Reviewerly/exHarmony huggingface dataset:
Advantages of a low coherence interferometer for optical testing

A new interferometer has been developed using an engineered low coherence LED illumination system, where both the spectral and spatial coherence are tailored to allow single surface interference from plane parallel transparent optical components such as optical windows, glass wafers, glass computer disk substrates, etc. without the need to paint the rear surface to suppress interference from that surface. Only the reflection from a surface at a specific optical path difference can interfere with the reference beam eliminating spurious interference. While the interference region due to spectral coherence is engineered to provide interference within a 280 micron zone, diffraction at surface defects and dust within this zone produces secondary wavefronts which can interfere with the test and reference wavefronts, producing phase artifacts which can limit measurement accuracy in standard coherent laser-based interferometers; these phase errors are eliminated by using a spatially extended LED source. For this low coherence interferometer, the “purity” of interference between the test surface and a high quality calibrated reference surface, along with high spatial resolution phase measurements, offers the ability to review waviness and surface polish variations to sub-nanometer height resolutions over a large 4 inch field-of-view. Examples of polish process variations of optical components, along with power spectral density plots (PSD) are shown where the data were acquired with OptoFlat, a downward-looking LED-based low coherence interferometer newly developed for the rapid measurement of precision polished flat surfaces including thin transparent optical components without special surface preparation.
Earthquake Sensor Based on Surface Water Wave in Cylindrical Container

The purposes of the study are to create a seismometer from water surface wave to solve the problem of the spring constant varying and friction of fixpoint pendulum, and to reduce the import seismometer. The operation of the machine uses the light from the LED illumination to the surface of the water that is vibrating due to the force of the earthquake and get the reflected ray back to the receiver (LDR), which will change the light into an electrical signal. The electrical signal will be sent to the portable computer via a sound card, and it is displayed on the screen by the MATLAB program. All the equipment will be assembled inside the PVC pipe that contains the water inside. The Electrical signal as mention above is recorded and the difference of p wave and s wave traveling time is determined. Those are the variables that are used to figure out earthquake magnitude. The system testing consists of 2 steps: 1) a graph showing on the computer screen every time 2.) earthquake magnitude calculated and displayed on the computer screen. From an experimental result, it showed that this technique can detect an earthquake event with R=0.95.
Structuralizing Disaster-scene Data through Auto-captioning

Disaster-scene images documenting the magnitude and effects of natural disasters nowadays can be easily collected through crowdsourcing aided by mobile technologies (e.g., smartphones or drones). One challenging issue that confronts the first-responders who desire the use of such data is the non-structured nature of these crowdsourced images. Among other techniques, one natural way is to structuralize disaster-scene images through captioning. Through captioning, their imagery contents are augmented by descriptive captions that further enable more effective search and query (S&Q). This work presents a preliminary test by exploiting an end-to-end deep learning framework with a linked CNN-LSTM architecture. Demonstration of the results and quantitative evaluation are presented that showcase the validity of the proposed concept.
Class Similarity Based Orthogonal Neighborhood Preserving Projections for Image Recognition

Recent Dimensionality reduction methods like Locality Preserving Projections and Neighborhood Preserving Projections learn local neighborhood characteristics and try to preserve these characteristics in the lower dimensional space. In supervised settings, conventional Orthogonal Neighborhood Preserving Projections (ONPP) uses knowledge of class label to identify the neighbors of data points. When data points are closely placed or the classes are overlapping, such hard decision rule may not help finding a good low dimensional representation. To overcome this limitation, we are proposing a novel neighborhood selection rule, where low dimensional representation is used with Logistic Regression to find the probability of every data point to belong into a particular class. Based on these probabilities a new distance measure - Class Similarity based distance is used to define a local neighborhood of data points. It is observed that class similarity based ONPP very well represents the relationship of neighbors in low dimensions. The proposed scheme is used to recognize face images and handwritten numerals images. The proposed Class Similarity based neighborhood scheme achieves same recognition performance with significantly less number of subspace dimensions.
Compound Dirichlet Processes

The compound Poisson process and the Dirichlet process are the pillar structures of renewal theory and Bayesian nonparametric theory, respectively. Both processes have many useful extensions to fulfill the practitioners’ needs to model the particularities of data structures. Accordingly, in this contribution, we join their primal ideas to construct the compound Dirichlet process and the compound Dirichlet process mixture. As a consequence, these new processes have a rich structure to model the time occurrence among events, with also a flexible structure on the arrival variables. These models have a direct Bayesian interpretation of their posterior estimators and are easy to implement. We obtain expressions of posterior distribution, nonconditional distribution, and expected values. In particular, to find these formulas, we analyze sums of random variables with Dirichlet process priors. We assess our approach by applying our model on a real data example of a contagious zoonotic disease.
Remote Two–Qubit Entanglement by Joint Homodyne Detection of Fluorescence

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text P. Lewalle, C. Elouard, S. K. Manikandan, X. –F. Qian, J. H. Eberly, and A. N. Jordan, "Remote Two–Qubit Entanglement by Joint Homodyne Detection of Fluorescence," in Rochester Conference on Coherence and Quantum Optics (CQO-11), OSA Technical Digest (Optica Publishing Group, 2019), paper W6A.22. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
Classification of Acute Leukemia Based on Multilayer Perceptron

Abstract In general, various artificial neural network have been applied in many areas such as modelling, pattern recognition, signal processing, diagnostic and prognostic. In this paper, artificial neural network are used to detect and classify the white blood cell (WBC) inside the acute leukemia blood samples. There are 25 features have been extracted from segmented WBC, which consist of shape, color and texture based features. Then, it have been fed up as the neural network inputs for the classification process in order to classify the segmented regions into two classes either B or T. The training algorithm for MLP network is Levenberg-Marquardt (LM). The MLP network achieves the highest testing accuracy of 96.99% for 4 hidden nodes at state of 5 by using the overall 25 input features. Thus, MLP network trained by using LM algorithm is suitable for acute leukemia cells detection in blood sample.
Digital mind maps in teaching materials science at the university level

Mind maps increase attractiveness of classes and motivation of students to create their own associations and stimulate them to expand the knowledge acquired during the course. The aim of the work was to review information technology (IT) tools for building digital mind maps in terms of their use for teaching materials at the university level and to examine students’ opinions on their use as a tool to support the acquisition of knowledge. The results of questionnaire surveys among university students from two different non-technical majors confirmed research hypotheses about the increase in student acceptance for introducing new educational techniques.
Robust Optical Flow Estimation Using the Monocular Epipolar Geometry

The estimation of optical flow in cases of illumination change, sparsely-textured regions or fast moving objects is a challenging problem. In this paper, we analyze the use of a texture constancy constraint based on local descriptors (i.e., HOG) integrated with the monocular epipolar geometry to estimate robustly optical flow. The framework is implemented in differential data fidelities using a total variation model in a multi-resolution scheme. Besides, we propose an effective method to refine the fundamental matrix along with the estimation of the optical flow. Experimental results based on the challenging KITTI dataset show that the integration of texture constancy constraint with the monocular epipolar line constraint and the enhancement of the fundamental matrix significantly increases the accuracy of the estimated optical flow. Furthermore, a comparison with existing state-of-the-art approaches shows better performance for the proposed approach.
A publicly verifiable watermarking scheme based on quantum chaos and DWT–DCT

One of the problems faced by the internet consumer in today’s data age is protection of digital rights or ownership of intellectual property (IP) due to multi-fold sharing of data without consent. As a result, anti-piracy, a domain of robust IP protection has become a cardinal subject of research in system design. In this regards, watermarking has emerged as one of the best protection of authorship since it provides protection against tampering without any perceivable change in the multimedia content. This research paper focuses on developing a robust watermarking scheme. The proposed approach employs quantum chaos based encryption algorithm having low time complexity and optimized entropy values thereby reducing the chances of statistical attacks. In addition, SHA-3 is used for creating the digest as it affords easier hardware implementation and high security. DCT–DWT hybrid model is used for embedding watermark to further enhance robustness and RC4 with quantum chaos coupled with neighbouring coupled map lattice sequence as key for allocating random position to watermark pixels. The scheme provides improved results against existing noise based geometric attacks, universal image quality index and visual assessments in comparison to other mechanism in literature.
Mathematical argumentation in the classroom

Abstract The article shares some elements of comprehensive type about “mathematical argumentation in the classroom”; whose analysis, was made from two fundamental categories in the development of an oral mathematical argumentation process for the conviction, contradiction and validation of a written mathematical argumentation process. The research addressed two central categories of argumentation as a discursive form, the first one is the epistemic position, and the second one is the discursive position that students unveil at the time of mathematically arguing the solution to a problem situation. The research was developed under the interpretative paradigm through the design of a case study directed by the theory and technique of a focal group, for the collection of information. In the findings, difficulties in the passage were evidenced from the semantic to the theoretical from the epistemic position; regarding the discursive position, the presence of three discursive forms was revealed: description, explanation and argumentation, the latter being the least used by the students.
Low Resolution Person Re-identification by an Adaptive Dual-Branch Network

Low-resolution person re-identification (LR-REID) refers to matching cross-view pedestrians from varying resolutions, which is common and challenging for realistic surveillance systems. The LR-REID is largely under-study and the performance of the existing methods drops dramatically in LR domain. In this paper, we propose a novel adaptive dual-branch network (ADBNet) to recover HR pedestrians and learn discriminative resolution-robust representations. The ADBNet employs a two-branch structure to obtain HR images, of which the SR-branch adopts the reconstruction loss to get the global image and the GAN-branch employs the generative loss to generate sharp details. These branches are combined into a universe model and the combination weights are adaptively learned. Furthermore, we propose a relative loss to guarantee shaper edges of the output results. Experimental results on two large-scale benchmarks prove the validity of the proposed approach.
Rest and Soap Comparison on Web Service Technology for Android Based Data Services

Comparison between ReST API and SOAP web services in providing data services that are carried out to support the improvement of these web services. The data tested is divided into 2, namely data with the same size format and data with different size formats. The test is divided into 3 stages, the first collecting with 10 data, the second 20 data, and the third 30 data, by carrying out data loading from each comparing web service. From the overall results obtained from the ReST API runs faster than SOAP. With an overall total data collection time on different devices with the same size format in the ReST API it produces 3.4 seconds and SOAP produces 3.9 seconds. While receiving data on different devices with different size formats on the ReST API, it takes 4.7 seconds and SOAP takes 5.3 seconds.
Comparison of K-Nearest Neighbour and support vector machine for choosing senior high school

Abstract The aim of this research is to compare K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) algorithm which are used for choosing senior high school recommendation. As we know that education is an important aspect in the development of a nation. The methodology that used in this research is data mining through Knowledge Discovery in Database (KDD) stages, which consist of cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge presentation. KNN and SVM are the most common algorithms used in data mining and decision support system. Either KNN or SVM in this research used for classifying the type of senior high school by input parameters, among others national examination score, student interest, and counsellor suggestion. Based on the experiment with several training and testing data, the result shows that SVM is better than KNN. SVM has an accuracy value around 97.1%, while KNN has an accuracy value around 88.5%. And also, SVM has processing time faster than KNN.
A Byte Pattern Based Method for File Compression

This research presents a method to allows the data compression from a file containing any type of information by combining the pattern theory with the theory of data compression. This proposal can reduce the storage space of a file data from any kind of computer, platform or operating system installed on that computer. According to the fundamentals of patterns, a pattern is a regularity of bytes contained within a file with self-similarity characteristics; if this concept applies to data files, we find certain amounts of auto-similar or patterns repeated several times throughout the file; with a store data representation and being referenced, at a certain point data can be recovered from the original file without losing a single data, and consequently saving space on the hard disk. In the search for various ways to compress data, led me to analyze and implement the proposed methodology in a beta mode compression software for Windows 10, which presents very compromising results.
Use of prediction based meter reputation factors in power systems

Reputation systems provide a protocol for participants to interact based on their past performance. The concept of a prediction based meter reputation factor is introduced as a number between 0.1 and 1 that is assigned to every meter and that varies based on the accuracy of a meter's predictions. A system architecture is presented that allows the instantiation of rules for economic interaction between metered participants in a power system using reputation factors. This will create a system in which individuals are incentivised to provide accurate predictions, giving planners more reliable information. It also provides a basis for the allocation of rewards for flexibility and penalties for inflexibility. Two algorithms to allocate meter reputation factors are presented and assessed using a defined performance index and metering information from the OpenLV project. It is demonstrated that the performance of the meter reputation algorithms can be moderated according to system requirements. It is concluded that instantiation of the algorithms in such a way that makes persecution of individuals impossible is crucial.
The Hough Transform and the Impact of Chronic Leukemia on the Compact Bone Tissue from CT-Images Analysis

Computational analysis of X-ray Computed Tomography (CT) images allows the assessment of alteration of bone structure in adult patients with Advanced Chronic Lymphocytic Leukemia (ACLL), and may even offer a powerful tool to assess the development of the disease (prognostic potential). The crucial requirement for this kind of analysis is the application of a pattern recognition method able to accurately segment the intra-bone space in clinical CT images of the human skeleton. Our purpose is to show how this task can be accomplished by a procedure based on the use of the Hough transform technique for special families of algebraic curves. The dataset used for this study is composed of sixteen subjects including eight control subjects, one ACLL survivor, and seven ACLL victims. We apply the Hough transform approach to the set of CT images of appendicular bones for detecting the compact and trabecular bone contours by using ellipses, and we use the computed semi-axes values to infer information on bone alterations in the population affected by ACLL. The effectiveness of this method is proved against ground truth comparison. We show that features depending on the semi-axes values detect a statistically significant difference between the class of control subjects plus the ACLL survivor and the class of ACLL victims.
A Key Update Scheme for Side-Channel Attack Mitigation

As two typical representatives of Side-Channel Attack (SCA), power analysis attack and electromagnetic attack show a very high efficiency to extract cryptographic keys and other secret information from the device non-invasively. [1]
Study on an Argumentation-Based Negotiation in Human-Computer Negotiation Service

With the rapid development of e-commerceE-commerce , the automatic transaction has been a potential demand of enterprises. Concentrating on B2C e-commerce scenario, this paper designs a human-computer negotiation model and algorithm, through which an agentAgent can negotiate with a human using natural language. To validate the model and algorithm, we conducted a between-group experiment based on a prototype system comparing the negotiation effect between the groups with and without arguments. The experimental results show that adding arguments into price negotiation can significantly increase the success rate of the human-computer negotiation. Moreover, there is a significant positive impact on the buyer’s subjective feelings on system using, as well as the seller agent’sAgent economic utility, so that it can finally help both sides to reach a win-win situation in the negotiation. The contribution of our study can apply to B2C e-commerceE-commerce platforms for improving the performance of their intelligent customer service agent. Our study is also meaningful for helping the two parties to increase their trading efficiency and decrease their trading cost.
A Comparison of Computational Intelligence Techniques for Real-Time Discrete Multivariate Time Series Classification of Conducting Gestures

Gesture classification is a computational process that can identify and classify human gestures. More specifically, gesture classification is often a discrete multivariate time series classification problem and various computational intelligence solutions have been developed for these problems. It is difficult to determine which existing techniques and approaches to algorithms will produce the most effective solutions for discrete multivariate time series classification problems. In this study, we compare twelve different classification algorithms to report which techniques and approaches are most effective for recognizing conducting beat pattern gestures. After performing 10-fold cross-validation tests on twelve commonly used algorithms, the results show that of the algorithms tested, the most accurate were RNN, LSTM, and DTW; all of which had an accuracy of 100%. We found that in general, algorithms which can take in a dynamic sequence input and classification algorithms that are discriminative performed consistently well, while their counterparts varied in performance. From these results we determine that when selecting a computational intelligence technique to solve these classification problems, it would be advantageous to consider the top performing algorithms along with furthering research into new dynamic input and discriminative algorithms.
Driving-Condition Dependent and Monte Carlo Simulation-Based Optimization Method for a Bayesian Localization Filter for Parking

In this article, a method for the automated adaptive tuning of a Bayesian localization filter is investigated, since the tuning parameters were often only empirically determined and constantly set. The method is applied to a previously developed Bayesian localization filter called Odometry 2.0 estimator. Its architecture makes it feasible to individually combine different dead-reckoning models because the input is redundant. However, under different driving-conditions (DCs), in particular when parking, the models have individual advantages and disadvantages. Therefore, different cluster analysis methods are examined in order to automatically divide a parking maneuver into different useful segments and to enable dc-dependent optimization. In the next step these segments and statistical error and coefficient models serve as input for a Monte Carlo Simulation (MCS). The error model provides potential sensor errors to ensure robust filter tuning and the coefficient model provides randomly found filter settings in the large parameter space. Finally, the MCS results and ground truth data are used for the optimization to find the filter tuning with minor model errors. The application of the so found adaptive tuning setting shows a significant increase in the robustness of the localization filter.
<i>Mineralogical Record</i> Turns Fifty: The Early Years

It is true what our elders have said: The older you get, the quicker time goes by. I can hardly believe that fifty years have passed, and for all of those fifty years, the Mineralogical Record (MR)...