Reviewerly/exHarmony huggingface dataset:
Fuzzy c-Means with Improved Particle Swarm Optimization

Fuzzy clustering algorithm has become a relevant research field of unsupervised learning due to that the uncertainties between patterns can be described more accurately. Based on objective function, fuzzy clustering algorithm uses a constrained optimization mathematical problem to represent the clustering problem. It then determines the division of data sets and fuzzy clustering results by solving the optimization problem. Fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency. However, it shows some weaknesses, particularly its tendency to fall into local optima and dependence on initial values. Particle Swarm Optimization (PSO) is one of the heuristic methods that usually implemented on function optimization problems since it has a robust global search capability. In this paper, a new concept of worst position is introduced to PSO that gives a chance for particles to change flying directions. Moreover, new hybrid algorithms based on FCM and improved PSO with worst position (PSOWP) both in L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm are proposed, which avoid falling into local optimum with faster convergence speed.
Developing an optical design pipeline for correcting lens aberrations and vignetting in light field cameras

Light field cameras have been employed in myriad applications thanks to their 3D imaging capability. By placing a microlens array in front of a conventional camera, one can measure both the spatial and angular information of incoming light rays and reconstruct a depth map. The unique optical architecture of light field cameras poses new challenges on controlling aberrations and vignetting in lens design process. The results of our study show that field curvature can be numerically corrected for by digital refocusing, and vignetting must be minimized because it reduces the depth reconstruction accuracy. To address this unmet need, we herein present an optical design pipeline for light field cameras and demonstrated its implementation in a light field endoscope.
MRF Model-Based Estimation of Camera Parameters and Detection of Underwater Moving Objects

The detection of underwater objects in a video is a challenging problem particularly when both the camera and the objects are in motion. In this article, this problem has been conceived as an incomplete data problem and hence the problem is formulated in expectation maximization (EM) framework. In the E-step, the frame labels are the maximum a posterior (MAP) estimates, which are obtained using simulated annealing (SA) and the iterated conditional mode (ICM) algorithm. In the M-step, the camera model parameters, both intrinsic and extrinsic, are estimated. In case of parameter estimation, the features are extracted at coarse and fine scale. In order to continuously detect the object in different video frames, EM algorithm is repeated for each frame. The performance of the proposed scheme has been compared with other algorithms and the proposed algorithm is found to outperform.
Intensity based Image Registration on Brain MRI Images

In recent days, people are prone to many diseases that can be detected effectively through scans. The severity of the problem can be analyzed by taking periodic scans and the treatment also depends on the same. The intensity based image registration was applied to the mammography images in the previous works. In this paper, the evaluation of the intensity based image registration is made on the brain MRI (Magnetic Resonance Imaging) images which are used frequently among people suffering from any brain disorders. The image registration is the process of detecting the distortion that occurs in the image from its original structure. The intensity based registration technique is used to avoid human intervention and make the process convenient for patients. The image registration is performed using various metrics and methods that vary from global transformations to the local transformations. The similarity metrics used for image registration are the sum of the squared differences and mutual information. These metrics are evaluated using the three registration methods namely: Affine, Demons and Hybrid (Affine + B-Spline). The algorithm that results with higher similarity image is taken to be the efficient image registration algorithm for Brain MRI images. MATLAB has been used to analyze this idea and had produced better results.
Learning a Convolutional Autoencoder for Nighttime Image Dehazing

Currently, haze removal of images captured at night for foggy scenes rely on the traditional, prior-based methods, but these methods are frequently ineffective at dealing with night hazy images. In addition, the light sources at night are complicated and there is a problem of inconsistent brightness. This makes the estimation of the transmission map complicated in the night scene. Based on the above analysis, we propose an autoencoder method to solve the problem of overestimation or underestimation of transmission captured by the traditional, prior-based methods. For nighttime hazy images, we first remove the color effect of the haze image with an edge-preserving maximum reflectance prior (MRP) method. Then, the hazy image without color influence is input into the self-encoder network with skip connections to obtain the transmission map. Moreover, instead of using the local maximum method, we estimate the ambient illumination through a guiding image filtering. In order to highlight the effectiveness of our experiments, a large number of comparison experiments were conducted between our method and the state-of-the-art methods. The results show that our method can effectively suppress the halo effect and reduce the effectiveness of glow. In the experimental part, we calculate that the average Peak Signal to Noise Ratio (PSNR) is 21.0968 and the average Structural Similarity (SSIM) is 0.6802.
MAMMSIT: A Database For The diagnosis and detection of Breast Cancer in Mammography images

In recent days Mammography is most widely used techniques to detect the Breast cancer. Masses and calcifications are the abnormalities indicate the breast cancer. . Breast cancer is the leading cause of cancer deaths in women. Advanced technologies with existing imaging modalities improve breast cancer screening. Most of the Mammographic image data sets are not easily available. The Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) are the most easily available data set for Mammographic images. MAMMSIT is another data set for the mammographic image analysis. The mammogram data set is named as MAMMSIT and it contains Normal and Malignant group images of mammography. Our proposed data set provided with appropriate annotations details such as age, background tissue, ultra sound screen position (i.e., left and right) and BIRADS level are included in the database.
Vertical Distribution of Major and Trace Elements in a Soil Profile from the Nile Delta, Egypt

Abstract The present study was conducted to highlight the elemental composition of ten soil samples collected at different depths along of a soil profile (0.25-17 m). The collected samples were subjected to epithermal neutron activation analysis at the pulsed reactor IBR-2 of Frank Laboratory of Neutron Physics - Joint Institute for Nuclear Research - Dubna - Russian Federation. The concentrations in mg/kg of 36 major and trace elements were determined. Symbatic behaviour of geochemically related elements was observed: Th and U; Cl and Br and Fe, Ti, Ca, Al, and Mg, etc. A sharp increase of certain concentrations at the depth of 8 m was observed. Significant mafic sources of elements were observed and mostly are attributed to Ethiopian High Plateau with small amount of felsic volcanic rocks.
Inserting Punctuation to ASR Output in a Real-Time Production Environment

The output of a speech recognition system is a continuous stream of words that has to be post-processed in various ways, out of which punctuation insertion is an essential step. Punctuated text is far more comprehensible to the reader, can be used for subtitling, and is necessary for further NLP processing, such as machine translation. In this article, we describe how state-of-the-art results in the field of punctuation restoration can be utilized in a production-ready business environment in the Czech language. A recurrent neural network based on long short-term memory is employed, making use of various features: textual based on pre-trained word embeddings, prosodic (mainly temporal), morphological, noise information, and speaker diarization. All the features except morphological tags were found to improve our baseline system. As we work in a real-time setup, it is not possible to employ information from the future of the word stream, yet we achieve significant improvements using LSTM. The usage of RNN also allows the model to learn longer dependencies than any n-gram-based language model can, which we find essential for the insertion of question marks. The deployment of an RNN-based model thus leads to a relative 22.6% decrease in punctuation errors and improvement in all metrics but one.
A novel modified particle swarm optimization algorithm with mutation for data clustering problem

Particle Swarm Optimization (PSO) and K-Means (KM) are widely used for solving data clustering. KM encounters the problem of initializing the cluster centers and the problem of trapping in local optimum. When PSO is applied with KM, it can decrease two problems from KM. Hence, the hybrid clustering technique based on PSO and KM that can enhance performance of clustering is more than using KM alone. However, the hybrid clustering technique encounters the trapping in local optimum problem. To solve this problem, this paper proposed improving hybrid technique by the mutation operation is applied with particles of PSO when swarm traps in local optimum. The proposed technique is tested on eight datasets from the UCI Machine Learning Repository and gives more satisfied search results in comparison with PSOs for the data clustering problems.
FDBSCAN-APT: A Fuzzy Density-based Clustering Algorithm with Automatic Parameter Tuning

Density-based clustering algorithms represent a convenient approach when the number of clusters is not known in advance and their shapes are arbitrary. Nevertheless, they are highly sensitive to the input parameter setting, especially when clusters' borders are close to each other, or even overlap. In this paper we propose FDBSCAN-APT, a fuzzy extension of the DBSCAN algorithm. FDBSCAN-APT is able to discover clusters with fuzzy overlapping borders and relies on the automatic setting of input parameters thanks to the definition of a novel heuristic based on the statistical modelling of the density distribution of objects. An extensive experimental analysis carried out on synthetic datasets shows that FDBSCAN-APT always finds reasonable parameter configurations and produces good clustering results in a variety of challenging scenarios.
Improved Particle Swarm Optimization Approach for Vibration Vision Measurement

Traditional template matching-based motion estimation is a popular but time-consuming method for vibration vision measurement. In this study, the particle swarm optimization (PSO) algorithm is improved to solve this time-consumption problem. The convergence speed of the algorithm is increased using the adjacent frames search method in the particle swarm initialization process. A flag array is created to avoid repeated calculation in the termination strategy. The subpixel positioning accuracy is ensured by applying the surface fitting method. The robustness of the algorithm is ensured by applying the zero-mean normalized cross correlation. Simulation results demonstrate that the average extraction error of the improved PSO algorithm is less than 1%. Compared with the commonly used three-step search algorithm, diamond search algorithm, and local search algorithm, the improved PSO algorithm consumes the least number of search points. Moreover, tests on real-world image sequences show good estimation accuracy at very low computational cost. The improved PSO algorithm proposed in this study is fast, accurate, and robust, and is suitable for plane motion estimation in vision measurement.
Relationship Between Gestalt and Usability Heuristics in Mobile Device Interfaces

While usability heuristics are universal concepts, visual references for using them in an application are limited, contradictory, and sometimes confusing, especially for novice developers. The present work has linked Nielsen's heuristics with Gestalt principles, which are the foundation of human perception, developed from psychology, and which have been used in art, design, and architecture, to understand how different visual stimulus can be interpreted by the human being. The heuristics, Match between system and the real world, Recognition rather than recall and Aesthetic and minimalistic design, have been linked with the Gestalt principles of Similarity, Proximity, Common direction, and Simplicity. This link is possible because Gestalt principles have certain conceptual similarities with usability heuristics, in addition to the fact that these fundamentals were identified in some mobile interfaces and are related to specific interactions. This work provides theoretical guidelines, visual references and practical examples on the use of some graphic components that are regularly used in mobile interfaces, such as buttons, menus, and sections; Besides, information is provided regarding how these visual components affect certain usability interactions such as learnability, efficiency, memorability, and usefulness.
Job Analyses of Earth Science Data Librarians and Data Managers

Abstract This study’s purpose is to capture the skills of Earth science data managers and librarians through interviews with current job holders. Job analysis interviews were conducted of 14 participants—six librarians and eight data managers—to assess the types and frequencies of job tasks. Participants identified tasks related to communication, including collaboration, teaching, and project management activities. Data-specific tasks included data discovery, processing, and curation, which require an understanding of the data, technology, and information infrastructures to support data use, reuse, and preservation. Most respondents had formal science education and six had a master’s degree in Library and Information Sciences. Most of the knowledge, skills, and abilities for these workers were acquired through on-the-job experience, but future professionals in these careers may benefit from tailored education informed through job analyses.
Lesamnta-LW Revisited: Improved Security Analysis of Primitive and New PRF Mode

In this paper we revisit the design of the Lesamnta-LW lightweight hash function, specified in ISO/IEC 29192-5:2016. Firstly, we present some updates on the bounds of the number of active S-boxes for the underlying cipher consisting of 64 rounds. The previous work showed that at least 24 active S-boxes are ensured after 24 rounds, while our tool based on Mixed Integer Linear Programming (MILP) in the framework of Mouha et al. shows that only 18 rounds are sufficient to ensure 24 active S-boxes. The tool can evaluate the tight bound of the number of active S-boxes for more rounds, which shows that 103 active S-boxes are ensured after full (64) rounds. We also provide security analysis of the Shuffle operation in the round function. Secondly, we propose a new mode for building a pseudo-random function (PRF) based on Lesamnta-LW. The previous PRF modes can only process 128 bits per block-cipher call, while the new mode can process 256 bits to achieve the double throughput. We prove its security both in the standard model and the ideal cipher model.
MIMO Neural Models for a Twin-Rotor Platform: Comparison Between Mathematical Simulations and Real Experiments

This work presents a neural model developed for a multivariable system with complex nonlinear dynamics, obtained through a tight methodology used both in simulation and in the real platform. In addition, this neural model has been studied and designed to meet the requirements of a predictive control strategy. A Twin-Rotor platform is used as an example of a Multi-Input Multi-Output (MIMO) system to study and analyse how a neural network is able to reproduce its nonlinear coupled dynamics and accurately estimate future system outputs. An in-depth study of the neural structures and their performance in the prediction of future states has been developed. Results show with comparisons, the modelization inaccuracies that appears when a proposal made just on the basis of a mathematical simulation is used to conclude the good performance of these MIMO neural models.
CBIR Systems: Techniques and Challenges

The vigorous growth and continual intelligibility of visual information on the cyberspace have led to the richness of research projects in image search and retrieval. With the lack of concern on the visual contents of an image, the text based search techniques for image retrieval often suffers variability between the text words and visual content. Hence Content Based Image Retrieval (CBIR) has achieved a great appeal as it models the perceptual content of an image to identify similar relevant images. This paper aims at discussing the evolution of retrieval techniques, paying particular attention on development, merits, demerits and challenges of the image retrieval. It focuses on reviewing the already existing techniques and its related issues. The enormous development of image data necessitates the need for the expansion of research and helps in the progress of efficient and accurate image retrieval. Also, CBIR maintains a sustained growth in the research field.
ESP corpus design: compilation of the Veterinary Nursing Medical Chart Corpus and the Veterinary Nursing Wordlist

This paper reports on two research results: ( 1) designing an English for Specific Purposes (esp) corpus architecture complete with annotations structured by regular expressions; and ( 2) a case study to test the design to cater for creating a specific vocabulary list using the compiled corpus. The first half of this study involved designing a precisely structured esp corpus from 190 veterinary medical charts with a hierarchy of the data. The data hierarchy in the corpus consists of document types, outline elements and inline elements, such as species and breed. Perl scripts extracted the data attached to veterinary-specific categories, and the extraction led to creating wordlists. The second part of the research tested the corpus mode, creating a list of commonly observed lexical items in veterinary medicine. The coverage rate of the wordlists by General Service List (gsl) and Academic Word List (awl) was tested, with the result that 66.4 percent of all lexical items appeared in gsl and awl, whereas 33.7 percent appeared in none of those lists. The corpus compilation procedures as well as the annotation scheme introduced in this study enable the compilation of specific corpora with explicit annotations, allowing teachers to have access to data required for creating esp classroom materials.
An Efficient Design for Testability Approach of Reversible Logic Circuits

Reversible circuits have been extensively investigated because of their applications in areas of quantum computing or low-power design. A reversible circuit is composed of only reversible gates and allow computations from primary inputs to primary outputs and vice-versa. In the last decades, synthesis of reversible circuits received significant interest. Additionally, testing of these kinds of circuits has been studied which included different fault models and test approaches dedicated for reversible circuits only. The analysis of testability issues in a reversible circuit commonly involves the detection of the missing gate faults that may occur during the physical realizations of the reversible gates. In this paper, we propose a design for testability (DFT) technique for reversible circuits in which the gates of a circuit are clustered into different sets and the gates from each cluster are then connected to an additional input line where, the additional line acts as an extra control input to the corresponding gate. Such arrangement makes it possible to achieve [Formula: see text] fault detection in any reversible circuit with a small increase in quantum cost. Experimental evaluations confirm that the proposed DFT technique incurs less quantum cost overhead with [Formula: see text] fault detection compared to existing DFT techniques for reversible circuits.
Analysis of enterprise site selection and R&amp;D innovation policy based on BP neural network and GIS system

The traditional spatial optimization location solution is difficult to solve the space optimization location problem under the condition of large data volume. However, GIS has the advantage of analyzing and processing spatial data, which can effectively compensate for this defect. In this paper, we analyze the enterprise site selection and R&amp;D innovation policy based on BP neural network and GIS system. As a tool for the government to guide, encourage, support and adjust innovation activities and application of achievements, science and technology policy can provide new support for the development of innovation by improving the industrial chain and innovating the industrial structure. Moreover, the quantitative analysis of the entropy weight method and the qualitative analysis of the AHP method are combined to analyze a number of influencing factors. Based on this, the overlay of various factors is further analyzed, and the maximum eigenvalues of the target layer and the criterion layer and the weights of each index are calculated using MATLAB tools. Therefore, according to the different characteristics of different periods and different fields, the government should formulate science and technology innovation policies to improve the specificity and applicability of the policies.