Reviewerly/exHarmony huggingface dataset:
QARPF: A QoS-Aware Active Resource Provisioning Framework Based on OpenStack

On-demand resource allocation is a crucial feature of Infrastructure as a Service (IaaS) in cloud computing environment. Aiming at the problem that existing cloud platforms cannot conduct active resource provisioning according to the Quality of Service (QoS) requirements of tenants' tasks, this paper designs and implements a resource actively provisioning framework based on OpenStack, which takes into account both the resource utilization efficiency of data center and the QoS requirements of task. The proposed framework consists of three stages: (1) tasks are encapsulated and forwarded according to resource requirements and time characteristics of tasks; (2) workload is dynamically analyzed based on priority-aware task scheduling model; (3) and efficient mapping between tasks and resources is accomplished based on QoS-aware resource provisioning model. Experimental evaluation on CloudSim simulation platform shows that the proposed framework allows tenants to directly submit task requirements to the cloud platform, and actively realize resource-efficient provisioning, which effectively improves the resource utilization efficiency of data center, reduces the task completion time and virtual machine (VM) rental cost of tenant.
Method of Arithmetic Comparison of Data in the Residue Numeral System

This article considers the method of arithmetic comparison of numbers in the residue numeral system (RNS). The use of RNS significantly increases the speed of implementation of modular arithmetic operations. However, the implementation of non-modular operations in RNS requires the development of effective methods for comparing numbers. The mathematical model and the method of arithmetic comparison of numbers, proposed in the paper, are based on the usage of the positional feature of the non-positional code in RNS. This method ensures the maximum accuracy of comparison with a minimum number of equipment of the comparing device. Also, the developed method is more effective in comparing numbers compared to a method that is based on the operation of zeroing numbers. Using the developed method expands the field of effective use of RNS as a number system of computing systems.
Comparative Analysis for KeyTerms Extraction Methods for Personalized Search Engines

Text Mining refers to an extraction of certain nontrivial, hidden and interesting knowledge from an unstructured textual data. In this paper, efforts are directed to interpret text mining queries in the healthcare domain. To do so, the dataset is taken from the 1mg-company that has emerged during 2015 to provide transparent, authentic and accessible healthcare information for the millions of people while guiding customers with the quality care that too at affordable prices. The different text mining algorithms are compared to generate knowledge extraction of keyterms while linking the personalized search concepts with respect to the healthcare domain, and for the better search recommendations. The algorithms are: basic TF-IDF, SGRank with IDF, TextRank, and modified TF-IDF. The best results are obtained with the modified TF-IDF with the Shingle analyzer where post-release overall is reduced.
University Informational Ecosystem with Target Contours

The chapter deals with the problem of creating an informational ecosystem of a higher educational institution based on target contours. Under the informational ecosystem, the authors understand a complex social system, which includes informational support for scientific and educational, innovative, organizational, and other processes using such infrastructure components as information and telecommunication systems, software applications, personal computers, mobile devices for students, parents, employees, employers, etc. The implementation of the informational ecosystem creates the necessary conditions for the university to become a competitive scientific and educational organization. It would improve the educational space, modernize educational, scientific innovation processes, enhance the quality of education, and increase the role of the university in the sustainable development of the region and the country. In the course of the study, the authors suggest scientifically grounded recommendations on the University Informational Ecosystem with the following main target contours: the applicant’s contour, the student contour, the teacher’s contour, the contour of the educational process administrator, the contour of the university administrator, and the contour of external partners. The results of the study ensure the effective interaction of all stakeholders of the educational process and the improvement of the quality of education.
Applying Data Visualization Guideline on Forest Fires in Argentina

Nowadays, the data has a dynamic never seen before. They have continuous growth which is associated with the permanent generation from both data entered by users and new data derived from systems. In this context, the data visualization plays a highlighted role because it allows synthetically communicating high-data volume making them understandable for the End-user. This aspect constitutes a key asset throughout the decision-making process in any organization because incorporates dynamism and fosters different kinds of analysis. For that reason, the use of guidelines allows tutoring a process and in particular, those related to data visualizations. In this work, the general visualization process is described in order to schematize the way in which user requirements and sketches converge. Next, the visualization design describes the way in which the sketches could be implemented using the software. As a contribution, here an application case using the forest's fires dataset of Argentina between 2011 and 2017 is shown in order to serve as a reference for the guidelines' using. The case was implemented using Qlik Sense Cloud, incorporating a set of dynamic behaviors included in the platform, such as the possibility of making zoom on maps or sharing the selections between visual components. The employed data are freely available on datos.gob.ar, the Open-data platform of Argentina's Government. The case allows exemplifying the use of guidelines, its applicability, and the chosen data visualization strategy in a consistent way.
Management of Scientific and Educational Communities in Terms of Digitalization

The chapter is dedicated to the study of developmental features of the education system and university science in terms of innovation processes. We examine the prospects to change the basic principles of university performance and management within the development of a digital society, including ideological concepts to intensify innovation growth. It is noted that the main trends of education and science follow global ones, but their implementation is determined by the sociocultural features of the evolution of respective social institutions. The high potential and readiness of universities for innovation based on the principles of the digital economy are often hindered by the existing socioeconomic conditions and traditions of traditional management of education and science systems.
Can translationese features help users select an MT system for post-editing?

This work explores the possibility of using translationese features as indicators of machine translation quality for users to select an MT system for post-editing assuming that a lower level of translationese will reveal a reduced need for editing. Results reveal that translationese and automatic metrics rank systems differently, opening an avenue for further research into the information each provides.
Salient Object Detection Based On Image Bit-Map

In this paper, we propose a novel salient object detection framework, which makes full use of the essential image compression. More specifically, we first compose an intuitive measure of compressibility from JPEG compression, namely bit-map. Then, depending on the relationship between bitmap and salient object, we generate the salient object window directly from bit-map without utilizing any features from the compressed image. Finally, the saliency map is calculated according to the salient object window and with a ranking algorithm. The proposed method achieves good performance as well as low complexity. The experimental results demonstrate the effectiveness of our proposed method compared with other existing approaches.
Human Action Image Generation with Differential Privacy

Large volumes of human action image data are becoming increasingly available due to the prevalence of surveillance cameras and smart personal devices. While such image data enables important applications such as activity recognition for health and safety enhancement, they often contain sensitive information such as identities that introduce high risks to individual privacy. Existing image privacy-enhancing techniques are either developed at the cost of sacrificing image utility or lack of provable privacy guarantees. We propose a novel human action image generation model that enforces rigorous differential privacy protection. Theoretical analysis is provided to quantify the privacy protection on the training data within the differential privacy framework. Experiments with real-world datasets demonstrate that images generated using our method achieve higher image utilities than baselines given similar degrees of privacy protection.
PickPatch: A Regularization Method for Deep Face Recognition

Abstract This paper proposes a simple and efficient regularization method, called PickPatch, for face recognition based on a deep convolutional neural network (DCNN). The proposed method randomly selects patches in the input face image and the intermediate feature maps as the activation region according to facial landmarks during the training phase. PickPatch is an approximation method that trains a series of models for different face patches and provides a combined model. This strategy introduces the idea of model combination for multiple face patches but does not change the model structure, which is both simple and efficient. Experiments on the public LFW database demonstrate that the proposed regularization method based on current deep convolutional neural networks can achieve obvious improvements of face recognition accuracy.
OCR Recognition System for Degraded Urdu and Devnagari Script

Any Optical character Recognition System has to undergo Pre-processing, Segmentation and classification to change the image into machine editable form. In this paper algorithm for Neural Network is applied for the recognition of Urdu and Devanagari Characterts. In all the phases segmentation plays an important role in character Recognition System. By applying the algorithm we can see that the system is able to recognize characters of Urdu and Devanagari Scripts. The result obtained are compared with other methods to prove that this method is better from others.
Two Can Play That Game

Cyber-security is an important societal concern. Cyber-attacks have increased in numbers as well as in the extent of damage caused in every attack. Large organizations operate a Cyber Security Operation Center (CSOC), which forms the first line of cyber-defense. The inspection of cyber-alerts is a critical part of CSOC operations (defender or blue team). Recent work proposed a reinforcement learning (RL) based approach for the defender’s decision-making to prevent the cyber-alert queue length from growing large and overwhelming the defender. In this article, we perform a red team (adversarial) evaluation of this approach. With the recent attacks on learning-based decision-making systems, it is even more important to test the limits of the defender’s RL approach. Toward that end, we learn several adversarial alert generation policies and the best response against them for various defender’s inspection policy. Surprisingly, we find the defender’s policies to be quite robust to the best response of the attacker. In order to explain this observation, we extend the earlier defender’s RL model to a game model with adversarial RL, and show that there exist defender policies that can be robust against any adversarial policy. We also derive a competitive baseline from the game theory model and compare it to the defender’s RL approach. However, when we go further to exploit the assumptions made in the Markov Decision Process (MDP) in the defender’s RL model, we discover an attacker policy that overwhelms the defender. We use a double oracle like approach to retrain the defender with episodes from this discovered attacker policy. This made the defender robust to the discovered attacker policy and no further harmful attacker policies were discovered. Overall, the adversarial RL and double oracle approach in RL are general techniques that are applicable to other RL usage in adversarial environments.
People Profile Metrics for Improved Classification of Defect Prone Files in Open Source Projects

Numerous models have been studied and presented in literature for classification of defect-prone source code files. Usually these models use static code metrics, process metrics, and change metrics as input and predict defect proneness of code. However, there has been limited use of people related metrics as input to the prediction models. Impact of using people related information should be studied for better classification of defect prone files in future releases of software projects. This study proposes the use of People Profile Metrics (PPM) of software development team members to improve the prediction of defect prone source code files. The experiment is performed on an open source project and the defect prone source code files have been classified. In addition, severity of defects has also been predicted. The PPM have been evaluated for three classifiers Decision Tree, Random Forest, and K-Nearest Neighbors using Weka. Significant improvement in classification of defect prone source code files, in terms of Precision, Recall and F-Measure has been achieved. The combination of existing static code metrics and the PPM will be tested for more projects and for unsupervised models.
Wideband Programmable Gaussian Noise Generator on FPGA

Gaussian Noise Generators are common in various applications of Electronic Countermeasures and Low Probability of Intercept radar. It can be applied to military applications, law enforcement and commercial segments. The key requirement for such a generator is the ability to operate in wide spectral band. The main driver for this requirement is the ever increasing radars bandwidth to achieve high radar resolution.In addition to wideband capabilities, it is important to have a fine control over the suppressed and unsuppressed spectral frequencies. The countermeasures must allow friendly system to continue to operate, while suppressing the adversary systems. Moreover, due to limited transmit power envelope on analog RF power amplifier chain, it is advantageous to limit a transmission only to specific band-limited regions. The countermeasures should be able to reconfigure its settings in terms of active spectral frequencies agilely, when operational conditions or power considerations change. This paper presents proposed implementation on FPGA that achieves such key metrics.
Aggregate Channel Features Based on Local Phase, Color, Texture, and Gradient Features for People Localization

Detection of human beings in a complex background environment is a great challenge in computer vision. For such a difficult task, most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. To improve the detection accuracy, we propose a new descriptor that is constructed from the channels of image gradients, texture features, local phase information, and color features. This information is fused together to build one descriptor named Channels of Chromatic domain, local Phase with Gradient and Texture features (CCPGT). The image gradients, and local phase information based on the phase congruency concept are used to extract the human body shape features. Local binary pattern approach (LBP) is used to capture the texture features, and additional significant information for human detection are added by the color channels. The phase congruency magnitude and orientation of each pixel in the input image is computed with respect to its neighborhood and then six channels representing the histogram of oriented phase are generated. Gradient magnitude channel, LBP channel, and three LUV color channels are also computed for the input image. A maximum pooling of the candidate features is applied for these generated channels. All these features are concatenated in one feature vector and fed to a decision tree Adaboost classifier for training and to classify between the different objects classes. Several experiments were conducted to evaluate the proposed approach using challenging INRIA dataset, and a promising performance is observed.
Design and Implementation of Cubical Robot Control System Based on STM32

In order to satisfy the verification requirements of unstable, non-linear, multi-input and single-output control algorithms, the research of complex inverted pendulum system has broad prospects for development and high scientific research value. The cubical robot inverted pendulum system built in this paper takes STM32F427II microcontroller as the control core. The mechanical structure is composed of three isometric and vertical momentum wheels, and the brake system with the steering gear as the power. The space attitude of the robot is calculated by using three axis acceleration and linear velocity solution, and the output torque of the motor is controlled by its own attitude, so that the robot can stand steadily on one side and at one point. This paper briefly introduces the development history and research status of the inverted pendulum system and cubical robot, gives the hardware and software design ideas and implementation methods of the cubical robot based on STM32, and builds the corresponding cubical robot. The experimental results show that the cubical robot built in this paper achieves the expected functions, and verifies the effectiveness of the control system.
Fuzzy Rule Based Inference System in Patient Diagnosis of Breast Tumor in Fine Needle Aspiration

Breast cancer is considered as one of the leading cause of death among women and even men worldwide. Early detection is very important for patient survival. In this paper, fuzzy logic is used to predict if the tumor of a patient is benign or malignant based on the fine needle aspiration test. This will aid experts to make decision in giving recommendation to patients on what medical process to undergo. WEKA is used to determine the best attribute in the UCI Breast Cancer Diagnosis dataset using Correlation Ranking Filter. The data set consists of 683 instances after removing 16 instances with incomplete information. The new constructed dataset has 444 benign cases and 239 malignant cases. Six (6) attributes are selected out of nine (9) given attributes in the dataset. Mamdani fuzzy inference method is used in the development of the fuzzy model. Rules are generated based on the experts' domain. Value of each selected attributes are inputted in the Rule Viewer to determine the class of breast cancer tumor using CRISP input. Performance shows that fuzzy logic has an accuracy of 96.93%, sensitivity and specificity of 97.06%. Thus, fuzzy logic is good for predicting the class of breast tumor.
Prediction of Voicing and the F0 Contour from Electromagnetic Articulography Data for Articulation-to-Speech Synthesis

Articulation-to-speech synthesis based solely on supraglottal articulation requires some sort of intonation control. This paper examines to what extent the f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> contour of an utterance can be predicted from such supraglottal articulation data. To that end, three groups of machine learning models (support vector machines, kernel ridge regression and neural networks) were trained and evaluated on the mngu0speech corpus containing synchronous articulatory and audio data. The best voiced/unvoiced/silence classification rates were achieved by a deep neural network with two hidden layers: 85.8 % with no look-ahead (important for on-line applications) and 86 % with a look-ahead of 50 ms. The best f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> prediction model without look-ahead scored a root-mean-square error (RMSE) (when compared to the original f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> contours) of 10.4 Hz using a neural network with one hidden layer, while the best prediction with a look-ahead of 50 ms was attained by kernel ridge regression and an RMSE of 10.3 Hz. The predicted f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> contours were also subjectively evaluated in a listening test by manipulating the f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> of the original speech files using PRAAT. The results are consistent with the objective evaluation.
Balancing Rates and Variance via Adaptive Batch-Sizes in First-Order Stochastic Optimization

Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics. In this work, we seek to balance the fact that attenuating step-sizes is required for exact asymptotic convergence with the fact that larger constant step-sizes learn faster in finite time up to an error. To do so, rather than fixing the mini-batch and step-size at the outset, we propose a strategy to allow parameters to evolve adaptively. Specifically, the batch-size is set to be a piecewise-constant increasing sequence where the increase occurs when a suitable error criterion is satisfied. Moreover, the step-size is selected as that which yields the fastest convergence. The overall algorithm, two scale adaptive (TSA) scheme, is shown to inherit the exact asymptotic convergence of stochastic gradient method. More importantly, the optimal error decreasing rate is achieved theoretically, as well as an overall reduction in sample computational cost. Experimentally, we observe a favorable tradeoff relative to standard SGD schemes absorbing their advantages, which illustrates the significant performance of proposed TSA scheme.
Blockchain provisioning over private cloud computing environments: Availability modeling and cost requirements

This paper proposes and evaluates availability models for blockchain provisioning over cloud computing infrastructures as well as their respective deployment expenses in order to establish a cost × benefit relationship. To demonstrate these models' feasibility, we provide two case studies considering blockchain provisioning over a baseline architecture, and three other alternative redundant environments.