Reviewerly/exHarmony huggingface dataset:
Comparison of Meta-heuristic with Evolutionary and Local Search Methods for Feature Selection

Feature selection is one of the most significant steps of pre-processing which deals with extracting the pertinent subset of features from the available feature set. This reduces the dimensionality of the dataset which is a key challenge in the field of data mining. In this chapter, three types of search methods are used for feature selection, viz. local search, evolutionary search and metaheuristic search. The goal of this chapter is to evaluate the search ability of metaheuristic methods as they overcome the drawbacks of local search and evolutionary search methods. For this, the authors have conducted an exhaustive comparison amongst the search methods where a collection of nine meta-heuristic search methods (Ant search, Bat search, Bee search, Cuckoo search, Elephant search, Firefly search, Flower search, Harmony search, Wolf search) are compared with three local search and four evolutionary search methods. These search methods are validated against two open source datasets belonging to different domains (allow to provide stronger and reliable conclusion), viz. Pima Indians Diabetes dataset and Hepatocellular carcinoma dataset found in UCI machine learning repository. After extracting the useful features using all the search methods, models are predicted using ensemble classifiers, Random Forest, Logitboost, Adaboost and Bagging. To the best of authors’ knowledge, there is no work in literature which has done such an exhaustive evaluation and comparison of search methods (total 16 search methods are used) and then constructed ensemble models to determine the effectiveness of each search method. The results proved that meta-heuristic search methods are more optimal for feature selection process and are better than local and evolutionary search methods.
On the stability of periodic binary sequences with zone restriction

Abstract Traditional global stability measure for sequences is hard to determine because of large search space. We propose the k -error linear complexity with a zone restriction for measuring the local stability of sequences. For several classes of sequences, we demonstrate that the k -error linear complexity is identical to the k -error linear complexity within a zone, while the length of a zone is much smaller than the whole period when the k -error linear complexity is large. These sequences have periods $$2^n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> </mml:math> , or $$2^v r$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>v</mml:mi> </mml:msup> <mml:mi>r</mml:mi> </mml:mrow> </mml:math> ( r odd prime and 2 is primitive modulo r ), or $$2^v p_1^{s_1} \cdots p_n^{s_n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>v</mml:mi> </mml:msup> <mml:msubsup> <mml:mi>p</mml:mi> <mml:mn>1</mml:mn> <mml:msub> <mml:mi>s</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:msubsup> <mml:mo>⋯</mml:mo> <mml:msubsup> <mml:mi>p</mml:mi> <mml:mi>n</mml:mi> <mml:msub> <mml:mi>s</mml:mi> <mml:mi>n</mml:mi> </mml:msub> </mml:msubsup> </mml:mrow> </mml:math> ( $$p_i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>p</mml:mi> <mml:mi>i</mml:mi> </mml:msub> </mml:math> is an odd prime and 2 is primitive modulo $$p_i^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>p</mml:mi> <mml:mi>i</mml:mi> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> , where $$1\le i \le n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>≤</mml:mo> <mml:mi>i</mml:mi> <mml:mo>≤</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> ) respectively. In particular, we completely determine the spectrum of 1-error linear complexity with any zone length for an arbitrary $$2^n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> </mml:math> -periodic binary sequence.
The use of profilometry techniques and eigenstrain theory for the analysis of creep behavior in nickel superalloy welds

This paper presents a summary of recently developed experimental and computational tools to reconstruct residual stress fields and analyze creep in nickel superalloy welds used in aerospace engineering components. This approach combines experimental data with eigenstrain theory to reconstruct stress fields at the macroscopic scale and provided reliable means for numerical prediction of creep behavior of welded components under complex loading conditions. Experimental data in the form of profilometry scans was interpreted using a range of iterative eigenstrain methods that included the adaptation of the contour method and artificial intelligence models for eigenstrain-creep analysis. The integration of principles of artificial intelligence with eigenstrain models allowed highly accurate results to be obtained which were validated by comparison with experimental data obtained using independent techniques such as neutron diffraction. The use of artificial intelligence models is discussed for residual stress reconstruction and creep behavior prediction in annular aeroengine parts manufactured using inertia friction welding. To extend the range of experimental data taken into consideration, the height Digital Image Correlation (hDIC) technique was introduced that utilizes information regarding triaxial displacements obtained from profilometry, allowing deeper and more reliable analysis to be conducted. The hDIC technique was validated using operando tensile testing data.
Crowd Counting with Spatial Normalization Network

Crowd counting, which requires to estimate crowd density from an image, is still a challenging task in computer vision. Most of the current methods are focused on large scale variation of people and ignore the huge distribution difference of crowd. To tackle these two problems together, we propose a novel framework named Spatial Normalization Network (SNNet). We normalize multi-scale features from parallel subnetworks to a particular scale and then fuse them to acquire rich spatial information for final accurate density map predictions. Furthermore, we propose a novel normalization layer called Spatial Group Normalization (SGN), which firstly split feature maps along the spatial dimension and then perform group-wise normalization. It's useful to solve statistic shift problems caused by the great difference of distribution in crowd counting. Moreover, SGN can be naturally plugged into existing solutions and brings significant improvement in crowd counting. Our proposed SNNet achieves state-of-the-art performance on four challenging crowd counting datasets (ShanghaiTech, UCFQNRF, GCC and TRANCOS datasets), which demonstrates the effectiveness and robust feature learning capability of our methods.
Fast 3D surface defect detection with fringe projection

Ensuring high quality standards at a competitive cost through rapid and accurate industrial inspection is a great challenge in the eld of intelligent manufacturing. Three-dimensional (3D) optical quality inspection technologies are gradually widely applied for surface defect detection of complex workpieces because of its non-contact, high- accuracy, digitization and automation. However, the contradiction between cost and eciency, dependence on additional position hardware, and compromised detection strategies remain the urgent obstacles to overcome. In this work, we propose a fast 3D surface defect inspection approach based on fringe projection pro-lometry (FPP) for complex objects without any auxiliary equipment for position and orientation control. Firstly, a multi- view 3D measurement based on geometric constraints is employed to acquire high-accuracy depth information from dierent perspectives. Then, a cycle-positioning-based registration scheme with the establishment of the pose-information-matched 3D standard digital model is proposed to realize rapid alignment of the measured point cloud and the standard model. Finally, a minimum 3D distance search method is driven by a dual-thread processing mechanism for simultaneous scanning and detection to quantify and locate 3D surface defects in real time. To validate the proposed inspection approach, a software that combines 3D imaging, point cloud registration, and surface defect calculation is developed to perform quality inspections on complicated objects. The experimental results show that our method can accurately detect the 3D surface defect of the workpiece through more economical hardware and more convenient means in real time, which is of great signicance to intelligent manufacturing.
A Fuzzy Logic Approach for Software Cost Prediction

Software cost prediction is the technique of accurately evaluating the amount while developing the software. Estimation involves the total time required for the completion of the software, effort required that is measured in terms of person per month (PM), and the total cost to complete the activity. Accuracy and duration are the two desirable criteria in the software estimation process. In software estimation process, there are several inputs that are being fed to the system and these inputs are used for the generation or calculation of the set of outputs. The important work of the software project managers in the present scenario is the computation of cost or effort before the absolute advancement of any particular software. There are several methods applied for software cost estimation but we will focus on the fuzzy logic which is a soft-computing method. We feel that model which is based on fuzzy logic for the software cost estimation should be able to give the uncertain values rather than other models which give precision and certain values and the results based on fuzzy logic model will be more accurate than other models. In this research paper, we have developed the fuzzy logic model by taking several inputs and membership functions, also the fuzzy rules have been deduced by expert-knowledge with the help of MATLAB’s Fuzzy logic toolbox and the results are then compared with the multiple regression model using SPSS Tool upon desharnais data set which gives less accuracy than fuzzy-based model.
The role of grammar in transition-probabilities of subsequent words in English text

Sentence formation is a highly structured, history-dependent, and sample-space reducing (SSR) process. While the first word in a sentence can be chosen from the entire vocabulary, typically, the freedom of choosing subsequent words gets more and more constrained by grammar and context, as the sentence progresses. This sample-space reducing property offers a natural explanation of Zipf’s law in word frequencies, however, it fails to capture the structure of the word-to-word transition probability matrices of English text. Here we adopt the view that grammatical constraints (such as subject–predicate–object) locally re-order the word order in sentences that are sampled by the word generation process. We demonstrate that superimposing grammatical structure–as a local word re-ordering (permutation) process–on a sample-space reducing word generation process is sufficient to explain both, word frequencies and word-to-word transition probabilities. We compare the performance of the grammatically ordered SSR model in reproducing several test statistics of real texts with other text generation models, such as the Bernoulli model, the Simon model, and the random typewriting model.
A Passage-Level Text Similarity Calculation

Along with the explosion of web information, information flow service has attracted the attentions of users. In this kind of service, how to measure the similarity between texts and further filter the redundant information collected from multiple sources becomes the key solution to meet user’s desire. One text often mentions several events. The core event mostly decides the main content carried by the text. It should take the pivotal position. For this reason, this paper aims to construct a passage-level event connection graph to model the relations among the events mentioned by one text. The core event can be revealed and is further chosen to measure the similarity between two texts. As shown by experimental results, after measuring text similarity from a passage-level event representation perspective, our unsupervised measuring method acquires superior results than unsupervised methods by a large margin and even comparable results with some popular supervised neuron based methods.
Rule‐based Verification of System Security using Feature‐Based Product Line Engineering

ABSTRACT Systems security engineering is a discipline to engineer a system of interest ensuring system functionality under disruptive conditions associated with misuse and malicious behavior. Today's cyber‐physical systems must survive in a constantly changing threat environment. Cybersecurity controls and cyber resiliency capabilities require continuous delivery to meet this challenge. This article describes creating and using a rule base as an integral part of a systems and software product line engineering (PLE) factory enabling continuous resilient and secure cyber‐physical systems delivery ensuring the system can function under disruptive conditions. A hypothetical intelligence gathering network comprising unmanned vehicles, a ground control segment, and an intelligence analytics segment is the system of interest. Systems engineering activities include continuously analyzing the cyber‐attack surface for each system variant in the product portfolio; creating or amending cyber resiliency capabilities and cybersecurity controls for each system variant addressing each attack vector; and a living rule base maintained within a PLE factory ensuring each system variant automatically generated by the factory contains the requisite system capabilities. Applying this approach results in rule‐based verification of continuously delivered cyber resiliency capabilities and cybersecurity controls for each fielded system variant. Moving at the speed of mission need is essential, and automated rule‐based verification of system deliveries meets that need.
Attendance Management Automated System Based on Face Recognition Algorithms

The management of automated attendance method in the paper. This structure is completely depending on face discovery other algorithms of recognition, robotically would detect a candidate whenever he would enter the class and attendance scores by identifying the person. The system algorithms architecture utilized in every phase have been explained here. Dissimilar actual situations were believed to assess the act of several systems of face acknowledgment. It also recommends the practices to be utilized to handle the hazards like spoofing. This system would save the time and also would support to supervise the candidates when it is evaluated to the customary marking of attendance.
Hybrid Dilated Convolution Network Using Attentive Kernels for Real-Time Semantic Segmentation

Though current semantic segmentation methods achieve high accuracy, most of them suffer from low speed, massive memory usage, and high computation complexity. To avoid these problems, we propose a light-weight network called Hybrid Dilated Convolution Network (HDCNet). HDCNet mainly consists of the Hybrid Scale-Aligned Block (HSAB) and the Attentive Depthwise Separable Block (ADSB). The HSAB adopts multiple small kernel convolutions with small-scale dilation rates to extract local information and applies several large kernel convolutions with large-scale dilation rates to encode global information, respectively. We further explore the best option to match the kernel size to the dilation scale. The ADSB is designed to decrease redundant parameters and enhance the critical information by depthwise separable convolution and mixed convolution kernels. In this way, ADSB and HSAB jointly encode multi-scale context information to improve model performance. Thereafter, we combine integrated local information with global information to generate final prediction results. Extensive experiments on Cityscape dataset have demonstrated that the proposed method reaches a better trade-off between accuracy and efficiency compared with other start-of-the-art methods. In particular, HDCNet obtains 72.82% MIoU with only 2.02M and 16.8 GFLOPs.
Compositional Data Analysis â€“ Coherent Forecasting Mortality Model with Cohort Effect

In this paper, an extension of the Coherent forecasts of mortality with compositional data analysis (CoDa) model of Bergeron-Boucher et al. (2017) to cohort effect is proposed applied to data from six African countries. The process of fitting this model starts by adapting the Renshaw and Haberman (2006) to compositional data analysis (CODA) as suggested by Bergeron-Boucher et al. (2017). The proposed CoDa-cohort model generally fits the data better than the original cohort model of Renshaw and Haberman (2006). To get the full CoDa-cohort-coherent model the multiple population factor is included in CoDa-cohort model. Then a comparison between CoDa -coherent and CoDa-cohort-coherent models revealed that they have similar accuracy for the selected countries in West Africa but not for countries in East Africa based on Aitchinson distance (AD). But for merged populations like male and female, the new model, CoDa-cohort-coherent, has generally better fits for Kenya mortality data.Â Â Keywords: Mortality, Compositional data analysis, coda, Coherent, Cohort, Forecast
Hyperspectral Image Denoising Based on Graph-Structured Low Rank and Non-local Constraint

Hyperspectral image (HSI) denoising is an effective image processing technology to improve the quality of HSI. By exploiting the spectral-spatial characteristics of HSI, this paper proposes a HSI denoising method based on the Graph-structured Low Rank model and Non-Local constraint (GLRNL). Graph-structured low rank model can reveal the spectral similarity in HSI and preserve the distribution characteristics of spectral bands. The graph structure is used to ensure that the denoised HSI has similar spectral characteristics as the real HSI, and reduce the spectral distortions. Moreover, the non-local constraint in GLRNL adopts the spatial-spectral self-similarity and provides an effective side prior information for low rank calculation. Comparing with some traditional low rank and sparse model based HSI denoising methods, the proposed method achieves the competitive results on different simulated and real HSI data with various types of noise.
Detection of White Blood Cells with Metaheuristic Computation

This chapter illustrates the use of metaheuristic computation schemes for the automatic detection of white blood cells embedded into complicated and cluttered smear images. The approach considers the identification problem as the process of detection of multi-ellipse shapes. The scheme uses the Differential Evolution (DE) method, which is easy to use, maintains a quite simple computation scheme presenting acceptable convergence properties. The approach considers the use of five edge points as agents which represent the candidate ellipses in the edge image of the smear. A cost function assesses if such candidate ellipses are present in the actual edge image. With the values of the cost function, the set of agents is modified by using the DE algorithm so that they can approximate the white blood cells contained in the edge-only map of the image.
Optimisation based on ‘Nearest Better Neighbor’: A Preliminary study

A multi agent optimization was proposed and evaluated in this research report. It incorporates multi agent search by learning from the 'nearest better solution'. Each agent learns from the nearest agent with better solution thereby ensures that the optimization explores solution spaces between the solutions. Weightage multipliers determine how much each solution segment adapts to the 'nearest better solution' based the ratio of the 'nearest better solution fitness' to the best population fitness. A small amount of random mutation is introduced in every iteration to prevent early convergence. A converging time period (50% of last iteration period) is assigned in order for the agents to converge to a specified range in which the random mutation mechanism is totally repressed. Results showed that the algorithm is able to produce comparable results as compared with other algorithm such as Genetic Algorithm (GA) and particle swarm optimization (PSO). Evaluations were performed using 11 benchmark functions based on average ranking.
Enhanced Security for the authentication of Digital Signature from the key generated by the CSTRNG method

Security is the most critical feature of the present e-world. Many applications are based on random number generation. In the present situation, it is very difficult to hide the data. So the key-based encryption process is suggested to keep the files secure. The protection of the data depends on the key. Key generation is more important for all cryptography applications, which is entirely dependent on randomness. As long as many designers have been able to develop many Random Number Generator-based keys. Also work is going on to make hacker data more stable. CSTRNG can be suggested in this work. One of the most commonly used technologies in the marketing field is the production of digital signatures. The generation of digital signatures can be generated using many keys that have the property of RNG. The most stable RNG is the Cryptographically Secured True Random Number Generator (CSTRNG). This could generate more unexpected numbers. Work has been built on the Xilinx ISE simulator.
Morphological Operators on Hypergraphs for Colour Image Processing

This article is an extension of morphological operators on hypergraphs to work with colour images. Morphological operators on hypergraphs are useful for binary and grayscale image processing. The preliminary experimental results related to the extension of these operators to colour images is presented in this paper. The results on colour images are promising and is a better alternative for the existing methods.
Improved three-dimensional reconstruction model based on coaxial structured light system

Complicated and time-consuming system calibration is indispensable for existing accurate three-dimensional reconstruction methods. To address this problem, this paper presents a simple and accurate optimized 3D reconstruction model based on a coaxial structured light system (CSLS). Compared to the traditional structured light system, CSLS contains a perspective projection unit and a telecentric imaging unit. Instead of obtaining the complicated structured light system parameters, the proposed model only requires the retrieval of a product matrix which could be easily calibrated by using an ordinary white plane. The influence of geometric distortion in the projector lenses has also been considered to further improve the measuring accuracy. Experiments were carried out to verify the validity of the proposed method.
Unsupervised Domain Adaptation Method Based on Discriminant Sample Selection

In order to solve the problem that low classification accuracy caused by the different distribution of training set and test set, an unsupervised domain adaptation method based on discriminant sample selection (DSS) is proposed. DSS projects the samples of different domains onto a same subspace to reduce the distribution discrepancy between the source domain and the target domain, and weights the source domain instances to make the samples more discriminant. Different from the previous method based on the probability density estimation of samples, DSS tries to obtain the sample weights by solving a quadratic programming problem, which avoids the distribution estimation of samples and can be applied to any fields without suffering from the dimensional trouble caused by high-dimensional density estimation. Finally, DSS congregates the same classes by minimizing the intra-class distance. Experimental results show that the proposed method improves the classification accuracy and robustness.