Reviewerly/exHarmony huggingface dataset:
DESIGN FEATURES OF MULTI-BIT CMOS-CNI-ADC TO CREATE MULTI-CHANNEL HIGH-SPEED DSP SYSTEMS WITH INCREASED FAULT TOLERANCE

The features and possibilities of using foreign and domestic foundry technologies in the creation of CMOS-ADC for high-speed multichannel DSP systems with increased fault resistance to the effects of TKCH are considered. The architecture and technique of ADC balancing, which provide an increase in the conversion rate when several ADCS operate in the alternating mode, are presented. The technique of double reservation of sources of «weight» currents is considered. The necessity of using an additional current source and dual series-connected CMOS transistors instead of single transistors of the same conductivity type is substantiated. It is noted that the proposed solutions provide effective amortization of the impact of TKCH with the most "dangerous" energies of 20-100 MeV/nucleon
A Deep Learning based Framework for UAV Trajectory Pattern Recognition

Recognizing the trajectory patterns of Unmanned Aerial Vehicles (UAV) allows to identify their missions also to detect abnormalities for intelligent supervision. To take advantage of the developments of deep learning on image classification, we proposed a method that converts 3D trajectory data into 2D images and trains a deep network adapted to sketch-like images. We achieved a promising recognition rate of 99.5% on the database of simulated UAV trajectory images.
Prevalence of Anemia and its Correlates among Elderly population of an Urban Slum in Meerut

Background: Anaemia is a major public health problem in elderly persons which is often overlooked. With rising urbanization and world is facing demographic transition, there is a need to increase focus on elderly residing in urban slums. Aim: The present study aims to find the prevalence of anaemia and its associated factors among the elderly population in an urban slum of Meerut. Methodology: The present study conducted in an urban slum of Meerut from November 2017 to January 2018. The sample size was calculated as 396, considering the prevalence of anaemia as 45.5% in this age group. Haemoglobin levels were estimated using paper chromatography methodby hemoc-heck rapid diagnostic kit. Data were compiled and analysed using epi info software version 3.7.2. Results: 49.5% of the elderly residing in urban slums were found to be anaemic. The anaemia in the elderly was found to be significantly associated with employment, socioeconomic status and chronic diseases like COPD, hypertension and Tuberculosis
AWSCPM: A Framework For Automation Of Web Services Composition Processes

A growing number of companies are using web services to make their expertise and data available through the network. The current problem is the integration of these services in order to implement inter-company collaboration. Research organizations and industrialists are trying to find an adequate solution to achieve this task called services composition. The development of composite services using the Models Driven Approach (MDA) principles is as follows: The developer specifies the composition scenario using a modeling language. Once the specification is complete, it is usually formally validated before proceeding with the implementation of the new composite service. In this paper we present a summary of our developed approach of web services composition based on MDA, and using different languages and systems, like Multi-Agent Reactive Decisional system (MARDS) for the modeling task, LOTOS language for formal verification, and BPEL language for implementation. In order to automate these processes of web services composition approach, we have developed as part of this research the framework AWSCPM "Automatic Web Services Composition Process based on MARDS".
Implementation of Cloud IoT - Based Patient Fall Detection System with GeoTagging using Tilt Sensors and Texas Instruments’ CC3200

Objective: Coupled with an elderly population and extended life expectancy, disorder in the movement is one of the leading causes of functional disability. In this study we have designed an IoT-based Patient Fall Detection System (PFDS) using a low-cost sensor SW-520D, cloud computing technology and GeoTagging. Methods/Analysis: The position of the patient is detected by using tilt sensor which uses the rolling ball technology and is interfaced with the CC3200 microcontroller which is Wi-Fi on chip module using 802.11 b/g/n network protocol. The location of the patient can easily be tracked by GeoTagging technique. Findings: when the patient is tilted on the wheel chair/adjustable bed the tilt sensor detects the tilted motion and data is processed to the microcontroller and the data will be uploaded through Thing Speak cloud which is used for storing the real time values and is represented graphically. SIM 900A GSM module is used to send an alert if abnormality detected in the patient position and the location of the patient can easily be accessed using GeoTagging. Conclusion: In this study we have developed a cost-effective IoT-based PFDS that can access the data in real time through Thing Speak cloud and an alert signal is sent to the nursing attendant and hospital dashboard. Keywords: GeoTagging, Internet of Things (IoT), Patient Fall Detection System, Tilt Sensor, CC3200
Tree pyramidal adaptive importance sampling

This paper introduces Tree-Pyramidal Adaptive Importance Sampling (TP-AIS), a novel iterated sampling method that outperforms state-of-the-art approaches like deterministic mixture population Monte Carlo (DM-PMC), mixture population Monte Carlo (M-PMC) and layered adaptive importance sampling (LAIS). TP-AIS iteratively builds a proposal distribution parameterized by a tree pyramid, where each tree leaf spans a subspace that represents its importance density. After each new sample operation, a set of tree leaves are subdivided for improving the approximation of the proposal distribution to the target density. Unlike the rest of the methods in the literature, TP-AIS is parameter free and requires no tuning to achieve its best performance. We evaluate TP-AIS with different complexity randomized target probability density functions (PDF) and also analyze its application to different dimensions. The results are compared to state-of-the-art iterative importance sampling approaches and other baseline MCMC approaches using Normalized Effective Sample Size (N-ESS), Jensen-Shannon Divergence, and time complexity.
Digital Image Processing with Python and OpenCV

OpenCV means Open Source Computer Vision Library. It is an open source computer vision library and it has bindings for C++, Python, and Java. It is used for a very wide range of applications including medical image analysis, stitching street view images, surveillance video, detecting and recognizing faces, tracking moving objects, extracting 3D models, and much more. OpenCV can take advantage of multicore processing and features GPU acceleration for real-time operation.
Hybrid of Cuckoo Search Algorithm with Lévy Flight and Neural Network for Crude Oil Prices Prediction

Accurate prediction of crude oil price can successfully be used to deviate from the negative impact of the crude oil price hike. Limitations of previous studies prompt the proposal of an alternative approach for the hybridization of Cuckoo Search Algorithm with lévy flight and Back-propagation neural network (CSBP) for the estimation of crude oil price to enhance convergence speed accuracy. To evaluate the effectiveness of the proposed CSBP, we used ABC to train BP neural network (ABCNN) and Levenberg-Marquardt neural network (ABCLM) to develop an approach for crude oil price prediction. The outcomes of simulated comparative indicate that the proposed CSBP outperforms the ABCLM and ABCNN in both accuracy and convergence speed. Analysis of variance including post hoc multiple comparative test shows that the performance of the proposed CSBP is statistically significant than the comparison algorithms. The negative side of the crude oil prices in the global market can be defeated with proper planning based on the crude oil prices predicted by the CSBP. The CSBP proposed in this paper could provide a new strategy for risk managers and analysts to create an efficient risk management framework to formulate policies related to risk issues in the global crude oil market.
Ontology-Based Fragmented Company Knowledge Integration: Multi-aspect Ontology Building

Early steps of ongoing digital transformation in companies is often driven by local business needs and results in usage of various information systems aimed at assisting in solving particular tasks. However, this leads to a dead end and further transformation is not possible without integration, what, in turn, requires interoperability support. The solution that seems to be the most simple is to use one complex system. However, various legacy systems used in various departments of companies have accumulated large volumes of corporate information and knowledge that are not easy to transfer into another system. Besides, spending time of specialists to learn different systems instead of those they are used to is not the best idea either. Though, the problem technical interoperability support is solved through usage of commonly accepted standards, the semantic interoperability is still an issue. In the previous paper, research results related to selection of the most appropriate solution for building a common information model enabling seamless knowledge exchange preserving existing information models was presented. This paper makes a step further through building a multi-aspect ontology taking into account differences between terminologies used in various information systems.
Increasing automation in policing

Seeking the delicate balance between civil liberties and policing public safety.
SIGNAL PROCESSING IN END DEVICES OF TELECOMMUNICATION AND MEDIA PATHS

In the paper, we carry out the analysis of works on the construction of television and multimedia (T and MM) systems, signal processing, transmission, and reproduction. The classification of color perception models and their possible application in the end devices to improve the quality of the transmitted image is provided. The data are presented that indicate the need to use digital adaptive video signal correction systems. As the source image, the image of the color stripes, their digital signals, which are transmitted through end-to-end (T and MM) devices, were selected. The values of signal levels were obtained which can be used to determine the magnitude of the error classified according to the scale of subjective assessments - "no noticeable distortion", "noticeable, but acceptable distortion", and "unacceptable distortion". We also present a comparison of signal levels that were obtained using earlier color perception models with inaccuracies in the description of the blue and yellow color regions.
Designing a Secure IoT Network by Using Blockchain

By the emergence of many intelligent application areas that can be diversified, such as homes and cities being smarter, creates new opportunities. It is clear that the Internet of Things (IoT) will be a large network with a growing number of connected devices at as a logarithmic speed in the coming years. IoT devices are embedded system elements that range from simple to complex. Typically, these IoT devices are more vulnerable to attack than other IoT endpoint devices, such as smartphones, tablets, computers, or servers, because they are limited in processing, storing, and networking capacity for information. Blockchain is a distributed, encrypted, immutable, and low-tolerance distributed ledger application. It is increasingly used in areas where data security is important. In this study, it is aimed to use IoT devices as blockchain elements as a solution to existing IoT security problems. A blockchain network was created by using Raspberry Pi 3 elements.
e-Learning Objects Designing Approach for Programming-based Problem Solving

Problem solving techniques are one of the primary topics introduced to the computing and information technology students in tertiary education. In general, those techniques are presented using a collection of traditional tools, such as texts, graphs, illustrations, procedural steps, pseudocodes, algorithms, and flowcharts. In many cases, there is no direct link between problem-solving from one side and the techniques and the programming structures used from the other side. This paper presents an approach that exploits computer interactive animations and graphics to implement learning objects for teaching and learning problem-solving based on computer programming. The main feature of this approach is its smoothness in associating the programming concepts and structures provided to the solution of a designated problem. Moreover, the developed learning objects are featured by the characteristic of reusability and easiness of inclusion in e-learning systems. The designed learning object can function as a workable hands-on model for the learners to get experience in pro. The technique we developed for the design and implementation of the learning objects for programming-based problem-solving can be utilized in other problem-solving subjects.
Solar Filament Detection using Mask R-CNN

Many algorithms have been proposed to detect solar filaments from H-Alpha images automatically, most of them use intensity thresholding method which requires intensity normalization. We propose a fast, automatic, and efficient method to detect filaments on H-Alpha images without carrying out intensity normalization using Mask R-CNN. The proposed method was applied on H-Alpha images obtained from Indonesian National Institute of Aeronautics and Space (LAPAN), resulting 96% precision. The average detection speed is 0.42 seconds per image. The method can be extended to perform detection on other solar features, such as sunspots, flares, and prominences.
Technological trends in modern libraries

Technological trends in modern libraries - IJLSIT- Print ISSN No: - 2582-1555 Online ISSN No:- 2456-9623 Article DOI No:- 10.18231/j.ijlsit.2019.018, IP Indian Journal of Library Science and Information Technology-IP Indian J Libr Sci Inf Technol
Combined Correlation Filters with Siamese Region Proposal Network for Visual Tracking

Siamese network based trackers have received extensive attention with their trade-off between accuracy and speed. In particular, Siamese Region Proposal Network (SiamRPN) tracker can get more accurate bounding box with proposal refinement, yet, most siamese trackers are lack of discrimination without target classification and robustness without online learning module. To tackle the problem, in this paper, we propose an ensemble tracking framework based on SiamRPN tracker, consisting of two components: (1) Correlation Filter module with hierarchical features fusion; and (2) SiamRPN module. The Correlation Filter module fully exploits both the semantic features for classification and the lower-level features for precise localization through online learning process. By cascading the Correlation Filter to SiamRPN tracker, which can equip with discrimination power. The entire network based on multitask learning strategy is trained in an end-to-end manner, which enhances both robustness and module adaptability effect. In extensive experiments evaluations on GOT-10K test dataset, OTB2015 and VOT2016 benchmarks, our tracking approach obtains better performance than other trackers, including SiamRPN tracker, by a notable margin.
User experience design in virtual reality medical training application

To investigate the effects of design parameters on the user experience of virtual reality medical training.The quantitative study was conducted at Punjab (Pakistan) from July 2018 to October 2018, and comprised final year students from eight medical colleges in Pakistan. Each respondent was given to experience laparoscopy operation in text, video and virtual reality-based learning methodologies. User experience and usefulness was assessed against a pre-validated scale and compared with the three learning methodologies.Of the 87, students, 50(57.5%) were male and 37(42.5%) were female. The overall mean age was 22.5±4 years. Result of virtual reality was better than others (p<0.05). Data was analysed using SPSS 20.Virtual reality-based learning provided better user experience than traditional learning methodologies.
Message-Level Security with JSON Web Signature

JavaScript Object Notation (JSON) provides a way of exchanging data in a language-neutral, text-based, and lightweight manner. It was originally derived from the ECMAScript programming language. JSON and XML are the most commonly used data exchange formats for APIs. Observing the trend over the last five years, it’s quite obvious that JSON is replacing XML. Most of the APIs out there have support for JSON, and some support both JSON and XML. XML-only APIs are quite rare.
Decoding As Dynamic Programming For Recurrent Autoregressive Models

Decoding in autoregressive models (ARMs) consists of searching for a high scoring output sequence under the trained model. Standard decoding methods, based on unidirectional greedy algorithm or beam search, are suboptimal due to error propagation and myopic decisions which do not account for future steps in the generation process. In this paper we present a novel decoding approach based on the method of auxiliary coordinates (Carreira-Perpinan & Wang, 2014) to address the aforementioned shortcomings. Our method introduces discrete variables for output tokens, and auxiliary continuous variables representing the states of the underlying ARM. The auxiliary variables lead to a factor graph approximation of the ARM, whose maximum a posteriori (MAP) inference is found exactly using dynamic programming. The MAP inference is then used to recreate an improved factor graph approximation of the ARM via updated auxiliary variables. We then extend our approach to decode in an ensemble of ARMs, possibly with different generation orders, which is out of reach for the standard unidirectional decoding algorithms. Experiments on the text infilling task over SWAG and Daily Dialogue datasets show that our decoding method is superior to strong unidirectional decoding baselines.
Automatic Detection of Riots Using Deep Learning

Riot like situations styles serious consequences on societal and individual security and a simple primary cautioning of any fierce, vehement, vicious, force-full or violent movement could significantly condense these dangers. At present, there are oodles of video surveillance kits applied in civic places, such as bus-stations, highways, airports, Signal-squares, crossings, and railway_stations. This effort focuses on the challenging task of detecting violent situations in videotapes & aims to propose a fangled way that could automatically distinguish violent behaviors by resources of computer vision methods. For our system, the primary motive is to detect furious activities from either video_streams or pre-recorded video-clips. We take a proportion of videotapes & we train those precise sequences as violent/non-violent situations & once we have a model ready, we deploy for example on intelligent surveillance camera, any action which is close to this precise entity would be classified as violent situation and we can direct an alarm/warning back to the control-room for further necessary steps with highest possible accuracy.
Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks

Deep neural networks (DNNs) can be huge in size, requiring a considerable a mount of energy and computational resources to operate, which limits their applications in numerous scenarios. It is thus of interest to compress DNNs while maintaining their performance levels. We here propose a probabilistic importance inference approach for pruning DNNs. Specifically, we test the significance of the relevance of a connection in a DNN to the DNN’s outputs using a nonparemtric scoring testand keep only those significant ones. Experimental results show that the proposed approach achieves better lossless compression rates than existing techniques
Masked Based Unsupervised Content Transfer

We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other. The proposed method disentangles the common and separate parts of these domains and, through the generation of a mask, focuses the attention of the underlying network to the desired augmentation alone, without wastefully reconstructing the entire target. This enables state-of-the-art quality and variety of content translation, as demonstrated through extensive quantitative and qualitative evaluation. Our method is also capable of adding the separate content of different guide images and domains as well as remove existing separate content. Furthermore, our method enables weakly-supervised semantic segmentation of the separate part of each domain, where only class labels are provided. Our code is available anonymously at http://bit.ly/2mXTizX.
Multilayer Joint Segmentation Using MRF and Graph Cuts

The problem of jointly segmenting objects, according to a set of labels (of cardinality L), from a set of images (of cardinality K) to produce K individual segmentations plus one joint segmentation, can be cast as a Markov random field model. Coupling terms in the considered energy function enforce the consistency between the individual segmentations and the joint segmentation. However, neither optimality on the minimizer (at least for particular cases), nor the sensitivity of the parameters, nor the robustness of this approach against standard ones has been clearly discussed before. This paper focuses on the case where $$L>1$$ , $$K>1$$ and the segmentation problem is handled using graph cuts. Noticeably, some properties of the considered energy function are demonstrated, such as global optimality when $$L=2$$ and $$K>1$$ , the link with majority voting and the link with naive Bayes segmentation. Experiments on synthetic and real images depict superior segmentation performance and better robustness against noisy observations.
Convolution Neural Networks for Binary Classification

Convolutional neural networks (CNNs) are similar to “ordinary” neural networks in the sense that they are made up of hidden layers consisting of neurons with “learnable” parameters. These neurons receive inputs, perform a dot product, and then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and their class scores. Conventionally, the Softmax function is the classifier used at the last layer of this network. However, there have been studies conducted to challenge this norm. Empirical data has shown that the CNN model was able to achieve a test accuracy of ≈99.04% using the MNIST dataset. MNIST dataset consists of 60,000 training images and 10,000 testing images. This experiment was inspired by following the experiment on MNIST dataset. The dataset we used in this experiment is collection of images consisting of cats and dogs. These images are gathered from different sources over internet. This dataset consists of 10,000 images of each class i.e., Cats and Dogs. The overall accuracy of training and validation set is 96.85%. The said results may be improved if data pre-processing techniques were employed on the datasets, and if the base CNN model was alternatively more sophisticated than the one used in this study.