Reviewerly/exHarmony huggingface dataset:
Building a trust-based doctor recommendation system on top of multilayer graph database

In healthcare applications, developing a data model for storing patient–doctor relationships is important. Though relational models are popular for many commercial and business applications, they may not be appropriate for modeling patient–doctor relationships due to their inherent irregular nature and complexities. In this paper, as a case study, we propose to build a doctor recommendation system for the patients. The recommendation system is built on top of a multilayer graph data model. Contemporary research papers have already shown that multilayer graph data models can be efficiently used in many applications where large, heterogeneous data are to be modeled. As part of the recommendation system, the paper also introduces a concept of trust which is one important ingredient of any kind of recommendation. The trust factor introduced in the paper exploits certain characteristics of the multilayer graph model. The paper also presents some analysis to demonstrate the efficiency of the graph data model in comparison with relational data model.
An online multi-source summarization algorithm for text readability in topic-based search

Web search users are likely to face problems related to the availability of large amounts of data. As the quantity of online content grows, the risk of missing relevant information during search can only increase. Moreover, external variables such as the users’ reading proficiency level can further complicate the task. This article proposes an online multi-document summarization algorithm for text readability, as a means to simplify web search. The algorithm is designed to work over collections of topic-related documents, such as the ones returned as the results to a web query. Contrary to most modern approaches, no preliminary training for the algorithm is required. The algorithm was tested in both English and Spanish language documents, using different metrics of term and sentence relevance. The results were compared against summaries created by both human summarizers and third-party Automatic Text Summarization (ATS) systems in terms of two variables: readability and information content. In both variables, the results show generalized gains with respect to both the human summarizers and the third-party ATS systems. Furthermore, the algorithm achieved these results with a time complexity strictly lower than O(n2); well below traditional machine learning approaches.
Comparative Study of Deep Learning Models for Vehicle Detection in an Unconstrained Road Scenario

Over the past few years, deep learning has played a tremendous role in real time object detection, recognition and classification. Today, algorithms use deep learning not only for simple object detection but have also paved way for vehicle detection that is complex situation such as Indian roads. Different deep learning models can learn features of varied complexity and perform according to surrounding environment in which they are deployed. In this paper a comparison of six models is performed for detection of vehicles on roads that are unstructured or unconstrained. Till date, deep learning algorithms are being used for vehicle (car, bus, truck, motorcycle etc.) detection. A dataset based on Indian road scenario has been used for training on models: faster RCNN and Single Shot Detector (SSD) combined with convolution classifiers mobilenet, resnet and inceptionnet. The six models are compared by evaluating mean average precision (mAP) and classification time complexity for IDD dataset. The observations suggested faster RCNN Resnet50 resolves the trade-off of time and accuracy. Whereas SSD mobilenet V1 takes 31.89% less time than faster RCNN resnet101. On the flipped side, faster RCNN resnet101is 34.38% more accurate than SSD mobilenet v1.
Evaluating Blockchain Using COSO

SUMMARY As companies begin to explore and develop technology solutions based on blockchain and smart contracts, there is a need to understand the impact of blockchain and smart contracts on the assessment of internal controls and enterprise risk. Especially since the distributed ledger and smart contracts blur the system boundaries between trading partners, there is a need to understand whether internal control assessments based on a single company approach is adequate in an integrated and collaborative environment. This paper provides an overview of smart contracts for practitioners and describes the associated risks of engaging in a blockchain consortium. We also list potential questions related to internal controls that may be considered when either engaging in a consortium or executing a smart contract. We then discuss whether current frameworks, specifically the Committee of Sponsoring Organizations' (COSO) integrated and COSO's Enterprise Risk Management (ERM) frameworks, adequately address a collaborative supply chain ecosystem.
Transfer learning-based one-class dictionary learning for recommendation data stream

Recommendation system has attracted growing attention in machine learning and data mining. In addition, one-class learning has been proposed to build a predictive classifier mainly based on the preferred data, and has been verified to be an efficient method for recommendation system. In this paper, we propose a novel recommendation model called transfer learning-based one-class dictionary learning for recommendation data stream (DOC-SVM), which incorporates transfer learning and discriminative dictionary learning into one-class data stream learning. The proposed method can reduce the effect of the uncertain data on the classifier and transfer knowledge from the recent historical chunk to the current chunk to construct a predictive classifier for the current chunk. We then propose an iterative framework to solve the optimization function, and obtain the predictive classifier for the current chunk. Extensive experiments has been conducted and the results show that the proposed method can deliver better performance than the state-of-the-art methods.
Inter-Hour Forecast of Solar Radiation Based on the Structural Equation Model and Ensemble Model

Given the wide applications of photovoltaic (PV) power generation, the volatility in generation caused by solar radiation, which limits the capacity of the power grid, cannot be ignored. Therefore, much research has aimed to address this issue through the development of methods for accurately predicting inter-hour solar radiation and then estimating PV power. However, most forecasting methods focus on adjusting the model structure or model parameters to achieve prediction accuracy. There is little research discussing how different factors influence solar radiation and, thereby, the effectiveness of these data-driven methods regarding their prediction accuracy. In this work, the effects of several potential factors on solar radiation are estimated using correlation analysis and a structural equation model; an ensemble model is developed for predicting inter-hour solar radiation based on the interaction of those key factors. Several experiments are carried out based on an open database provided by the National Renewable Energy Laboratory. The results show that solar zenith angle, cloud cover, aerosols, and airmass have great effects on solar radiation. It is also shown that the selection of the key factor is more important than the model structure construction for predicting solar radiation precisely. The proposed ensemble model proves to outperform all sub-models and achieves about a 12% improvement over the persistent model based on the normalized root mean squared error statistic.
Blockchain Based Secure Model for Sensor Data in Wireless Sensor Network

Sensor nodes in Wireless Sensor Networks (WSNs) are known for fast data transfer thus, there is a need of decentralized authentication authority for the task of secure and transparent migration of the data from the nodes at a global stage in the entire network. Thus we need a Blockchain(BC) based network which involves transactions which can be duplicated and be spread across multiple nodes in a peer-peer network enabling mutual trust and data immutability enabling the network to go on and on in case some nodes get compromised or die due to heavy energy consumption. However, BC involves consensus as well before the chain is updated and traditional Proof of Work(PoW)method were being used for consensus and which we all know, consume high energy while utilizing large computational power and which makes it unfit for WSNs given they send data at a faster rate and will not be able to bear such high strain. Thus, we can either move towards an improved Proof of Stake(PoS) based method or any popular consensus method like Practical Byzatine Fault Tolerance(PBFT). We also plan to stick to a single base station(BS) having a lot many gateway nodes which perform the BC updation after consensus based on data received from the nodes.
Task Space Trajectory Planning for Robot Manipulators to Follow 3-D Curved Contours

The demand for robots has increased in the industrial field, where robots are utilized in tasks that require them to move through complex paths. In the motion planning of a manipulator, path planning is carried out to determine a series of the positions of robot end effectors without collision. Therefore, it is necessary to carry out trajectory planning to determine position, velocity, and acceleration over time and to control an actual industrial manipulator. Although several methods have already been introduced for point-to-point trajectory planning, a trajectory plan which moves through multiple knots is required to allow robots to adapt to more complicated tasks. In this study, a trajectory planning based on the Catmull–Rom spline is proposed to allow a robot to move via several points in a task space. A method is presented to assign intermediate velocities and time to satisfy the velocity conditions of initial and final knots. To optimize the motion of the robot, a time-scaling method is presented to minimize the margin between the physical maximum values of velocity and acceleration in real robots and the planned trajectory, respectively. A simulation is then performed to verify that the proposed method can plan the trajectory for moving multiple knots without stopping, and also to check the effects of control parameters. The results obtained show that the proposed methods are applicable to trajectory planning and require less computation compared with the cubic spline method. Furthermore, the robot follows the planned trajectory, and its motion does not exceed the maximum values of velocity and acceleration. An experiment is also executed to prove that the proposed method can be applied to real robotic tasks to dispense glue onto the sole in the shoe manufacturing process. The results from this experiment show that the robot can follow the 3-D curved contour in uniform speed using the proposed method.
FIFNET: A convolutional neural network for motion-based multiframe super-resolution using fusion of interpolated frames

We present a novel motion-based multiframe image super-resolution (SR) algorithm using a convolutional neural network (CNN) that fuses multiple interpolated input frames to produce an SR output. We refer to the proposed CNN and associated preprocessing as the Fusion of Interpolated Frames Network (FIFNET). We believe this is the first such CNN approach in the literature to perform motion-based multiframe SR by fusing multiple input frames in a single network. We study the FIFNET using translational interframe motion with both fixed and random frame shifts. The input to the network is a sequence of interpolated and aligned frames. One key innovation is that we compute subpixel interframe registration information for each interpolated pixel and feed this into the network as additional input channels. We demonstrate that this subpixel registration information is critical to network performance. We also employ a realistic camera-specific optical transfer function model that accounts for diffraction and detector integration when generating training data. We present a number of experimental results to demonstrate the efficacy of the proposed FIFNET using both simulated and real camera data. The real data come directly from a camera and are not artificially downsampled or degraded. In the quantitative results with simulated data, we show that the FIFNET performs favorably in comparison to the benchmark methods tested.
Intelligibility Improvement of Dysarthric Speech using MMSE DiscoGAN

Dysarthria is a manifestation of the disordering in articulatory parts that are used during speech production, which results in uneven, slow, slurred, monotone speech or speech in an abnormal rhythm. People with dysarthria produce less intelligible speech. Improving the intelligibility of dysarthric speech is challenging because unlike normal speech, there is less amount of data for dysarthric speech. It is a known fact that dysarthric speech and normal speech are different in speech production-perception perspectives. Recently, Generative Adversarial Network (GAN)-based architectures have become more popular to learn such kind of cross-domain relationships efficiently. In this paper, we propose to use Discover GAN (DiscoGAN) along with Mean Square Error (MSE) regularization (i.e., MMSE DiscoGAN) for Dysarthric-to-Normal speech conversion. In particular, a direct feature-based mapping technique is used to train all the models. In the end, we use the Automatic Speech Recognition (ASR) to measure the Phoneme Error Rate (PER) for a particular speaker. Proposed method is compared with baseline Deep Neural Network (DNN)-based system. Training of both the architectures and the evaluations were carried out on UA corpus. By analyzing the results, we observed that MMSE DiscoGAN outperforms DNN by 13.16% and 9.64% for male and female, respectively. Moreover, proposed GAN-based frameworks efficiently improve the intelligibility of dysarthric speech, and generate more naturalsounding speech compared to the DNN-based models.
Skeletal Data based Activity Recognition System

In recent years, Human activity recognition and research related to it are in high demand owing to its application in various fields such as healthcare systems, assisted living, surveillance etc. Activity recognition system in general aims at identifying activities performed by person in an environment. In this work, skeletal data-based activity recognition system is presented. Microsoft Kinect sensor is a motion capture sensor developed by Microsoft for xbox one. It has become most popular for its effortless operation and low cost. The 3D skeletal joint positions obtained from this kinect sensor are used as raw data for classification purpose. Set of statistical features are computed from these skeletal data. The dimensions of statistical features computed are reduced to eliminate correlated and redundant features among them. Principal component analysis (PCA), a technique used for finding smaller number of uncorrelated data is employed for reducing the feature dimension. The dimensionally reduced features are used as training data for training the classifier. The ability of K-nearest neighbour (KNN) classifier and Support vector machine (SVM) classifier in classifying actions are analysed. The classification method proposed is tested on most popular KARD (Kinect activity recognition dataset) dataset and various classification parameters are computed. Among the two classifiers considered, SVM classifier performed better with an average overall accuracy of 97% which is 3.44% higher than the accuracy produced by KNN classifier.
Using Biosignals for Objective Measurement of Presence in Virtual Reality Environments

The concept of 'presence' in the context of virtual reality (VR) refers to the experience of being in the virtual environment, even when one is physically situated in the real world. Therefore, it is a key parameter of assessing a VR system, based on which, improvements can be made to it. To overcome the limitations of existing methods that are based on standard questionnaires and behavioral analysis, this study proposes to investigate the suitability of biosignals of the user to derive an objective measure of presence. The proposed approach includes experiments conducted on 20 users, recording EEG, ECG and electrodermal activity (EDA) signals while experiencing custom designed VR scenarios with factors contributing to presence suppressed and unsuppressed. Mutual Information based feature selection and subsequent paired t-tests used to identify significant variations in biosignal features when each factor of presence is suppressed revealed significant (p < 0.05) differences in the mean values of EEG signal power and coherence within alpha, beta and gamma bands distributed in specific regions of the brain. Statistical features showed a significant variation with the suppression of realism factor. The variations of activity in the temporal region lead to the assumption of insula activation which may be related to the sense of presence. Therefore, the use of biosignals for an objective measurement of presence in VR systems indicates promise.
Networked Chaotic Map Model and Its Applications in Color Multiple Image Encryption

In this study, a networked spatiotemporal chaotic map model is proposed based on the traditional coupled map lattice system.In addition, the time delay and variable topology parameters are considered to make the system more practical.The chaotic properties are analyzed using a bifurcation diagram, Lyapunov exponents, and entropies.In addition, institute of standards and technology tests are conducted to further analyze the randomness of the system signal.Based on the proposed system, a three-dimensional encryption algorithm is designed to encrypt multiple color images with scrambling and diffusion at the bit level.Several analyses, including histogram analysis, information entropy analysis, correlation analysis, key sensitivity, differential attack analysis, and resistance analyses, are performed to test the effectiveness and robustness of the proposed multiple image encryption algorithm with a networked chaotic map model.
FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language

This paper presents an R package FuzzyR which is an extended fuzzy logic toolbox for the R programming language. FuzzyR is a continuation of the previous Fuzzy R toolboxes such as FuzzyToolkitUoN. Whilst keeping existing functionalities of the previous toolboxes, the main extension in the FuzzyR toolbox is the capability of optimising type-1 and interval type-2 fuzzy inference systems based on an extended ANFIS architecture. An accuracy function is also added to provide performance indicators featuring eight alternative accuracy measures, including a new measure UMBRAE. In addition, graphical user interfaces have been provided so that the properties of a fuzzy inference system can be visualised and manipulated, which is particularly useful for teaching and learning. Note that this paper illustrates some of the new features of the FuzzyR toolbox, but does not provide a complete list of all functions available. More details about the new features of FuzzyR and a complete description of all functions can be found in the manual of the toolbox.
A Method of Human Activity Recognition in Transitional Period

Human activity recognition (HAR) has been increasingly used in medical care, behavior analysis, and entertainment industry to improve the experience of users. Most of the existing works use fixed models to identify various activities. However, they do not adapt well to the dynamic nature of human activities. We investigated the activity recognition with postural transition awareness. The inertial sensor data was processed by filters and we used both time domain and frequency domain of the signals to extract the feature set. For the corresponding posture classification, three feature selection algorithms were considered to select 585 features to obtain the optimal feature subset for the posture classification. And We adopted three classifiers (support vector machine, decision tree, and random forest) for comparative analysis. After experiments, the support vector machine gave better classification results than other two methods. By using the support vector machine, we could achieve up to 98% accuracy in the Multi-class classification. Finally, the results were verified by probability estimation.
A Weak Supervision-based Framework for Automatic Lung Cancer Classification on Whole Slide Image

Classification of normal lung tissue, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by pathological images is significant for clinical diagnosis and treatment. Due to the large scale of pathological images and the absence of definitive morphological features between LUAD and LUSC, it is time-consuming, laborious and challenging for pathologists to analyze the microscopic histopathology slides by visual observation. In this paper, a pixel-level annotation-free framework was proposed to classify normal tissue, LUAD and LUSC slides. This framework can be divided into two stages: tumor classification and localization, and subtype classification. In the first stage, EM-CNN was utilized to distinguish tumor slides from normal tissue slides and locate the discriminative regions for subsequent analysis with only image-level labels provided. In the second stage, a multi-scale network was proposed to improve the accuracy of subtype classification. This method achieved an AUC of 0.9978 for tumor classification and an AUC of 0.9684 for subtype classification, showing its superiority in lung pathological image classification compared with other methods.
Improved estimation of motion blur parameters for restoration from a single image

This paper presents an improved method to estimate the blur parameters of motion deblurring algorithm for single image restoration based on the point spread function (PSF) in frequency spectrum. We then introduce a modification to the Radon transform in the blur angle estimation scheme with our proposed difference value vs angle curve. Subsequently, the auto-correlation matrix is employed to estimate the blur angle by measuring the distance between the conjugated-correlated troughs. Finally, we evaluate the accuracy, robustness and time efficiency of our proposed method with the existing algorithms on the public benchmarks and the natural real motion blurred images. The experimental results demonstrate that the proposed PSF estimation scheme not only could obtain a higher accuracy for the blur angle and blur length, but also demonstrate stronger robustness and higher time efficiency under different circumstances.