Reviewerly/exHarmony huggingface dataset:
A cognitive approach to teaching deictic motion verbs to German and Italian students of Spanish

Abstract In this study we compare two instruction approaches (cognitive and traditional) to the teaching of Spanish deictic motion verbs – ir, venir, llevar and traer – to German and Italian learners. We also analyse whether the students’ first language (Italian or German) influences the results of the cognitive methodology we applied. The Cognitive Instruction combined the basic principles of Cognitive Grammar with those of Processing Instruction for activities in which students practice both comprehension and production. We carried out a survey of 274 university students who were learning Spanish (Level B1) at universities in Italy and Germany. Students carried out a test prior to receiving the instruction and three tests subsequently, one immediately afterwards, the second a week later and the third, a month later. The cognitive methodology proved to be beneficial and positive. The students who received cognitive instruction made better form-meaning connections and showed higher performances in the use of deictic motion verbs than those who received traditional instruction. The learners’ L1 did not appear to influence the results of the groups that received the cognitive method of instruction.
A More Informative Way to Name Plutonic Rocks

The International Union of Geological Sciences (IUGS) system for rock classification, introduced more than 40 years ago, has served geologists well but suffers from the problem of dividing a continuum of rock compositions into arbitrary bins.As a result, closely related rocks can be given unrelated names (e.g., granodiorite and tonalite), and the names themselves, which were generally derived from the names of places or people, rarely contribute to understanding the processes that generate the diversity of igneous rocks.Here we propose a quantitative modification to the IUGS system that reduces the number of distinct names but more effectively communicates the inherent variability of plutonic rocks.The system recognizes that mapped plutonic rock units are characterized by recognizable textures and mineral assemblages, but that mineral proportions within those units can be highly variable.Adding quantitative data to rock names is an important step toward moving geologic field observations into quantitative digital form and preparing them for advanced data mining and analysis.One thing quarks do have going for them: all their names are simple-something chemists, biologists, and especially geologists seem incapable of achieving when naming their own stuff.-Neil deGrasse Tyson, Astrophysics for People in a Hurry
Echo-State Restricted Boltzmann Machines: A Perspective on Information Compensation

Feature learning has been introduced in the modeling of echo state networks (ESNs). However, the procedure of feature learning is generally accompanied by certain information loss. In this regard, this paper proposes a hybrid neural network model from the information compensation view, named echo-state restricted Boltzmann machine (ERBM). It is deemed as a unified and coherent architecture with the successive functionalities of feature learning, information compensation, input superposition, and supervised nonlinear approximation. This is the first systematic model seeking to improve the ESN representative by that a direct weighted compensation channel is built to enhance feature learning. On the widely used benchmarks, we demonstrate that ERBM is fully competent to handle the nonlinear approximation tasks and superior to the state of the art in nonlinear approximation, robustness, and memory capacity.
Contesting #StopIslam: The Dynamics of a Counter-narrative Against Right-wing Populism

This paper sets out quantitative findings from a research project examining the dynamics of online counter-narratives against hate speech, focusing on #StopIslam, a hashtag that spread racialized hate speech and disinformation directed towards Islam and Muslims and which trended on Twitter after the March 2016 terror attacks in Brussels. We elucidate the dynamics of the counter-narrative through contrasting it with the affordances of the original anti-Islamic narrative it was trying to contest. We then explore the extent to which each narrative was taken up by the mainstream media. Our findings show that actors who disseminated the original hashtag with the most frequency were tightly-knit clusters of self-defined conservative actors based in the US. The hashtag was also routinely used in relation to other pro-Trump, anti-Clinton hashtags in the run-up to the 2016 presidential election, forming part of a broader, racialized, anti-immigration narrative. In contrast, the most widely shared and disseminated messages were attempts to challenge the original narrative that were produced by a geographically dispersed network of self-identified Muslims and allies. The counter-narrative was significant in gaining purchase in the wider media ecology associated with this event, due to being reported by mainstream media outlets. We ultimately argue for the need for further research that combines ‘big data’ approaches with a conceptual focus on the broader media ecologies in which counter-narratives emerge and circulate, in order to better understand how opposition to hate speech can be sustained in the face of the tight-knit right-wing networks that often outlast dissenting voices.
Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) is to detect the largest number of defective software modules in one project by using historical data collected from other projects with different metrics. However, these data can not be directly used because of different metrics set among projects. Meanwhile, software data have more non-defective instances than defective instances which may cause a significant bias towards defective instances. To completely solve these two restrictions, we propose unsupervised deep domain adaptation approach to build a HDP model. Specifically, we firstly map the data of source and target projects into a unified metric representation (UMR). Then, we design a simple neural network (SNN) model to deal with the heterogeneous and class-imbalanced problems in software defect prediction (SDP). In particular, our model introduces the Maximum Mean Discrepancy (MMD) as the distance between the source and target data to reduce the distribution mismatch, and use the cross-entropy loss function as the classification loss. Extensive experiments on 18 public projects from four datasets indicate that the proposed approach can build an effective prediction model for heterogeneous defect prediction (HDP) and outperforms the related competing approaches.
Distortion-specific feature selection algorithm for universal blind image quality assessment

Blind image quality assessment (BIQA) aims to use objective measures for predicting the quality score of distorted images without any prior information regarding the reference image. Several BIQA techniques are proposed in literature that use a two-step approach, i.e., feature extraction for distortion classification and regression for predicting the quality score. In this paper, a three-step approach is proposed that aims to improve the performance of BIQA techniques. In the first step, feature extraction is performed using existing BIQA techniques to determine the distortion type. Secondly, features are selected for each distortion type based on the mean value of Spearman rank ordered correlation constant (SROCC) and linear correlation constant (LCC). Lastly, distortion-specific features are used by regression model to predict the quality score. Experimental results show that the predicted quality score using distortion-specific features strongly correlates with the subjective quality score, improves the overall performance of existing BIQA techniques, and reduces the processing time.
Framework for enabling order management process in a decentralized production network based on the blockchain-technology

The promise of immutable documents to make it easier and less expensive for consumers and producers to collaborate in a verifiable way would represent an enormous progress, especially as companies strive for establish service contracts which are based on the flow of many small transactions using machine-to-machine communication. The blockchain technology logs these data, verifies the authenticity and make them available for service offers. This work deal with an architecture enabling to setup order processing between consumers and producers using blockchain. In this way, the technical feasibility is shown and the special characteristics of blockchain production networks will be discussed.
Tensors.jl — Tensor Computations in Julia

Tensors.jl is a Julia package that provides efficient computations with symmetric and non-symmetric tensors. The focus is on the kind of tensors commonly used in e.g. continuum mechanics and fluid dynamics. Exploiting Julia's ability to overload Unicode infix operators and using Unicode in identifiers, implemented tensor expressions commonly look very similar to their mathematical writing. This possibly reduces the number of bugs in implementations. Operations on tensors are often compiled into the minimum assembly instructions required, and, when beneficial, SIMD-instructions are used. Computations involving symmetric tensors take symmetry into account to reduce computational cost. Automatic differentiation is supported, which means that most functions written in pure Julia can be efficiently differentiated without having to implement the derivative by hand. The package is useful in applications where efficient tensor operations are required, e.g. in the Finite Element Method. Funding statement: Support for this research was provided by the Swedish Research Council (VR), grant no. 621-2013-3901 and grant no. 2015-05422.
Application of Computer Data Analysis Technology in the Development of a Physical Education Examination Platform

This physical education assessment platform is designed based on the data min-ing technology to address a number of problems like large workload in the actual physical education performance examination of school students so as to help teachers offer individualized teaching. In the design and development of the com-puter-data-analysis-based physical education assessment platform, this paper adopts the ASP.NET technology and employs the three-layer architecture. The application of this platform can help solve the problems currently existing in the physical education management and realize information-based and automated physical education performance examination.
Double Circulant Self-Dual and LCD Codes Over ℤp2

We study double circulant LCD codes over [Formula: see text] for all odd primes [Formula: see text] and self-dual double circulant codes over [Formula: see text] for primes [Formula: see text]. We derive exact enumeration formulae, and asymptotic lower bounds on the minimum distance of the [Formula: see text]-ary images of these codes by the classical Gray maps.
Effective shadow removal via multi-scale image decomposition

Shadow removal is a fundamental and challenging problem in image processing field. Current approaches can only process shadows with simple scenes. For complex texture and illumination, the performance is less impressive. In this paper, we propose a novel shadow removal algorithm based on multi-scale image decomposition, which can recover the illumination for complex shadows with inconsistent illumination and different surface materials. Independent of shadow detection, our algorithm only requires a rough boundary distinguishing shadow regions from non-shadow regions. It first performs a multi-scale decomposition for the input image based on an illumination-sensitive smoothing process and then removes shadows in the basic layer using a local-to-global optimization strategy, which fuses all local shadow-free results in a global manner. Finally, we recover the texture details for the shadow-free basic layer and obtain the final shadow-free image. We validate the performance of the proposed method under various lighting and texture conditions and show consistent illumination between shadow and surrounding regions in the shadow removal results.
An Improved Classifier Based on Entropy and Deep Learning for Bug Priority Prediction

The bug reports are reported at a faster rate, resulting in uncertainties and irregularities in the bug reporting process. The noise and uncertainty also generated due to increasing enormous size of the bugs to the bug tracking system. In order to build a better classifier, we need to take care of these uncertainties and irregularity. In this paper, we built classifiers based on machine learning techniques Naïve Bayes (NB) and Deep Learning (DL) using entropy based measures for bug priority prediction. We have considered severity, summary weight and entropy attribute to predict the bug priority. The experimental analysis is conducted on eight products of an open source project OpenOffice. We have considered the performance measures, namely accuracy, precision, recall and f-measure to compare the proposed approach. We observed that the attribute entropy has improved the performance of classifier in both the cases NB and DL. DL with entropy is performing better than NB with entropy.
Cloud Computing: Review on Recent Research Progress and Issues

The current trend of research in cloud computing is challenging and keenly marginal to understand for the researchers and the learners due to its over-cover and complementary nature towards the new technologies.Cloud Computing is part and parcel of its applications with a majority of the extending concepts of Fog Computing, Internet of Things (IoT) and Big Data Analytics.In the current scenario, it is difficult for a researcher to identify the domain applicability and interrelationship among all the specified areas.In view of this, the current study is planned to focus on the research requirements and the basics for majorly two categories of stakeholders -the learners and the researchers exploring the needs of users to cater by cloud and also the current research trends in cloud computing.
Towards a Mechanism for Protecting Seller’s Interest of Cash on Delivery by using Smart Contract in Hyperledger

In emerging economies, with the explosion of ecommerce, payment methods have increasingly enhanced security.However, Cash-on-Delivery (COD) payment method still prevails in cash-based economies.Although COD allows consumers to be more proactive in making payments, it still appears to be vulnerable by the appearance of a third party (shipping companies).In this paper, we proposed a payment system based on "smart contract" implemented on top of blockchain technology to minimize risks for parties.The platform consists of a set of rules that each party must follow including specific delivery time and place, cost of delivery, mortgage money; thereby, forcing parties to be responsible for their tasks in order to complete the contract.We also provided a detailed implementation to illustrate the efficiency of our model.
A Feature based Reconstruction Model for Fluorescence Microscopy Image Denoising

Abstract The advent of Fluorescence Microscopy over the last few years have dramatically improved the problem of visualization and tracking of specific cellular objects for biological inference. But like any other imaging system, fluorescence microscopy has its own limitations. The resultant images suffer from the effect of noise due to both signal dependent and signal independent factors, thereby limiting the possibility of biological inferencing. Denoising is a class of image processing algorithms that aim to remove noise from acquired images and has gained wide attention in the field of fluorescence microscopy image restoration. In this paper, we propose an image denoising algorithm based on the concept of feature extraction through multifractal decomposition and then estimate a noise free image from the gradients restricted to these features. Experimental results over simulated and real fluorescence microscopy data prove the merit of the proposed approach, both visually and quantitatively.
Information retrieval system based semantique and big data

In traditional word-based information retrieval systems, a document is considered a set of words representing graphs without semantics. In this paper, we focus on enriching the similarity measure by using synonymy and performance evaluation of semantic indexing approaches to a document corpus. We will also present comparisons showing that the use of synonymy with Leacock and Chodorow measures increases the semantic similarity that makes research more efficient.
Time-evolution of squeezed coherent states of a generalized quantum parametric oscillator

Time evolution of squeezed coherent states for a quantum parametric oscillator with the most general self-adjoint quadratic Hamiltonian is found explicitly. For this, we use the unitary displacement and squeeze operators in coordinate representation and the evolution operator obtained by the Wei-Norman Lie algebraic approach. Then, we analyze squeezing properties of the wave packets according to the complex parameter of the squeeze operator and the time-variable parameters of the Hamiltonian. As an application, we construct all exactly solvable generalized quantum oscillator models classically corresponding to a driven simple harmonic oscillator. For each model, defined according to the frequency modification in position space, we describe explicitly the squeezing and displacement properties of the wave packets. This allows us to see the exact influence of all parameters and make a basic comparison between the different models.
HDFS Optimization Strategy Based On Hierarchical Storage of Hot and Cold Data

HDFS has been the core storage service of Hadoop big data platform for many years, with the continuous development of deep learning, artificial intelligence and other technologies, the amount of data continues to grow, the initial batch processing offline scenario did not fit the user’s needs, the data’s access heat appears big difference. Therefore, the HDFS storage system needs to be optimized according to the degree of heat and cold of the data. The heterogeneous storage and erasure-correction code technology of HDFS can improve the storage efficiency to some extent, but users need to specify the behavior of the storage for that particular data. Therefore, it will cause a waste of cluster resources. SSM systems can follow a predetermined set of rules, according to the degree of heat and cold, it uses different storage strategies to optimize the data, so as to improve the efficiency of the whole storage system.
Living in a Spamster's Paradise: Deceit and Threats in Phishing Emails

The prevalence of using email as a communication tool for personal and professional purposes makes it a significant attack vector for cybercriminals. Consensus exists that phishing, i.e. use of socially engineered messages to convince recipients into performing actions that benefit the sender, is widespread as a negative phenomenon. However, little is known about its true extent from a criminal law perspective. Similar to how the treatment of phishing in a generic manner does not adequately inform the relevant law, a case-by-case legal analysis of seemingly independent offences would not reveal the true scale and extent of phishing as a social phenomenon. The current research addresses this significant gap in the literature. To study this issue, a qualitative text analysis was performed on (N=42) emails collected over a 30-day period from two email accounts. Secondly, the phishing emails were analysed from an Estonian criminal law perspective. The legal analysis shows that in the period of only one month, the accounts received what amounts to 3 instances of extortion, 29 fraud attempts and 10 cases of personal data processing related misdemeanour offences.
Lemma Discovery for Induction

Automating proofs by induction can be challenging, not least because proofs might need auxiliary lemmas, which themselves need to be proved by induction. In this paper we survey various techniques for automating the discovery of such lemmas, including both top-down techniques attempting to generate a lemma from an ongoing proof attempt, as well as bottom-up theory exploration techniques trying to construct interesting lemmas about available functions and datatypes, thus constructing a richer background theory.
AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects

We propose a novel approach for class-agnostic object proposal generation, which is efficient and especially well-suited to detect small objects. Efficiency is achieved by scale-specific objectness attention maps which focus the processing on promising parts of the image and reduce the amount of sampled windows strongly. This leads to a system, which is $$33\%$$ faster than the state-of-the-art and clearly outperforming state-of-the-art in terms of average recall. Secondly, we add a module for detecting small objects, which are often missed by recent models. We show that this module improves the average recall for small objects by about $$53\%$$ . Our implementation is available at: https://www.inf.uni-hamburg.de/en/inst/ab/cv/people/wilms/attentionmask .
Detection of sparse mixtures: higher criticism and scan statistic

We consider the problem of detecting a sparse mixture as studied by Ingster (1997) and Donoho and Jin (2004).We consider a wide array of base distributions.In particular, we study the situation when the base distribution has polynomial tails, a situation that has not received much attention in the literature.Perhaps surprisingly, we find that in the context of such a power-law distribution, the higher criticism does not achieve the detection boundary.However, the scan statistic does.