Reviewerly/exHarmony huggingface dataset:
A virtual assistant in cultural heritage scenarios

Summary New technologies, tools, and methodologies have been used in the Cultural Heritage (CH) scenarios to assist the visitor to enrich and enjoy his experiences during the visit. Intelligent information systems based upon machine learning approaches have been specifically designed for CH to enhance the quality of services in art exhibitions and events. In this work, we show an innovative framework that can be specifically designed to help visitors during his/her visit by answering to their questions. We also describe a system architecture and a case study in a well‐defined CH context.
Hight Speed Low Area VLSI Architecture for LEA Encryption Algorithm

A novel hardware architecture for LEA encryption algorithm has been implemented in this paper. LEA algorithm is available in three key sizes of 128 bit, 192 bit and 256 bit. This paper focuses on the architecture of 128 bit key with 128-bit block size. The hardware implementation utilizes multiplexers to select the data, shift registers to store 32-bit data as hardware elements. The implementation results have been verified using Xilinx ISE. Virtex5 and Virtex4 FPGA family are used to test and validate the results. It is evident from the results that the proposed architecture utilizes 1% of the total slices available. Further, the processing speed for Virtex4 implementation is less in comparison to the processing speed of Virtex5. The total power consumption of virtex-5 and virtex-4 are 0.566 w and 0.348 w, respectively. The maximum operating frequency of the proposed architecture is compared with the existing literature and better performance is achieved.
Compiling Permutations for Superconducting QPUs

In this paper we consider the compilation of quantum state permutations into quantum gates for physical quantum computers. A sequence of generic single-target gates, which realize the input permutation, are extracted using a decomposition based reversible logic synthesis algorithm. We present a compilation algorithm that translates single-target gates into a quantum circuit composed of the elementary quantum gate sets that are supported by IBM's 5-qubit and 16-qubit, and Rigetti's 8-qubit and 19-qubit superconducting transmon QPUs. Compared to generic state-of-the-art compilation techniques, our technique improves gate volume and gate depth by up to 59% and 53%, respectively.
Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.
Attention Based Echo State Network

Recurrent neural networks (RNNs) are widely studied in recent years, since RNNs are capable of modeling the significant nonlinear dynamical systems. Echo state network (ESN) is a novel type of RNN with an interconnected reservoir to model temporal dynamics of complex sequential information. In this paper, a novel ESN structure is developed and employed to conduct fault prognosis. Fault prognosis is vital in predictive maintenance, which is a prevalent research area that mainly concentrates on predicting the remaining useful life of a machine and reducing the machine's downtime. Attention model is integrated to a typical ESN and thus different importance levels of different input elements can be adaptively treated. To further enhance the generalization of the prediction model, genetic algorithm is applied to adaptively optimize the parameters of the attention-based ESN. The proposed prognostic approach is verified on the NASA's turbofan benchmark dataset. Experimental results show that the attention-based ESN can not only achieve superior prediction accuracy but also obtain substantial improvement on stability.
Automated firearms detection in cargo x-ray images using RetinaNet

We present a method for the automated detection of firearms in cargo x-ray images using RetinaNet. RetinaNet is a recently proposed powerful object detection framework that is shown to surpass the detection performance of state-of-art two-stage R-CNN family object detectors while matching the speed of one-stage object detection algorithms. We trained our models from scratch by generating training data with threat image projection (TIP) that alleviates the class imbalance problem inherent to the x-ray security inspection and eliminates the need for costly and tedious staged data collection. The method is tested on unseen weapons that are also injected into unseen cargo images using TIP. Variations in cargo content and background clutter is considered in training and testing datasets. We demonstrated RetinaNet-based firearm detection model matches the detection accuracies of traditional sliding-windows convolutional neural net firearm detectors while offering more precise object localization, and significantly faster detection speed.
Indicator Proposal for Measuring Regional Political Support for the Electoral Process on Twitter: The Case of Spain’s 2015 and 2016 General Elections

Research on electoral events in conjunction with social media provides opportunities to describe an interesting phenomenon that can be analyzed using sentiment analysis techniques.The goal of the study is to analyze the support of political parties during electoral periods from Twitter comments, including 250 000 tweets regarding the Spanish general elections of 2015 and 2016, respectively.Text mining and natural language processing techniques enable information analysis, and the methodology emphasizes good practices for large-scale data collection retrieved from Twitter through a quantitative analysis of text collection written in the Spanish language.After information extraction obtained in three Spanish regions defined by geolocation, as well as feature selection based on keywords of the main four political parties, we conducted an in-depth examination of Twitter users' support during the course of the election.By weighting the tendency of tweets, we were able to obtain a proposed indicator of support: the positiveness ratio (PR).The results suggest that PR is a feasible barometer to demonstrate the measurable patterns of support tendency regarding political parties and users' behavioral activity to track their affinity on Twitter.The findings indicate consistent support behavior by users toward traditional parties and optimistic users' behavior regarding emerging political parties.
Relative motion and thrust estimation of a non-cooperative maneuvering target with adaptive filter

With the rapid increase in the number of spacecraft in outer space, the unknown maneuvers of a spacecraft significantly increase the probability of a collision between neighborhoods. In order to guarantee the safety of an operational spacecraft, we need to estimate and predict the relative trajectories of a non-cooperative target in real time. Traditional filter routines are ineffective when the target spacecraft performs unknown continuous maneuvers. In this work, we focus on the development of relative estimators with enhanced robustness against unknown maneuvers. First, a variable state dimension estimator based on filter switching and covariance inflation is proposed to adaptively cope with a constant unknown maneuver. To strengthen the robustness in response to the time-varying maneuvers, a novel observer-enhanced Kalman filter is subsequently proposed by incorporating an observer into the filtering routines. Finally, the performance of the proposed estimators is evaluated with a series of numerical simulations, both the advantages and the disadvantages are analyzed using the simulation results.
Lithology identification on well logs by fuzzy inference

The purpose of this work is to present a fuzzy inference system in order to identify lithologies from wireline logs and core data from a specific borehole and transport this information to nearby uncored wells in the same oilfield. Input variables in this inference system are natural gamma ray log (GR) and porosity logs (density, neutron porosity and sonic) used to obtain M and N parameters from M-N plot in order to reduce the number of variables used and obtaining more discrete numerical intervals for the variables. The database of this inference system is built from the core information with evidence of the presence of recognised lithologies. The response of this inference system (output) is indicated along the depth of uncored boreholes where the lithologies occurring. The proposed methodology was applied to real well log data recorded in two boreholes in the Campo de Namorado (Bacia de Campos, Rio de Janeiro). In general, the system obtained 97% of accuracy, getting the conclusion that the methodology presented is applicable to the mapping of one reservoir layer (sandstone, for example) along oilfield, even at complex geologic scenarios which cause the thickness variations and of the depth of the layer of interest.
Optical 3-D Profilometry for Measuring Semiconductor Wafer Surfaces with Extremely Variant Reflectivities

A new surface profilometry technique is proposed for profiling a wafer surface with both diffuse and specular reflective properties. Most moiré projection scanning techniques using triangulation principle work effectively on diffuse reflective surfaces, on which the reflected light beams are assumed to be well captured by optical sensors. In reality, this assumption is no longer valid when measuring a semiconductor wafer surface having both diffuse and specular reflectivities. To resolve the above problem, the proposed technique uses a dual optical sensing configuration by engaging two optical sensors at two different viewing angles, with one acquiring diffuse reflective light and the other detecting at the same time specular surface light for achieving simultaneous full-field surface profilometry. The deformed fringes measured by both sensors could be further transformed into a 3-D profile and merged seamlessly for full-field surface reconstruction. Several calibration targets and industrial parts were measured to evaluate the feasibility and accuracy of the developed technique. Experimental results showed that the technique can effectively detect diffuse and specular light with repeatability of one standard deviation below 0.3 µm on a specular surface and 2.0 µm on a diffuse wafer surface when the vertical measuring range reaches 1.0 mm. The present findings indicate that the proposed technique is effective for 3-D microscale surface profilometry in in-situ semiconductor automated optical inspection (AOI).
Feature Extraction of Colorectal Endoscopic Images for Computer-Aided Diagnosis with CNN

This paper introduces a feature extraction method for Narrow-Band Imaging (NBI) colorectal endoscopic images with Convolutional Neural Network (CNN) for Support Vector Machine (SVM) as a Computer-Aided Diagnosis (CAD) system. The proposed method using the result of pre-learned CNN as a feature extraction module on Bag-of-Features (BoF) framework and SVM inputs the result for classification. We estimated identification accuracy compare with the BoF framework and the proposed method. As an estimation result, we achieved that the proposed method can identify cancer or not with about over 90% accuracy.
SPBSO: self-adaptive brain storm optimization algorithm with pbest guided step-size

As a new and promising swarm intelligence algorithm, brain storm optimization (BSO) has drawn more attention of researches and has been successfully applied to solve the real-world optimization problems. However, too many parameters make the algorithm more complex and greatly limit the convergence performance. Thus, this paper proposed a novel BSO variant, named self-adaptive BSO with pbest guided step-size (SPBSO), in which a simple self-adaptive strategy is employed to choose a creating strategy in a random manner rather than depending on several adjustable parameters. In addition, the pbest guided step-size and dynamic clustering number are used to accelerate the convergence speed. The experimental studies have been tested on a set of widely used benchmark functions (including the CEC 2014 problems). Experimental results and comparison with the state-of-the-art BSO variants and some recently proposed PSO and DE algorithms, have proved that the proposed algorithm is competitive.
Modeling the spatial distribution of Global Solar Radiation (GSR) over Ghana using the Ångström-Prescott sunshine duration model

Solar radiation is an important geological and meteorological parameter. In most developing countries, data is readily unavailable owing to lack of instrumentation and skilled personnel. In this study, Global solar radiation (GSR) over Ghana has been quantified using the Ångström–Prescott sunshine model with sunshine duration data from 22 synoptic stations distributed across the country's ecological zones. The simulated data was gridded at 10 km by 10 km, establishing the spatial distribution of solar radiation over the country. Comparison with satellite data showed good results with root mean square error (RMSE) values of 1–5 MJm−2day−1 and correlation coefficient of 60 - 66%. Meanwhile, the estimated total GSR over the country was found to be 412.82 MJm−2day−1. The savanna zone had the maximum estimated total monthly mean GSR for the year, with the highest value of 20.76 MJm−2day−1 in Navrongo. The forest zone had the minimum estimated total annual mean GSR, with the lowest radiation level in Oda (17.11 MJm−2day−1). A maximum and minimum mean clearness index of 0.59 and 0.48 respectively are estimated, implying that about 53% of solar radiation at the top of the atmosphere reaches the study area after attenuation. The satellite data has a total monthly mean horizontal Global Solar irradiance of 366.62 MJm−2day−1. The study shows that the region is a potential field to harness and optimize solar energy for the operation of photovoltaic systems and solar collectors for industrial and domestic applications.
Mineral discrimination by combination of multispectral image and surrounding hyperspectral image

A hyperspectral (HS) imager is more effective than a multispectral (MS) imager in mineral discrimination, but spatial coverage of HS images is limited in comparison to that of MS images.Thus Kruse and Perry have proposed a method that uses coincident HS imaging and MS imaging data to extend mineral mapping to larger areas.We propose a method modified from the Kruse and Perry's (K&P) method.Though the K&P method derives the MS-based endmember spectra by weighting the HS-based endmember spectra with the response functions of the MS sensor bands, the proposed method obtains the MS-based endmember spectra from surface reflectance spectra of the MS pixels at the same positions with the HS pixels selected as the HS-based endmembers in the overlapping area.The validation study using airborne visible/infrared imaging spectrometer and advanced spaceborne thermal emission and reflection radiometer images over Cuprite and Goldfield areas, Nevada, USA, demonstrates that the proposed method is more robust against spectral inconsistency between the HS-and the MS-images caused by calibration and/or atmospheric correction errors than the K&P method, though the proposed method is more sensitive to co-registration errors between the HS-and the MS-images than the K&P method.
Shape Detection of Silicon Single Crystal Based on the MUSIC Algorithm

To improve the accuracy and speed of detecting crystal shape during the growth of Czochralski silicon single crystals, a straight line detection method is proposed based on the multiple signal classification (MUSIC) algorithm. First, in order to obtain multi-snapshot signals to meet the MUSIC algorithm requirements, we add multiple sets of zero-mean Gaussian noise. These sets of noise obey the same distribution (i.e., zero-mean Gaussian). This supports the generation of multiple images, which are converted into multi-snapshot signals of a virtual sensor array. These multi-snapshot signals are used to construct a far-field model; the tilt angle estimation problem of a straight line is transformed into a search problem, which seeks the direction of arrival in the virtual input sources. Finally, a near-field model is constructed, and the estimated potential offsets corresponding to the tilt angles are calculated to determine the parameters of the straight line. The simulation and engineering experiments show that the proposed method can accurately and quickly detect the tilt angle and the offset of the straight line, and is superior to existing methods.
A comparative Study of Augmented Reality SDKs to Develop an Educational Application in Chemical Field

Augmented Reality "AR" is an increasingly pervasive newcomer in higher-education; it offers good advantages for learners to learn anywhere and at any time. Working in this new promoter research area can be attractive for researchers, due in one hand, to the lack of such applications used in education, on the other hand to the higher quality of the developed applications. In this paper, we present a comparative study concerning some AR software development kits "SDKs", this comparison will be based on several significant criteria, in order, to choose the most suitable SDK adapted for the learning process in higher education, more especially the chemical scope. Thereafter, according to the analysis of the obtained results, we will develop an educational AR application based on the chosen SDK. The proposed application combines Augmented Reality techniques, game engine utilities, rendering of both 2D and 3D resources, and objects detection and tracking algorithm, and its main objective is to allow learners to learn new concepts in organic chemistry e.g. "stereochemistry", and to understand the compositions of both atoms and molecules in 3D environment.
Hybrid of Intelligent Minority Oversampling and PSO-Based Intelligent Majority Undersampling for Learning from Imbalanced Datasets

Learning from imbalanced datasets poses a major research challenge today due to the imbalanced nature of real-world datasets where samples of some entities are few in number, while some other entities have thousands of samples available. A novel hybrid scheme of intelligently oversampling the minority class followed by subsequent intelligent undersampling of the majority class, is proposed in this paper for learning from imbalanced datasets. Different oversampling techniques: SMOTE and the intelligent oversampling versions of Borderline-SMOTE, Adaptive Synthetic Sampling (ADASYN) and MWMOTE, are considered in combination with Sample Subset Optimization (SSO) that is an intelligent majority undersampling technique based on the evolutionary optimization algorithm of Particle Swarm Optimization (PSO). The datasets after balance-correction are applied to the decision tree classifier. Experiments on benchmark datasets from the UCI repository prove the efficiency of our method due to the higher classification accuracies obtained as compared to the baseline methods.
Simultaneous learning of reduced prototypes and local metric for image set classification

Classification based on image set is recently a competitive technique, where each set contains multiple images of a person or an object. As a widely used model, affine hull has shown its power in modeling image set. However, due to the existence of noise and outliers, the over-large affine hull usually matches fails when two hulls overlapped. Aiming at alleviating this handicap, this paper proposes a novel method for image set classification, namely Learning of Reduced Prototypes and Local Metric (LRPLM). Specifically, for each gallery image set, a reduced set of prototypes and an optimal local feature-wise metric are simultaneously learned, which jointly minimize the loss function involved the estimation of classification error probability. In doing so, LRPLM inherits the merits of affine hull with better representation to account for the unseen appearances and makes use of the powerful discriminative ability improved by the local metric. It looks like that LRPLM pulls similar image sets with the same class label “closer” to each other, while pushing dissimilar ones “far away”. Extensive experiments illustrate the considerable effectiveness of LRPLM on three widely used datasets. As we know, classification is a research hotspot in expert and intelligent systems. Different from the previous classification methods, LRPLM focuses on image set-based classification technology, while most of them are single-shot classification technology. Thus, the proposed method can be considered as an expert system technology for medical diagnosis, security monitoring, object categorization, and biometrics recognition applications.
Theorem proof based gate level information flow tracking for hardware security verification

Digital hardware lies in the heart of systems deployed in finance, medical care, basic infrastructure and national defense systems. However, due to the lack of effective security verification tools, hardware designs may contain security vulnerabilities resulting from performance optimizations, side channels, insecure debug ports and hardware Trojans, which provide attackers low level access to critical resources and effective attack surface to compromise a system. To address these security threats, we propose a theorem proof based gate level information flow tracking (GLIFT) method for formal verification of security properties and identifying security vulnerabilities that cause security property violations. Our method integrates a precise information flow tracking (IFT) model with the Coq theorem proof environment, inheriting the accuracy of GLIFT and the scalability of theorem proving based formal verification. We formalize semantic circuits and security theorems for proving security properties in order to detect security violations. The proposed method has been demonstrated on an OpenRISC development interface core and an RSA implementation both from OpenCores as well as several Trojan benchmarks from Trust-HUB. Experimental results show that the proposed method detects insecure debug port, timing channel and hardware Trojans that cause violation of important security properties such as confidentiality, integrity and isolation and also derives the trigger condition of hardware Trojans.