Reviewerly/exHarmony huggingface dataset:
Geochemical patterns of Cu, Au, Pb and Zn in stream sediments from Tongling of East China: Compositional and geostatistical insights

Geochemical patterns of elements in surficial sediments are of significance in deciphering processes and locating mineral resources. Stream sediment geochemical data of four ore-forming elements (Cu, Au, Pb and Zn) and fourteen associated elements (Ag, As, Bi, Cd, La, Mn, Mo, Nb, Sb, Th, U, W, Y and Zr) from the Tongling Ore Cluster District (TOCD), East China were analyzed by compositional multivariate and geostatistical approaches. It is shown that the median values of the four ore-forming and main associated elements including Ag, As, Bi, Cd and Sb are at least two times larger than those in stream sediments of China, indicating that there was a noteworthy addition of such ore minerals as (gold-rich) chalcopyrite and pyrite, galena and sphalerite into the sediments. The first three factors that explain 71.4% of the total variance could represent the dominant geology in the TOCD including the felsic intrusive rocks by F1 (−) (Zr-Th-Nb-Y-La) and F2 (−) (U-Y-Th-Nb-La), Pb and Zn-bearing strata and related mineralization by F1 (+) (Pb-Cd-Ag-Mn-Sb-Zn), skarn Cu mineralization by F2 (+) (Bi- Cu-Au-As) and porphyry Cu mineralization by F3 (−) (MoW). Simultaneously, regression analysis exhibited a closer relationship of Cu in the stream sediments with the skarn-type Cu mineralization than the porphyry counterpart. The geostatistical semivariogram modeling reflected that the greatest continuity of Cu, Au, Pb and Zn is at NE4°, SE95°, NE50° and NE42°, respectively. Moreover, the factor score and balance maps from factor analysis and sequential binary partition (SBP) illustrated the geochemical patterns of the elements. According to the modeling and spatial patterns, not only the possible sources (geogenic vs. anthropogenic), but also the controlling factors have determined. High levels of Cu, Bi, Mo and W are dominated by the felsic intrusives and related mineralization. That of Au, however, is governed by combined effect of the basement fault system and felsic intrusives. In contrast, high concentrations of Pb and Zn and such associated elements as Cd and Sb are controlled by the Permian and Triassic strata and associated PbZn mineralization. Exploration suggestions and targets were proposed accordingly. It demonstrates that compositional and geostatistical analyses are effective to characterize geochemical patterns of elements.
Neural Comment Generation for Source Code with Auxiliary Code Classification Task

Code comments help program developers understand programs, read and navigate source code, thus resulting in more efficient software maintenance. Unfortunately, many codes are not commented adequately, or the code comments are missing. So developers have to spend additional time in reading source code. In this paper, we propose a new approach to automatically generating comments for source codes. Following the intuition behind the traditional sequence-to-sequence (Seq2Seq) model for machine translation, we propose a tree-to-sequence (Tree2Seq) model for code comment generation, which leverages an encoder to capture the structure information of source code. More importantly, code classification is involved as an auxiliary task for aiding the Tree2Seq model. We build a multi-task learning model to achieve this goal. We evaluate our models on a benchmark dataset with automatic metrics like BLEU, ROUGE, and METEOR. Experimental results show that our proposed Tree2Seq model outperforms traditional Seq2Seq model with attention, and our proposed multi-task learning model outperforms the state-of-the-art approaches by a substantial margin.
Calibrating generative models: The probabilistic Chomsky–Schützenberger hierarchy

A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied, with the aim of understanding the expressive power of generative models. We offer characterizations of the distributions definable at each level of the hierarchy, including probabilistic regular, context-free, (linear) indexed, context-sensitive, and unrestricted grammars, each corresponding to familiar probabilistic machine classes. Special attention is given to distributions on (unary notations for) positive integers. Unlike in the classical case where the "semi-linear" languages all collapse into the regular languages, using analytic tools adapted from the classical setting we show there is no collapse in the probabilistic hierarchy: more distributions become definable at each level. We also address related issues such as closure under probabilistic conditioning.
About Generative Aspects of Variational Autoencoders

An essential prerequisite for random generation of good quality samples in Variational Autoencoders (VAE) is that the distribution of variables in the latent space has a known distribution, typically a normal distribution N(0, 1). This should be induced by a regularization term in the loss function, minimizing for each data X, the Kullback-Leibler distance between the posterior inference distribution of latent variables Q(z|X) and N(0, 1). In this article, we investigate the marginal inference distribution Q(z) as a Gaussian Mixture Model, proving, under a few reasonable assumptions, that although the first and second moment of Q(z) might indeed be coherent with those of a normal distribution, there is no reason to believe the same for other moments; in particular, its Kurtosis is likely to be different from 3. The actual distribution of Q(z) is possibly very far from a Normal, raising doubts on the effectiveness of generative sampling according to the vanilla VAE framework.
Classification of Changes in API Evolution

Applications typically communicate with each other, accessing and exposing data and features by using Application Programming Interfaces (APIs).Even though API consumers expect APIs to be steady and well established, APIs are prone to continuous changes, experiencing different evolutive phases through their lifecycle.These changes are of different types, caused by different needs and are affecting consumers in different ways.In this paper, we identify and classify the changes that often happen to APIs, and investigate how all these changes are reflected in the documentation, release notes, issue tracker and API usage logs.The analysis of each step of a change, from its implementation to the impact that it has on API consumers, will help us to have a bigger picture of API evolution.Thus, we review the current state of the art in API evolution and, as a result, we define a classification framework considering both the changes that may occur to APIs and the reasons behind them.In addition, we exemplify the framework using a software platform offering a Web API, called District Health Information System (DHIS2), used collaboratively by several departments of World Health Organization (WHO).
Code Smell Prediction Employing Machine Learning Meets Emerging Java Language Constructs

Background: Defining code smell is not a trivial task. Their recognition tends to be highly subjective. Nevertheless some code smells detection tools have been proposed. Other recent approaches incline towards machine learning (ML) techniques to overcome disadvantages of using automatic detection tools. Objectives: We aim to develop a research infrastructure and reproduce the process of code smell prediction proposed by Arcelli Fontana et al. We investigate ML algorithms performance for samples including major modern Java language features. Those such as lambdas can shorten the code causing code smell presence not as obvious to detect and thus pose a challenge to both existing code smell detection tools and ML algorithms. Method: We extend the study with dataset consisting of 281 Java projects. For driving samples selection we define metrics considering lambdas and method reference, derived using custom JavaParser-based solution. Tagged samples with new constructions are used as an input for the utilized detection techniques. Results: Detection rules derived from the best performing algorithms like J48 and JRip incorporate newly introduced metrics. Conclusions: Presence of certain new Java language constructs may hide Long Method code smell or indicate a God Class. On the other hand, their absence or low number can suggest a Data Class.
Exploiting color name space for salient object detection

In this paper, we will investigate the contribution of color names for the task of salient object detection. An input image is first converted to color name space, which is consisted of 11 probabilistic channels. By exploiting a surroundedness cue, we obtain a saliency map through a linear combination of a set of sequential attention maps. To overcome the limitation of only using the surroundedness cue, two global cues with respect to color names are invoked to guide the computation of a weighted saliency map. Finally, we integrate the above two saliency maps into a unified framework to generate the final result. In addition, an improved post-processing procedure is introduced to effectively suppress image backgrounds while uniformly highlight salient objects. Experimental results show that the proposed model produces more accurate saliency maps and performs well against twenty-one saliency models in terms of three evaluation metrics on three public data sets.
SIFT-Based Visual Tracking using Optical Flow and Belief Propagation Algorithm

Abstract Perceptible visual tracking acts as an important module for distinct perception tasks of autonomous robots. Better features help in easier decision-making process. The evaluation of tracking objects, dynamic positions and their visual information in results are quite difficult tasks. Until now, most real-time visual tracking algorithms suffer from poor robustness and low occurrence as they deal with complex real-world data. In this paper, we have proposed more robust and faster visual tracking framework using scale invariant feature transform (SIFT) and the optical flow in belief propagation (BF) algorithm for efficient processing in real scenarios. Here, a new feature-based optical flow along with BF algorithm is utilized to compute the affine matrix of a regional center on SIFT key points in frames. Experimental results depict that the proposed approach is more efficient and more robust in comparison with the state-of-the-art tracking algorithms with more complex scenarios.
Classifying Approaches for Constructing Single Underlying Models

Multi-view environments for software development allow different views of a software system to be defined to cover the requirements of different stakeholders. One way of ensuring consistency of overlapping information often contained in such views is to project them “on demand” from a Single Underlying Model (SUM). However, there are several ways to construct and adapt such SUMs. This paper presents four archetypal approaches and analyses their advantages and disadvantages based on several new criteria. In addition, guidelines are presented for selecting a suitable SUM construction approach for a specific project.
A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.
Objective Assessment of Perceived Geometric Distortions in Viewport Rendering of 360° Images

To render omnidirectional (or 360°) visual content, a projection that maps the pixels from a portion of the viewing sphere to a 2D plane must be employed; this projection creates the viewport image shown to the user and thus has an important role on the quality of experience, for this type of content. However, a sphere to planar projection always introduces geometrical distortions on the rendered image, such as stretching and/or bending of some image regions and structures, which may impact negatively the quality offered to the users. In this article, a content-aware objective quality metric, that predicts the perceived viewport quality, is proposed for the general perspective projection (GPP) rendering of 360° images, which includes the popular rectilinear and stereographic projections. The proposed metric relies on two set of features that characterize the bending of straight lines and stretching of image regions. The extracted features, and the corresponding viewport subjective quality CMOS (comparative mean opinion scores) are then used to build a quality prediction model, based on Support Vector Regression (SVR). The experimental results show that the proposed metric is able to predict the viewport CMOS with a Pearson correlation coefficient close to 0.8. Furthermore, it is shown that the proposed metric may be used to accurately find the GPP projection center that minimizes the perceived geometric distortions, according to the viewport content.
Investigating Examination Behavior in Mobile Search

Examination is one of the most important user interactions in Web search. A number of works studied examination behavior in Web search and helped researchers better understand how users allocate their attention on search engine result pages (SERPs). Compared to desktop search, mobile search has a number of differences such as fewer results on the screen. These differences bring in mobile-specific factors affecting users' examination behavior. However, there still lacks research on users' attention allocation mechanism via viewports in mobile search. Therefore, we design a lab-based study to collect user's rich interaction behavior in mobile search. Based on the collected data, we first analyze how users examine SERPs and allocate their attention to heterogeneous results. Then we investigate the effect of mobile-specific factors and other common factors on users allocating attention. Finally, we apply the findings of user attention allocation from the user study into click model construction efforts, which significantly improves the state-of-the-art click model. Our work brings insights into a better understanding of users' interaction patterns in mobile search and may benefit other mobile search-related research.
Nonlinear gated channels networks for action recognition

Video-based convolutional neural networks (CNNs) involve a large amount of training parameters, leading to the enormous computational complexity, which thereby delays the network convergence. Therefore, training successful CNN for action recognition rapidly is non-trivial. In this paper, a novel encoding called nonlinear gated channels unit (NGCU) is proposed to facilitate network training by encoding the global channel-level relationship. Based on this, nonlinear gated channels network (NGCN) is constructed for the end-to-end encoding, and the corresponding convergence performance is evaluated on the standard benchmarks UCF101 and HMDB51. Experimental results demonstrate that the proposed method is conducive to the convergence process of CNN based action recognition models.
Echo State Network for Extended State Observer and Sliding Mode Control of Vehicle Drive Motor with Unknown Hysteresis Nonlinearity

An echo state network (ESN) for extended state observer (ESO) and sliding mode control (SMC) of permanent magnet synchronous motor (PMSM) in an electric vehicle system is investigated in this paper. For the PMSM model, most researches neglect the hysteresis loss and other nonlinear factors, which reduces the accuracy of the PMSM model. We present a modified PMSM model considering the hysteresis loss and then transform the new PMSM model to a canonical form to simplify the controller design. In order to deal with the hysteresis loss, an ESN is utilized to estimate the nonlinearity. Considering that some states cannot be directly obtained, an ESO with ESN is proposed to estimate unknown system states of the electric vehicle PMSM system. Afterwards, an SMC is adopted to control the closed-loop system based on the ESO with ESN, and a double hyperbolic function instead of the sign function is used to suppress the chattering of the SMC. The stabilities of the observer and the controller are all guaranteed by Lyapunov functions. Finally, simulations are presented to verify the validity of the echo state network for extended state observer and the neural network sliding mode control.
Hybridization of cognitive computing for food services

The application of data mining technology to food services and the restaurant industry has certain social value. By predicting customer traffic and needs, a restaurant can prepare a reasonable amount of meals for customers according to predicted needs which is conducive to improving the dining experience of customers and also improving the quality of food preparation and making the restaurant itself operate more efficiently. In recent years, we have seen the use of collaborative robots for use in the fast food industry. In Asia and more specifically in Japan, we have seen many fast-food chains implement the use of robots to better serve their customers. By studying the linear regression algorithm and the random forest algorithm, this paper proposes a new interwoven novel fusion approach of combining both algorithms and applies the new model to restaurant data to assist in the prediction of customer traffic in the restaurant industry. This predictive algorithm using cognitive techniques can assist these newly place robots in the food industry better serve their client base and in doing so make the industry more efficient. Experimental, comparison, and analysis are reported in the paper. The error rate of the fusion solution is reduced by approximately 5.503% compared with the linear regression algorithm and is approximately 3.719% lower than the error rate of the random forest algorithm. Results show that the new fusion algorithm can achieve better prediction results of customer traffic prediction for the restaurant industry. Furthermore, we also provide a new take on the application of data mining technology in the restaurant industry itself.
Genetic programming based feature construction methods for foreground object segmentation

Foreground object segmentation is a crucial preprocessing step for many high-level computer vision tasks, e.g. object recognition. It is still challenging to achieve accurate segmentation, especially for complex images (e.g. with high variations). Feature construction can help to improve the segmentation performance by extracting more distinctive features for foreground/background regions from the original features. However, commonly-used feature construction methods (e.g. principle component analysis) often involve certain assumptions/constraints, and the constructed features cannot be interpreted. To address these problems, genetic programming (GP) is employed in this paper, which is a well-suited feature construction technique. The aim of this work is to design new feature construction methods using GP, and analyse/compare popular GP-based feature construction methods for foreground object segmentation, especially on complex image datasets with high variations. Specifically, one new feature construction method that incorporates the subtree technique in GP is designed, which can construct multiple features simultaneously (called SubtMFC, Subtree Multiple Feature Construction). Moreover, a parsimony pressure technique is introduced to improve SubtMFC for bloat control (a common issue for GP-based methods), which forms the method, PSubtMFC (Parsimony SubtMFC). In addition, comparison of popular GP-based feature construction methods for foreground object segmentation is conducted for the first time. Results show that SubtMFC achieves better or similar performance compared with three reference methods. In addition, compared with SubtMFC that does not control bloat, PSubtMFC can significantly reduce the solution size while maintain similar performance in the segmentation accuracy. The GP-based feature construction framework is further extended for feature representation based knowledge transfer, which can handle the problem of the scare labelled training data. Moreover, after GP is thoroughly investigated on benchmark datasets with one type of foreground objects (i.e. the Weizmann horse dataset and Pascal aeroplane dataset), it is considered whether the GP methods can perform well on datasets containing multiple types of foreground objects. Compared with three other well-performing GP-based feature construction methods, the proposed method achieves better or comparable results for the given segmentation tasks. In addition, this paper thoroughly compares/analyses popular GP-based feature construction methods for complex figure-ground segmentation for the first time. Moreover, further analyses on the input features frequently used by the GP-evolved feature construction functions reflect the effectiveness of the extracted high-level features.
A Randomized Cross-Over Trial Focused on Breast Core Needle Biopsy Skill Acquisition and Safety Using High Fidelity Versus Low Fidelity Simulation Models in Rwanda

Breast cancer is the most common cancer diagnosed in low and middle-income countries. Growing the number of health care personnel trained in diagnostic procedures like breast core needle biopsy (BCNB) is critical. We developed a BCNB simulation-training course that evaluated skill acquisition, confidence, and safety, comparing low-cost low fidelity (LF) models to expensive high fidelity (HF) models. A single-center randomized education crossover trial was implemented. Participants were randomized to HF or LF groups. A preintervention baseline exam followed by lectures and training sessions with a HF or LF model was implemented. A postintervention simulation exam was conducted, and participants crossed over to the other simulation model. The study was implemented at the University Teaching Hospital, Kigali (CHUK) in Rwanda, Africa from October 2014 to March 2015. Residents training in surgery or obstetrics and gynecology participated in a 1-day BCNB training course. A total of 36 residents were analyzed, 19 in the HF arm and 17 in the LF arm. Mean difference in exam scores for HF and LF groups in the baseline exam (exam 1) (0.067, p = 0.94, standard error [SE] of 1.57) postintervention exam (exam 2) (1.85, SE 1.46, p = 0.33), and the crossover exam (exam 3) (4.39, SE = 1.90, p = 0.11) were not significantly different between HF and LF. Overall exam scores improved from pre- to postintervention. Our results indicate that mean difference in exams scores were not significantly different between residents trained with HF versus LF models. In resources poor areas—LF models can be utilized as effective teaching tools for skill acquisition for diagnostic surgical procedures.