Reviewerly/exHarmony huggingface dataset:
A hybrid feature selection on AIRS method for identifying breast cancer diseases

Breast cancer may cause a death due to the late diagnosis. A cheap and accurate tool for early detection of this disease is essential to prevent fatal incidence. In general, the cheap and less invasive method to diagnose the disease could be done by biopsy using fine needle aspirates from breast tissue. However, rapid and accurate identification of the cancer cell pattern from the cell biopsy is still challenging task. This diagnostic tool can be developed using machine learning as a classification problem. The performance of the classifier depends on the interrelationship between sample sizes, some features, and classifier complexity. Thus, the removal of some irrelevant features may increase classification accuracy. In this study, a new hybrid feature selection fast correlation based feature (FCBF) and information gain (IG) was used to select features on identifying breast cancer using AIRS algorithm. The results of 10 times the crossing (CF) of our validation on various AIRS seeds indicate that the proposed method can achieve the best performance with accuracy =0.9797 and AUC=0.9777 at k=6 and seed=50.
Robust Collective Classification against Structural Attacks.

Collective learning methods exploit relations among data points to enhance classification performance. However, such relations, represented as edges in the underlying graphical model, expose an extra attack surface to the adversaries. We study adversarial robustness of an important class of such graphical models, Associative Markov Networks (AMN), to structural attacks, where an attacker can modify the graph structure at test time. We formulate the task of learning a robust AMN classifier as a bi-level program, where the inner problem is a challenging non-linear integer program that computes optimal structural changes to the AMN. To address this technical challenge, we first relax the attacker problem, and then use duality to obtain a convex quadratic upper bound for the robust AMN problem. We then prove a bound on the quality of the resulting approximately optimal solutions, and experimentally demonstrate the efficacy of our approach. Finally, we apply our approach in a transductive learning setting, and show that robust AMN is much more robust than state-of-the-art deep learning methods, while sacrificing little in accuracy on non-adversarial data.
The Comparison of Imbalanced Data Handling Method in Software Defect Prediction

Software testing is a crucial process in software development life cycle which will affect the software quality. However, testing is a tedious task and resource consuming. Software testing can be conducted more efficiently by focusing this activitiy to software modules which is prone to defect. Therefore, an automated software defect prediction is needed. This research implemented Extreme Learning Machine (ELM) as classification algorithm because of its simplicity in training process and good generalization performance. Aside classification algorithm, the most important problem need to be addressed is imbalanced data between samples of positive class (prone to defect) and negative class. Such imbalance problem could bias the performance of classifier. Therefore, this research compared some approaches to handle imbalance problem between SMOTE (resampling method) and weighted-ELM (algorithm-level method).The results of experiment using 10-fold cross validation on NASA MDP dataset show that including imbalance problem handling in building software defect prediction model is able to increase the specificity and g-mean of model. When the value of imbalance ratio is not very small, the SMOTE is better than weighted-ELM. Otherwise, weighted-ELM is better than SMOTE in term of sensitivity and g-mean, but worse in term of specificity and accuracy.Software testing is a crucial process in software development life cycle which will affect the software quality. However, testing is a tedious task and resource consuming. Software testing can be conducted more efficiently by focusing this activitiy to software modules which is prone to defect. Therefore, an automated software defect prediction is needed. This research implemented Extreme Learning Machine (ELM) as classification algorithm because of its simplicity in training process and good generalization performance. Aside classification algorithm, the most important problem need to be addressed is imbalanced data between samples of positive class (prone to defect) and negative class. Such imbalance problem could bias the performance of classifier. Therefore, this research compared some approaches to handle imbalance problem between SMOTE (resampling method) and weighted-ELM (algorithm-level method).The results of experiment using 10-fold cross validation on NASA MDP dataset show that including imbalance problem handling in building software defect prediction model is able to increase the specificity and g-mean of model. When the value of imbalance ratio is not very small, the SMOTE is better than weighted-ELM. Otherwise, weighted-ELM is better than SMOTE in term of sensitivity and g-mean, but worse in term of specificity and accuracy.
Distributed Injection-Locking in Analog Ising Machines to Solve Combinatorial Optimizations

The oscillator-based Ising machine (OIM) is a network of coupled CMOS oscillators that solves combinatorial optimization problems. In this paper, the distribution of the injection-locking oscillations throughout the circuit is proposed to accelerate the phase-locking of the OIM. The implications of the proposed technique theoretically investigated and verified by extensive simulations in EDA tools with a 130 nm PTM model. By distributing the injective signal of the super-harmonic oscillator, the speed is increased by 219.8% with negligible increase in the power dissipation and phase-locking error of the device due to the distributed technique.
A Lagrangian Approach to Information Propagation in Graph Neural Networks.

In many real world applications, data are characterized by a complex structure, that can be naturally encoded as a graph. In the last years, the popularity of deep learning techniques has renewed the interest in neural models able to process complex patterns. In particular, inspired by the Graph Neural Network (GNN) model, different architectures have been proposed to extend the original GNN scheme. GNNs exploit a set of state variables, each assigned to a graph node, and a diffusion mechanism of the states among neighbor nodes, to implement an iterative procedure to compute the fixed point of the (learnable) state transition function. In this paper, we propose a novel approach to the state computation and the learning algorithm for GNNs, based on a constraint optimisation task solved in the Lagrangian framework. The state convergence procedure is implicitly expressed by the constraint satisfaction mechanism and does not require a separate iterative phase for each epoch of the learning procedure. In fact, the computational structure is based on the search for saddle points of the Lagrangian in the adjoint space composed of weights, neural outputs (node states), and Lagrange multipliers. The proposed approach is compared experimentally with other popular models for processing graphs.
Digital rock image inpanting using GANs

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Digital rock image inpanting using GANsAuthors: Yong Zheng OngNan YouYunyue Elita LiHaizhao YangYong Zheng OngNational University of SingaporeSearch for more papers by this author, Nan YouNational University of SingaporeSearch for more papers by this author, Yunyue Elita LiNational University of SingaporeSearch for more papers by this author, and Haizhao YangPurdue UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3427515.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThis extended abstract explores the problem of reconstructing 3D μCT samples by filling in ”gaps” between parallel 2D μCT of isotropic rock samples, which reduce technical cost of performing full 3D scans of 3D rock samples. We propose a novel inpainting technique which exploits the isotropic property of such 3D rocks by considering 2D μCT from a different perspective from the given 2D scans. We then employ state of the art invert GAN and AE techniques to produce good inpainting results to reconstruct the 3D sample from the concatenation of inpainted 2D samples. Empirical evidence is presented to show how our proposed method can produce accurate 2D inpainting samples.Presentation Date: Tuesday, October 13, 2020Session Start Time: 9:20 AMPresentation Time: 11:00 AMLocation: Poster Station 7Presentation Type: PosterKeywords: sandstone, neural networks, imaging, 3D, reconstructionPermalink: https://doi.org/10.1190/segam2020-3427515.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Yong Zheng Ong, Nan You, Yunyue Elita Li, and Haizhao Yang, (2020), "Digital rock image inpanting using GANs," SEG Technical Program Expanded Abstracts : 2525-2529. https://doi.org/10.1190/segam2020-3427515.1 Plain-Language Summary Keywordssandstoneneural networksimaging3DreconstructionPDF DownloadLoading ...
An Exact Quantum Principal Component Analysis Algorithm Based on Quantum Singular Value Threshold

Quantum principal component analysis (qPCA) is the quantum version of principal component analysis (PCA). In this paper, based on the quantum singular value threshold (qSVT), we propose an exact quantum principal component analysis algorithm, which screens the data components through the threshold, rather than output all components of data. Compared with other improved qPCA algorithms, our proposed algorithm does not require to adjust the parameters to obtain estimated results. Instead, it yields exact results directly, and the quantum circuit designed is simpler because almost half of the quantum gates are reduced. We implemented our qPCA algorithm on the IBM quantum computing platform: IBM Quantum Experience, and the experimental results verified correctness of our algorithm.
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models.

Expectation-Maximization (EM) is a prominent approach for parameter estimation of hidden (aka latent) variable models. Given the full batch of data, EM forms an upper-bound of the negative log-likelihood of the at each iteration and updates to the minimizer of this upper-bound. We first provide a model level interpretation of the EM upper-bound as sum of relative entropy divergences to a set of singleton models, induced by the set of observations. Our alternative motivation unifies the observation level and the model level view of the EM. As a result, we formulate an online version of the EM algorithm by adding an analogous inertia term which corresponds to the relative entropy divergence to the old model. Our motivation is more widely applicable than the previous approaches and leads to simple online updates for mixture of exponential distributions, hidden Markov models, and the first known online update for Kalman filters. Additionally, the finite sample form of the inertia term lets us derive online updates when there is no closed-form solution. Finally, we extend the analysis to the distributed setting where we motivate a systematic way of combining multiple hidden variable models. Experimentally, we validate the results on synthetic as well as real-world datasets.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS KOMIK PADA KONSTRUKSI BANGUNAN 1

The purpose of this research is to develop learning specifically on digital comic media, State University of Jakarta. The rmethod non-test research instruments in the form of student needs analysis questionnaires, feasibility questionnaires to media experts and material experts as well as test questionnaires to lecturers and students or questionnaires. Then at the stage of research and information gathering a needs analysis survey was conducted of students and students about the development of comic learning media to determine the needs and constraints faced by students. The results of the assessment in the form of digital comic product designs amounted to 90% (very good). The number of final average aspects and indicators of the characteristics of learning media amounted to 91.3% (very good / decent). The assessment of the material presented in digital comics media is considered good with results reaching 73%. The results of the product feasibility assessment assessed by 20 students from the Building Engineering Education Study Program were obtained at 91% (very good). Then the material validation and media on digital comic products developed were obtained an average score of 82.33% and included in the excellent category. Therefore the product can be said to be feasible to use.&#x0D; &#x0D; Keywords: Learning Media, Digital Comics, Building Construction.
Routing on quantum repeater networks

The design of large-scale quantum networks and any future quantum internet will rely on quantum repeaters and how quantum information flows through it. Tasks performed on such networks will go well beyond quantum key distribution and are likely to include quantum remote sensing and distributed quantum computation. In this presentation we will explore the various ways that such networks could be designed to support those advanced tasks. Critical to this will be quantum routing which we should is highly dependent on the repeater architecture. We introduce a quantum quality of service to help characterize the systems performance and shows how it leads interesting network and routing behavior.
Empirical Analysis for English Teaching Integration and Optimization Based on Big Data Mining Technology

This article takes English teaching behavior analysis as a breakthrough point, combines big data mining technology, optimizes English teaching plan analysis methods, and uses data mining methods on the basis of inductive analysis to compare the K nearest neighbour algorithm and the support vector machine algorithm. The algorithm is combined with the support vector machine algorithm to obtain the advantages and disadvantages of the new algorithm. A classification prediction model is built. Through the built prediction model, the prediction of English teaching integration is optimized. The classifier's prediction results for accuracy, precision, and recall are 89.63%, 90.63%, and 71.01% respectively. This can provide a basis for educators to optimize decision-making and optimize teaching methods.
Conversational Frames

This paper extends the previous work of Almousa and Migdady by proposing and implementing three models to improve the correctness and naturality of the given answers of smart personal assistants (SPAs). The motivation behind the three proposed models is the failure of some well-known existing SPAs (Siri and Salma) to extract contextual information from a conversation. The authors call this kind of information: the conversational frame. For evaluating the suggested models, the authors also implement an abstraction of the existing SPAs that they call non-framed model. To evaluate the four implementations, 36 respondents answered an on-line questionnaire after watching four videos that the authors recorded per implementation. The authors statistically prove that their three suggested models precede the non-framed model. The researchers attribute this to the fact that our models overcome the shortcoming present in non-framed model. Moreover, their model that implements the conversational frame precedes all other models.
Riesz transform for fringes pattern analysis: advantages and limitations

Fringe pattern analysis is an essential step in optical techniques including the digital speckle pattern interferometry (DSPI), digital speckle shearing interferometry (DSPSI), digital holographic interferometry (DHI), moire interferometry, and others. This step enables to evaluate the coded phase distribution related to a physical magnitude of the material under study such as deformation, displacement, refractive index, strain, temperature. Several methods have been proposed for the extraction of phase distribution such as phase-shifting techniques and other transform-based methods like Fourier, Hilbert, and wavelet transforms. In phase-shifting techniques, the intensity is sampled spatially or temporally, and the object should be stable during the acquisition of at least three frames. So, this technique is not suitable for the analysis of dynamic events. Recently, Riesz transform, two-dimensional extension of the Hilbert transform, has been exploited in several works including the phase evaluation. In this work, we present a variety of methods based on the Riesz transform for fringe pattern analysis. The analysis concerns the extraction of the encoded phase distribution from the recorded/processes fringe patterns obtained from the interferometric techniques and their horizontal and vertical phase derivatives. Using numerical simulation, we study the performance of these Riesz transform-based methods with a quantitative appraisal, and finally, the experimental application will be presented. The advantages and limitations of the Riesz transform-based methods will be discussed.
A recursive approach to long-term prediction of monthly precipitation using genetic programming: case of the Three-River Headwaters Region

&amp;lt;p&amp;gt;This study develops a recursive approach to long-term prediction of monthly precipitation using genetic programming (GP), and the study area is the Three-River Headwaters Region (TRHR) in China. The daily precipitation data recorded at 29 meteorological stations during 1961-2014 are collected, among which the data during 1961-2000 are used for calibration and the remaining data are for validation. To develop this approach, first, the preliminary estimations of annual precipitation are computed based on a statistical method. Second, the percentage of the monthly precipitation for each month of a year is calculated as the mean monthly precipitation divided by the mean annual precipitation during the study period, and then the preliminary estimation of monthly precipitation for each month of a year is obtained. Third, GP is adopted to improve the preliminary estimations through establishing the relationship of the observations with the preliminary estimations at the past and current times. The calibration and validation results reveal that the recursive approach involving GP can provide the more accurate predictions of monthly precipitation. Finally, this approach is used to predict the monthly precipitation over the TRHR till 2050.&amp;lt;/p&amp;gt;
Insider Collusion Attack on Distributed Machine Learning System and its Solutions - A Case of SVM

Among several known data breaching problems, insider collusion attacks have been one of the fastest-growing threats. However, there are very few insider-related researches involving the distribution of kernel-based data mining, such as distributed Support Vector Machine (SVM). Prior works often fall short by addressing a multifactorial model that is more limited in scope and implementation than addressing "insiders within an organization" colluding with outsiders. In this talk, I will introduce a novel insider collusion attack, in which a faulty system allows collusion to go unnoticed when an insider shares data with an outsider, who can then recover the original data from message transmissions (intermediary kernel values) among organizations. This attack requires only accessibility to a few data entries within the organizations rather than requiring the encrypted administrative privileges typically found in the distribution of data mining scenarios. I will also provide the analysis about the minimum amount of insider data necessary to launch the insider attack. Finally, I will discuss several proposed privacy-preserving schemes, like Differential Privacy, Homomorphic Proxy Re-Encryption, and etc., to counter the described attack.
Identification of Parasite Presence on Thin Blood Splotch Images

Parasite is a bacterium that lives in a separate organism that functions as a host known as plasmodium.The parasite is vulnerable to malaria, dengue, typhoid diseases, etc.The presence of the parasite in blood smears can often lead to human death for some times.So, detecting and recognizing the parasite in blood splotch images at the early stages is very important to save human life. Aim and Objective:The primary consideration in this article is to detect the parasite which occurs in red blood cells through blood splotch images in early stages in less time using a new image processing method.Method: The method which is followed identifies the presence of parasite on blood smear images, was done in several steps.The first step of the method is to collect the input image from a laboratory taken through an electronic microscope.Then the image is further sent by converting the input image to the grayscale image using the standard method.Once the grayscale is obtained, the output image is further converted to the monochrome image.The pixel values of the image consist of only binary values using the "Otsu Threshold form."Then this monochrome image is converted to a matrix model and printed with the binary values. Conclusion:The presence of parasites on the images will be displayed with the binary values by either one or zero on the output matrix model.Suppose the entire image is displayed as all zeros, In that case, it can be concluded to no parasite presence, and if any one's presence on the matrix model, then it can be observed that there is a presence of parasite on the blood smear blotch images.
Data Anonymization through Collaborative Multi-view Microaggregation

Abstract The interest in data anonymization is exponentially growing, motivated by the will of the governments to open their data. The main challenge of data anonymization is to find a balance between data utility and the amount of disclosure risk. One of the most known frameworks of data anonymization is k -anonymity, this method assumes that a dataset is anonymous if and only if for each element of the dataset, there exist at least k − 1 elements identical to it. In this paper, we propose two techniques to achieve k -anonymity through microaggregation: k -CMVM and Constrained-CMVM. Both, use topological collaborative clustering to obtain k -anonymous data. The first one determines the k levels automatically and the second defines it by exploration. We also improved the results of these two approaches by using pLVQ2 as a weighted vector quantization method. The four methods proposed were proven to be efficient using two data utility measures, the separability utility and the structural utility. The experimental results have shown a very promising performance.
Design of Two Way Solar Tracking

Solar tracking has created a great trend in the field of producing renewable source of energy. In this paper we propose a two way solar tracking system which would increase the total efficiency, this involves tracking of the sun in dual axis. To accomplish this we have used two motors for the movement in two axis. This paper would imply that the use of the mentioned method would result in more power consumption, high operating cost.