Reviewerly/exHarmony huggingface dataset:
PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions

Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR. MDR was used to detect multilocus interactions based on the PSO algorithm. A test data set was simulated from the genotype frequencies of 26 SNPs from eight breast-cancer-related gene. In simulated disease models, we demonstrated that PBMDR outperforms existing global optimization algorithms in terms of its ability to explore and power to detect specific SNP-genotype combinations. In addition, the PBMDR algorithm was compared with other algorithms, including PSO and chaotic PSOs, and the results revealed that the PBMDR algorithm yielded higher accuracy and chi-square values than other algorithms did.
Entangling and disentangling in Grover's search algorithm

Entanglement is believed to be crucial in making quantum algorithms more powerful than their classical counterparts for certain computational tasks. In Grover's search algorithm, the Grover's operator/iteration G can be decomposed into two basic operators, i.e., G=RO, where O is so called the Oracle operator and R is the Reflection operator. To probe the production/depletion of entanglement from basic operator level, we investigate the roles the Oracle and the Reflection operators play in the entanglement dynamics during Grover's search algorithm application. Using geometric measure of entanglement (GME), we show that the Oracle operator is an entangling operator which almost always produces (increases) entanglement while the Reflection operator is a disentangling operator which mainly depletes (decreases) entanglement. We explicitly demonstrate that there exists a turning point during the Grover's iteration application with the following properties. Before that turning point, the entanglement is almost always increased when the Oracle operator is applied, and the effect of the Reflection operator on the level of entanglement can be almost ignored. However, after the turning point, both the Oracle and the Reflection operators play important roles to the entanglement, more exactly, the Reflection operator significantly decreases entanglement while the Oracle operator increases entanglement. All these results are carefully demonstrated.
Agile Uncertainty Assessment for Benefit Points and Story Points

Estimates are inherently uncertain, and it is fair to expect that benefit estimates are even more so. By making this uncertainty explicit, you can manage and monitor your agile project according to upper and lower margins of uncertainty.
GPU based parallel optimization for real time panoramic video stitching

Panoramic video is a sort of video recorded at the same point of view to record the full scene. With the development of video surveillance and the requirement for 3D converged video surveillance in smart cities, CPU and GPU are required to possess strong processing abilities to make panoramic video. The traditional panoramic products depend on post processing, which results in high power consumption, low stability and unsatisfying performance in real time. In order to solve these problems, we propose a real-time panoramic video stitching framework. The framework we propose mainly consists of three algorithms, L-ORB image feature extraction algorithm, feature point matching algorithm based on LSH and GPU parallel video stitching algorithm based on CUDA. The experiment results show that the algorithm mentioned can improve the performance in the stages of feature extraction of images stitching and matching, the running speed of which is 11.3 times than that of the traditional ORB algorithm and 641 times than that of the traditional SIFT algorithm. Based on analyzing the GPU resources occupancy rate of each resolution image stitching, we further propose a stream parallel strategy to maximize the utilization of GPU resources. Compared with the L-ORB algorithm, the efficiency of this strategy is improved by 1.6–2.5 times, and it can make full use of GPU resources. The performance of the system accomplished in the paper is 29.2 times than that of the former embedded one, while the power dissipation is reduced to 10 W.
Validating the usability attributes of AHP-software risk prioritization model using partial least square-structural equation modeling

Purpose This study aims to develop a software risk prioritization model using analytic hierarchy process (AHP) and further validate the usability attributes of the model in prioritizing operational, technical, technological, strategic and environmental software risks. Design/methodology/approach Questionnaire was used to collect data from software practitioners to evaluate the usability attributes of the AHP-software risk prioritization model. Accordingly, partial least square-structural equation modeling was used to analyze the collected data. Findings Results reveal that the developed AHP-software risk prioritization model is efficient and effective in facilitating software risk factor prioritization. In addition, results suggest that the experts are satisfied with the learnability, accessibility and navigation capability of the model. Besides, results indicate that the model provides a useable interface and system design for content availability of information needed by software practitioners in evaluating and prioritizing operational, technical, technological, strategic and environmental risk. Furthermore, results show that the experts intend to adopt the model to prioritize identified software risk in their firm. Research limitations/implications Methodologically, the developed AHP-software risk prioritization model is faced with issues such as inconsistency in judgments, weakness of confronting ambiguities and uncertainties of high complexity. Empirically, data were collected from software practitioners in Malaysia to validate the AHP-software risk prioritization model. Hence, results from this study cannot be generalized to other software practitioners in different countries. Practical implications This study developed a software risk prioritization model to evaluate and prioritize software risks that occur in software organizations by deploying AHP to carryout risk factor priority selection. Moreover, the model provides risk knowledge as guidelines for evaluating software risks in software organizations. Social implications The developed AHP-software risk prioritization model computes risk prioritization factor priority selection and further supports software practitioners and evaluates risks and associated risk factors. Besides, this study develops an instrument that can be used in project risk management to validate the usability attributes of software risk approaches. Originality/value This research designs use case and class diagram to show how the AHP-software risk prioritization model evaluates and prioritizes software risks factors by using risk evaluation questions. Additionally, the AHP-software risk prioritization model computes, evaluates and prioritizes software risk factors using risk factor priority selection for software project management.
Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms

Understandability plays a key role in ensuring that people accessing health information are capable of gaining insights that can assist them with their health concerns and choices. The access to unclear or misleading information has been shown to negatively impact the health decisions of the general public.The aim of this study was to investigate methods to estimate the understandability of health Web pages and use these to improve the retrieval of information for people seeking health advice on the Web.Our investigation considered methods to automatically estimate the understandability of health information in Web pages, and it provided a thorough evaluation of these methods using human assessments as well as an analysis of preprocessing factors affecting understandability estimations and associated pitfalls. Furthermore, lessons learned for estimating Web page understandability were applied to the construction of retrieval methods, with specific attention to retrieving information understandable by the general public.We found that machine learning techniques were more suitable to estimate health Web page understandability than traditional readability formulae, which are often used as guidelines and benchmark by health information providers on the Web (larger difference found for Pearson correlation of .602 using gradient boosting regressor compared with .438 using Simple Measure of Gobbledygook Index with the Conference and Labs of the Evaluation Forum eHealth 2015 collection).The findings reported in this paper are important for specialized search services tailored to support the general public in seeking health advice on the Web, as they document and empirically validate state-of-the-art techniques and settings for this domain application.
Performance Issues? Hey DevOps, Mind the Uncertainty

DevOps is a novel trend that aims to bridge the gap between software development and operation teams. This article presents an experience report that better identifies performance uncertainties through a case study and provides a step-by-step guide to practitioners for controlling system uncertainties.
Moving from Waterfall to Agile: Perspectives from IT Portuguese Companies

This study investigates the main motivations, difficulties and good practices in the migration process of software development models from Waterfall to Agile experienced by IT Portuguese companies. For that, we adopted a quantitative methodology based on multiple case studies that allowed us to explore five research questions. In a first step, we propose to identify the main reasons that motivate the companies to adopt Agile development processes and we intend to characterize this migration process. We also explore a set of good practices that can be followed in this migration process and we suggest a set of measures that can be used to assess the success of projects in an Agile environment. Finally, we explore the organizational and management impacts with the introduction of Agile processes.
The Online Controlled Experiment Lifecycle

Unlike other techniques for learning from customers, online controlled experiments (OCEs) establish an accurate and causal relationship between a change and the impact observed. We show that OCEs help optimize infrastructure needs and aid in project planning and measuring team efforts. We conclude that product development should fully integrate the experiment lifecycle to benefit from the OCEs.
Accurate fuzzy predictive models through complexity reduction based on decision of needed fuzzy rules

Fuzzy predictive models (FPMs) are employed in a wide variety of domains for their high prediction accuracy and good interpretability. For a FPM, the fuzzy rules from data or expert experience are always firstly extracted to establish a relatively complete fuzzy rule base. However, it is found that a large amount of extracted fuzzy rules which are always not used for prediction increase the complexity of the FPM. Moreover, inaccurate fuzzy rules may be used for prediction to degrade the accuracy of the FPM. Therefore, we proposed a method to establish an accurate FPM through complexity reduction based on decision of needed fuzzy rules. To avoid extractions of the rules which are not needed for prediction, reduce the complexity of the FPM, and suppress the influence of some inaccurate fuzzy rules, we add the decision of fuzzy rules needed for prediction based on membership values to predictive modeling before the extraction of fuzzy rules. And then an improved WM method based on optimization of centers of output fuzzy subsets for fuzzy rules (COiWM), which was proven to possess high completeness, robustness, and accuracy in our previous work, is used to generate the needed fuzzy rules directly from historical data. The rules which can not to be generated are extrapolated based on these data-generated fuzzy rules. At last, two fuzzy rule bases are built and used together to fuzzy predictive modeling. Experimental results of a case study on short term daily maximum electric load forecasting and Mackey–Glass chaotic time-series prediction show that our proposed method reduces the complexity of the FPMs and enhances the prediction accuracy.
Detecting environmentally-related problems on Twitter

Social media networks such as Facebook and Twitter can be used as a valuable tool to report on environmentally-related problems, e.g. landslides or wildfires, that are about to occur or have just occurred, so that response actions can be promptly executed. The goal of this article is to describe a knowledge-based system that is able to analyse tweets in Spanish to detect a variety of such problems. This research resulted in the implementation of CASPER, a proof-of-concept workbench where multi-domain problem detection has been devised as a two-fold task: topic categorisation and sentiment analysis.
Return-oriented programming on a resource constrained device

Microcontrollers are found in many everyday devices and will only become more prevalent as the Internet of Things (IoT) and other low power devices gain momentum. As such, it is increasingly important that they are reasonably resilient to known exploitation techniques. Modern enterprise-grade systems with virtually unlimited resources have many options when it comes to implementing state of the art intrusion prevention and detection solutions. These solutions are costly in terms of energy, execution time, circuit board area, and — of course — money. Sustainable IoT devices and power-constrained embedded systems cannot afford such costs and are forced to make suboptimal security trade-offs. One such trade-off is the design of architectures which prevent execution of injected shell code, yet have allowed Return Oriented Programming (ROP) to emerge as a more reliable way to execute malicious code following attacks. ROP is a method used to take over the execution of a program by causing the return address of a function to be modified through an exploit vector, then returning to small segments of otherwise innocuous code located in executable memory one after the other to carry out the attacker's aims. It will be shown that the Tiva TM4C123GH6PM microcontroller, which utilizes a Cortex-M4F processor, can be fully controlled with this technique. Sufficient code is pre-loaded into a ROM on Tiva microcontrollers to erase and rewrite the flash memory where the program resides. Then, that same ROM is searched for a Turing-complete gadget set which would allow for arbitrary execution. This allows an attacker to re-purpose the microcontroller, altering the original functionality to their own malicious ends. Our results show that advanced exploitation techniques are still effective against embedded systems which prioritize energy-efficiency and that more research needs to be focused on finding the right balance of security for devices with a small energy footprint.
A comparison of three differential evolution strategies in terms of early convergence with different population sizes

Differential Evolution (DE) is a popular population-based continuous optimization algorithm that generates new can-didate solutions by perturbing the existing ones, using scaled differences of randomly selected solutions in the population. While the number of generation increases, the differences between the solutions in the population decrease and the population tends to converge to a small hyper-volume within the search space. When these differences become too small, the evolutionary process becomes inefficient as no further improvements on the fitness value can be made - unless specific mechanisms for diversity preser-vation or restart are implemented. In this work, we present a set of preliminary results on measuring the population diversity during the DE process, to investigate how different DE strategies and population sizes can lead to early convergence. In particular, we compare two standard DE strategies, namely “DE/rand/1/bin” and “DE/rand/1/exp”, and a rotation-invariant strategy, “DE/current-to-random/1”, with populations of 10, 30, 50, 100, 200 solutions. Our results show, quite intuitively, that the lower is the population size, the higher is the chance of observing early convergence. Furthermore, the comparison of the different strategies shows that “DE/rand/1/exp” preserves the population diversity the most, whereas “DE/current-to-random/1” preserves diversity the least.
Interferometric angular velocity measurement of rotating blades: theoretical analysis, modeling and simulation study

Doppler radar can only measure the radial velocity of a moving object. If an object is moving along a curved path, when its radial velocity decreases, the angular velocity must increase. Thus, if the angular velocity can be measured, the problem caused by little or no radial velocity can be solved. In this paper, we provide detailed theoretical analysis and establish the mathematical model of the interferometric frequency shifts of rotating blades. We first analyze the instantaneous frequency of a SINC function, which comprises a pair of sinusoidals and a train of strong spectrum lines. Then, we utilize the convolution theory in time-frequency domain to calculate the interferometric frequency shifts of rotating blades. Simulation results manifest that some important parameters and features of rotating blades, such as blade length, rotating rate and blade number, can be accurately estimated from the time-varying interferometric frequency signatures.
An aggregated coupling measure for the analysis of object-oriented software systems

Coupling is a fundamental property of software systems which is strongly connected with the quality of software design and has high impact on program understanding. The coupling between software components influences software maintenance and evolution as well. In order to ease the maintenance and evolution processes it is essential to estimate the impact of changes made in the software system, coupling indicating such a possible impact. This paper introduces a new aggregated coupling measurement which captures both the structural and the conceptual characteristics of coupling between the software components. The proposed measure combines the textual information contained in the source code with the structural relationships between software components. We conduct several experiments which underline that the proposed aggregated coupling measure reveals new characteristics of coupling and is also effective for change impact analysis.
Captured multi-label relations via joint deep supervised autoencoder

The mapping relations learning between instances and multiple labels should reflect the underlying joint probability distribution following by the data sets. The general solution of such problem is to assume that the samples are subject to a certain distribution, i.e. normal distribution, but this hypothesis cannot excavate the real underlying mapping relations hidden in the data sets. Meanwhile, it is not advisable to suppose that multiple labels are independent of each other. Therefore, we propose the deep supervised autoencoder as a generative model to learn the posterior conditional probability rather than assigning the specific distribution in advance. In this way, we propose the different joint augmented matrices of training instances Xi and corresponding label sets Yi under the three multi-label relations assumptions as the inputs to learn the posterior probability distribution. Finally, the experiments under model assumptions are conducted on six data sets, and we also set different noise levels to verify whether the optimal hypothesis has the ability to handle the corrupted labels. Experiments on images, biology and music real-world data sets show that our method outperforms most of state-of-the-art multi-label classifiers.
Elastic-net regularized latent factor analysis-based models for recommender systems

Latent factor analysis (LFA)-based models are highly efficient in recommender systems. The problem of LFA is defined on high-dimensional and sparse (HiDS) matrices corresponding to relationships among numerous entities in industrial applications. It is ill-posed without a unique and optimal solution, making regularization vital in improving the generality of an LFA-based model. Current models mostly adopt l2-norm-based regularization, which cannot regularize the latent factor distributions. For addressing this issue, this work applies the elastic-net-based regularization to an LFA-based model, thereby achieving an elastic-net regularized latent factor analysis-based (ERLFA) model. We further adopt two efficient learning algorithms, i.e., forward-looking sub-gradients and forward-backward splitting and stochastic proximal gradient descent, to train desired latent factors in an ERLFA-based model, resulting in two novel ERLFA-based models relying on different learning schemes. Experimental results on four large industrial datasets show that by regularizing the latent factor distribution, the proposed ERLFA-based models are able to achieve high prediction accuracy for missing data of an HiDS matrix without additional computational burden.
CQASUMM

Answers submitted to CQA forums are often elaborate, contain spam, are marred by slurs and business promotions. It is difficult for a reader to go through numerous such answers to gauge community opinion. As a result summarization becomes a prioritized task. However, there is a dearth of neural approaches for CQA summarization due to the lack of large scale annotated dataset. We create CQASUMM, the first annotated CQA summarization dataset by filtering the 4.4 million Yahoo! Answers L6 dataset. We sample threads where the best answer can double up as a reference and build hundred word summaries from them. We provide scripts1 to reconstruct the dataset and introduce the new task of Community Question Answering Summarization.
Weighted Hierarchical Grammatical Evolution

Grammatical evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings, of a language defined by a user-provided context-free grammar. In this paper, we propose a novel procedure for mapping genotypes to phenotypes that we call weighted hierarchical GE (WHGE). WHGE imposes a form of hierarchy on the genotype and encodes grammar symbols with a varying number of bits based on the relative expressive power of those symbols. WHGE does not impose any constraint on the overall GE framework, in particular, WHGE may handle recursive grammars, uses the classical genetic operators, and does not need to define any bound in advance on the size of phenotypes. We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results of the standard GE framework as well as two of the most significant enhancements proposed in the literature: 1) position-independent GE and 2) structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure.