Miracle-dz/UniFAR-FTUs huggingface dataset:
Cytotoxic effect of gold and silver nanoparticles synthesized by chemical reduction method using sucrose on in vitro model of cardiomyocytes

Using disaccharides in green synthesis of metallic nanoparticles (NPs) is an alternative route to traditional processes of nanostructure development, which reduces the degree of incompatibility of these structures with the biological model. The aim of this study was to evaluate the cytotoxic effect of gold and silver nanoparticles synthesized from chemical reduction method with sucrose on in vitro model of cardiomyocytes RL-14. The nanoparticles obtained because of synthesis were characterized by UV-visible spectroscopy, which allowed determining the surface plasmon resonance and experimental particle sizes; likewise, the cell viability assay by MTT was performed to find IC50 of cell growth inhibition of cardiomyocytes. The results showed that variation in cytotoxicity levels are dependent on particle diameter and concentrations of each type of nanostructure.
Maze-solving with a memristive grid of charge-controlled memristors

Memristive grids have been recently used as emerging analogue processors in various fields of application. This paper introduces a memristive grid for maze-solving applications. A specially suited fully symbolic charge-controlled model for the memristor has been developed. Electrical simulation is used due to the fact that the model has been recast in a VERILOG-A module. The memristive grid is able to handle mazes with single and multiple solutions.
The Modelling of Hand, Foot, and Mouth Disease in Contaminated Environments in Bangkok, Thailand

Hand, foot, and mouth disease (HFMD) has spread widely in a continuing endemic in Thailand. There are no specific vaccines or antiviral treatments available that specifically target HFMD. Indirect transmission via free-living viruses from the environment may influence HFMD infections because the virus can survive for long periods in the environment. In this study, a new mathematical model is proposed to investigate the effect of indirect transmission from contaminated environments and the impact of asymptomatic individuals. By fitting our model to reported data on hospitalized individuals of HFMD endemic in Bangkok, Thailand, 2016, the basic reproduction number was estimated as 1.441, which suggests that the disease will remain under current conditions. Numerical simulations show that the direct transmission from asymptomatic individuals and indirect transmission via free-living viruses are important factors which contribute to new HFMD infections. Sensitivity analysis indicates that the basic reproduction number is sensitive to the transmission rate of asymptomatic and symptomatic subgroups and indirect transmission. Our findings suggest that cleaning the environment frequently and healthcare precautions which include the reduction of direct transmission rates should be promoted as effective control strategies for preventing the HFMD spread.
Measuring evolvability and accessibility using the hyperlink-induced topic search algorithm

The redundant mapping from genotype to phenotype is common in evolutionary algorithms, where multiple genotypes can map to the same phenotype. Such a redundancy has been suggested to make an evolutionary system robust as well as evolvable. However, the impact of the redundant genotype-to-phenotype mapping and its resulted robustness and evolvability have not been well characterized quantitatively. In this article, we used a Boolean linear genetic programming system to construct a weighted and directed phenotype network, where vertices are phenotypes and a weighted link represents the number of possible point mutations that can transition genotypes from one phenotype to another. The direction of the links ensures moving from less fit phenotypes to fitter or equally fit ones. We used two fitness functions to investigate how it influences the network structure. Then we employed the Hyperlink-Induced Topic Search (HITS) algorithm to quantitatively characterize the evolvability and accessibility of phenotypes in the network. We found more robust phenotypes are both more evolvable and accessible. Our results help elucidate the effects of redundant mapping and the relationship of robustness, evolvability, and accessibility.
Exposure to Bisphenol A, Bisphenol F, and Bisphenol S in U.S. Adults and Children: The National Health and Nutrition Examination Survey 2013–2014

Bisphenol F (BPF) and bisphenol S (BPS) are replacing bisphenol A (BPA) in the manufacturing of products containing polycarbonates and epoxy resins. Data on current human exposure levels of these substitutes are needed to aid in the assessment of their human health risks. This study analyzed urinary bisphenol levels in adults (N = 1808) and children (N = 868) participating in the National Health and Nutrition Examination Survey (NHANES) 2013–2014 and investigated demographic and lifestyle factors associated with urinary levels of bisphenols. BPA, BPS, and BPF were detected in 95.7, 89.4, and 66.5% of randomly selected urine samples analyzed as part of NHANES 2013–2014, respectively. Median levels of BPA in U.S. adult were higher (1.24 μg/L) than BPF and BPS levels (0.35 and 0.37 μg/L, respectively). For children, median BPA levels were also higher (1.25 μg/L) than BPF and BPS levels (0.32 and 0.29 μg/L, respectively). The limits of detection for BPA, BPF, and BPS were 0.2, 0.2, and 0.1 μg/L, respectively. Urinary levels showed associations with gender, race/ethnicity, family income, physical activity, smoking, and/or alcohol intake that depended on the specific bisphenol. The results of this study indicate that exposure of the general U.S. population to BPA substitutes is almost ubiquitous. Because exposures differ across the U.S. population, further studies of environmental, consumer, and lifestyle factors affecting BPF and BPS exposures are warranted.
Deep and Wide Neural Networks on Multiple sets of Temporal data with Correlation

Both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have played important roles in deep learning in recent years. The combination of them is an important direction to improve the performance of the prediction model. In the process of studying temporal data, it is found that temporal data are closely related to each other rather than isolated. Therefore it is necessary to take into account the correlation between different temporal data while processing the temporal data along the time steps. However general RNN model cannot achieve the requirement. In this paper, RNN and CNN are combined to extract the correlation characteristics of different RNNs by using CNNs while the RNNs run along the time steps. Thus, we propose the Deep and Wide Neual Networks (DWNNs) which can not only process temporal data in depth direction (time step), but also extract the correlation feature in breadth direction (multiple sets of data). We use temporal data from the stock market for our experiment, which has an interesting correlation structure. The empirical results show that the performance of DWNN is 30% higher than that of ordinary RNN.
Mining concepts from code using community detection in co-occurrence graphs

Software modularity is a quality that determines how fluently individual parts (modules) of a system can vary and be understood if taken by themselves. However, modularity tends to degrade during program evolution – old concepts may get entangled with code introduced into their modules, while new concepts can be scattered over many existing modules. In this work, we propose to infer high-level concepts and relations between them independently from the current module decomposition by exploiting the vocabulary used by programmers. Our approach uses an extensible graph-based vocabulary representation in which we detect latent communities representing our concepts. Inferred concepts can be used to support program comprehension, track architectural drift over time, and provide recommendations for related code or refactorings.
Optimization framework model for retrospective tweet summarization

Twitter is a valuable source of information to keep users up to date on topics they care about. However, timely following the development of long-running events is too difficult due to the velocity and the volume of the published information. Automatically generating a concise summary containing relevant and non-redundant posts that capture key aspects of information need, is one solution to keep users up to date. In this paper, we propose a novel approach that formulates the summary generation as an optimization problem modeled using Integer Linear Programming whereas the majority of traditional methods generate the summary by selecting iteratively top weighted tweets and ignores the mutual relation among messages. To overcome this issue, the generation of the summary is formulated as an optimization problem to select a subset of tweets that maximizes the global summary relevance and fulfills constraints related to non-redundancy, coverage, temporal diversity and summary length. Our experiments on TREC RTF 2015 and TREC RTS 2016 datasets have shown the effectiveness of our approach.
What to write and why: a recommender for news media

The way in which people acquire information on events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to news stimulates a demand for additional information which is often met through online encyclopedias, such as Wikipedia. This behavior has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests the readers. The goal of this paper is to present a recommender system, What To Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers' communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter and Wikipedia, either not covered or poorly covered in the published news articles.
A survey on measuring the effect of website on customer satisfaction

Article history: Received December 25, 2011 Received in Revised form March, 25, 2012 Accepted 24 April 2012 Available online April 28 2012 During the past few years, there has an increasing trend on using internet facilities to do different personal banking. People transfer money, pay their bills and even purchase shares of different companies using internet. There is also an increasing competition among banks and other financial institutions through providing new web based facilities. There is no doubt that a bank with no online banking can easily lose market share. The primary purpose of this paper is to investigate how a bank website can impact customer loyalty. The proposed study is implemented for one of governmental Iranian banks called Bank of Industry and Mine. The study distributes a questionnaire among 330 people who use the internet banking of this bank. We then analyze their feedback using structural equation system and all the hypothesis are confirmed. © 2012 Growing Science Ltd. All rights reserved.
Maximally informative k-itemset mining from massively distributed data streams

We address the problem of mining maximally informative k-itemsets (miki) in data streams based on joint entropy. We propose PentroS, a highly scalable parallel miki mining algorithm. PentroS renders the mining process of large volumes of incoming data very efficient. It is designed to take into account the continuous aspect of data streams, particularly by reducing the computations of need for updating the miki results after arrival/departure of transactions to/from the sliding window. PentroS has been extensively evaluated using massive real-world data streams. Our experimental results confirm the effectiveness of our proposal which allows excellent throughput with high itemset length.
Fuzzy ontologies for search results diversification: application to medical data

Fuzzy ontologies offer an efficient representation of uncertain information in natural language and this representation allows a better interpretation of user queries and documents. Integrating fuzzy ontologies in a search results diversification process may improve the quality of returned documents since diversification helps covering the maximum of user's needs. In this context, we propose an ontology based diversification approach for search results applied to medical domain. The proposal first analyses the query in order to extract medical concepts. A contextual ontology fuzzification is then applied in order to offer an understanding of the user's information needs and finally a fuzzy search result diversification is performed in order to improve the ranking quality of returned documents. We perform a thorough experimental evaluation of our proposal with CLEF e-health 2016 topics. Evaluation results show a major improvement in precision and ranking.
Automated Binary Analysis on iOS: A Case Study on Cryptographic Misuse in iOS Applications

A wide range of mobile applications for Apple's iOS platform process sensitive data and, therefore, rely on protective mechanisms natively provided by the operating system. A wrong application of cryptography or security-critical APIs, however, exposes secrets to unrelated parties and undermines the overall security. We introduce an approach for uncovering cryptographic misuse in iOS applications. We present a way to decompile 64-bit ARM binaries to their LLVM intermediate representation (IR). Based on the reverse-engineered code, static program slicing is applied to determine the data flow in relevant code segments. For this analysis to be most accurate, we propose an adapted version of Andersen's pointer analysis, capable of handling decompiled LLVM IR code with type information recovered from the binary. To finally highlight the improper usage of cryptographic APIs, a set of predefined security rules is checked against the extracted execution paths. As a result, we are not only able to confirm the existence of problematic statements in iOS applications but can also pinpoint their origin. To evaluate the applicability of our solution and to disclose possible weaknesses, we conducted a manual and automated inspection on a set of iOS applications that include cryptographic functionality. We found that 343 out of 417 applications (82%) are subject to at least one security misconception. Among the most common flaws are the usage of non-random initialization vectors and constant encryption keys as input to cryptographic primitives.
Investigation of the exponential population scheme for genetic algorithms

Early development of GAs requires many parameters to be tuned. The tuning process increases the difficulty for inexperienced practitioners. Modern GAs have most of these parameters pre-determined, and therefore recent research concerning parameterless schemes has focused on population size. The techniques developed in this paper are mainly based on Harik and Lobo's work and the exponential population scheme (EPS), which double the population until the solution is satisfactory. In this paper, we modify EPS based on theoretical analyses. Specifically, we propose a new termination criterion and an optimized population multiplier. The experiment results show that our scheme reduces 33.4%, 19.1% and 29.6% number of function evaluations (NFE) on hBOA (the parameter-less hBOA), LT-GOMEA and DSMGA-II respectively when compared to Harik-Lobo scheme, and reduces 28.5%, 4.7% and 11.0% NFE on hBOA, LT-GOMEA and DSMGA-II respectively when compared to EPS. In addition, compared to EPS, our scheme empirically reduces the number of failures when using LT-GOMEA to solve the folded trap and MAX-SAT problems.
Metamorphic Testing for Adobe Analytics Data Collection JavaScript Library

In recent years, metamorphic testing has been successfully and systematically adopted within Adobe Systems to improve the cost effectiveness of its software process. In this industry experience report, we present a case where metamorphic testing has been applied to the Data Collection JavaScript Library of Adobe Analytics. This type of software is difficult to test using traditional approaches. The application of metamorphic testing alleviated the oracle problem, and detected real-life bugs in the system under test as well as compatibility problems between the system and its environment, namely, the Internet Explorer browser. Our results further justify the adoption of metamorphic testing as a simple yet effective approach in industrial settings.
Semi-supervised Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data

The detection and the quantification of anomalies in image data are critical tasks in industrial scenes such as detecting micro scratches on product. In recent years, due to the difficulty of defining anomalies and the limit of correcting their labels, research on unsupervised anomaly detection using generative models has attracted attention. Generally, in those studies, only normal images are used for training to model the distribution of normal images. The model measures the anomalies in the target images by reproducing the most similar images and scoring image patches indicating their fit to the learned distribution. This approach is based on a strong presumption; the trained model should not be able to generate abnormal images. However, in reality, the model can generate abnormal images mainly due to noisy normal data which include small abnormal pixels, and such noise severely affects the accuracy of the model. Therefore, we propose a novel semi-supervised method to distort the distribution of the model with existing abnormal images. The proposed method detects pixel-level micro anomalies with a high accuracy from 1024x1024 high resolution images which are actually used in an industrial scene. In this paper, we share experimental results on open datasets, due to the confidentiality of the data.
Urban scene 3D reconstruction optimization leveraged by line information

Recently, great progress has been made in large scale scene reconstruction based on structure from motion and multi-view stereo approaches. However, the accuracy of this kind model still needs to be improved in the urban scene reconstruction. In this study, a novel method was proposed to optimize the 3D reconstruction result from multi-view images with 3D line information. The joint estimation of the line and dense points information are used to refine all the existing edges in scenes. The dense points and 3D line information were firstly reconstructed from the structure and motion result. Then, the wrong lines were removed based on the dense points and 3D line information. The incorrect points are removed based on the structural line segments and the mesh curvature. Finally, all the point position information are fused with normal information. The strategy is illustrated on several real sets of images of urban buildings and the experiments demonstrate the performance of this method is convincing and the reconstruction results is definitely better than the original ones.
Detecting energy bugs and hotspots in control software using model checking

We explore a way to find energy deficiencies in code by model checking, both properties related to utilisation (energy hotspots) and not related to utilisation (energy bugs). Temporal logic properties (expressed using LTL) finds these deficiencies during the development of a system, employing a novel energy-inefficiency metric. The formal model is deduced from hardware specifications, a definition of utilisation and a control software application written in C. Model checking results in a set of traces (formally a counter-example) that can be related to the matching execution path in the source code by the software developer. The traces include energy footprints and enable developers to improve the energy efficiency of the application. A smart light system serves as a case study to evaluate the proposed approach.
Predicting competitor links in company networks

The scored task at FEIII Challenge 2018 proposed the identification of competitor relationships in a network of companies from the financial and IT sectors. This article describe our BBVA Data & Analytics submission to the challenge and our experiments with three different approaches to predict competitor links: a local classifier, a recommender and a relational classifier. Our best results were obtained by a recommender where we included seed and non-seed companies and data from Thomson Reuters and TNIC to set the probability of the link. In perspective, this simple approach achieves good precision and moderate recall, but also highlights that solving link prediction in company networks may require additional data sources, larger training sets or more elaborated models.
New approaches for evaluation: correctness and freshness: Extended Abstract

The main goal of a Recommender System is to suggest relevant items to users, although other utility dimensions -- such as diversity, novelty, confidence, possibility of providing explanations -- are often considered. In this work, we study two dimensions that have been neglected so far in the literature: coverage and temporal novelty. On the one hand, we present a family of metrics that combine precision and coverage in a principled manner (correctness); on the other hand, we provide a measure to account for how much a system is promoting fresh items in its recommendations (freshness). Empirical results show the usefulness of these new metrics to capture more nuances of the recommendation quality.
Solid Geometry Processing on Deconstructed Domains

Many tasks in geometry processing are modelled as variational problems solved numerically using the finite element method. For solid shapes, this requires a volumetric discretization, such as a boundary conforming tetrahedral mesh. Unfortunately, tetrahedral meshing remains an open challenge and existing methods either struggle to conform to complex boundary surfaces or require manual intervention to prevent failure. Rather than create a single volumetric mesh for the entire shape, we advocate for solid geometry processing on deconstructed domains, where a large and complex shape is composed of overlapping solid subdomains. As each smaller and simpler part is now easier to tetrahedralize, the question becomes how to account for overlaps during problem modelling and how to couple solutions on each subdomain together algebraically. We explore how and why previous coupling methods fail, and propose a method that couples solid domains only along their boundary surfaces. We demonstrate the superiority of this method through empirical convergence tests and qualitative applications to solid geometry processing on a variety of popular second‐order and fourth‐order partial differential equations.