenon, and end users have, as a result, achieved various benefits such as autonomy, scalability and agility and improved return on investment by migrating to Cloud. Cloud environment is inherently elastic with respect to applications, infrastructure and platform resources which consequentially translate to the benefits mentioned. The rapid consumer adoption of Cloud paradigm mandates software testing in order to ensure that services over the Cloud are working as expected. In addition to the need for testing Cloud services and applications, the emergence of Cloud computing has opened new avenues for providing testing services over the Cloud in the form of testing as a service (TaaS)Testing as a Service (TaaS). Many quality assurance (QA) processesQA processes which have a direct impact on testing cycles, like test environment management and test dataData management, can be provisioned via the Cloud, resulting in immense additional benefits. This chapter sets the context of Cloud computing and its growing significance for the software industry before focusing on Cloud TaaSTesting as a Service (TaaS). Additionally, different types of Cloud deployments in testing ecosystem are discussed in this chapter including: testing on the Cloud, testing models, test processes relating to the Cloud, tools and frameworks.
Compact interferometer for precise shape testing of large-size convexaspherical mirrors

Abstract A model of an interferometer for testing of convex aspherical mirrors up to 250 mm in diameter based on the scheme of orthogonal rays is presented. An algorithm for interferogram processing has been developed. The results of measurements of the profile of a convex spherical mirror on the presented interferometer are presented.
SPPBPS: A Secure Privacy-Preservation Bilinear Pairing Scheme for Bitcoin Cryptocurrency

Abstract Despite the existing advances on research and education in cybersecurity and its applications, the field is still under discovery and new technologies are evolving. In recent years, Bitcoin as a cryptocurrency cyber application that records all financial transactions present on a blockchain where users can reach a secure and robust consensus on transactions emerges as the most popular peer-to-peer electronic payment system. Cryptocurrencies mostly rely on blockchain, as a secure decentralised append-only ledger to exchange digital currencies, and thereby attracting a billion-dollar economy. Bitcoin allows input transactions to link addresses and this can be used by an adversary to reveal real user identity. This obviously attracts privacy issues such as user’s information and identity leakage. Several existing techniques are found not satisfactory to fulfil practical and compatible anonymity requirements for users and their transaction. We present in this paper, a secure privacy-preservation scheme based on bilinear pairing and other cryptographic primitives. We proved that our proposed scheme is robust and can be implemented to preserve users and transaction privacy in the Bitcoin system by theoretical analysis and evaluation.
Sentiment Analysis of Smartphone Product Reviews Using Weightage Calculation

Sentiment analysis is extremely useful in monitoring the social media and the most popular tool for text classification, and analyzes the input and informs you if it is positive, negative, or neutral. It helps to collect large amount of data systematically and it extracts the subjective information from them. Humans have the indelible ability to determine sentiment which is time-consuming process, conflicting, and costly in a business context. It is not practical to have people individually read all the reviews of the customer and scores them for sentiment. So, to overcome this, sentimental analysis models has been developed. In our proposed system, we are using weightage classification model to analyze the tweets from Twitter API and classify based on their respective sentiment.
Edge Sign Prediction Based on Orthogonal Graph Regularized Nonnegative Matrix Factorization for Transfer Learning

In a signed graph, the edges have binary labels that indicate positive or negative relationships. In scenarios where some of the edge signs are unavailable, conventional learning methods will be ineffective. In contrast, transfer learning methods can improve the learning performance by using another network with adequate signs. In a social network, the problem often facedis that the network dimension is too high. Nonnegative Matrix Factorization (NMF) is a widely used matrix decomposition method to decrease the high dimensionality. However, the matrix that is generated may not be sparse enough, which can impact its representation ability. To address this problem, we propose Orthogonal Graph Regularized Nonnegative Matrix Factorization (OGNMF) to extract latent features from social networks and prove its convergence theoretically. Based on TrAdaBoost, a classical transfer learning algorithm, the experimental results using benchmark datasets demonstrate that our method has superior performance to the other baseline methods.
Action and movements recognition methods

The article describes the formulation of the problem of recognition of the movements of objects in a video sequence, the stages of its solution, the analysis of the basic methods of each of the stages. A wide range of applications and growing requirements on the quality of recognition determines the relevance of the study. The process of action recognition and detection begins with extracting useful features, from the input video sequence. Features are then processed through a classier to identify the action class (for example, running, walking, jumping, various gestures). The article describes the main feature descriptors, in the filter-based category: histogram of oriented gradients, cuboid descriptor, scale-invariant feature transform, gradient location-orientation histogram, local trinary patterns, and spatiotemporal patches, optical flow-based descriptors: histograms of optical flow, the motion boundary histogram, dense trajectory, convolutional neural network-based descriptors. Some algorithms require the extraction of primitive features and further refinement of the auxiliary features before they can be passed to the classifier. Examples of the use of specialized primitive features are methods based on silhouettes / contours and methods based on object tracking. There are methods for classifying extracted features, including the following: support vector machines, adaptive boost, artificial neural networks, convolutional neural networks. The key difficulties arising in solving the problem are considered. There are ways to compare various methods. One of the ways to draw comparisons is to quantitatively evaluate each approach on the same database with the same protocol. From simple KTH datasets and Weizmannnd to Carnegie Mellon University Crowded Videos dataset and Microsoft Research Action Group dataset to more complex video conditions and large-scale UCF101 and ActivityNet datasets. Existing approaches to recognition of motion in video sequences are analyzed. The article reveals characteristics, strengths and weaknesses of the various methods of detecting features and their classification. Leading methods that show the best results widely use convolutional neural networks. One of such methods is a spatio-temporal graph convolutional neural network for action recognition based on the object's skeleton. A method for further research and improvement was chosen.
Neoproterozoic Rare Element Pegmatites from Gitarama and Gatumba Areas, Rwanda: Understanding Their Nb-Ta and Sn Mineralisation

The aim of this work was to study the petrography, geochemistry of the pegmatites, their relationship to the mineralisation in Gitarama and Gatumba areas, and current processes that occurred after the primary emplaced neoproterozoic rare element pegmatites.Previous works on pegmatites were geochemistry and geological maps which are not enough for focused exploration and mine planning.Therefore, geological, petrographic, geochemical studies of neoproterozoic rare element pegmatites of Gatumba and Gitarama areas in relation to their mineralisation were carried out.The samples were analysed for mineral assemblages by petrographic light microscope; major elements by ICP AES; trace and rare earth elements by ICP MS.Petrographic studies revealed the mineral assemblages included quartz, microcline, biotite and major muscovites, which implied that there was the process of muscovitisation occurred after the primary emplacement of pegmatites.The results of geochemical analysis revealed that the silica content (in wt%) ranges from 59.5 -80.5 with an average of 67.13 (in wt%) for the weathered pegmatite in Gatumba area ,and high percentages of SiO 2 (in wt%) range 73.9 -75.0 with an average of 73.15 (in wt%) for fresh pegmatite in Gitarama area.The pegmatites from Gatumba area were altered and much enriched in Rb (227 -3460 ppm), Cs (2.59 -24.7 ppm), Ta (2.6 -268 ppm), Li (40 -9224 ppm), W (240 -10,000 ppm), Nb (13 -517 ppm), Sn (24 -8870 ppm).Their enrichment is commonly used as a marker of a magmatic-hydrothermal alteration.Conversely, the pegmatites from Gitarama area showed the low to moderate concentrations in Rb (321 -337 ppm), Cs (5.47 -5.62 ppm), Ta (1.3 -1.6 ppm), Li (~20 ppm), W (5540 -6410 ppm), Nb (3.9 -4.3 ppm), How to cite this paper:
Image Captioning Based on Visual and Semantic Attention

Most of the existing image captioning methods only use the visual information of the image to guide the generation of the captions, lack the guidance of effective scene semantic information, and the current visual attention mechanism cannot adjust the focus intensity on the image. In this paper, we first propose an improved visual attention model. At each time step, we calculate the focus intensity coefficient of the attention mechanism through the context information of the model, and automatically adjust the focus intensity of the attention mechanism through the coefficient, so as to extract more accurate image visual information. In addition, we represent the scene semantic information of the image through some topic words related to the image scene, and add them to the language model. We use attention mechanism to determine the image visual information and scene semantic information that the model pays attention to at each time step, and combine them to guide the model to generate more accurate and scene-specific captions. Finally, we evaluate our model on MSCOCO dataset. The experimental results show that our approach can generate more accurate captions, and outperforms many recent advanced models on various evaluation metrics.
Single View Depth Estimation via Dense Convolution Network with Self-supervision

Depth estimation from single image by deep learning is a hot topic of research nowadays. Existing methods mainly focus on learning neural network supervised by ground truth. This paper proposes a method for single view depth estimation based on convolution neural network with self-supervision. Firstly, a modified dense encoder-decoder architecture is employed to predict the disparity maps of image which can then be converted into depth and only one single image is fed to predict depth at test time. Secondly, the stereo pairs without ground truth are used as samples to generate supervision signals by synthesizing the predicted results during network training, which is referenced to network training in self-supervision manner. Finally, a novel loss function is defined which considers not only the similarity between the stereo and synthesized images, but also the inconsistency between the predicted disparities, which can decrease the influence of illumination of images. Experimental comparisons against the state-of-the-art both supervised and unsupervised methods on two public datasets prove that the proposed method performs very well for single view depth estimation.
Estimation of the image quality correction efficiency in optical measurement systems under Scheimpflug condition

A criterion for evaluating the image quality correction efficiency in optical systems under Scheimpflug conditions is proposed. The limits of applicability of the geometric optics approximation are clarified for the sensor tilt angle calculating. The results of a simulation optical stereoscopic system arm for three cases: normal, oblique system not under the Scheimpflug condition and oblique system under the Scheimpflug condition are presented. The quantitative estimation of the image quality correction efficiency is performed. The ranges of object distances and viewing angles, providing the maximum image quality correction without loss of accuracy due to perspective distortion are defined.
Path Planning for Multi-Object Push Problems in Continuous Domain

The task of bulldozing has many aspects that need more research before they can be fully automated. This paper presents a new method for calculating a path for vehicles tasked with pushing multiple objects to multiple goal locations. Unlike previous approaches which either focus on a single object, apply both push and pull operations, or operate in discrete domain, this solution works in a continuous space whilst handling multiple objects in a push-only scenario and doesn’t use computationally heavy methods to find a potential path planning solution. To achieve the results, two graph structures, one for the active vehicle and one for the passive pushable objects, is used. These graphs are then combined with an A⁎ Search Algorithm to find a correct path for the bulldozer to take. The algorithm is tested on a Microban problem set which results in a 96% success rate.
Agents based Context-Aware Framework for Facial Identification System

Research in face recognition systems has been focused on improving algorithm performance under specific conditions or to increase the average performance under heterogeneous conditions. Typical systems do not adjust well to perform at optimal level for a given instant of identification because the algorithms, face feature representations are fixed to achieve the best average result. Therefore, there is a real need to design a context-aware adaptive face identification system that can select the best pre-processing, features, and classifier for any given instance of identification. This paper focuses on the practical implementation and evaluation of our proposed framework [1] [2] that is aware of its operational context and adapt itself to select a suitable approach to identify a given face image. This is by using agent technology to give the system an intelligent and adaptive mechanism to make decisions at the key stages of the facial identification process. The agents will use context information such as environment conditions and application requirements to select the most appropriate pre-processing, features and match scores to optimise the best identification accuracy for a given test image. Within our framework, we propose the use of agents in two strategies: 1) an agent-based adaptive score selection technique as an alternative to the traditional fusion approaches, and 2) an agent-based integrated technique as an improvement to the existing adaptive and non-adaptive techniques. The experimental results presented here demonstrate that our techniques of using agents outperform the traditional fusion strategy that is commonly used in face recognition systems as well as the performance of other existing techniques.
A Chained Neural Network Model for Photovoltaic Power Forecast

Photovoltaic (PV) power forecasting is an important task preceding the scheduling of dispatchable power plants for the day-ahead market. Commercially available methods rely on conventional meteorological data and parameters to produce reliable predictions. These costs increase linearly with a rising number of plants. Recently, publicly available sources of free meteorological data have become available which allows for forecasting models based on machine learning, albeit offering heterogeneous data quality. We investigate a chained neural network model for PV power forecasting that takes into account varying data quality and follows the business requirement of frequently introducing new plants. This two-step model allows for easier integration of new plants in terms of manual efforts and achieves high-quality forecasts comparable to those of raw forecasting models from meteorological data.
Improved Search Strategy for Active Shape Model of Face Alignment

Face alignment is to look for the location of face features such as eyes, nose and mouth in the face image. Based on the face alignment of the active shape model, the point distribution statistical probability model was established, and the mahalanobis distance between the local features was calculated based on the iterative optimization of the gray information around the feature points, so as to find the position of the feature points in the new image. The traditional search process is that after initializing the shape, affine transformation parameters are calculated according to the updated position of the feature points, until the parameters converge, that is, the face alignment is completed and the position of the feature points is obtained. However, this process requires a lot of calculation, and the weights in the updating process are obtained by experience. In this paper, a search process based on genetic algorithm is proposed. The initial parameter of affine transformation is the value when training the model, and the objective function is the new shape vector multiplied by affine transformation parameter to get the difference between the result and the average shape, and the search is completed when the difference converges or reaches the number of evolution.
Prática e análise paleográfica: manuscritos da Colonia de Blumenau

This article deals with the paleographic transcription of two documents belonging to the colony of Blumenau, Santa Catarina, a collection of the rare works sector of the University Library of the Federal University of Santa Catarina (UFSC). The research question that guides the study is to highlight the importance of paleography as well as the difficulties arising from its practice. As for the general objective, it is a study that is organized to understand the context of the documents from the paleographic transcription. The methodological procedures of the research were done by means of a bibliographical survey, in search of general information about the paleology and the colony of Blumenau, the practice was carried out by means of the technical standards for transcription and editing of manuscript documents. It concludes by emphasizing the importance of the theoretical basis for transcription of historical documents. Based on the preliminary results, we understand that the difficulties encountered enrich the knowledge about the subject and prepare the students for the job market.
Text Categorization Using Sentiment Analysis

Twitter is an online social networking site where people can write short messages called tweets to express their emotions on any issue. In the existing system, classification of the tweets has been done in two main categories (positive and negative) using Naïve Bayes classifier and also categorized into three main categories (i.e., positive, negative and neutral). For classification, they have used “Random Forest” classifier. Dataset consists of tweets for training and testing, which is collected manually. In the proposed approach, the classification of the tweets is doing in three main categories (i.e., positive, negative and neutral). In this approach, we are classifying tweets into positive, negative and neutral class, and for classification, we are presenting a hybrid approach of Naive Bayes and K-Nearest Neighbor (KNN) classifiers. Dataset consists of 21,000 tweets which are taken by using Twitter API, and performance is calculated in terms of accuracy of tweet classification.
Fruit-Fly Optimization Algorithm for Disability-Specific Teaching Based on Interval Trapezoidal Type-2 Fuzzy Numbers

Student-centric learning methodology is vital in handling specific learning disabilities (SLD) that spotlight students for the teaching-learning process, improve the effectiveness of the learning and respond to the student's need. The educational psychologist with diverse skills diagnoses a SLD in different ways which contribute to decision-making. As a result, the deviation between each decision maker matrix has to be lowered to find the optimal weights. The proposed model develops the fruit fly optimization algorithm (FOA) based on the interval trapezoidal type-2 fuzzy number (ITrT2FN). Since the problem is multi-attribute decision making, the proposal was for a group decision-making model based on ITrT2FNs and a multi-attributive border approximation area comparison method. The model helps to ease the decision making related to the type of teaching-learning methodologies to be followed for a student with SLD. The model is tested with a ten attribute SLD problem, and a comparative study is made to measure the efficiency of the FOA.
On Integral Distinguishers for Ciphers Based on the Feistel Network Generalizations

Lightweight block ciphers are designed to ensure high-performance and security under resource constraint environments. They can be used to provide security for internet of things, cyber physical system and radio frequency identification. The Feistel network generalizations are often suited to the design of lightweight block ciphers. To evaluate the security of new cryptosystems they need to be tested with various cryptanalytical methods. In this paper, the PICARO and QTL block ciphers based on the Feistel network modifications are studied using integral technique which considers a propagation of multiset property during the encryption process in order to determine the type of cipher. The QTL cipher is proved to have 1.5-round integral distinguisher. Its round function is studied against integral attack. The propagation of some integral properties in the PICARO expansion layer is shown. Integral distinguishers for generalized Feistel networks of type 1, 2, 3 are proposed for some restricted component functions.
Modularized Musculoskeletal Platforms for the Investigation of Learning Control System

In this study, we develop MusashiLarm, TWIMP, and Musashi, as hardware platforms to investigate learning control systems of musculoskeletal structures, such as self-body image acquisition. First, we develop sphere-shaped joint modules which can directly measure joint angles, compact muscle modules including muscle tension measurement units, actuators, circuits, etc., compact and reliable nonlinear elastic units using grommet structures, standardized muscle relay units, and so on. Second, we develop MusashiLarm composed of only joint modules, muscle modules, muscle wire units, generic bone frames, and a few attachments, TWIMP which has high mobility by using an inverted pendulum as its lower limb, and Musashi which extends MusashiLarm to its whole body design.