Reviewerly/exHarmony huggingface dataset:
Automatic Segmentation and Functional Assessment of the Left Ventricle using U-net Fully Convolutional Network

A new method for the automatic segmentation and quantitative assessment of the left ventricle (LV) is proposed in this paper. The method is composed of two steps. First, a fully convolutional U-net is used for the segmentation of the epi- and endo-cardial boundaries of the LV from cine MR images. This step incorporates a novel loss function that accounts for the class imbalance problem caused by the binary cross entropy (BCE) loss function. Our novel loss function maximizes the segmentation accuracy and penalizes the effect of the class-imbalance caused by BCE. In the second step, the ventricular volume curves are constructed from which LV function parameter is estimated (i.e., ejection fraction). Our method demonstrated a statistical significance in the segmentation of the epi- and endo-cardial boundaries (Dice score of 0.94 and 0.96, respectively) compared with the BCE loss (Dice score of 0.89 and 0.86, respectively). Furthermore, a high positive correlation of 0.97 between the estimated ejection fraction and the gold standard was obtained.
T-REC: Towards Accurate Bug Triage for Technical Groups

With ever-larger software development systems involving more people with different skills, it is necessary to think about the process of bug assignment to groups of developers and not just to a developer alone. This work aims to leverage Bug Triage Process by suggesting a list of specialized groups of developers, or Technical Groups (TG's), to be attributed to a new bug report, based on other bugs that are similar and have been resolved by these TG's in the past. In the dataset used in our experiments, the mean time to correctly assign bug reports to their proper TG is 14 days, and just by then, the bug fixing process starts. This is a critical problem for software development and management since issues tend to accumulate a high-resolution time, which compromises developer performance and deliveries. In order to enhance the Bug Triage Process, we propose T-REC, an auxiliary SW Project Management system that accurately and efficiently analyzes similar issues to provide personalized TG recommendation. T-REC is a method that ensemble Machine Learning (ML) and Information Retrieval (IR) algorithms to recommend a list of TG's to handle an issue. Our experiments show that T-REC recommendation reaches an overall Acc@1 of 50.9%, Acc@2 of 63.2%, Acc@5 of 76.1%, Acc@10 of 83.6%, and Acc@20 of 89.7%. To the best of our knowledge, our work is the first to associate multiple machine learning strategies (classifiers, attributes, and training history) on the prediction of specialized groups of developers. We validate our approach on a real-world dataset from a large company that comprises 9.5M mobile-related bug reports from January 2001 to January 2019.
On the strength of asymmetric code cryptosystems based on the merging of generating matrices of linear codes

The resistance to attacks on a key of code cryptosystems based on the merging of generating matrices of u(∈ ℕ) linear [n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , k, d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> -codes C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> (⊆ F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n(i)</sup> ), i = 1, ..., u, are considered. The resistance of such cryptosystems in particular is based on the assumption that for u ≥ 2 in the public key matrix G̃ it is computationally difficult to find such (k × n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> )-submatrices composed of n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> columns of the matrix G̃, which would be generating matrices of codes permutably equivalent to the codes C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , i = 1, ..., u. In the present paper an algorithm is constructed which can be used to efficiently find such submatrices when a number of easily verified conditions are met. It is shown that the constructed algorithm can be used to clarify the strength of the Sidelnikov cryptosystem and its modification for u = 2.
Fast Frequent Pattern Mining without Candidate Generations on GPU by Low Latency Memory Allocation

In this work, we propose a GPU-accelerated algorithm for frequent pattern(FP) mining without candidate generation. We observe that the existing FP-growth algorithm has critical characteristics unsuitable for GPU, including the tree data structure, deep recursion and heavy dynamic memory allocations. By utilizing iterative execution and collectively allocating memory on GPU, our proposed method significantly reduce the latency caused by large memory allocations of original FP-growth. Experiment results show that our solution outperforms baselines, including sequential FP-growth with CPU only and existing GPU-accelerated Apriori and FP-growth, on various data sets with a significant speedup, from several times to hundred times.
Optimizing Neural Network Embeddings Using a Pair-Wise Loss for Text-Independent Speaker Verification

This paper proposes a new loss function called the “quartet” loss for the better optimization of the neural networks for matching tasks. For such tasks, where neural network embeddings are the key component, the optimization of the network for better embeddings is critical. The embeddings are required to be class discriminative, resulting in minimal inter-class variation and maximal intra-class variation even for unseen classes for better generalization of the network. The quartet loss explicitly computes the distance metric between pairs of inputs and increases the gap between the similarity score distributions between the same class pairs and the different class pairs. We evaluate on the speaker verification task and demonstrate the performance of the loss on our proposed neural network.
Comparison between segmentation performances of a tool based on wavelet decomposition and multithreshold and of a U-net convolutional neural network applied to shearography images of carbon fiber reinforced plastic plates with low-velocity impact damages

Carbon fiber reinforced plastics (CFRPs) have been used to replace metallic alloys in many industries because of their high strength-to-weight ratio. Due to their anisotropic behavior, low-velocity impacts can produce defects whose effects on material performance are hard to foresee. Nondestructive testing (NDT) methods are a convenient alternative to evaluate their integrity. Shearography, an image-based optical interferometric technique for measuring deformation, is one of these NDT possibilities. The segmentation of defects in the resulting images provided by such a method is essential to correctly locate and indicate the severity of impact damage. This task is especially intricate for shearography images with barely visible impact damage because of their usual low signal-to-noise ratio. We compare a combination of wavelet decomposition with multithresholds introduced in a previous publication with a U-net convolutional neural network for analyzing impact damage in CFRP plates. Both tools are detailed and then evaluated using the Matthews correlation coefficient and the equivalent diameter criterion. The results showed that U-net provided a better impact damage characterization in both evaluation metrics, allowing a safer defect detection that is less dependent on the inspector’s ability to interpret them.
Many-Modal Optimization by Difficulty-Based Cooperative Co-evolution

Evolutionary multimodal optimization has received considerable attention in the past decade. Most existing evolutionary multimodal optimization algorithms are designed to solve problems with relatively few global optima. However, in real-world applications, the problems can possess a lot of global optima (and sometimes acceptable local optima). Finding more global optima can help us learn more about their landscapes and distributions. However, solving these problems with limited computational resources is a challenge for current algorithms. In this paper, many-modal optimization problems are studied, and each of them has more than 100 global optima. We first present a benchmark with 10 many-modal problems based on the existing multimodal optimization benchmarks. The numbers of global optima of these 10 problems vary from 108 to 7776. Second, we propose the difficulty-based cooperative co-evolution (DBCC) strategy for solving many-modal optimization problems. DBCC comprises four primary steps: problem separation, resource allocation, optimization, and solution reconstruction. The clonal selection algorithm is selected as the optimizer in DBCC. Experimental results demonstrate that DBCC provides satisfactory performance.
Assessing the reliability of a clothing-based forensic identification

A 2009 report by the National Academy of Sciences was highly critical of many forensic practices. This report concluded that significant changes and advances were required to ensure the reliability across the forensic sciences. We examine the reliability of one such forensic technique used for identification based on purported distinct patterns on the seams of denim pants. Although first proposed more than 20 years ago, no thorough analysis of reliability or reproducibility of this forensic technique has previously been reported. We performed a detailed analysis of this forensic technique to determine its reliability and efficacy.
End-effector force estimation method for an unmanned aerial manipulator

This paper presents a detailed description of the end-effector force estimation of a robotic manipulator attached to an unmanned aerial vehicle. In order to estimate the force, it is assumed that there is a link between the force acting on the end-effector and the change of the torques in the joints of the manipulator in relation to the unloaded manipulator. This link was presented with Newton-Euler's dynamic model and the Jacobian matrix of the manipulator. The force estimation was tested and validated on several experiments of a manipulator mounted on the aerial vehicle.
How C++ Templates Are Used for Generic Programming

Generic programming is a key paradigm for developing reusable software components. The inherent support for generic constructs is therefore important in programming languages. As for C++, the generic construct, templates, has been supported since the language was first released. However, little is currently known about how C++ templates are actually used in developing real software. In this study, we conduct an experiment to investigate the use of templates in practice. We analyze 1,267 historical revisions of 50 open source systems, consisting of 566 million lines of C++ code, to collect the data of the practical use of templates. We perform statistical analyses on the collected data and produce many interesting results. We uncover the following important findings: (1) templates are practically used to prevent code duplication, but this benefit is largely confined to a few highly used templates; (2) function templates do not effectively replace C-style generics, and developers with a C background do not show significant preference between the two language constructs; (3) developers seldom convert dynamic polymorphism to static polymorphism by using CRTP (Curiously Recursive Template Pattern); (4) the use of templates follows a power-law distribution in most cases, and C++ developers who prefer using templates are those without other language background; (5) C developer background seems to override C++ project guidelines. These findings are helpful not only for researchers to understand the tendency of template use but also for tool builders to implement better tools to support generic programming.
Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments

Assessing whether an agent has abandoned a goal or is actively pursuing it is important when multiple agents are trying to achieve joint goals, or when agents commit to achieving goals for each other. Making such a determination for a single goal by observing only plan traces is not trivial, as agents often deviate from optimal plans for various reasons, including the pursuit of multiple goals or the inability to act optimally. In this article, we develop an approach based on domain independent heuristics from automated planning, landmarks, and fact partitions to identify sub-optimal action steps—with respect to a plan—within a fully observable plan execution trace. Such capability is very important in domains where multiple agents cooperate and delegate tasks among themselves, such as through social commitments , and need to ensure that a delegating agent can infer whether or not another agent is actually progressing towards a delegated task. We demonstrate how a creditor can use our technique to determine—by observing a trace—whether a debtor is honouring a commitment. We empirically show, for a number of representative domains, that our approach infers sub-optimal action steps with very high accuracy and detects commitment abandonment in nearly all cases.
A Simple Yet Effective Resampling Rule in Noisy Evolutionary Optimization

Noisy optimization refers to the optimization of objective functions corrupted by noise, which happens in many real-world optimization problems. Resampling has been widely used in evolutionary algorithms for noisy optimization. It has been theoretically proved that evolutionary algorithms with resampling can achieve a “log-log convergence” slope of - 21when optimizing functions corrupted by unbiased additive noise [1]. Various dynamic resampling rules have been proposed in the literature. However, determining their optimal hyperparameter values for reaching the optimal slope is hard. In this work, we reach this slope using resampling rules optimized numerically though automatic parameter tuning. We have found a parameter-free yet effective new resampling rule depending on the iteration number and the problem dimension. This simple parameter-free resampling rule is compared to several state-of-the-art rules and achieved superior performance on functions corrupted by asymmetric additive noise or in case of very high noise levels.
Big Data Analytic for Cascading Failure Analysis

With the challenges of increased grid dynamics and more variability of power generation from renewable energy sources, rapidly increasing complexity in the grid model, and abundant data from measurements and simulations, the requirements for computational analysis have also increased dramatically. This paper proposes a novel big data analysis approach for power system cascading analysis, prevention, and remediation. The developed techniques will be capable of cascading analysis, better assessment of the systems vulnerability level, as well as proposing potential remediation. Case studies using IEEE 118- bus system and a 563-bus system, with comparisons against a commercial tool, validate the advantages of the developed big data approach: accurate prediction, and more importantly, faster and effective correction actions. The developed techniques could be further used for other power system applications.
Complexity of full counting statistics of free quantum particles in product states

We study the computational complexity of quantum-mechanical expectation values of single-particle operators in bosonic and fermionic multiparticle product states. Such expectation values appear, in particular, in full-counting-statistics problems. Depending on the initial multiparticle product state, the expectation values may be either easy to compute (the required number of operations scales polynomially with the particle number) or hard to compute (at least as hard as a permanent of a matrix). However, if we only consider full counting statistics in a finite number of final single-particle states, then the full-counting-statistics generating function becomes easy to compute in all the analyzed cases. We prove the latter statement for the general case of the fermionic product state and for the single-boson product state (the same as used in the boson-sampling proposal). This result may be relevant for using multiparticle product states as a resource for quantum computing.
Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine

This paper describes a machine learning approach to the solar flare prediction competition, a track in IEEE Big Data 2019 Big Data Cup. The competition task is to predict whether or not there is a solar flare event basing on a given time series of solar magnetic field parameters. Our method involves exploring and constructing data-driven machine learning models for the classification task of two imbalanced class labels from time series. Specifically, the investigated models include boosting, logistic regression, multilayer perceptron neural network, and long short-term memory neural network. These models have been successfully deployed and combined in an ensemble framework with two tiers in our final proposed solution for this competition. Our proposed approach ranked at the second place in the competition (the first on the private board and the eleventh on the public board).
Principal Component Analysis on Graph-Hessian

Principal Component Analysis (PCA) is a widely used linear dimensionality reduction method, which assumes that the data are drawn from a low-dimensional affine subspace of a high-dimensional space. However, it only uses the feature information of the samples. By exploiting structural information of data and embedding it into the PCA framework, the local positional relationship between samples in the original space can be preserved, so that the performance of downstream tasks based on PCA can be improved. In this paper, we introduce Hessian regularization into PCA and propose a new model called Graph-Hessian Principal Component Analysis (GHPCA). Hessian can correctly use the intrinsic local geometry of the data manifold. It is better able to maintain the neighborhood relationship between data in high-dimensional space. Compared with other Laplacian-based models, our model can obtain more abundant structural information after dimensionality reduction, and it can better restore low-dimensional structures. By comparing with several methods of PCA, GLPCA, RPCA and RPCAG, through the K-means clustering experiments on USPS handwritten digital dataset, YALE face dataset and COIL20 object image dataset, it is proved that our models are superior to other principal component analysis models in clustering tasks.
The Effect of Time on the Maintenance of a Predictive Model

Periodic updating of a machine learning model may become necessary because new data could have a distribution that has drifted significantly over time from the original data distribution, thus impacting the model's usefulness. The primary objective of this paper is to evaluate temporal influence on the maintenance of a predictive model. We investigate the impact of using training data from various year-groupings on a model designed to detect Medicare Part B billing fraud. Training datasets are obtained from year-groupings of 2015, 2014-2015, 2013-2015, and 2012-2015. The test dataset is represented by 2016 data. Our study utilizes five popular learners and five class ratios obtained by Random Undersampling. Using the Area Under the Receiver Operating Characteristic (ROC) Curve as the performance metric, our case study indicates that the Logistic Regression learner yields the highest overall value for the yeargrouping of 2013-2015, with a majority-to-minority ratio of 90:10. For the problem of maintaining predictive models for Medicare fraud, we conclude that a sampled dataset should be chosen over the full dataset and that the largest training dataset (i.e., 2012- 2015) does not always produce the best results. To the best of our knowledge, this is the first big data study that examines the influence of time on the maintenance of machine learning models.
Automatic Detection of Temples in consumer Images using histogram of Gradient

Automatic detection of Temples in consumer images is a very complex due to very less information to guess the variations in the testing sample. Object identification is a technically hard and practically useful problem in the field of computer vision. In this paper, we present a vision based system to recognize temples that considers a great deal of work has been done on vehicle, individual, bicycle and so forth. Hence, plan to work on temple detection that are included on our own dataset, Up to now no experimentation has been done on temple detection in image processing. According to computer vision point of view, temple detection is difficult because representation or shapes of various buildings are same. So we propose a novel approach to handle shape on temple detection using histogram of gradient. This approach mainly used in pedestrian detection and face detection by Navneet Dalal and Bill Triggs. Proposed work also includes those scenes which have same shape in temple and same in house detection. In the proposed work uses a model in which shape feature are taken to detect temple in images even when same shape is found in house.
Development of mobile markerless augmented reality for cardiovascular system in anatomy and physiology courses in physiotherapy education

This paper focuses on the development of markerless Augmented Reality (AR) using ARCore platform, where interactive three-dimensional (3D) content was designed and developed based on the learning outcome syllabus to enhance the visualization and understanding of the anatomy and physiology for cardiovascular system topic. Currently, learning method is based on 2D images and slides, plastic models and cadavers have to deal with students' experience issues such as lack of interactive, uneasy feeling with dead body and cadavers storing and donation. Therefore, more advances using technology such as Augmented Reality (AR) in learning method are needed to overcome the current gap and enhance the students' learning. Thus, this study aims to develop markerless AR specifically focus on the cardiovascular system for undergraduate physiotherapy program at UniKL, RCMP. In this study, we describe a method used to create markerless AR content using 3D data from MRI images and 3D unity as an authoring tool. We present three processes, where the first design consideration based on author's previous works derived from systematic search strategy were outlined, the second 3D model was developed using a real object and subsequently converted to an AR asset that can be linked to a unique markerless using ARCore platform and the third AR content creation using 3D unity authoring tool. This application provides a better visualization for the anatomical parts to support for an innovative and flexible learning process. We have successfully analyzed the design consideration using a systematic search strategy and developed the markerless AR specifically for cardiovascular system in anatomy and physiology courses. This study has contributed to knowledge in design and development of AR used in physiotherapy education. Therefore, this will be a step forward to an exploration of design-based research for an AR benefit in experienced-learning approach application.