Reviewerly/exHarmony huggingface dataset:
Image De-noising with a New Threshold Value Using Wavelets

The image de-noising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among the various methods for recovering infinite dimensional objects like curves, densities, images etc. The wavelet techniques are very effective to remove the noise because of its ability to capture the energy of a signal in few energy transform values. The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain. This paper concentrates on selecting a threshold for wavelet function estimation. A new threshold value is pro-posed to shrink the wavelet coefficients obtained by wavelet decomposition of a noisy image by considering that the sub band coefficients have a generalized Gaussian distribution. The proposed threshold value is based on the power of 2 in the size 2^J x 2^J of the data that can be computed efficiently. The experiment has been conducted on various test images to compare with the established threshold parameters. The result shows that the proposed threshold value removes the noise significantly.
Enhancing the TMS320C6713 DSK for DSP Education

Abstract The introduction of the Texas Instruments TMS320C6713 DSP Starter Kit (DSK) brought a much more capable, stable, and robust DSP development environment to DSP education. However, while the DSK had many improvements over the TMS320C6711 DSK, it did not include any way to transfer data to and from the host computer except through the debugger interface, which is extremely limited in bandwidth and requires that the TI software tools be available. This means that the existing suite of winDSK6 demonstration tools cannot be run on the 6713 DSK, denying educators a valuable teaching and classroom demonstration resource. Also, there is no way to interface an application on the host PC directly to the DSK, limiting the ability of students to create stand-alone, interactive projects using the DSK. To solve this problem, the authors have created an interface to the TMS320C6713 DSK that uses the Host Port Interface (HPI) to provide both a means for a PC host application to boot software onto the DSK, and to permit the transfer of data between the DSK and the host PC application. A software package makes it possible for students to create stand-alone Windows applications that communicate directly with the DSK. In addition to parallel port communication, the interface provides USB, RS-232, and digital input/output ports as user selectable resources available to the DSK software. This paper discusses the specific capabilities of the hardware and software interface, summarizes the software applications and library calls available, and relates our experiences in using all of it. The presentation will include a demonstration of the capabilities and uses of this interface. The authors freely distribute the software components of the interface for educational use.
A Comprehensive Suite Of Tools For Teaching Communications Courses

Both the U.S. Naval Academy and the University of Wyoming offer a wide variety of electrical engineering courses concerning communications.Additionally, required design courses offer opportunities for exposure to a wide variety of real-world communication systems and topics.Whether these courses are discussing the basics of analog and digital communications, or the details of advanced digital modulation schemes and error performance, until very recently, we have found it exceeding difficult to perform communications systems demonstrations and the subsequent signal analysis without a phenomenal amount of specialized hardware and personal effort.This all changed when both schools started using a National Instrument (NI) vector signal analyzer (VSA) and vector signal generator (VSG).Both of these functions are contained within a standalone PXI chassis.These hardware functions are enabled and controlled by LabView and the vast array of toolkits available from NI.This paper discusses the use of this hardware and software in both the lecture and design environment.
Scalable Nonlinear AUC Maximization Methods

The area under the ROC curve (AUC) is a widely used measure for evaluating classification performance on heavily imbalanced data. The kernelized AUC maximization machines have established a superior generalization ability compared to linear AUC machines because of their capability in modeling the complex nonlinear structures underlying most real-world data. However, the high training complexity renders the kernelized AUC machines infeasible for large-scale data. In this paper, we present two nonlinear AUC maximization algorithms that optimize linear classifiers over a finite-dimensional feature space constructed via the k-means Nyström approximation. Our first algorithm maximizes the AUC metric by optimizing a pairwise squared hinge loss function using the truncated Newton method. However, the second-order batch AUC maximization method becomes expensive to optimize for extremely massive datasets. This motivates us to develop a first-order stochastic AUC maximization algorithm that incorporates a scheduled regularization update and scheduled averaging to accelerate the convergence of the classifier. Experiments on several benchmark datasets demonstrate that the proposed AUC classifiers are more efficient than kernelized AUC machines while they are able to surpass or at least match the AUC performance of the kernelized AUC machines. We also show experimentally that the proposed stochastic AUC classifier is able to reach the optimal solution, while the other state-of-the-art online and stochastic AUC maximization methods are prone to suboptimal convergence. Code related to this paper is available at: https://sites.google.com/view/majdikhalid/ .
Universal and Succinct Source Coding of Deep Neural Networks

Deep neural networks have shown incredible performance for inference tasks in a variety of domains, but require significant storage space, which limits scaling and use for on-device intelligence. This paper is concerned with finding universal lossless compressed representations of deep feedforward networks with synaptic weights drawn from discrete sets, and directly performing inference without full decompression. The basic insight that allows less rate than naïve approaches is recognizing that the bipartite graph layers of feedforward networks have a kind of permutation invariance to the labeling of nodes, in terms of inferential operation. We provide efficient algorithms to dissipate this irrelevant uncertainty and then use arithmetic coding to nearly achieve the entropy bound in a universal manner. We also provide experimental results of our approach on several standard datasets.
A survey on Automatic Text Summarization

Text summarization endeavors to produce a summary version of a text, while maintaining the original ideas. The textual content on the web, in particular, is growing at an exponential rate. The ability to decipher through such massive amount of data, in order to extract the useful information, is a major undertaking and requires an automatic mechanism to aid with the extant repository of information. Text summarization systems intent to assist with content reduction by means of keeping the relevant information and filtering the non-relevant parts of the text. In terms of the input, there are two fundamental approaches among text summarization systems. The first approach summarizes a single document. In other words, the system takes one document as an input and produce a summary version as its output. The alternative approach is to take several documents as its input and produce a single summary document as its output. In terms of output, summarization systems are also categorized into two major types. One approach would be to extract exact sentences from the original document in order to build the summary output. The alternative would be a more complex approach, in which the rendered text is a rephrased version of the original document. This paper will offer an in-depth introduction to automatic text summarization. We also mention some evaluation techniques to evaluate the quality of automatic text summarization.
The dual phase moisture conductivity of fibrous materials using random walk techniques in X-ray microcomputed tomographic structures

Molecular and energy transport in several porous materials can occur through both the pore space as well as the solid phase. In the case of paper and paperboard structures, moisture can be conducted via diffusion in the air through the pore space under low moisture conditions, but primarily through the fiber phase at high moisture contents. Prediction of the overall diffusivity or conductivity in such cases can become quite complex and the impact of the pore-fiber microstructure on transport is not possible. In the present work, a new method based on random walk inside the pore space allowing for a restricted walk inside the conducting fiber phases was applied to show the impact of this dual conduction on the overall transport of moisture (or analogously, heat). Model verification using periodic array of spheres showed the non-linear relationship between normalized effective diffusivity and porosity of the structure in correspondence with Archie’s equation and agreeing reasonably well with literature. The structures of samples of paperboard were first assembled using X Ray Computed Tomographic (XR-CT) reconstruction after which a hybrid random walk algorithm was implemented. The walker was allowed to penetrate the fiber space in proportion to the conductivity ratio and also with an adjusted path length. Paper and fiber structures are strongly anisotropic, with the structure in the thickness dimension (z) being different from the in-plane dimensions (x, y). This is reflected in the anisotropy of the diffusivity tensor. Assuming fibers to be nonconductive, laboratory made paper fiber structures showed a non-linear relationship between normalized effective diffusivity and porosity with in-plane direction being more conductive than the transverse direction. Since both the pore and fiber spaces are conductive to moisture, the diffusivity tensor has contributions from each space and can be assembled from the diffusivities in each of these phases individually. An important result from the present work is that initially anisotropic porous structures become more isotropic as the fiber phase becomes more conductive. Experimental diffusivity data and model simulations were compared showing the effect of structure on the anisotropic diffusion characteristics of paper. The results are presented in the form of a proposed ‘effective diffusional tortuosity’ and the intrinsic fiber diffusivity, as fundamental characteristics of cellulose fibers. With the development of new nanofibrous cellulosic materials, it is expected that net effective conduction parameters accounting for multiple phase random walks will be more prominent in controlling material properties.
Inferring frame conditions with static correlation analysis

We introduce the abstract domain of correlations to denote equality relations between parts of inputs and outputs of programs. We formalise the theory of correlations, and mechanically verify their semantic properties. We design a static inter-procedural dataflow analysis for automatically inferring correlations for programs written in a first-order language equipped with algebraic data-types and arrays. The analysis, its precision and execution cost, have been evaluated on the code and functional specification of an industrial-size micro-kernel. We exploit the inferred correlations to automatically discharge two thirds of the proof obligations related to the preservation of invariants for this micro-kernel.
Cell Detection With Deep Convolutional Networks Trained With Minimal Annotations

In this paper, we propose and thoroughly compare strategies for training convolutional neural networks (CNNs) for cell localization and segmentation in microscopy images with both little training data and in presence of significant label noise. Insufficient availability of ground truth (GT) is a common issue in the field of microscopy image analysis, hence the usefulness of such approaches. Performance evaluation is done using phase contrast microscopy human fibrosarcoma (HT1080) cells and comparing the resulting F-scores.
Android-based automatic detection and measurement system of highway billboard for tax calculation in indonesia

&lt;span&gt;Billboards are objects, tools or actions, which based on the characteristics serve its own purpose to earn profits, advertise certain people or service, and to draw public’s attention by placing it in a very strategic place. It has led the government to charge tax on billboards based on its location, dimensions, and viewpoints. Therefore, authorized parties have to be able to ensure the data authenticity of the proposed billboards. One of the obstacles in data verification is the time of billboards measurement process due to its size and height from the ground, based on this problem, and we developed a system which can measure the dimensions of billboards without physically touching it by implementing image processing methods to identify the billboards. The implementation is by measuring the dimensions of the billboards using perspective concept, then calculates the distance between the camera and the object using two-point distance calculation GPS coordinates. The results showed that the distance calculation using the GPS method generated inaccurate values, whereas the systematic distance method generated a result of errors’ range from 0.5 to 25 cm if the image acquisition is performed nearly perpendicular to the object.&lt;/span&gt;
Timeline Storyteller: The Design & Deployment of an Interactive Authoring Tool for Expressive Timeline Narratives

The interface of Timeline Storyteller, where the timeline canvas spans the entire browser window. Interface components include: (A) toggles for alternative timeline representations, scales, and layouts; (B) a movable event legend; (C) widgets for loading data, annotating the canvas with captions and images, filtering and highlighting events, and exporting content; (D) scene recording and playback controls along with a scene list; and (E) tutorial and help widgets. This example displays a scene from a story about the performance record of professional golfers at the US Open golf tournament throughout their careers (see the interactive story at https://aka.ms/timelinestoryteller-golf-story). Golfer images: Wikimedia (CC). Timeline Storyteller (timelinestoryteller.com) is an open-sourced interactive application for authoring expressive visualization-based narratives about event sequences. Its design reflects a recently proposed timeline design space, which itself is grounded in an extensive survey. In this paper, we highlight its capabilities and reflect upon its usage and adoption.
Predicting Vulnerable Software Components via Bellwethers

Software vulnerabilities are weakness, flaws or errors introduced during the life cycle of a software system. Although, previous studies have demonstrated the practical significance of using software metrics to predict vulnerable software components, empirical evidence shows that these metrics are plagued with issues pertaining to their effectiveness and robustness. This paper investigates the feasibility of using Bellwethers (i.e., exemplary data) for predicting and classifying software vulnerabilities. We introduced a Bellwether method using the following operators, PARTITION, SAMPLE + TRAIN and APPLY. The Bellwethers sampled by the three operators are used to train a learner (i.e., deep neural networks) with the aim of predicting essential or non-essential vulnerabilities. We evaluate the proposed Bellwether method using vulnerability reports extracted from three popular web browsers offered by CVE. Again, the mean absolute error (MAE), Welch’s t-test and Cliff’s δ effect size are used to further evaluate the prediction performance and practical statistical significant difference between the Bellwethers and the growing portfolio. We found that there exist subsets of vulnerability records (Bellwethers) in the studied datasets that can yield improved accuracy for software vulnerability prediction. The result shows that recall and precision measures from the text mining process were in a range of 73.9%–85.3% and 67.9%–81.8% respectively across the three studied datasets. The findings further show that the use of the Bellwethers for predictive modelling is a promising research direction for assisting software engineers and practitioners when seeking to predict instances of vulnerability records that demand much attention prior to software release.
PEMBELAJARAN E-LEARNING

The internet can be used as a way to transfer knowledge from teachers to students. Learning that utilizes the development of technology and information, namely the internet, one of which is the e-learning learning system. E-learning is a form of conventional learning that is transferred in digital format through internet technology, not only to present subject matter on the internet but also must be in accordance with the principles of learning.The e-learning learning model results in changes in learning culture in the context of learning. Learning becomes very flexible, because it can be adjusted to the availability of time from students in learning the material provided by the teacher.The teacher determines the success of students in learning, therefore teachers are required to have the ability to adapt to technological progress.&#x0D; &#x0D; Keywords: ICT,e-learning, e-learning learning
Formation of Inter-Frame Deformation Field of Images Using Reverse Stochastic Gradient Estimation

The effectiveness of the use of stochastic gradient estimation to detect motion in a sequence of images is investigated. Pixel-by-pixel reverse estimation of the deformation field is used. The representation of the shift vector is considered both by the projections on the basic axes and by the polar parameters. Two approaches to estimate the parameters of the deformation field are proposed and analyzed. In the first approach, the stochastic gradient procedure sequentially processes all rows of an image to find estimates of shifts for all points of the reference image. The joint processing of the results allows compensating the inertia of the stochastic estimation. In the second approach, to improve the accuracy of estimation, the correlation of image rows is taken into account. As a criterion for the formation of the resulting estimate, the minimum of gradient estimation and correlation maximum were investigated. The computational complexity of the proposed algorithms is investigated. The algorithms are compared with the MVFAST algorithm. Examples of experimental results on the formation of the deformation field, the selection of a moving object area, and the finding of the movement and trajectory parameters of a moving object in a video sequence are given.
Critical slowing down and entanglement protection

We consider a quantum device $D$ interacting with a quantum many-body environment $R$ which features a second-order phase transition at $T=0$. Exploiting the description of the critical slowing down undergone by $R$ according to the Kibble-Zurek mechanism, we explore the possibility to freeze the environment in a configuration such that its impact on the device is significantly reduced. Within this framework, we focus on the magnetic-domain formation typical of the critical behavior in spin models and propose a strategy that allows one to protect the entanglement between different components of $D$ from the detrimental effects of the environment.
Evaluating the performance of a neural network-based multi-criteria recommender system

Frequent use of internet applications and rapid increase in volumes of resources have made it difficult for online users to effectively make decisions on the kinds of information or items to select.Recommender systems (RSs) are intelligent decision-support tools that exploit preferences of users and suggest items that might be interesting to them.RSs are one of the various solutions proposed to address the problems of information overload.Traditionally, RSs use single rating techniques to predict and represent preferences of users for items that are not yet seen.Multi-criteria RSs use multiple ratings to various attributes of items for improving prediction and recommendation accuracy of the systems.However, one major challenge of multi-criteria RSs is the choice of an efficient approach for modelling the criteria ratings.Therefore, this paper aimed at employing artificial neural networks to model the criteria ratings and determine the predictive performance of the systems based on aggregation function approach.Seven evaluation metrics have been used to evaluate and measure the accuracy of the systems.The empirical results of the study have shown that the proposed technique has the highest prediction and recommendation than the corresponding traditional technique.
Qubitization of Arbitrary Basis Quantum Chemistry by Low Rank Factorization

Recent work has dramatically reduced the gate complexity required to quantum simulate chemistry by using linear combinations of unitaries based methods to exploit structure in the plane wave basis Coulomb operator. Here, we show that one can achieve similar scaling even for arbitrary basis sets (which can be hundreds of times more compact than plane waves) by using qubitized quantum walks in a fashion that takes advantage of structure in the Coulomb operator, either by directly exploiting sparseness, or via a low rank tensor factorization. We provide circuits for several variants of our algorithm (which all improve over the scaling of prior methods) including one with $\widetilde{\cal O}(N^{3/2} \lambda)$ T complexity, where $N$ is number of orbitals and $\lambda$ is the 1-norm of the chemistry Hamiltonian. We deploy our algorithms to simulate the FeMoco molecule (relevant to Nitrogen fixation) and obtain circuits requiring about seven hundred times less surface code spacetime volume than prior quantum algorithms for this system, despite us using a larger and more accurate active space.
BALG: An alternative for fast and robust feature matching

Robust feature matcher is usually inapplicable to real time computer vision applications as its high computational complexity, while fast feature matcher is short of robustness to geometric transformations. By studying the sampling pattern of binary descriptor and local geometric statistics of features, this paper proposes a fast and robust feature matching method with binary affine invariant descriptor and local geometric consistency check (BALG). The sampling pattern is adaptively adjusted according local affine transformation that can be effectively estimated from the local intensity moments. The affine sampling pattern improves the affine invariance of binary descriptor while enable fast processing. Furthermore, candidate matches are sorted into local groups along different orientations with sequence order constraint, and then followed by local geometric consistency check. False matches are efficiently filtered out with high recall. Extensive experiments on four publicly benchmark datasets prove the proposed method to be an alternative for time critical feature matcher.