Reviewerly/exHarmony huggingface dataset:
Spatial distribution and controls on organic and inorganic carbon in the soils of Cyprus

This study examines the spatial distribution of soil inorganic carbon (SIC) and soil organic carbon (SOC) in samples from over 3200 sites across Cyprus, collected as part of the Geochemical Atlas of Cyprus project. The concentration of SIC is largely controlled by underlying lithology, with distinct variations between the main geological terranes, especially across the boundaries between the mafic-ultramafic units of the Troodos Ophiolite and surrounding carbonate-dominated sedimentary units. Geology, land use, major and trace element concentrations, climatic factors and SIC have little influence on SOC, whereas vegetation type does have an influence as reflected in slightly elevated values in the forested Troodos Mountains and coastal wetlands compared with other areas. SOC displays more restricted spatial correlation than most other soil geochemical variables, indicating complex controls by specific site characteristics and other factors. The mean SIC and SOC values of 2.15% and 1.52% respectively are broadly consistent with previous localised studies of soils in Cyprus but less than that predicted using the low-density sampling of the GEMAS project. The average values are similar to other regions in southern Europe with comparable climatic, geological and soil characteristics. The results yield an estimated 27 Mt. of SOC contained in the top 20 cm of soils in Cyprus including 6 Mt. in forested areas. The extensive database and network of sampling locations provides an important resource for both long-term direct monitoring of changes in SOC in the soils of Cyprus and the development and testing of alternate SOC mapping methods including remote sensing.
Distributed variance-constrained robust filtering with randomly occurring nonlinearities and missing measurements over sensor networks

This paper is concerned with the distributed variance-constrained robust filtering problem for a class of time-varying stochastic systems subject to both randomly occurring nonlinearities and missing measurements. The target plant is disturbed by the multiplicative noises, randomly occurring nonlinearities as well as additive noises. The phenomena of the randomly occurring nonlinearities and missing measurements are modeled by the Bernoulli distributed random variables with known occurrence probabilities. The available measurements of each sensor node and its neighbor nodes can be communicated based on the network topology structure. Attention is focused on the design of a new distributed variance-constrained robust filtering algorithm such that, in the simultaneous presence of the missing measurements, multiplicative noises and randomly occurring nonlinearities, an upper bound of the filtering error covariance is obtained via the solutions to two recursive matrix equations. Subsequently, the filter parameters are designed to minimize the obtained upper bound of the filtering error covariance. Furthermore, by utilizing the mathematical induction method, a sufficient condition is provided to guarantee the boundedness of the upper bound of the filtering error covariance. At last, we provide a numerical simulation to illustrate the effectiveness of distributed variance-constrained robust filtering method.
Dual pattern-enhanced representations model for query-focused multi-document summarisation

To address the problem of query-focused multi-document summarisation, we present a novel unsupervised pattern-enhanced approach for representing coherent topics across documents, as well as the query relevance, in order to generate topically coherent summaries that meet the information needs of users. The proposed model employs not only a pattern-enhanced topic model to generate discriminative and semantic rich representations for topics and documents, but also a pattern-based relevance model for the query relevance of sentences. With these dual pattern-based representations for sentences, we are able to integrate various indicative metrics, such as rational coverage of document topics and sentence relevance, into a unified model. When evaluated on the datasets of the document understanding conferences of 2006 and 2007, the proposed approach shows a performance improvement as compared to a number of state-of-the-art methods and unsupervised baseline systems.
Differential Privacy and Federal Data Releases

Federal statistics agencies strive to release data products that are informative for many purposes, yet also protect the privacy and confidentiality of data subjects’ identities and sensitive attributes. This article reviews the role that differential privacy, a disclosure risk criterion developed in the cryptography community, can and does play in federal data releases. The article describes potential benefits and limitations of using differential privacy for federal data, reviews current federal data products that satisfy differential privacy, and outlines research needed for adoption of differential privacy to become widespread among federal agencies.
Underwater video mosaicing using topology and superpixel-based pairwise stitching

Expert and intelligent systems based on computer-vision algorithms are becoming a common part of our daily lives, as they help us solve problems in areas such as medicine, agriculture, transportation, and ecology. In this paper, we focus on the application of computer vision in marine ecology. Here, we propose a new algorithm for mosaicing images of coral reefs captured by scuba divers using hand-held cameras during reef surveys. Such images often capture partial views of the surveyed area that are then collated into mosaics of the reef assemblages. Accurate mosaics will help coral-reef researchers rapidly assess not only the status of coral reefs, but also determine rates of recovery or rates of decline using longitudinal data. Most standard mosaicing algorithms distort the images, however, and moving objects and parallax errors, which usually happen in underwater images, hinder mosaic construction. To overcome these issues, our new algorithm uses a two-step approach that first detects and then removes loops and low-quality regions from an estimated camera trajectory. The algorithm then stitches the remaining images by warping parallax-affected image regions with a geometric transformation different from the one used for parallax-unaffected regions. We tested our method on images captured during a large spatial survey of coral reef sites throughout the Caribbean region. Our method obtained better-quality mosaics than other state-of-the-art algorithms.
A Multitier Stacked Ensemble Algorithm for Improving Classification Accuracy

For real-world problems, ensemble learning performs better than the individual classifiers. This is true for datasets that have many instances closer to the decision boundary. Using a meta-learner to learn from the predictions of the base classifiers generalizes better. Hence, stacked ensemble (SE) is preferred over other ensemble methods. We extend the SE and propose a multitier stacked ensemble (MTSE) algorithm with three tiers, namely, a base tier, an ensemble tier, and a generalization tier. The base tier uses the traditional classifiers to predict the labels. Tenfold cross-validation is used to validate the models in the base tiers. The cross-validated predictions are combined using combination schemes in the next tier. The predictions from the ensemble tier are generalized using meta-learning to give the final prediction. When tested with 36 datasets, the MTSE gives superior performance over the SE. It achieves high accuracy and does not suffer from over-fitting/under-fitting.
Unsupervised Oil Tank Detection by Shape-Guide Saliency Model

In this letter, a novel oil tank detection framework based on a shape-guide saliency (SGS) model is proposed. Beyond the low-level visual stimuli, SGS focuses more on simulating the selective visual searching, which is dominated by the goal in human minds. Using a top–down strategy, SGS breaks the limitation of the low-level visual features and introduces the high-level task concept to measure saliency. For the oil tank detection, SGS model skillfully extracts the contour shape cue (CSC) as the target-oriented information and uses CSC to guide the selective saliency value calculation. Specifically, a sparse reconstruction with the target-specific dictionary is implemented to generate the saliency map. This saliency map only assigns high values to oil tank regions instead of highlighting all high-contrast regions. Consequently, SGS model is capable of accurately locating oil tanks and eliminating the interferences of high-contrast backgrounds. Experimental results on a remote sensing data set demonstrate that the proposed SGS model outperforms five class-independent saliency models. Comparisons with the state-of-the-art oil tank detection approaches demonstrate the effectiveness of the proposed method.
An intelligent validation system for diagnostic and prognosis of ultrasound fetal growth analysis using Neuro-Fuzzy based on genetic algorithm

Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. Based upon the analysis of existing algorithms for the automatic fetal development measurement, a new algorithm known as Neuro-Fuzzy based on genetic algorithm is developed. Firstly, the fetal ultrasound benchmark image is auto-pre-processed using Normal Shrink Homomorphic technique. Secondly, the features are extracted using Gray Level Co-occurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), Intensity Histogram (IH) and Rotation Invariant Moments (IM). Thirdly, Neuro-Fuzzy using Genetic approach is used to distinguish among the fetus growth as abnormal or normal. Experimental results using benchmark and live dataset demonstrate that the developed method achieves an accuracy of 97% as compared to the state-of- art methods in terms of parameters such as Sensitivity, Specificity, Recall, F-Measure &Precision Rate. The use of area under the receiver of characteristics(AUC) and confusion matrix as assessment indicators is also cross-validated using various classification methods by achieving best accuracy rate of Support Vector Machine (SVM) i.e. 98.7% as compare to other classification methods such as KNN, Ensemble methods, Linear Discriminant Analysis(LDA) and Decision Tree whereas ROC curve covers 0.9992 SVM.
Interpretable fuzzy partitioning of classified data with variable granularity

Fuzzy rule-based systems are effective tools for acquiring knowledge from data and represent it in a linguistically interpretable form. To achieve interpretability, input features are granulated in fuzzy partitions. A critical design decision is the selection of the granularity level for each input feature. This paper presents an approach, called DC* (Double Clustering with A*), for automatically designing interpretable fuzzy partitions with optimal granularity. DC* is specific for classification problems and is mainly based on a two-stage process: the first stage identifies clusters of multidimensional samples in order to derive class-labeled prototypes; in the second stage the one-dimensional projections of such prototypes are further clustered along each dimension simultaneously, thus minimizing the number of clusters for each feature. Moreover, the resulting one-dimensional clusters provide information to define fuzzy partitions that satisfy a number of interpretability constraints and exhibit variable granularity levels. The fuzzy sets in each partition can be labeled by meaningful linguistic terms and used to represent knowledge in a natural language form. Experimental results on both synthetic and real data show that the derived fuzzy partitions can be exploited to define very compact fuzzy rule-based systems that exhibit high linguistic interpretability and good classification accuracy.
Reexamination of the decoherence of spin registers

We revisit decoherence process of a qubit register interacting with a thermal bosonic bath. We generalize the previous studies by considering not only the register's behavior but also of a part of its environment. In particular, we are interested in information flow from the register to the environment, which we describe using recently introduced multipartite quantum state structures called Spectrum Broadcast Structures. Working in two specific cases of: i) two-qubit register and ii) collective decoherence, we identify the regimes where the environment acquires almost complete information about the register state. We also study in more detail the interesting causal aspects, related to the finite propagation time of the field disturbances between the qubits. Finally, we describe quantum state structures which appear due to the presence of protected spaces.
RETRACTED: The impact of socio-cultural differences on formation of intrinsic motivation: The case of local and foreign students

Abstract Currently, intrinsic motivation becomes a key focus in the classroom processes, including language learning. This paper examines the commonalities and differences in the formation of intrinsic motivation of local and foreign students based on their socio-cultural background. The sample consisted of 107 students of Foreign Languages Department in Kazan Federal University (56% Russians, 44% foreign). Thus, foreign students from Uzbekistan, Tadzhikistan and Turkmenistan tend to experience mental and even physical discomfort, which strengthens their general belief in achieving their goal. Local students on the contrary feel at home thus they do not fall apart from the usual way of life and have to search for some inner inspiration. The article describes the psychological process in interaction with social and cultural environment and may serve as a tool for teachers to support developmental strategies. Findings aim to increase intercultural understanding between international students and foreign language teachers as well as to improve university policies that address students’ needs.
TAPON: A two-phase machine learning approach for semantic labelling

Through semantic labelling we enrich structured information from sources such as HTML pages, tables, or JSON files, with labels to integrate it into a local ontology. This process involves measuring some features of the information and then finding the classes that best describe it. The problem with current techniques is that they do not model relationships between classes. Their features fall short when some classes have very similar structures or textual formats. In order to deal with this problem, we have devised TAPON: a new semantic labelling technique that computes novel features that take into account the relationships. TAPON computes these features by means of a two-phase approach. In the first phase, we compute simple features and obtain a preliminary set of labels (hints). In the second phase, we inject our novel features and obtain a refined set of labels. Our experimental results show that our technique, thanks to our rich feature catalogue and novel modelling, achieves higher accuracy than other state-of-the-art techniques.
Try to Start It! The Challenge of Reusing Code in Robotics Research

This letter reviews the source code published with the papers of a flagship robotics research conference, 2017 International Conference on Robotics and Automation. The aim is to investigate whether the code is actually useful, i.e., can be reused by an interested reader without much effort. The interest is twofold: for one side, it makes possible to replicate and validate the results of the research; for another side, it facilitates new progress on the field, since researchers can build new systems on top of existing work. Unfortunately, reusing code is not as straightforward as it could seem, and there is a need for tools that alleviate the effort for integrating someone else's code into the own user's system. We propose the use of Docker, a Linux container technology, to turn the source code repositories into executable images, that can be run and tested locally, in an isolated environment, without the need of a costly integration with the host system.
Abstracting extensible data types: or, rows by any other name

We present a novel typed language for extensible data types, generalizing and abstracting existing systems of row types and row polymorphism. Extensible data types are a powerful addition to traditional functional programming languages, capturing ideas from OOP-like record extension and polymorphism to modular compositional interpreters. We introduce row theories, a monoidal generalization of row types, giving a general account of record concatenation and projection (dually, variant injection and branching). We realize them via qualified types, abstracting the interpretation of records and variants over different row theories. Our approach naturally types terms untypable in other systems of extensible data types, while maintaining strong metatheoretic properties, such as coherence and principal types. Evidence for type qualifiers has computational content, determining the implementation of record and variant operations; we demonstrate this in giving a modular translation from our calculus, instantiated with various row theories, to polymorphic λ-calculus.
That Again: A Multivariate Analysis of the Factors Conditioning Syntactic Explicitness in Translated English

Abstract This article aims to disentangle three explanations that have been proposed for the increased explicitness of translated English, as reflected in the more frequent use of the complementiser that in translated English texts compared to non-translated English texts. These three explanations are designated as the cognitive complexity (or processing strain) hypothesis, the pragmatic risk-aversion hypothesis and the source-language transfer hypothesis. Four comparable register-controlled corpora are used for the analysis: a corpus of English translated from Afrikaans, a corpus of written Afrikaans, and corpora of written British and native South African English. A multivariate analysis of the factors conditioning complementiser omission across the four corpora is used to test the three hypotheses proposed. The transfer hypothesis is tested by investigating whether the translation corpus demonstrates overall omission preferences that are more similar to the omission preferences of Afrikaans than of English. The cognitive complexity hypothesis is tested by investigating whether translated English is more sensitive to the complexity-related factors that are known to condition omission than non-translated English. The risk-aversion hypothesis is tested by investigating whether translations opt for the communicatively and normatively “safer” choice of including the complementiser in contexts where non-translated writing would typically omit it, and therefore demonstrate less sensitivity to register and frequency effects than non-translated English. The findings of the study provide strong evidence against the transfer hypothesis and find stronger support for the pragmatic risk-aversion hypothesis although the cognitive complexity hypothesis cannot be ruled out.
The application of SLM in shearography detecting system

The paper presents a new shearography system which applies a spatial light modulator (SLM). The system can control the shearing amount and direction as well as introduce the phase shift accurately and automatically, making shearographic testing easier and more practical. Shearography has the known advantages for nondestructive test and strain measurements of being fast, full-field, and having high sensitivity. Traditionally, the shearing amount is controlled by adjusting a reflector mechanically. Mechanical adjustment of the reflector is slow, inconvenient and not well suited for in-field NDT practice. For application of phase shift techniques, a piezoelectric transducer (PZT), which is usually used for application of phase shift techniques, introduces random error due to its tendency to be nonlinear. This paper introduces a new shearographic detection system with a spatial light modulator. The spatial light modulator controls the liquid crystal by using a birefringence effect and twisted nematic effect. The spatial light modulator can change the phase of the light and shearing amount accurately and automatically, thus eliminating the nonlinear random error in the phase shift method. Furthermore, this system also improves testing efficiency. In this paper, the fundamental of SLM shearography is presented. The capability of this optical configuration is demonstrated theoretically as well as through experiments.
A novel algorithm for handling reducer side data skew in MapReduce based on a learning automata game

In many MapReduce applications, there is an unbalanced distribution of intermediate map-outputs to the reducers. The partitioner determines the load on the reducers. The completion time for a MapReduce job is determined as the slowest reduce task. Under normal conditions assigning a huge amount of data to a task will increase the time required for completion. The current study presents an adaptive algorithm called LAHP (learning automata hash partitioner) that is based on a learning automata game for custom distribution of intermediate key-value pairs to reducers. In this algorithm, a learning automaton on every mapper node is set to control the load on the reducers. This leads to a learning automata game during the execution of a job. This algorithm can partition the intermediate key-value pairs arbitrarily regardless of the statistical distribution of input data and pre-processing. Using the Bonett-test at a confidence level of 95%, the standard deviation ratio of hash-to-LAHP was [0.1, 2858]. This means that LAHP showed much lower dispersion. The results show that the proposed algorithm can successfully distribute any custom load to reducers with an accuracy of over 99% and can speed up the execution of popular applications more than four-fold.
A feature learning approach for face recognition with robustness to noisy label based on top-N prediction

Collecting a vast amount of face data with identity labels to train a convolutional neural network is an effective mean to learn a discriminative feature representation for face recognition. However, the datasets with larger scale often contain more noisy labels, that directly affects the ultimate performance of the learned model. This paper proposes an end-to-end feature learning method with robustness to noisy label. First, a data filtering method is proposed to automatically online filter out the data with false label, by checking the consistency between the annotated label and the results of top-N prediction. Then the loss functions of softmax and center loss are simply revised to only supervise the reserved feature. Finally, we use MS-Celeb-1M dataset, which contains massive noisy labels, to train a 128-D feature representation without any pre-train or data pre-clean. A single learned model gets an accuracy of 99.43% on LFW test set, that is very close to the model trained using the clean data.