Reviewerly/exHarmony huggingface dataset:
Automated breast ultrasound system (ABUS): can it replace mammography as a screening tool?

Abstract Background Mammography is the most accepted, accurate, and effective modality in screening of breast cancer, yet its sensitivity is affected by the density of the breast tissue. Alternative methods for screening are the sonography and MRI but both had their limitations. A new option named ABUS (automated breast ultrasound system) is now proposed to overcome the breast US limitation as it is time-consuming and operator-dependent and to overcome the costly time-consuming MRI. The objectives of the study are to evaluate the accuracy of ABUS in the detection of different breast lesions as a substitution for mammography. This prospective study included 25 women outreached for digital mammography or handheld ultrasound examination at the period between January 2017 and February 2018. Women have no specific age group. Results Significant improvement in the detection of breast lesions by ABUS use with mammogram especially in dense breasts (ACR class C and D) Conclusion ABUS is a promising competitor to mammogram in screening of breast lesions
Describing quasi-graphic matroids

The class of quasi-graphic matroids recently introduced by Geelen, Gerards, and Whittle generalises each of the classes of frame matroids and lifted-graphic matroids introduced earlier by Zaslavsky. For each biased graph (G,B) Zaslavsky defined a unique lift matroid L(G,B) and a unique frame matroid F(G,B), each on ground set E(G). We show that in general there may be many quasi-graphic matroids on E(G) and describe them all: for each graph G and partition (B,L,F) of its cycles such that B satisfies the theta property and each cycle in L meets each cycle in F, there is a quasi-graphic matroid M(G,B,L,F) on E(G). Moreover, every quasi-graphic matroid arises in this way. We provide cryptomorphic descriptions in terms of subgraphs corresponding to circuits, cocircuits, independent sets, and bases. Equipped with these descriptions, we prove some results about quasi-graphic matroids. In particular, we provide alternate proofs that do not require 3-connectivity of two results of Geelen, Gerards, and Whittle for 3-connected matroids from their introductory paper: namely, that every quasi-graphic matroid linearly representable over a field is either lifted-graphic or frame, and that if a matroid M has a framework with a loop that is not a loop of M then M is either lifted-graphic or frame. We also provide sufficient conditions for a quasi-graphic matroid to have a unique framework. Zaslavsky has asked for those matroids whose independent sets are contained in the collection of independent sets of F(G,B) while containing those of L(G,B), for some biased graph (G,B). Adding a natural (and necessary) non-degeneracy condition defines a class of matroids, which we call biased-graphic. We show that the class of biased-graphic matroids almost coincides with the class of quasi-graphic matroids: every quasi-graphic matroid is biased-graphic, and if M is a biased-graphic matroid that is not quasi-graphic then M is a 2-sum of a frame matroid with one or more lifted-graphic matroids.
Land Value Capture Modeling in Commercial and Office Areas using a Big Data Approach

Infrastructure development in Indonesia creates massive impacts on the economy.The Light Rail Transit (LRT) of the greater Jakarta (Jabodebek) project has an estimated cost of more than 29 trillion rupiahs due to land acquisition and route planning.The urban transit development may impact to the price of property including residential, commercial and offices along the route.This research aims to determine variables affecting the price elasticity of property and the correlation to station proximity.Data mining through web scrapping was used to assess the degree of correlation between price elasticity and station location.The result shows that approximately 13% of commercial property was spread over a distance of 1 km from the LRT station.The closer a property to transit station, the more likely a price will be twice as cheap compared to those further away.The findings also show variables that highly contribute to property prices, including schools and hospitals, linked to proximity of some transit stations located in city center of Jakarta and building density.
Comparative Study of Anomaly Detection in Wireless Sensor Networks Using Different Kernel Functions

Wireless sensor network (WSN) is defined as an autonomous network composed of low power sensor nodes having limited computational, communication, and energy resources. Being short at resources they require efficient use of each resource to prolong network lifetime. Sensor networks are exposed to noise, compromised nodes, low battery levels, and damaged sensors, all these results in anomalous readings or anomaly. Presence of anomaly in system deteriorates the performance of WSN in terms of efficiency, accuracy, and reliability. Hence anomaly detection becomes a major challenge to decide the performance of network. Support vector machine (SVM) is a light weight, learning-based binary classifier that can classify the raw data into normal and anomalous. SVM suffers from computational complexity while handling large datasets, so sequential minimal optimization SVM (SMO-SVM) is used to reduce the complexity. In this paper, a comparative study is made on anomaly detection using SMO-SVM classifier utilizing different kernel functions.
Developing a Catalogue of Explainability Methods to Support Expert and Non-expert Users

Organisations face growing legal requirements and ethical responsibilities to ensure that decisions made by their intelligent systems are explainable. However, provisioning of an explanation is often application dependent, causing an extended design phase and delayed deployment. In this paper we present an explainability framework formed of a catalogue of explanation methods, allowing integration to a range of projects within a telecommunications organisation. These methods are split into low-level explanations, high-level explanations and co-created explanations. We motivate and evaluate this framework using the specific case-study of explaining the conclusions of field engineering experts to non-technical planning staff. Feedback from an iterative co-creation process and a qualitative evaluation is indicative that this is a valuable development tool for use in future company projects.
SIFO: Secure Computational Infrastructure Using FPGA Overlays

Secure Function Evaluation (SFE) has received recent attention due to the massive collection and mining of personal data, but remains impractical due to its large computational cost. Garbled Circuits (GC) is a protocol for implementing SFE which can evaluate any function that can be expressed as a Boolean circuit and obtain the result while keeping each party’s input private. Recent advances have led to a surge of garbled circuit implementations in software for a variety of different tasks. However, these implementations are inefficient, and therefore GC is not widely used, especially for large problems. This research investigates, implements, and evaluates secure computation generation using a heterogeneous computing platform featuring FPGAs. We have designed and implemented SIFO: secure computational infrastructure using FPGA overlays. Unlike traditional FPGA design, a coarse-grained overlay architecture is adopted which supports mapping SFE problems that are too large to map to a single FPGA. Host tools provided include SFE problem generator, parser, and automatic host code generation. Our design allows repurposing an FPGA to evaluate different SFE tasks without the need for reprogramming and fully explores the parallelism for any GC problem. Our system demonstrates an order of magnitude speedup compared with an existing software platform.
Prediction of Heavy Metals Concentration in the Groundwater Resources in Razan Plain: Extreme Learning Machine vs. Artificial Neural Network and Multivariate Adaptive Regression Spline

Background: The groundwater is known as a major water source for domestic, industrial and agricultural purposes in the Razan Plain. Therefore, the prediction of toxic and essential elements (arsenic, lead, and zinc) content in groundwater resources of this area is important. Objectives: The main aim of this study was to investigate the extreme learning machine model as a novel model for the prediction of heavy metals concentration at Razan Plain, Hamedan province, Iran. Methods: In this descriptive study, a total of 60 groundwater specimens were collected from 20 semi-deep and deep wells across the studied area. After preparing the specimens in the laboratory, the elementsâ content was detected using inductively coupled plasma-optical emission spectrometry (ICP-OES) in three replicates. In the next step, three types of machine learning methods, including, extreme learning machine (ELM), artificial neural network (ANN) and multivariate adaptive regression spline (MARS) were used to predict the heavy metals concentration in groundwater resources in Razan Plain, Hamedan, Iran. The models were trained using training data (the first 80% of the data) to find optimum values for weights and biases followed by testing using testing data (the first 20% of the data) collected from the study area. The used data were representative of the concentration of the As, Zn and Pb in Razan Plain. Three evaluation measures, correlation coefficient (r), coefficient of determination (R2) and root mean square error (RMSE) were applied to investigate the accuracy of models in estimation of heavy metals concentration. Results: The results showed that the mean content (Âµg/L) of the analyzed elements in groundwater samples of Razan Plain was 6.35 for As, 5.24 for Pb, and 32.4 for Zn. In addition, based on the findings the superiority of ELM was confirmed compared with the ANN and MARS models. ELM model decreased RMSE for ANN and MARS by 39.8% and 47.8% for As, 38.5% and 59.8% for Zn, and 64.4% and 75.5% for Pb, respectively. The results indicated that the ELM model can be successfully utilized for predicting heavy metals concentration in groundwater resources. Conclusions: The developed ELM approach can be successfully applied to estimate the concentration of As, Pb, and Zn in Razan Palin.
Politics of Participation in Design Research: Learning from Participatory Art

In design research, critique has recently been voiced against the multiple ways the notion of participation is understood and practiced. Studies of performativity and performance art have been used to account for this methodological multiplicity. However, in this article, we argue that participation still has much to offer design research as a foundational concept, but that a more nuanced understanding is required. Further, we offer such an understanding by presenting three theories and methodologies from participatory art. The value of participatory art for design research will then be exemplified by a practice-based design research project exploring forms of patient democracy.
Comparative Study of Interval Type-2 and Type-1 Fuzzy Genetic and Flower Pollination Algorithms in Optimization of Fuzzy Fractional Order <i>PI<sup>λ</sup>D<sup>μ</sup> </i> Controllers

In this chapter, a comparison between fuzzy genetic optimization algorithm (FGOA) and fuzzy flower pollination optimization algorithm (FFPOA) is bestowed.In extension, the prime parameters of each algorithm adapted using interval type-2 and type-1 fuzzy logic system (FLS) are presented.The key feature of type-2 fuzzy system is alimenting the modeling uncertainty to the algorithms, and hence it is a prime motivation of using interval type-2 fuzzy systems for dynamic parameter adaption.These fuzzy algorithms (type-1 and type-2 fuzzy system versions) are compared with the design of fuzzy control systems used for controlling the dihybrid level control process subject to system component (leak) fault.Simulation results reveal that interval type-2 fuzzy-based FPO algorithm outperforms the results of the type-1 and type-2 fuzzy GO algorithm.
Research on TSP Solution Based on Genetic Algorithm

Genetic algorithm has been widely used in many fields because of its good global search capability. In this paper, we mainly introduce the research on Traveling Salesman Problem (TSP) solution in 26 cities based on genetic algorithm. We adopt tournament selection, order insert crossover and exchange mutation methods to solve this problem. However, it does not perform sufficiently well enough, so we optimize the operators by using elitist selection and inversion mutation methods. With the improved operators, we can prevalent the shortcomings of traditional genetic algorithm, prevent convergence into a local optimum, guarantee the stability of population evolution, dismantle cross paths and finally find the optimal solution. The obtained information is useful for further genetic algorithm parameters, operators settings and practical problems.
Mundos virtuales y el aprendizaje inmersivo en educación superior

Today's society has become a constant swing of changes in every aspect of life; academic, economic, cultural, political, among others. The changes have been generated with the insertion of technology in daily activities, where learning is not exempt from these changes. According to Negroponte (1995), when science meets technology, it produces significant changes in our lives, as well as the way we understand reality. Today the knowledge society is empowered by technology and by the creation of new and diverse ways of interacting as people; the virtual worlds that are a graphic representation of the reality designed in 3D, is one of them. Second Life is one of these virtual worlds, becoming a site of mere entertainment and social life on the web, to be established in an environment that fosters creativity, collaborative work, business vision, the development of technological skills, the development of experiences of learning in 3D environments, among other aspects. Likewise, immersive learning enables interaction in three-dimensional environments. There the activities are carried out within an artificially constructed world which resembles reality, this allows us to build learning through the person's own experiences in interaction with the environment and the experiences among the users of this virtual world. For this reason, the use of the Second World virtual world is proposed as a training tool in immersive learning.
Sentiment Extraction from Image-Based Memes Using Natural Language Processing and Machine Learning

The widespread use of image-based memes on socioeconomic or political issues has witnessed a booming effect unparallel to any form of media in the recent years. The ability to go viral on social media in seconds and the popularity of memes on online platforms give a wide scope and pathway for research as it will help in understanding the usage patterns of the public and in turn be used for analyzing their sentiment toward a specific topic/event. In this paper, initially gap analysis on the features used for sentiment extraction on memes is presented. Exploring the correlation of image based and textual features, this paper gives a novel approach (correlating the facial features along with the text in the meme itself) for the extraction of sentiment from image-based memes. This paper also addresses the challenges faced in this relatively new area of sentiment extraction on memes. Finally, this paper concludes with insightful results.
A Web Services, Ontology and Big Data Analysis Technology-Based Cloud Case-Based Reasoning Agent for Energy Conservation of Sustainability Science

Energy conservation is one of the important topics for sustainability science, while case-based reasoning is one of the most important techniques for sustainable processing. This study aimed to develop a cloud case-based reasoning agent that integrates multiple intelligent technologies and supports, which can help users to quickly, accurately, and effectively obtain useful cloud energy-saving information in a timely manner for sustainability science. The system was successfully built with the support of Web services technology, ontology, and big data analytics. To set up this energy-saving case-based reasoning agent, this study reviewed the relevant technologies for building a web services platform and explored how to widely integrate and support the cloud interaction of the energy-saving data processing agent via the technologies. In addition to presenting relevant R&amp;D technologies and results in detail, this study carefully conducted performance and learning experiments to prove the system’s effectiveness. The results showed that the core technology of the case-based reasoning agent achieved good performance and that the learning effectiveness of the overall system was also great.
State-of-the-Art Object Recognition Techniques: A Comparative Study

Object recognition has a remarkable contribution in the field of computer vision. It has many areas of application like security, human–computer interface, industrial inspection and automation, etc. This paper presents the distinct object recognition approaches like feature-based method, appearance-based method and artificial neural network. Further, the various state-of-the-art algorithms like Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) of these approaches are introduced along their pros and cons. Finally, we conclude with a comparison of these algorithms on the basis of robustness (in terms of rotation, illumination, occlusion and speed), complexity (computation load and memory usage) and accuracy.
Sentiment analysis of Romanized Sindhi text

Sentiment Analysis have also an important role in natural language processing to evaluate and analyzing the public opinion, sentiments and views about social activities such as product, services, Academic institutes, organizations etc. Lot of work has been done on English language in natural langua ge processing. However, it is found out from the literature that still huge research gap is available for the Romanized Sindhi and there sentiment analysis in the field of natural language processing and also no any trained data is available for the testing. Classification of sentiment of Romanized Sindhi text is very difficult task. For the evaluation of sentiment of Romanized Sindhi text easily available online Python tool were used. In this research work thousand words of Romanized Sindhi text/data were used for the sentiment classification. Also discussed issues in sentiment classification in Python tool on Romanized Sindhi text.
Linear Complexity of a Family of Binary <i>pq</i> <sup>2</sup>-Periodic Sequences From Euler Quotients

We first introduce a family of binary pq <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -periodic sequences based on the Euler quotients modulo pq, where p and q are two distinct odd primes and p divides q - 1. The minimal polynomials and linear complexities are determined for the proposed sequences provided that 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q-1</sup> ≠ 1 mod q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The results show that the proposed sequences have high linear complexities.
Jaynes-Cummings model under monochromatic driving

We study analytically and numerically the properties of Jaynes-Cummings model under monochromatic driving. The analytical results allow to understand the regime of two branches of multi-photon excitation in the case of close resonance between resonator and driven frequencies. The rotating wave approximation allows to reduce the description of original driven model to an effective Jaynes-Cummings model with strong coupling between photons and qubit. The analytical results are in a good agreement with the numerical ones even if there are certain deviations between the theory and numerics in the close vicinity of the resonance. We argue that the rich properties of driven Jaynes-Cummings model represent a new area for experimental investigations with superconducting qubits and other systems.
Automatic Report Generation for Chest X-Ray Images: A Multilevel Multi-attention Approach

A comprehensive X-ray imaging report greatly assists the medical professional to investigate an indispensable condition and medication. The preparation of an extensive and diversified medical report by analysing the chest X-ray image is a time-consuming task and requires highly experienced professionals. This work targets the fundamental problem of generating a long and multifarious medical report for the chest X-ray image. It introduces a novel Multilevel Multi-Attention based encoder-decoder approach by combining Context Level Visual Attention and Textual Attention to generate a plausible medical report for different views of chest X-ray images. It exploited the proven ability of the Convolutional Neural Network to acquire course information of visual-spatial regions as an encoder. It leverages the strength of the Long Short-Term Memory network to learn long sequential dependencies and the ability of attention to focus on the prominent section as a decoder. The proposed method emphasizes on contextual coherence in intra and inter-sentence dependency within a report to improve the overall medical report generation quality. The effectiveness of the proposed model is evaluated on the publicly available IU chest X-ray dataset consisting of chest images along with multifarious radiology reports. The final performance of the proposed model is reported using the COCO-caption evaluation API. It shows a significant improvement in a medical report generation task compared to state-of-the-art methods.
The Vese-Chan model without redundant parameter estimation for multiphase image segmentation

Abstract The Vese-Chan model for multiphase image segmentation uses m binary label functions to construct 2 m characteristic functions for different phases/regions systematically; the terms in this model have moderate degrees comparing with other schemes of multiphase segmentation. However, if the number of desired regions is less than 2 m , there exist some empty phases which need costly parameter estimation for segmentation purpose. In this paper, we propose an automatic construction method for characteristic functions via transformation between a natural number and its binary expression, and thus, the characteristic functions of empty phases can be written and recognized naturally. In order to avoid the redundant parameter estimations of these regions, we add area constraints in the original model to replace the corresponding region terms to preserve its systematic form and achieve high efficiency. Additionally, we design the alternating direction method of multipliers (ADMM) for the proposed modified model to decompose it into some simple sub-problems of optimization, which can be solved using Gauss-Seidel iterative method or generalized soft thresholding formulas. Some numerical examples for gray images and color images are presented finally to demonstrate that the proposed model has the same or better segmentation effects as the original one, and it reduces the estimation of redundant parameters and improves the segmentation efficiency.
Using 5G Mobile to Enable the Growing Slate of VR and AR Applications

With the arrival of the fifth generation (5G) radio access, there are numerous opportunities and challenges for the new worlds of Virtual Reality (VR) and Augmented Reality (AR). This abstract examines the promises of 5G networks, the future needs of VR and AR, and how 5G will integrate with VR and AR, including the impact of edge computing on VR and AR and the benefits that edge computing will offer in an intelligent 5G mobile environment. The possible challenges on devices and power consumption are also discussed. We take AR navigation as a mobility-based example to explain how 5G will enable AR and compare the different AR experiences.