Reviewerly/exHarmony huggingface dataset:
Enhancing Intricate Details of Ultrasound PCOD Scan Images Using Tailored Anisotropic Diffusion Filter (TADF)

Polycystic ovary disorder (PCOD) is a prevalent reproductive and endocrinology chaos found in 6–10% of the female inhabitants. PCOD caused infertility in women if not detected early. PCOD is analyzed by using ultrasound B-scan modality images. One of the challenging tasks in medical image analysis is speckle noise reduction problem. To eradicate unwanted noise in the B-scan representation of image which is low cost and noninvasive for noise removal, the conventional methods namely—Gabor, Laplacian, medium, and curvlet were adopted. The above-mentioned methods have drawbacks which has not been able to sense fragile details noises that hold the miniature detail of cysts. To conquer the above-said problem, a new speckle reduction method, i.e., tailored anisotropic diffusion filter (TADF), is proposed. The key purpose of this research work is to enhance intricate details of ultrasound PCOD scan images by using TADF. In the proposed method, a diffusivity method and global gradient threshold is applied in all the four directions to an image to eliminate noise and preserves intricate features concurrently. The result of this approach has been tested on 120 test images out of 400 images in a database. The PSNR and CNR parameters are evaluated and compared to the existing speckle reducing methods. These experimental outcomes specify that the planned TADF can successfully diminish speckles while enhancing image boundaries for preserving intricate types like benign PCOD, chocolate polycystics, and lesions at its early stages.
Dynamic Chaotic Biometric Identity Isomorphic Elliptic Curve (DCBI-IEC) for Crypto Images

In this paper, a novel Dynamic Chaotic Biometric Identity Isomorphic Elliptic Curve (DCBI-IEC) has been introduced for Image Encryption. The biometric digital identity is extracted from the user fingerprint image as fingerprint minutia data incorporated with the chaotic logistic map and hence, a new DCBDI-IEC has been suggested. DCBI-IEC is used to control the key schedule for all encryption and decryption processing. Statistical analysis, differential analysis and key sensitivity test are performed to estimate the security strengths of the proposed DCBI-IEC system. The experimental results show that the proposed algorithm is robust against common signal processing attacks and provides a high security level for image encryption application.
The Internet of Things in the Russian Federation

The chapter presents a study on ensuring the information security of the Russian Federation in the field of the internet of things (IoT), an analysis of Russian state policy in this field and methods for its implementation in terms of technical and legal regulation, and the directions of state development in field creating a system of regulation of relations in the field of IoT. To present the general picture of the state's opposition to the risks and threats arising from the use of the IoT, a comparison is made of the risks and threats used by the FSTEC of Russia and ENISA. The authors disclose Russian approaches to ensuring information security, reflected in state strategic documents, including the strategy adopted in 2015 in the field of ensuring information security by switching to their own information technologies. In conclusion, recommendations are made for government bodies and users to ensure integrated information security.
The development of information system for garments industry using the android platform

Abstract Nowadays, almost all cellphones are connected to internet and they are referred to as smartphones. Smartphones can be utilized by the garment industry to assist monitoring the production and financial management processes. The purpose of this study is to develop an Android-based mobile application in the “X” garment industry which is integrated with the existing web-based information systems. The application was developed using the Rapid Application Development (RAD) software development method. The program code for the mobile application was created using Android Studio 3 and database access (MySQL DBMS) on the server via Web Service (REST API). Application evaluation was done by two methods, namely System Usability Scaling (SUS) and Black Box Test. The Black Box Test results showed that all application functions were running appropriately, and value of the System Usability Scaling (SUS) test was 83.5. This indicates that the application is feasible to be implemented in the “X” garment industry.
A Lightweight Reinforcement-Learning-Based Mechanism for Bandwidth Provisioning on Multitenant Data Center

Efficient management of cloud traffic leads to resource sharing among clients. To meet clients' performance restrictions, however, cloud providers adopt strict resource allocation that implies idle resources as clients' bandwidth thresholds are over-provisioned. We propose a dynamic mechanism to provide resources on-demand in a multitenant data center. The mechanism relies on Q-learning multi-agent for managing each client access to the cloud resources. The proposed mechanism either measure the throughput continuously or map the CPU usage into bandwidth usage. We assess our mechanism in an emulated environment, in which a network controller provisions bandwidth. Results show that the mechanism reduces cloud idleness, allowing low-priority clients to use bandwidth while there is idle capacity. Our mechanism incurs low processing overhead, even when the number of states and action space grow, while the memory cost per agent increases, peaking at 300 kB for 200 states and actions. When mapping CPU usage into bandwidth usage, the mechanism achieves fair bandwidth sharing at the cost of small increase on the convergence time to an optimal policy.
Single-Shot 3D Multi-Person Shape Reconstruction from a Single RGB Image

Although the performance of the 3D human shape reconstruction method has improved considerably in recent years, most methods focus on a single person, reconstruct a root-relative 3D shape, and rely on ground-truth information about the absolute depth to convert the reconstruction result to the camera coordinate system. In this paper, we propose an end-to-end learning-based model for single-shot, 3D, multi-person shape reconstruction in the camera coordinate system from a single RGB image. Our network produces output tensors divided into grid cells to reconstruct the 3D shapes of multiple persons in a single-shot manner, where each grid cell contains information about the subject. Moreover, our network predicts the absolute position of the root joint while reconstructing the root-relative 3D shape, which enables reconstructing the 3D shapes of multiple persons in the camera coordinate system. The proposed network can be learned in an end-to-end manner and process images at about 37 fps to perform the 3D multi-person shape reconstruction task in real time.
Fusing deep learning and statistical visual features for no-reference image quality assessment

Fusing the pertinence of natural scene statistics-based methods and the ubiquity of convolutional neural network-based methods, a no-reference image quality assessment (IQA) method fusing deep learning and statistical visual features for no-reference image quality assessment (FDSVIQA) is proposed. For the statistical visual features, a local normalized luminance map and a local normalized local binary pattern (LBP) map of the image are constructed, and the local normalized luminance features and the gradient-weighted local normalized LBP features are extracted on the two maps, respectively. These two kinds of features are concatenated to build the image statistical visual features. For deep learning, the local normalized luminance block and the localized normalized LBP block are input into a double-path deep learning network, and the statistical visual features are input into the deep learning network to be integrated with the depth features. After learning and training, IQA is achieved. The performance of the proposed FDSVIQA algorithm is tested on the Laboratory for Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality Database, and Multiply Distortion Optics Remote Sensing Image databases. Experimental results show that the FDSVIQA algorithm has excellent subjective and objective consistency and good robustness for both distorted natural images and distorted remote sensing images. In addition, the FDSVIQA has database independence.
Energy Consumption Prediction using Data Stream Learning for Commercial Buildings

Smart cities should be built on harmonious relationships with the environment, with rational measures to use environmental resources in many ways. Resources essential to human life such as water and energy must have special attention. With the advance of technologies use, human being becomes one of the main promoters of information and solutions for smart cities. Stream machine learning is an approach where data becomes available sequentially, called data stream, which adapts the prediction model with fixed window-sized data stream. The objective of this work is to analyze the effectiveness of buildings energy consumption using stream learning. To achieve this goal, MOA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Massive Online Analysis tool was used to evaluate the performance of the selected stream learning algorithms on an an open database. Batch algorithms were also used as testbed, to analyze the performance of stream algorithms related to them. Our work shows that stream learning can show good results in this scenario.
Research paper abstracts in <i>Monthly Notices of the Royal Astronomical Society</i> (1943–2018)

Abstract We report the findings of a diachronic study of 100 research paper abstracts published in four different periods (from 1943 to 2018) in Monthly Notices of the Royal Astronomical Society , one of the most prestigious astrophysics journals written in English. Our main results show that research paper abstracts have evolved over time in the sense that they have become longer, more informative and more precise. They also reveal an overall increase in the number of authors, active and modal verbs, self-mentions and compound groups per total number of words. Likewise, compound structures are becoming more and more complex. These outcomes may be explained in terms of a rising collaboration scenario and an attempt to increase authors’ visibility.
Adaption of cloud computing types of software as a service

Abstract Cloud computing is an information technology service that can be used or accessed by users to manage data and applications through the internet network. Software as a service is one type of cloud computing service in the form of software. The purpose of this study is to analyze the use and students’ acceptance on cloud computing types of Software as a service including the most used applications, factors that influence usage, benefits obtained, and to find out their use related to learning and learning activities in school. The researcher used a mixed method research design approach to obtain qualitative and quantitative data. Data collection by conducting interviews and giving questionnaires to 367 students from 10 high schools in Garut. The findings show that the Google application is widely used by students. The compatibility factor is the reason for using cloud computing technology as a type of software as a service. Broad access is a benefit obtained by students in using cloud computing type of software as a service. It needs to be given an understanding to students about the use of cloud computing type of software as a service for learning activities in education at school.
Online Detection of Stalling and Scrubbing in Adaptive Video Streaming

Whether it is for network engineering or business intelligence insight purposes, it is crucial for an Internet Service Provider (ISP) to infer the Quality of Experience (QoE) perceived by the end user during a video streaming session. Specifically, it is important to detect video stalls as soon as they occur, to rapidly take counter-measures such as re-allocating resources more fairly among users. Video stalls fall into two different classes: (i) those caused by poor network conditions and (ii) those caused directly by the user when scrubbing or dragging the video playback forwards or backwards. However, only the former type of stalls degrade the QoE perceived by the end user. Therefore, in this paper we propose a technique to detect and classify stall events by observing the packets associated to a streaming session. We solve a least squares problem to minimize the distance between the estimated chunk's bitrate and the potential bitrate sequence that a plausible playback buffer dynamics would produce. This amounts to finding the maximally likely state sequence for a properly defined Hidden Markov Model. We propose two polynomial dynamic programming algorithms, one of which running in online fashion, computing the exact solution in the ideal case of complete and exact measurement set. We claim that our method is also applicable in an encrypted scenario, since it is robust with respect to the estimation error of a number of parameters, as we show via simulations.
Reliable Multi-Kernel Subtask Graph Correlation Tracker

Many astonishing correlation filter trackers pay limited concentration on the tracking reliability and locating accuracy. To solve the issues, we propose a reliable and accurate cross correlation particle filter tracker via graph regularized multi-kernel multi-subtask learning. Specifically, multiple non-linear kernels are assigned to multi-channel features with reliable feature selection. Each kernel space corresponds to one type of reliable and discriminative features. Then, we define the trace of each target subregion with one feature as a single view, and their multi-view cooperations and interdependencies are exploited to jointly learn multi-kernel subtask cross correlation particle filters, and make them complement and boost each other. The learned filters consist of two complementary parts: weighted combination of base kernels and reliable integration of base filters. The former is associated to feature reliability with importance map, and the weighted information reflects different tracking contribution to accurate location. The second part is to find the reliable target subtasks via the response map, to exclude the distractive subtasks or backgrounds. Besides, the proposed tracker constructs the Laplacian graph regularization via cross similarity of different subtasks, which not only exploits the intrinsic structure among subtasks, and preserves their spatial layout structure, but also maintains the temporal-spatial consistency of subtasks. Comprehensive experiments on five datasets demonstrate its remarkable and competitive performance against state-of-the-art methods.
Cognitive Hybrid PSO/SA Combinatorial Optimization

This paper presents a population based simulated annealing algorithm to improve modelling of cognitive processes. Particle Swarm Optimization (PSO) is embedded within the basic Simulated Annealing (SA) algorithm to allow for multiple concurrent candidate solutions through the use of a population-driven social coefficient updating the other population members. A modified ramping strategy which balances inertial, personal and swarm coefficients is introduced. The hybrid PSO/SA algorithm was tested on the travelling salesperson problem (TSP), and was shown to outperform the individual algorithms by improving their limitations in exploration and exploitation.
Broad Autoencoder Features Learning for Pattern Classification Problems

Deep Neural Networks (DNNs) demonstrate great performances in pattern classification problems. There are several available activation functions for DNNs while the Sigmoid and the Tanh functions are most widely used choices. In this work, we propose the Broad Autoencoder Features (BAF) to better utilize advantages of different activation functions. The BAF consists of four parallel connected Stacked AutoEncoders (SAEs) with different activation functions: the Sigmoid, the Tanh, the ReLu, and the Softplus. With this broad setting, the final learned features merge learn features using diversified nonlinear mappings from the original input features and such that more information is mined from the original input features. Experimental results show that the BAF yields better learned features in comparison with merging four SAEs using the same activation functions.
Dynamic Path Optimization for Robot Route Planning

Robot is an autonomous system that integrates advances AI technologies. This paper deals with the adaptive path planning and optimization problems for robots in dynamic environments. We propose a novel route planning method based on the maze representation of workplace layouts. We generate a universal path tree by a path optimization algorithm. Then, any given entrances and exits of target nodes can be reduced to a deterministic path searching problem. Our method can quickly determine the optimal path between any pair of entrance/exit nodes. The maze-based method provides an efficient and robust route planning solution for robots in real-time and dynamic workplaces. Experiments have demonstrated the effectiveness of the method beyond traditional heuristic technologies.
Application Research of the New Mode of “Blockchain + Higher Education”

As one of the subversive technologies that cause great changes in human society after information technology and Internet technology, blockchain technology has been applied in many fields, and its performance in the field of education and teaching is also expected. Against this background, this paper studies the application of the new mode of "blockchain + higher education". Based on the analysis of the connotation and technical characteristics of "blockchain +", it explains the various application scenarios of the new mode of "blockchain + higher education", studies the problems existing in the application, and finally puts forward the corresponding improvement ideas, so as to provide reference for the follow-up research of higher education theory and practice based on blockchain technology.
Security for Data in IOT Using a New APS Elliptic Curve Light Weight Cryptography Algorithm

A thought process on my mind says that IOT is expanding every minute and second due to many newer devices who join in the IOT chain every time. Necessity is the mother of invention, as per this when private and confidential data are moving across in the IOT environment across homogeneous and heterogeneous devices then in this case we should think about the data care. In this paper, a strong cryptographic algorithm is coined which will secure the data getting transformed over the IOT devices. A New APS Elliptic Curve Light Weight Cryptography algorithm is used which satisfies all the parameters in ensuring security across the IOT for data. If a text is given the user will have the private key and the hashing function is applied over the text and then the cipher text which moves across the signed hash checkers and after that the other user at the recipient side will be able to decrypt the cipher text using his public key and can read the message. This is actually done in the case of a Elliptic Curve Cryptography. The lightweight cipher text is mainly to handle the low resource device on the IOT chain. By considering the naturality of all the devices over the IOT chain and to satisfy all those parameters which are very essential to meet data security on IOT devices, the New APS Elliptic Curve Light Weight Cryptography algorithm is used.
An Exploratory Study Using Stream Learning Algorithms to Predict Duration Time of Vehicle Routes

The time required for a vehicle to travel different routes in the daily traffic of large cities varies and changes constantly, impacting the daily lives of everyone dwelling in those cities. Trying to predict such time is essential to evolve in understanding the behavior of vehicle traffic. On the other hand, due to the vast amount of data generated in this context, it is necessary to use new ways of dealing with this problem. This paper presents an exploratory analysis of the behavior of batch and stream learning algorithms for predicting the trip duration time for vehicles going through different routes. We understand that batch learning algorithms are not necessarily adequate for being used in stream mining situations. However, we would like to have a testbed to analyze the behavior of stream learning algorithms. For our experimental analysis, we used real data from three specific routes. The results show that the use of data stream learning for this domain yields promising results.
Complex-Event Processing for diabetic patients in the Internet of Medical Things: Semantic-based Approach

The adoption of the Internet of Things (IoT) in the medical sector improves health care services and guarantees continuous and efficient monitoring of patients with chronic disease. Accordingly, heterogeneous health data (medical devices and medical records) is obtained and requires to be efficiently managed and diverse risk factors should be smartly considered in order to predict and prevent complex and dangerous events. The modeling and processing of this kind of events present an emerging challenge that necessitates to be addressed. In this context, we aim to propose a semantic-driven complex event processing approach for Cardiovascular Disease (CVD) prevention for diabetic patients. Particularly, we focus on the semantic modeling of medical events; Then, we propose diverse rules for processing purposes.
Hijaiyah Letters Sign Language Recognition for Deaf and Hearing Impaired Children Based on Principal Component Analysis Method

Abstract Knowing and learning hijaiyah letters is a very important basis in reading Qur’an from an early age. For some normal people knowing and learning hijaiyah letters is relatively easy but unlike the children who have special needs like deaf and hearing impaired children. They can only communicate using sign language both in the form of hand gesture, body gesture, and facial expression. From these problems, the author the author is motivated to make a system for implementing the hijaiyah letters cues for deaf and hearing impaired children using PCA (Principal Component Analysis) method which was installed on desktop computer. The process of hand gesture recognition consists of several steps: camera initialization, preprocessing, feature extraction using PCA to find eigen value and eigen vector, and hand classification using euclidean distance. The output of hand gesture recognition is hijaiyah letters identification from alif to ya’ in the form of text and sounds.