Reviewerly/exHarmony huggingface dataset:
Service Composition in IoT using Genetic algorithm and Particle swarm optimization

Abstract Web service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.
Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique

Photovoltaic Solar Energy (PSE) sector has sparked great interest from governments over the last decade towards diminution of world’s dependency to fossil fuels, greenhouse gas emissions reduction and global warming mitigation. Photovoltaic solar energy also holds a significant role in the transition to sustainable energy systems. These systems and their optimal exploitation require an effective supply chain management system, such as design of the network, collection, storage, or transportation of this energy resource, without disregarding a country’s certain socio-economic and political conditions. In Brazil, the adoption of photovoltaic solar energy has been motivated not only by the energy matrix diversification but also from the shortages, problems, and barriers that the Brazilian energy sector has faced, lately. However, PSE development is affected by various factors with high uncertainty, such as political, social, economic, and environmental, that include critical operational sustainability issues. Thus, an elaborate modelling of energy management and a well-structured decision support process are needed to enhance the performance efficiency of Brazilian PSE supply chain management. This study focuses on the investigation of certain factors and their influence on the development of the Brazilian PSE with the help of Fuzzy Cognitive Maps. Fuzzy Cognitive Map is an established methodology for scenario analysis and management in diverse domains, inheriting the advancements of fuzzy logic and neural networks. In this context, a semi-quantitative model was designed with the help of various stakeholders from the specific energy domain and three plausible scenarios were conducted in order to support a decision-making process on PSE sector development and the country’s economic potential. The outcome of this analysis reveals that the development of the PSE sector in Brazil is mainly affected by economic and political factors.
Personalized Knee Geometry Modeling Based on Multi-Atlas Segmentation and Mesh Refinement

The development of personalized finite element models of the knee anatomy is critically important in the simulation of knee joint mechanics, prediction of optimal treatments in cases of pathological conditions and prevention of injuries.Subject-specific models can be obtained from diagnostic images with multi-atlas segmentation being a pertinent choice when prior anatomical information of the structures of interest is available.Although multi-atlas segmentation has been prevalent in some parts of the body, its exploitation for the segmentation of the knee complex has not been illustrated yet.This work utilizes a multi-atlas segmentation method based on deformable registration and joint label fusion in conjunction with anatomically-adopted mesh refinement in order to generate subject-specific models of the knee.The success of finite element simulations strongly depends on the properties of the 3D surface and the quality of the volumetric meshes.Therefore, emphasis was given to create structured meshes with well-shaped hexahedra for the knee cartilages and menisci.The segmentation performance is assessed using cross-validation on 7 subjects from the Open Knee project and 78 subjects from the Osteoarthritis Initiative.Our results indicate that our developed state-of-the-art processing scheme can achieve competitive performance, opening the path for better diagnostics and patient-specific interventions.The developed tools are freely available to download from SimTK at https://simtk.org/projects/knee-segment.
Modified Elman Spike Neural Network for Identification and Control of Dynamic System

The utilization of conventional modeling strategies in the identification and control of a nonlinear dynamical system suffers from some weaknesses.These include absence of precise, conventional knowledge about the system, a high degree of uncertainty, strongly nonlinear and time-varying behavior.In this paper, a modified training algorithm for the identification and control of a nonlinear system using a soft-computing approach is proposed.Specifically, a modified structure of the Elman neural network with spike neural networks is proposed.This modified structure includes self-feedback, which provides a dynamic trace of the training algorithm.This self-feedback has weights, which can be trained during the training process.The simulation results show that the modified structure with the modified training algorithm is capable of the identification and control of a dynamic system in a more robust manor than when solely applying the other types of neural networks by 70% in terms of minimization of the percentage of error.
Hilbert transform-based crosstalk compensation for color fringe projection profilometry

This Letter presents an effective crosstalk compensation method for color fringe projection profilometry. The crosstalk-induced phase error has been studied, and we find that the phase error doubles the frequency of the color fringe. Therefore, Hilbert transform is applied on the color fringe to shift the carried phase by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>π<!-- π --></mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> . Two phase maps are recovered from the normalized and the transformed patterns, and their average phase is used for three-dimensional (3D) reconstruction. Simulations and experiments confirm that the proposed method can effectively suppress the crosstalk-induced phase error to improve the measurement accuracy.
Pose Guided Gated Fusion for Person Re-identification

Person re-identification is an important yet challenging problem in visual recognition. Despite the recent advances with deep learning (DL) models for spatio-temporal and multi-modal fusion, re-identification approaches often fail to leverage the contextual information (e.g., pose and illumination) to dynamically select the most discriminant con-volutional filters (i.e., appearance features) for feature representation and inference. State-of-the-art techniques for gated fusion employ complex dedicated part- or attention-based architectures for late fusion, and do not incorporate pose and appearance information to train the backbone network. In this paper, a new DL model is proposed for pose-guided re-identification, comprised of a deep backbone, pose estimation, and gated fusion network. Given a query image of an individual, the backbone convolutional NN produces a feature embedding required for pair-wise matching with embeddings for reference images, where feature maps from the pose network and from mid-level CNN layers are combined by the gated fusion network to generate pose-guided gating. The proposed framework allows to dynamically activate the most discriminant CNN filters based on pose information in order to perform a finer grained recognition. Extensive experiments on three challenging benchmark datasets indicate that integrating the pose-guided gated fusion into the state-of-the-art re-identification backbone architecture allows to improve their recognition accuracy. Experimental results also support our intuition on the advantages of gating backbone appearance information using the pose feature maps at mid-level CNN layers.
Cost-sensitive learning for imbalanced data streams

The data imbalance problem hampers the classification task. In streaming environments, this becomes even more cumbersome as the proportion of classes can vary over time. Approaches based on misclassification costs can be used to mitigate this problem. In this paper, we present the Cost-sensitive Adaptive Random Forest (CSARF) and compare it to the Adaptive Random Forest (ARF) and ARF with Resampling (ARFRE) in six real-world and six synthetic data sets with different class ratios. The empirical study analyzes two misclassification costs strategies of the CSARF and shows that the CSARF obtained statistically superior w.r.t. the average recall and average F1 when compared to ARF.
Vulnerability Attacks of SVD-Based Video Watermarking Scheme in an IoT Environment

This study reviews the superiority and limitations of the Singular Value Decomposition (SVD)-based video watermarking scheme.First applies key frame selection to the video sequence to obtain an appropriate video frame for the purpose of watermark embedding in this paper.The Fibonacci-Lucas image transformation scrambles the grayscale watermark image before the embedding process.The former scheme satisfies the imperceptibility and robustness aspects.However, it can suffer from the False-Positive-Problem (FPP) in which a counterfeit watermark image can be easily reconstructed by a malicious attacker.This study presents a new technique for overcoming the FPP of the aforementioned scheme.The proposed method embeds the principal components of the watermark image rather than the Singular Value Matrix (SVM) into the host video.As results, the proposed methods yield promising results in terms of imperceptibility as well as robustness.At the same time, it solves the FPP.A theoretical analysis of embedded watermark information is also provided in this work.The theoretical analysis meets the experimental-based results in the SVD-based video watermarking scheme.
Exocentric to Egocentric Image Generation Via Parallel Generative Adversarial Network

Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate exocentric (third-person) view to egocentric (first-person) view image generation. This is a challenging task since egocentric view sometimes is remarkably different from exocentric view. Thus, transforming the appearances across the two views is a nontrivial task. To this end, we propose a novel Parallel Generative Adversarial Network (P-GAN) with a novel cross-cycle loss to learn the shared information for generating egocentric images from exocentric view. We also incorporate a novel contextual feature loss in the learning procedure to capture the contextual information in images. Extensive experiments on the Exo-Ego datasets [1] show that our model outperforms the state-of-the-art approaches.
Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge

This paper presents an analysis of our diarization system winning the second DIHARD speech diarization challenge, track 1. This system is based on clustering x-vector speaker embeddings extracted every 0.25s from short segments of the input recording. In this paper, we focus on the two x-vector clustering methods employed, namely Agglomerative Hierarchical Clustering followed by a clustering based on Bayesian Hidden Markov Model (BHMM). Even though the system submitted to the challenge had further post-processing steps, we will show that using this BHMM solely is enough to achieve the best performance in the challenge. The analysis will show improvements achieved by optimizing individual processing steps, including a simple procedure to effectively perform "domain adaptation" by Probabilistic Linear Discriminant Analysis model interpolation. All experiments are performed in the DIHARD II evaluation framework.
Maximum Correntropy Square-Root Cubature Kalman Filter for Non-Gaussian Measurement Noise

Cubature Kalman filter (CKF) is widely used for non-linear state estimation under Gaussian noise. However, the estimation performance may degrade greatly in presence of heavy-tailed measurement noise. Recently, maximum correntropy square-root cubature Kalman filter (MCSCKF) has been proposed to enhance the robustness against measurement outliers. As is generally known, the square-root algorithms have the benefit of low computational complexity and guaranteed positive semi-definiteness of the state covariances. Therefore, MCSCKF not only possesses the advantages of square-root cubature Kalman filter (SCKF), but also is robust against the heavy-tailed measurement noise. Nevertheless, MCSCKF is prone to the numerical problems. In this paper, we propose a new maximum correntropy square-root cubature Kalman filter (NMCSCKF) based on a cost function which is obtained by a combination of weighted least squares (WLS) to handle the Gaussian process noise and maximum correntropy criterion (MCC) to handle the heavy-tailed measurement noise. Compared to MCSCKF, the proposed method is more time-efficient and most importantly, it avoids the numerical problem. A univariate non-stationary growth model and a multi-rate vision/IMU integrated attitude measurement model are used to demonstrate the superior performance of the proposed method.
Spatial binary coding method for stripe-wise phase unwrapping

Binary coding methods have been widely used for phase unwrapping. However, traditional temporal binary coding methods require a sequence of binary patterns to encode the fringe order information. This paper presents a spatial binary coding (SBC) method that encodes the fringe order into only one binary pattern. Each stripe of the sinusoidal phase-shifting patterns is corresponding to an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> </mml:math> -bit codeword of the binary pattern. A robust stripe-wise decoding scheme is also developed to extract the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> </mml:math> -bit codeword, then fringe order can be determined, and stripe-wise phase unwrapping can be performed. Experiment results confirm that the SBC method can correctly recover the absolute phase of measured objects with only one additional binary pattern.
Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design

In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classification is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to significantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low efficiency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive synchronizations, which are very difficult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's inefficiency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference efficiency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability in optimizing the routing procedure with increasing network size.
Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera

Characterizing the atmosphere is one of the most complex studies one can undertake due to the non-linearity and phenomenological variability. Clouds are also among the most variable atmospheric constituents, changing their size and shape over a short period of time. There are several sectors in which the study of cloudiness is of vital importance. In the renewable field, the increasing development of solar technology and the emerging trend for constructing and operating solar plants across the earth’s surface requires very precise control systems that provide optimal energy production management. Similarly, airports are hubs where cloud coverage is required to provide high-precision periodic observations that inform airport operators about the state of the atmosphere. This work presents an autonomous cloud detection system, in real time, based on the digital image processing of a low-cost sky camera. An algorithm was developed to identify the clouds in the whole image using the relationships established between the channels of the RGB and Hue, Saturation, Value (HSV) color spaces. The system’s overall success rate is approximately 94% for all types of sky conditions; this is a novel development which makes it possible to identify clouds from a ground perspective without the use of radiometric parameters.
An Analysis of Machine Learning Classifiers in Breast Cancer Diagnosis

In the field of assisted cancer diagnosis, it is expected that the involvement of machine learning in diseases will give doctors a second opinion and help them to make a faster / better determination. There are a huge number of studies in this area using traditional machine learning methods and in other cases, using deep learning for this purpose. This article aims to evaluate the predictive models of machine learning classification regarding the accuracy, objectivity, and reproducible of the diagnosis of malignant neoplasm with fine needle aspiration. Also, we seek to add one more class for testing in this database as recommended in previous studies. We present six different classification methods: Multilayer Perceptron, Decision Tree, Random Forest, Support Vector Machine and Deep Neural Network for evaluation. For this work, we used at University of Wisconsin Hospital database which is composed of thirty values which characterize the properties of the nucleus of the breast mass. As we showed in result sections, DNN classifier has a great performance in accuracy level (92%), indicating better results in relation to traditional models. Random forest 50 and 100 presented the best results for the ROC curve metric, considered an excellent prediction when compared to other previous studies published.
Needs Analysis English For Specific Purposes (ESP) For Vocational Pharmacy Students

Increased development of study programs in vocational education programs that are predicted to be a solution to increase human resources in Indonesia. This also underlies the improvement of English language competence in Indonesia as outlined in Minister of Education and Culture Decree No. 096/1967 December 12, 1967. The Ministry of Education and Culture developed the communicative abilities of English vocational education students who needed help listening, reading, writing and speaking. This study discusses the analysis of the needs of English courses (ESP) for educational students who need analysis of targets and learning needs. Data obtained from questionnaires and interviews. The results of data analysis about active communication skills (35%), grammar difficulties (25%), being able to read (25%) difficulty in content (10%) and writing (5%).Introduction to basic pharmacy (20%), prescription pharmacy (20%), Health and drugs (20%), Healthy lifestyle (20%) and microbiology and pharmacy (20%). The English language used in technology is English Media (40%), crosswords (20%, dialogue content (20%) and multiple choice (20%). The results of the study constitute an analysis of needs and targets referred to as part of the English learning process The results of the analysis of learning needs and the achievement of learning English obtained by students both reading, writing, grammar and other needs in order to prepare themselves to meet their needs going forward.
A Countermeasure Against Statistical Ineffective Fault Analysis

Current state-of-the-art countermeasures against Fault Injection Attacks (FIA) provide good protection against analysis methods that require the differences in the correct and faulty ciphertext to derive the secret information, such as Differential Fault Analysis (DFA) or collision fault analysis. However, recent progress in Ineffective Fault Analysis (IFA) and Statistical IFA (SIFA) constitutes a real threat against cryptographic implementations. Such methods cannot be thwarted by standard FIA countermeasures that focus on detecting the change in the intermediate data. In this brief, we present a novel method based on error correcting codes that protects implementations against SIFA. We design a set of universal error-correcting gates that can be used for block cipher implementations. We analyze a hardware implementation of protected GIFT-64 and show that our method provides 100% protection against SIFA.