Reviewerly/exHarmony huggingface dataset:
Robust Prior-Based Single Image Super Resolution Under Multiple Gaussian Degradations

Although SISR (Single Image Super Resolution) problem can be effectively solved by deep learning based methods, the training phase often considers single degradation type such as bicubic interpolation or Gaussian blur with fixed variance. These priori hypotheses often fail and lead to reconstruction error in real scenario. In this paper, we propose an end-to-end CNN model RPSRMD to handle SR problem in multiple Gaussian degradations by extracting and using as side information a shared image prior that is consistent in different Gaussian degradations. The shared image prior is generated by an AED network RPGen with a rationally designed loss function that contains two parts: consistency loss and validity loss. These losses supervise the training of AED to guarantee that the image priors of one image with different Gaussian blurs to be very similar. Afterwards we carefully designed a SR network, which is termed as PResNet (Prior based Residual Network) in this paper, to efficiently use the image priors and generate high quality and robust SR images when unknown Gaussian blur is presented. When we applied variant Gaussian blurs to the low resolution images, the experiments prove that our proposed RPSRMD, which includes RPGen and PResNet as two core components, is superior to many state-of-the-art SR methods that were designed and trained to handle multi-degradation.
Model-based security analysis of feature-oriented software product lines

Today's software systems are too complex to ensure security after the fact – security has to be built into systems by design. To this end, model-based techniques such as UMLsec support the design-time specification and analysis of security requirements by providing custom model annotations and checks. Yet, a particularly challenging type of complexity arises from the variability of software product lines. Analyzing the security of all products separately is generally infeasible. In this work, we propose SecPL, a methodology for ensuring security in a software product line. SecPL allows developers to annotate the system design model with product-line variability and security requirements. To keep the exponentially large configuration space tractable during security checks, SecPL provides a family-based security analysis. In our experiments, this analysis outperforms the naive strategy of checking all products individually. Finally, we present the results of a user study that indicates the usability of our overall methodology.
Facial Keypoint Detection with Convolutional Neural Networks

Facial keypoint detection is a challenging problem in the field of computer vision. The keypoint detection is done by predicting the coordinates of certain facial features. In this paper, facial keypoint detection is predicted using convolutional neural networks. The models are trained to predict facial key points using the webcam input data. The facial keypoints includes eyebrow corners, nose tip, eye corners and center, and lip points. The predicted keypoints are mapped onto the webcam input data and compared with different models for better detection of keypoints. The mean square error is used to estimate the loss of each model.
Whose text, whose mining, and to whose benefit?

Scholarly content has become more difficult to find as information retrieval has devolved from bespoke systems that exploit disciplinary ontologies to keyword search on generic search engines. In parallel, more scholarly content is available through open access mechanisms. These trends have failed to converge in ways that would facilitate text data mining, both for information retrieval and as a research method for the quantitative social sciences. Scholarly content has become open to read without becoming open to mine, due both to constraints by publishers and to lack of attention in scholarly communication. The quantity of available text has grown faster than has the quality. Academic dossier systems are among the means to acquire more quality data for mining. Universities, publishers, and private enterprise may be able to mine these data for strategic purposes, however. On the positive front, changes in copyright may allow more data mining. Privacy, intellectual freedom, and access to knowledge are at stake. The next frontier of activism in open access scholarship is control over content for mining as a means to democratize knowledge.
Design of A Recurrent Neural Network Model for Machine Reading Comprehension

Reading paragraphs, understanding the related questions and answering them has always been a difficult task for the machines. Humans have the capability to understand the logic and meaning of a question and answer it to maintain a proper mode of interaction. But for machines, this is a complex task. The main focus of this research work is to explore the various machine learning and neural networks based techniques to develop and train a model on context of paragraphs related questions and answers and then test the model on an user given paragraph and question. This paper presents a thorough understanding of data analysis of the SQuAD data set and word embedding applied on the questions and answers of training set of the SQuAD data set.
A class of two-mode clustering algorithms in a fuzzy setting

Two-mode clustering consists in simultaneously partitioning modes (e.g., objects and variables) of an observed two-mode data matrix. A class of two-mode clustering algorithms in a fuzzy framework is proposed. Starting from the Double k-Means algorithm, different fuzzy proposals are addressed. The first one is the Fuzzy Double k-Means (FDkM) algorithm, providing two fuzzy partitions, one for each mode. A second proposal is the Fuzzy Double k-Means with polynomial fuzzifiers (FDkMpf) algorithm, a general method that includes the FDkM one as a particular case. Finally, a robust extension is introduced and analyzed by using the concept of noise cluster. The adequacy of the proposed algorithms is checked by means of a simulation and two real-case studies.
Design and simulation linear quadratic gaussian (LQG) for pan-tilt face tracking camera servos

The 2DOF face tracking system consists of two servo motors (pan and tilt) and a camera. Both are integrated with each other and are tasked with accurately tracking each facial movement. The high performance of the system depends on the desired specifications, that is the servo can move smoothly according to facial movements. Therefore it is necessary to design an appropriate servo control method. This paper presents the Linear Quadratic Controller (LQG) control method that is simulated to the servos. The design steps include system modeling, LQG control design, and simulation. The LQG control method is one of the optimal control methods, so this paper also discusses the basic principles of optimal control design. The output of the paper can be a reference in the implementation of the servo control system that will be connected with a face tracking control system. The simulation results show that the pan system produces a system response with a rise time of 0.8614 ms and 0% overshoot. While on the tilt system, the system response with rising time is 0.951067 ms and overshoot is 0%.
Progressive Machine Learning Approach with WebAstro for Web Usage Mining

Information retrieval is a scorching today when the flare-up of data on the Internet increasingly day by day. Due to the infinite growth and availability of the Internet; numbers of educational sites are amplified. Huge Expansion in knowledge puts a special effect on the education system. This helps in upgrading higher education. Interest in Web usage mining has grown swiftly in its short history, both in exploring interest and research. Web usage mining is a type of data mining that extracts the server log information after users browse web pages. For extracting and retrieval information machine learning approaches are designed to recognize useful information from text documents automatically. The main aim of this paper is to cram weblogs using the clustering technique with WEBASTRO tool for predicting next user movement and user behaviour analysis. This paper presents an ideal approach for web mining and predicting their behaviour for the next visit and automatic web site modification.
BcIoT: Blockchain based DDos Prevention Architecture for IoT

The Internet of Things (IoT) visualizes a massive network with billions of interaction among smart things which are capable of contributing all sorts of services. Self-configuring things (nodes) are connected dynamically with a global network in IoT scenario. The small things are widely spread in a real world paradigm with minimal processing capacity and limited storage. The recent IoT technologies have more concerns about the security, privacy and reliability. Sharing personal data over the centralized system still remains as a challenging task. If the infrastructure is able to provide the assurance for transferring the data but for now it requires special attention on security and data consistency. Because, centralized system and infrastructure is viewed as a more attractive point for hacker or cyber-attacker. To solve this we present a secured smart contract based on Blockchain to develop a secured communicative network. A Hash based secret key is used for encryption and decryption purposes. A demo attack is done for developing a better understanding on blockchain technology in terms of their comparison and calculation.
Energy Aware VM Consolidation Using Dynamic Threshold in Cloud Computing

Cloud datacenter consumes a lot of energy and there are significant opportunities to optimize the energy consumption. VM migration is one of the most renowned technique to migrate the VMs from the least used host and put it on sleep mode. However, this process also consumes a lot of energy and VM migration cost. Dynamic Voltage and Frequency Scaling (DVFS) can strength this process by giving only the required voltage to the running VMs on the host. In this paper, we proposed a hybrid approach for VM consolidation technique for energy optimization. The VM migration and DVFS decision applied for VM consolidation. A Workload-based Dynamic Threshold (WBDTH) approach design with a prediction technique to adjust the upper and lower threshold values according to the arrival rate. The proposed algorithm optimized energy consumption with a minimum Service Level Agreement (SLA) violations.
Unbalanced Data Clustering with K-Means and Euclidean Distance Algorithm Approach Case Study Population and Refugee Data

Abstract There is a lot of data that does not have a pattern and unbalanced that is difficult to classify, such as the total population of each country in the world is very varied especially when compared with the number of refugees from each of these countries, China and India numbered more than 2 billion people but the number of refugees is only 0, 01%, while Syria around 70% of the 18 million more residents are refugees. By using the K-Means algorithm, we can group countries that have similar characteristics of population and number of refugees, the average percentage of refugees to the population in each cluster is the character of the cluster. The methodology used in this study are: measures the distance of the data using the Euclidean distance formula, runs the K-Means algorithm, calculate the percentage value of each cluster and find conclusions from the characteristics of the clusters formed. We found how machine learning made a pattern of data without political and social issues, the result is K-Means describing machine learning can grouped every country in cluster that make sense.
Low-Light Image Enhancement via Adaptive Shape and Texture Prior

Low light images affect various computer vision algorithms due to their low visibility and much noise hidden in dark regions. Although many methods based on the Retinex theory, which decomposes an observed image into the reflectance and illumination, have been proposed to alleviate the problem, existing methods inevitably cause under-and over-enhancement. In this paper, we propose a new joint optimization equation that sufficiently considers the features of both reflectance and illumination. More concretely, we adopt L2-Lp norm regularization terms to estimate the reflectance as much as possible to preserve details and textures, and the illumination as much as possible to preserve the structure information with texture-less. We solve the optimization equation in an alternating minimization method. Furthermore, we introduce a new adaptive texture prior to reveal more details and textures with noise reduction on both bright and dark regions. Experimental results, including qualitative and quantitative evaluations, show that the proposed method can establish a better performance than the other state-of-the-art methods.
Interactive Visual Procedure using an extended FMEA and Mixed-Reality

In this paper, an application is created for the HoloLens, a headgear that enables users to experience mixed reality. The application proves that it is plausible to create an interactive visual guide in the form of a Unity application, in order to support operators with the fault detection assessment of a machine. This application utilizes WorldAnchors and a Vuforia engine to fix the holograms spatially. It shows the steps to solve detected faults through holograms and CAD models. The troubleshooting steps are modelled using an extended Failure Mode and Effects Analysis (FMEA) as a knowledge extraction method. This method extracts expert knowledge from the operators in a systematic way, as well as it maps the faults with the current feed of the machine data. Users can interact with buttons through the guide on the HoloLens until the fault is solved. The fault detection system is located in a software framework, which collects data from the machine using the OPC-UA communication protocol. This system is proven to be useful, as well as it has a low latency of 51 ms.
Formalizing the use case model: A model-based approach

In general, requirements expressed in natural language are the first step in the software development process and are documented in the form of use cases. These requirements can be specified formally using some precise mathematical notation (e.g. Linear Temporal Logic (LTL), Computational Tree Logic (CTL) etc.) or using some modeling formalism (e.g. a Kripke structure). The rigor involved in writing formal requirements requires extra time and effort, which is not feasible in several software development scenarios. A number of existing approaches are able to transform informal software requirements to formal specifications. However, most of these approaches require additional skills like understanding of specification languages additional artifacts, or services of domain expert(s). Consequently, an automated approach is required to reduce the overhead of effort for converting informal requirements to formal specifications. This work introduces an approach that takes a use case model as input in the proposed template and produces a Kripke structure and LTL specifications as output. The proposed approach also considers the common use case relationships (i.e., include and extend). The generated Kripke structure model of the software allows analysis of software behavior early at the requirements specification stage which otherwise would not be possible before the design stage of the software development process. The generated LTL formal specifications can be used against a formal model like a Kripke structure generated during the software development process for verification purpose. We demonstrate the working of the proposed approach by a SIM vending machine example, where the use cases of this system are inputs in the proposed template and the corresponding Kripke structure and LTL formal specifications are produced as final output. Additionally, we use the NuSMV tool to verify the generated LTL specifications against the Kripke structure model of the software, which reports no counterexamples thus validating the proposed approach.
Object Detection Framework for High Mobility Vehicles Tracking in Night-Time

Optical Vehicular Communication is one of the most promising areas in which Optical Camera Communication (OCC) involves as the core technology. Due to a lot of existing challenges in Optical Vehicular Communication, there is still a big gap between research works and commercialized products. Recent developments in computer vision and advanced image processing techniques could enhance the performance of vehicular OCC system significantly. Among those techniques, there are many of robust image processing tools which are developed based on the theory of Machine Learning, Deep Learning and Artificial Intelligence (AI), which could be applied in a wide range of applications including vehicular OCC application. In this paper, we would like to introduce our recent work on applying a well-known object detection framework - You Only Look Once (YOLO) to detect and track a large number of high mobility vehicles, which is also considered as region of interests (RoIs) in vehicular OCC system. From that, we conducted a test on the Korea highway at a raining night to analyze the effectiveness of this method in the vehicular OCC system.
Determining worst-case eye height in low BER channels using Bayesian optimization

Eye diagram simulation and bit error rate (BER) estimation is an essential task in signal integrity. A lengthy time domain simulation is required for non-LTI systems where statistical methods are generally inaccurate. However, with the BER reaching less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-12</sup> , and with exponential increase in bandwidth, this task is expected to become more challenging and exorbitantly time consuming. In particular, the concern is with inter-symbol interference (ISI) effect, which can be caused by the state of several earlier bits. Therefore, this paper suggests an optimization method to find the bit patterns causing the lowest received high symbol, and the highest received low symbol, at the sampling time point. Difference of these values can be used to estimate the worst-case eye height. The proposed approach is based on a mapping method and Bayesian optimization, which provides a significant speedup compared to the traditional transient eye. This optimization technique is capable of solving both non-linear and non-convex problems. A numerical example is provided to show performance of the proposed approach.
Image De-Raining for Driver Assistance Systems using U-Net based GAN

The visual appeal of images gets deteriorated when captured under unfavorable weather conditions thus rendering them unsuitable for optimum performance on vision-based systems. Performance of Driver Assistance Systems (DAS) depends on the quality of the images fed to the computer vision algorithms. This paper attempts to leverage generative modelling capabilities of Generative Adversarial Networks by utilizing special architectures for generator and discriminator. The loss function utilized is the efficient pixel mean square loss. Discriminator used here is a standard binary cross-entropy classifier intended to classify whether the predicted de-rained image of the generator matches the real high-resolution image or not. The generator network is a Fully Convolutional U-Net architecture. Experiments indicate that the proposed architecture produces de-rained images indistinguishable from their clean images using only 30 epochs and performs better than various novel approaches.
A hybrid matchmaking approach in the ambient assisted living domain

Abstract During the recent years, several new Information and Communication Technology solutions have been developed in order to meet the increasing needs of elderly with cognitive impairments and support their autonomous living. Most of these solutions follow a human-centred paradigm that aims to provide users with personalised services according to their needs by also ensuring their safety with mechanisms that can automatically trigger appropriate actions in situations where there may be a risk for an elderly. The present paper presents a hybrid matchmaking approach that uses efficiently both a rule-based and a statistical matchmaker in order to (a) propose ambient assisted living services to the end-users, based on their role, status and context of use and (b) identify and resolve problematic cases by automatically selecting the most proper set of services to be called in a single or combined manner.
Target detection in SAR images using Bayesian Saliency and Morphological attribute profiles

The Synthetic Aperture Radar (SAR) images have become a considerable and vital application in the optical satellite images due to their ability of function in all kinds of weather situations. In synthetic aperture radar (SAR) images Target detection is a demanding task to check presence of targets in images and to exactly locate that target. This paper propose a process for the detection of automatic target in SAR images which has three different steps, that is super pixel segmentation, Morphological attribute profiles and Bayesian Saliency Map. In first stage the SAR input image is then segmented into small regions by super pixels after that the clustering procedure is followed. In the second stage Bayesian saliency map is used to highpoint the clear objects including target and shadow. In the third stage the morphological attribute profile is used to suppress the shadow and highlight the target. The experiment results on MSTAR dataset are compared with some popular existing algorithms (Super pixel CFAR, local CFAR, PR, Global CFAR, MS, BS) which will proves that the proposed mechanism is effective and robust.
Distortion Rectification From Static to Dynamic: A Distortion Sequence Construction Perspective

Distortion rectification is a fundamental task in the field of computer vision and image processing. Nevertheless, previous methods have regarded distortion rectification as a static problem that learns a mapping function and corrects the distorted image to a unique state. However, this state is generally not the optimal solution, as it would result in an under-rectified or over-rectified structure. In this study, we revisit the classical distortion rectification task with a new perspective and redesign the algorithm, inspired by video processing techniques. Specifically, we regard distortion rectification as a dynamic problem that can be extended to a sequence of different distortion states: the input distorted image (t), under-rectified image (t+1), ideal-rectified image (t+2), and over-rectified image (t+3). We first estimate the residual distortion map (RDM) between the input distorted image and the coarse-rectified (t+1 or t+3) image. Here, RDM indicates the motion difference between two distorted images. Subsequently, the RDM is used to guide the refinement rectification process, aiming to convert the coarse-rectified state into the ideal-rectified state. In addition, the flexible implementation of the proposed refinement process with RDM to improve the rectification results of any method is appealing. The experimental results demonstrate that our method outperforms the state-of-the-art schemes by a significant margin, revealing approximately 40% improvement through quantitative evaluation.
Regional Weighted Generative Adversarial Network for LDR to HDR Image Conversion

For the image conversion from low dynamic range images to high dynamic range images, we considered the possibility of using the Generative Adversarial Network (GAN) as one of the deep neural network methods. Deep learning requires a lot of data before building a module, but once creation is done, it is convenient to use it. In this paper, we proposed a weighting map for a local luminance based learning to reconstruct locally tone-mapped images.