Reviewerly/exHarmony huggingface dataset:
Automatic Detection of Translation Direction

Parallel corpora are crucial resources for NLP applications, most notably for machine translation.The direction of the (human) translation of parallel corpora has been shown to have significant implications for the quality of statistical machine translation systems that are trained with such corpora.We describe a method for determining the direction of the (manual) translation of parallel corpora at the sentence-pair level.Using several linguistically-motivated features, coupled with a neural network model, we obtain high accuracy on several language pairs.Furthermore, we demonstrate that the accuracy is correlated with the (typological) distance between the two languages.
Achieving 98% system efficiency at 1550 nm in superconducting nanowire single photon detectors

We present a fiber-coupled single-photon detector with 98% system detection efficiencies at 1550 nm. We characterize beam divergence within the optical stack and the electronic compensations required for large-active-area detectors.
AWide-Coverage Context-Free Grammar for Icelandic and an Accompanying Parsing System

We present an open-source, widecoverage context-free grammar (CFG) for Icelandic, and an accompanying parsing system.The grammar has over 5,600 nonterminals, 4,600 terminals and 19,000 productions in fully expanded form, with feature agreement constraints for case, gender, number and person.The parsing system consists of an enhanced Earley-based parser and a mechanism to select best-scoring parse trees from shared packed parse forests.Our parsing system is able to parse about 90% of all sentences in articles published on the main Icelandic news websites.Preliminary evaluation with evalb shows an F-measure of 71.90% on parsed sentences.Our system demonstrates that parsing a morphologically rich language using a wide-coverage CFG can be practical.
Linked Data Exploration With RDF Surveyor

Linked Data exploration is an essential task in the process of understanding, assessing, and using datasets made available in the Resource Description Framework (RDF) format. Current solutions for exploration of RDF data are mainly targeted at Semantic Web experts, require non-trivial deployments, and do not scale to the increasing amounts of data published in RDF. The lack of simple, intuitive, and efficient solutions for exploring RDF data, especially for lay users, is the main motivation behind the work presented in this paper. We propose RDF Surveyor, an easy-to-use and lightweight tool for exploring RDF datasets. Its visual interface hides the intricacies of Semantic Web technologies from the user, while providing intuitive overviews of datasets, class navigation, and visualization of class instances. Furthermore, RDF Surveyor does not require any installation and can handle large datasets such as DBpedia. We provide a detailed overview of RDF Surveyor and illustrate its capabilities in two different scenarios. We also analyze the uptake, performance and usability of RDF Surveyor, showing its suitability for exploring Linked Data at scale.
Investigating the Impact of Combining Handwritten Signature and Keyboard Keystroke Dynamics for Gender Prediction

The use of soft-biometric data as an auxiliary tool on user identification is already well known. Gender, hand-orientation and emotional state are some examples which can be called soft-biometrics. These soft-biometric data can be predicted directly from the biometric templates. It is very common to find researches using physiological modalities for soft-biometric prediction, but behavioural biometric is often not well explored for this context. Among the behavioural biometric modalities, keystroke dynamics and handwriting signature have been widely explored for user identification, including some soft-biometric predictions. However, in these modalities, the soft-biometric prediction is usually done in an individual way. In order to fill this space, this study aims to investigate whether the combination of those two biometric modalities can impact the performance of a soft-biometric data, gender prediction. The main aim is to assess the impact of combining data from two different biometric sources in gender prediction. Our findings indicated gains in terms of performance for gender prediction when combining these two biometric modalities, when compared to the individual ones.
Automated Retraining of Machine Learning Models

Data is the most crucial component of a successful ML system. Once a machine learning model is developed, it gets obsolete over time due to presence of new input data being generated every second. In order to keep our predictions accurate we need to find a way to keep our models up to date. Our research work involves finding a mechanism which can retrain the model with new data automatically. This research also involves exploring the possibilities of automating machine learning processes. We started this project by training and testing our model using conventional machine learning methods. The outcome was then compared with the outcome of those experiments conducted using the AutoML methods like TPOT. This helped us in finding an efficient technique to retrain our models. These techniques can be used in areas where people do not deal with the actual working of a ML model but only require the outputs of ML processes.
Understanding Why Neural Networks Generalize Well Through GSNR of Parameters

As deep neural networks (DNNs) achieve tremendous success across many application domains, researchers tried to explore in many aspects on why they generalize well. In this paper, we provide a novel perspective on these issues using the gradient signal to noise ratio (GSNR) of parameters during training process of DNNs. The GSNR of a parameter is simply defined as the ratio between its gradient's squared mean and variance, over the data distribution. Based on several approximations, we establish a quantitative relationship between model parameters' GSNR and the generalization gap. This relationship indicates that larger GSNR during training process leads to better generalization performance. Futher, we show that, different from that of shallow models (e.g. logistic regression, support vector machines), the gradient descent optimization dynamics of DNNs naturally produces large GSNR during training, which is probably the key to DNNs’ remarkable generalization ability.
Low Level Linguistic Controls for Style Transfer and Content Preservation

Despite the success of style transfer in image processing, it has seen limited progress in natural language generation. Part of the problem is that content is not as easily decoupled from style in the text domain. Curiously, in the field of stylometry, content does not figure prominently in practical methods of discriminating stylistic elements, such as authorship and genre. Rather, syntax and function words are the most salient features. Drawing on this work, we model style as a suite of low-level linguistic controls, such as frequency of pronouns, prepositions, and subordinate clause constructions. We train a neural encoder-decoder model to reconstruct reference sentences given only content words and the setting of the controls. We perform style transfer by keeping the content words fixed while adjusting the controls to be indicative of another style. In experiments, we show that the model reliably responds to the linguistic controls and perform both automatic and manual evaluations on style transfer. We find we can fool a style classifier 84% of the time, and that our model produces highly diverse and stylistically distinctive outputs. This work introduces a formal, extendable model of style that can add control to any neural text generation system.
Smart Agriculture Information System Based on Cloud Computing and NB-IoT

The typical projects of NB-IoT technology in the agricultural Internet of Things are being planned, which has broad application prospects and development space. NB-IoT and cloud computing promotes the development of the agricultural Internet of Things and improves the development model. Smart Agriculture Information System Based on Cloud Computing and NB-IoT designed in this paper monitors crops or farmland through information transmission equipment in real time, and records the growth status of crops, so as to adjust the planting technology and planting methods.
Actionable Knowledge Discovery for Increasing Enterprise Profit, Using Domain Driven-Data Mining

Actionable Knowledge Discovery approaches to extract the business and technical significant actions/patterns to support direct decision making.These actions suggest how to transform an object from an undesirable status to a desirable status by incurring less cost and high profit.This article aims to propose a work that generates actionable patterns efficiently.It reduces the search space and number of iterations for attribute value change during action generation.Performance of the proposed method is compared with Yang's method and OF-CEAMA on the basis of four parameters i.e. the total number of rules required for action generation, run time of the methods, the total number of generated actions, total net profit and time and space complexity.Experiments have been carried out on four datasets retrieved from UCI Machine learning repository.Experimental results show that the proposed work takes less time than the other two methods to extract actions for all datasets.Also, the number of rules required to generate actions are less than the other two methods.Results also suggest that a decrease in execution time does not compromise the information and proposed work generates the same actions and net profit.Moreover, the proposed work tries to transfer an object from undesired status to the desired status.
Exploring the Learning Outcomes with Various Technologies - Proposing Design Principles for Virtual Reality Learning Environments

The study compares three virtual learning environments: VR, 3D videos and 2D videos.Following Bloom's taxonomy of learning outcomes, we measure remembering, understanding and ability to apply.We also apply the affordance theory framework to explain the differences between these virtual learning environments.Based on the results, we propose design principles for VR learning environments.The results suggest that VR has its advantages on the apply -level, or higher, as it outperforms the other two technologies at this level.In addition, several design principles are suggested, such as customized learning, challenging learning environments, multi-sensory effects, immersion, interactivity, 3D-dimensionality, engagement as well as motivation towards the content and technology.The results highlight the importance of choosing the right technology when designing virtual learning environments.This study demonstrates how virtual environment affordances and equivalent scales can be used in making those decisions.
Neural Conversation Model Controllable by Given Dialogue Act Based on Adversarial Learning and Label-aware Objective

Building a controllable neural conversation model (NCM) is an important task.In this paper, we focus on controlling the responses of NCMs by using dialogue act labels of responses as conditions.We introduce an adversarial learning framework for the task of generating conditional responses with a new objective to a discriminator, which explicitly distinguishes sentences by using labels.This change strongly encourages the generation of label-conditioned sentences.We compared the proposed method with some existing methods for generating conditional responses.The experimental results show that our proposed method has higher controllability for dialogue acts even though it has higher or comparable naturalness to existing methods.
Detection of metallic prospects using staged factor and fractal analysis in Zouzan region, NE Iran

The Zouzan area is located in the SE of Torbat-e- Heydarieh city (NE Iran). This area is located on north part of the Lut block which consists of basaltic andesite, tuff and porphyritic andesite. The purpose of this research is to investigate the enrichment of metal elements in the Zouzan area and provide a metallic geochemical model. For this purpose, 488 stream samples were analyzed using by ICP-MS. The results were interpreted using staged factor analysis to determine the components that are rich in the study area. However, Concentration-Number (C-N) fractal modeling was used for interpretation of these data and presenting a final model. The results of this study indicate the concentration of anomalies of the Au, Ag, Cu, Pb and Zn as main elements in the central, NE and SW of the Zouzan area. Moreover, results obtained by the staged factor analysis shows the elements of the specified components by this method that enriched together in the study area. The place of enrichment is in the central NE and SW parts of the study area.
The Impact of Genre Based Instruction on EFL Learners’ Writing Development

This study evaluated the effect of the teaching cycle adapted from the genre-based approach for teaching EFL learners in Thailand.As English writing is claimed to be the most serious problem among Thai EFL learners, the instruction aimed at developing writing competence of Thai EFL learners.One-group pretest-posttest design was employed in the study.The participants were 44 undergraduate students enrolling in a course of English writing for English teachers.This 12 week course aimed at teaching writing in the news report genre.After completing the course, two sets of tests were administered to measure their writing developments.The first test measured the students' development in linguistic features.The second test evaluated the students' performance in a writing task.A semi-structured interview was used to show students' attitudes towards the instruction.Quantitative results showed students made greater improvement in linguistic features and rhetoric organization, the majority of the students gained development at 'Moderately Improved' level.Qualitative results indicated students expressed positive attitudes towards autonomous and explicit learning.The students reflected that collaborative writing helped to improve their writing competence and increased their confidence in composing texts.The study concluded by stating the effectiveness of a genrebased approach to the development of EFL writing competence.
Performance Analysis of Isolated Speech Recognition Technique Using MFCC and Cross-Correlation

Speech signal processing has become an important mode of interaction with computer. In this paper, Mel Frequency Cepstral Coefficient (MFCC) technique has been used to process speech samples to attain the recognition. MFCC is a term which narrate the short-term power spectrum of a speech signal, depend on a linear cosine transform (FFT and DCT we have used in our work) of a log power spectrum on a nonlinear Mel scale of frequency. We have used Dynamic Time Warping algorithm and Cross Correlation algorithm to match feature vectors. We have taken five recorded reference word through “One” to “Five”. Then the feature vectors generated from this reference signals are stored in database. A test sample of any numeric in “One” to “Five” is again recorded and then the algorithm is applied to recognize the same with recorded reference voices. In our paper, the recognition techniques show different percentages of accuracy. The highest recognition accuracy we got 92% for Dynamic Time Warping algorithm with FFT transformation. The accuracy for Dynamic Time Warping algorithm using DCT is 86% which is less than DP algorithm using FFT. The average accuracy for Cross Correlation using FFT is 78% and average accuracy for DCT is only 60%.
CAG: A Real-Time Low-Cost Enhanced-Robustness High-Transferability Content-Aware Adversarial Attack Generator

Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many artificial intelligence fields. Adversarial attack is a method that causes the intended misclassfication by adding imperceptible perturbations to legitimate inputs. To date, researchers have developed numerous types of adversarial attack methods. However, from the perspective of practical deployment, these methods suffer from several drawbacks such as long attack generating time, high memory cost, insufficient robustness and low transferability. To address the drawbacks, we propose a Content-aware Adversarial Attack Generator (CAG) to achieve real-time, low-cost, enhanced-robustness and high-transferability adversarial attack. First, as a type of generative model-based attack, CAG shows significant speedup (at least 500 times) in generating adversarial examples compared to the state-of-the-art attacks such as PGD and C&amp;W. Furthermore, CAG only needs a single generative model to perform targeted attack to any targeted class. Because CAG encodes the label information into a trainable embedding layer, it differs from prior generative model-based adversarial attacks that use n different copies of generative models for n different targeted classes. As a result, CAG significantly reduces the required memory cost for generating adversarial examples. Moreover, CAG can generate adversarial perturbations that focus on the critical areas of input by integrating the class activation maps information in the training process, and hence improve the robustness of CAG attack against the state-of-art adversarial defenses. In addition, CAG exhibits high transferability across different DNN classifier models in black-box attack scenario by introducing random dropout in the process of generating perturbations. Extensive experiments on different datasets and DNN models have verified the real-time, low-cost, enhanced-robustness, and high-transferability benefits of CAG.
Hybrid DE-EM Algorithm for Gaussian Mixture Model-Based Wireless Channel Multipath Clustering

In this paper, the Gaussian mixture model (GMM) is introduced to the channel multipath clustering. In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters. However, the EM widely converges into local optimization. To address this issue, a hybrid differential evolution (DE) and EM (DE-EM) algorithms are proposed in this paper. To be specific, the DE is employed to initialize the GMM parameters. Then, the parameters are estimated with the EM algorithm. Thanks to the global searching ability of DE, the proposed hybrid DE-EM algorithm is more likely to obtain the global optimization. Simulations demonstrate that our proposed DE-EM clustering algorithm can significantly improve the clustering performance.
A 1.15-TOPS 6.57-TOPS/W Neural Network Processor for Multi-Scale Object Detection With Reduced Convolutional Operations

For automated driving cars, we present a 40-nm dedicated object detection processor with only three operations: 3 × 3 convolution, 1 × 1 convolution, and 4 × 4 deconvolution. Multi-scale object detection at high recognition accuracy is possible by virtue of the deconvolution feature and concatenation. The input memory for a feature map has 8-bit width. A multiplier for the inputs has 8-bit precision. Partial-sum memory, however, has 16-bit width to suppress detection accuracy deterioration in a layer with 1024 channels in the target network. By fixed-point bit precision, the external memory bandwidth and internal memory capacity are reduced. Optimized parallelization in input and output channels reduces the external memory bandwidth to 0.062 billion accesses per 1280 × 384 image with internal memory capacity of 400 kB. The detection error is 1.9% of that using single-precision floating point. The maximum operating frequency is 500 MHz at a supply voltage of 1 V. Its peak performance is 1.15 TOPS. The maximum energy efficiency is 6.57 TOPS/W at 174 MHz and 0.6 V.
Predicting Saccadic Eye Movements in Free Viewing of Webpages

Attention modeling for webpages has emerged as a new research direction in computer vision. Despite an amount of research effort, most studies have focused on estimating webpage saliency to reveal the static location of human fixations. Without temporal information, existing models cannot interpret the dynamic properties of the actual attention process in free-viewing webpages. To solve this problem, we propose a webpage-based saccadic model in this study to model dynamic visual search behaviors of humans when they view webpages. In the first stage, we utilize the support vector machine to learn the mapping from multilevel saliency features to an initial probability of being fixated. In the second stage, we combine the mechanisms of spatial bias and inhibition of return with the estimation of the initial probability to iteratively predict a sequence of successive fixations for each webpage image. Experimental results on a benchmark eye-tracking data set for webpages have demonstrated that the proposed model outperforms the state-of-the-art saccadic methods.
An extension of the differential image foresting transform and its application to superpixel generation

The Image Foresting Transform (IFT) is a graph-based framework to develop image operators based on optimum connectivity between a root set and the remaining nodes, according to a given path-cost function. Its applications involve a variety of tasks, such as segmentation, boundary tracking, skeletonization, filtering, among others. The Differential Image Foresting Transform (DIFT) allows multiple IFT executions for different root sets and a same monotonically incremental path-cost function, making the processing time proportional to the number of modified nodes. In this paper, we extend the DIFT algorithm for non-monotonically incremental functions with root-based increases. This proposed extension, called Generalized DIFT (GDIFT), has been successfully used as the core part of some modern superpixels methods with state-of-the-art results. Experimental results show considerable efficiency gains over the sequential flow of IFTs for the generation of superpixels, also avoiding inconsistencies in image segmentation, which could occur with the regular DIFT algorithm.
Understanding self-efficacy and performance of salespersons in Islamic banking

Purpose This study aims to investigate the relationship between organization image, awareness effort, customer demandingness, self-efficacy and self-rated performance among salespersons of Islamic banking products in the UAE. It also explores the mediating role of awareness effort and self-efficacy, using a theoretical framework derived from social cognitive theory. Design/methodology/approach Data are collected by surveying salespersons in a mixed bank (a conventional bank with an Islamic banking department) in the UAE. The researcher uses structural equation modeling to analyze the data. Findings The research concludes that customer demandingness positively affects awareness effort, awareness effort positively affects self-efficacy, self-efficacy positively affects performance and image positively affects salespersons’ self-efficacy and performance. Furthermore, the study highlights the mediating role of awareness effort and self-efficacy in the model. Research limitations/implications The sample consists of 217 salespersons working in the same bank, covering three cities in the UAE. Hence, the rest of the country is not included. Practical implications The study shows the importance of awareness efforts in achieving better performance. It also demonstrates the importance of addressing customer requirements in the banking environment. Furthermore, it illuminates the role of organization image in enforcing salespersons’ self-efficacy and performance. Social implications The paper sheds a light on salespersons’ personalities and the factors that reinforce their performance and self-efficacy. Originality/value The research is an empirical study that addresses the relationship between performance, self-efficacy, image, awareness effort and customer demandingness in Islamic banking in the UAE.