Reviewerly/exHarmony huggingface dataset:
Don’t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction

Human translators routinely have to translate rare inflections of words-due to the Zipfian distribution of words in a language.When translating from Spanish, a good translator would have no problem identifying the proper translation of a statistically rare inflection such as hablarámos.Note the lexeme itself, hablar, is relatively common.In this work, we investigate whether state-of-the-art bilingual lexicon inducers are capable of learning this kind of generalization.We introduce 40 morphologically complete dictionaries in 10 languages 1 and evaluate three of the state-of-the-art models on the task of translation of less frequent morphological forms.We demonstrate that the performance of state-of-the-art models drops considerably when evaluated on infrequent morphological inflections and then show that adding a simple morphological constraint at training time improves the performance, proving that the bilingual lexicon inducers can benefit from better encoding of morphology.
Healthcare information exchange using blockchain technology

Current trend in health-care industry is to shift its data on the cloud, to increase availability of Electronic Health Records (EHR) e.g. Patient’s medical history in real time, which will allow sharing of EHR with ease. However, this conventional cloud-based data sharing environment has data security and privacy issues. This paper proposes a distributed solution based on blockchain technology for trusted Health Information Exchange (HIE). In addition to exchange of EHR between patient and doctor, the proposed system is also used in other aspects of healthcare such as improving the insurance claim and making data available for research organizations. Medical data is very sensitive, in both social as well as legal aspects, so permissioned block-chain such as Hyperledger Fabric is used to retain the necessary privacy required in the proposed system. As, this is highly permissioned network where the owner of the network i.e. patient holds all the access rights, so in case of emergency situations the proposed system has a Backup Access System which will allow healthcare professionals to access partial EHR and this backup access is provided by using wearable IOT device.
Large Scale Structure of Neural Network Loss Landscapes

There are many surprising and perhaps counter-intuitive properties of optimization of deep neural networks. We propose and experimentally verify a unified phenomenological model of the loss landscape that incorporates many of them. High dimensionality plays a key role in our model. Our core idea is to model the loss landscape as a set of high dimensional \emph{wedges} that together form a large-scale, inter-connected structure and towards which optimization is drawn. We first show that hyperparameter choices such as learning rate, network width and $L_2$ regularization, affect the path optimizer takes through the landscape in similar ways, influencing the large scale curvature of the regions the optimizer explores. Finally, we predict and demonstrate new counter-intuitive properties of the loss-landscape. We show an existence of low loss subspaces connecting a set (not only a pair) of solutions, and verify it experimentally. Finally, we analyze recently popular ensembling techniques for deep networks in the light of our model.
Security durability assessment through fuzzy analytic hierarchy process

Background Security is an integral aspect of the development of quality software. Furthermore, security durability is even more imperative and in persistent demand due to high investment in recent years. To achieve the desired target of efficacious and viable durability of security services, there needs to be nodal focus on durability along with security. Unfortunately, the highly secure design of software becomes worthless because the durability of security services is not as it should be. Methods Security durability attributes have their own impact while integrating security with durability and assessment of security durability plays a crucial role during software development. Within this context, this paper estimates the security durability of the two alternatives versions of a locally developed software called version 1 and version 2. To assess the security durability, authors are using the hybrid fuzzy analytic hierarchy process decision analysis approach. Results The impact of the security durability on other attributes has been evaluated quantitatively. The result obtained contains the assessment of security durability. The study posits conclusions which are based on this result and are useful for practitioners to assess and improve the security life span of software services.
optimParallel: An R Package Providing a Parallel Version of the L-BFGS-B Optimization Method

The R package optimParallel provides a parallel version of the L-BFGS-B optimization method of optim().The main function of the package is optimParallel(), which has the same usage and output as optim().Using optimParallel() can significantly reduce the optimization time, especially when the evaluation time of the objective function is large and no analytical gradient is available.We introduce the R package and illustrate its implementation, which takes advantage of the lexical scoping mechanism of R.
Don’t paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing

A major hurdle on the road to conversational interfaces is the difficulty in collecting data that maps language utterances to logical forms.One prominent approach for data collection has been to automatically generate pseudo-language paired with logical forms, and paraphrase the pseudo-language to natural language through crowdsourcing (Wang et al., 2015).However, this data collection procedure often leads to low performance on real data, due to a mismatch between the true distribution of examples and the distribution induced by the data collection procedure.In this paper, we thoroughly analyze two sources of mismatch in this process: the mismatch in logical form distribution and the mismatch in language distribution between the true and induced distributions.We quantify the effects of these mismatches, and propose a new data collection approach that mitigates them.Assuming access to unlabeled utterances from the true distribution, we combine crowdsourcing with a paraphrase model to detect correct logical forms for the unlabeled utterances.On two datasets, our method leads to 70.6 accuracy on average on the true distribution, compared to 51.3 in paraphrasing-based data collection.
Generating Personalized Recipes from Historical User Preferences

Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes.We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete naturaltext instructions aligned with the user's historical preferences.We attend on technique-and recipe-level representations of a user's previously consumed recipes, fusing these 'useraware' representations in an attention fusion layer to control recipe text generation.Experiments on a new dataset of 180K recipes and 700K interactions show our model's ability to generate plausible and personalized recipes compared to non-personalized baselines.1 Our source code and appendix are at https://github.com/majumderb/recipe-personalization
Real-time 3D shape measurement using 3LCD projection and deep machine learning

For 3D imaging and shape measurement, simultaneously achieving real-time and high-accuracy performance remains a challenging task in practice. In this paper, a fringe-projection-based 3D imaging and shape measurement technique using a three-chip liquid-crystal-display (3LCD) projector and a deep machine learning scheme is presented. By encoding three phase-shifted fringe patterns into the red, green, and blue (RGB) channels of a color image and controlling the 3LCD projector to project the RGB channels individually, the technique can synchronize the projector and the camera to capture the required fringe images at a fast speed. In the meantime, the 3D imaging and shape measurement accuracy is dramatically improved by introducing a novel phase determination approach built on a fully connected deep neural network (DNN) learning model. The proposed system allows performing 3D imaging and shape measurement of multiple complex objects at a real-time speed of 25.6 fps with relative accuracy of 0.012%. Experiments have shown great promise for advancing scientific and engineering applications.
On Measuring Gender Bias in Translation of Gender-neutral Pronouns

Ethics regarding social bias has recently thrown striking issues in natural language processing.Especially for gender-related topics, the need for a system that reduces the model bias has grown in areas such as image captioning, content recommendation, and automated employment.However, detection and evaluation of gender bias in the machine translation systems are not yet thoroughly investigated, for the task being cross-lingual and challenging to define.In this paper, we propose a scheme for making up a test set that evaluates the gender bias in a machine translation system, with Korean, a language with genderneutral pronouns.Three word/phrase sets are primarily constructed, each incorporating positive/negative expressions or occupations; all the terms are gender-independent or at least not biased to one side severely.Then, additional sentence lists are constructed concerning formality of the pronouns and politeness of the sentences.With the generated sentence set of size 4,236 in total, we evaluate gender bias in conventional machine translation systems utilizing the proposed measure, which is termed here as translation gender bias index (TGBI).The corpus and the code for evaluation is available on-line 1 .
Empirical calculation of the optimal tilt angle for solar collectors in northern hemisphere

Panel tilt angles (0°–90°) need to be in a proper position and location to get maximum productivity from solar energy. Values used in solar energy applications are generally computed by (global, diffuse, and direct) variation on horizontal surfaces calculated using isotropic sky and a mean albedo method. Being parallel to the available literature concerning such applications, this study focuses on the optimum solar panel angle. In this study, optimum solar panel angle value by months was determined for three sample provinces (Antalya, Kayseri, and Trabzon) first and North Hemisphere then. Capacity calculation of sample provinces was performed based on monthly, seasonal, and annual angle values and horizontal situation. Monthly and annual optimum angle values for Northern Hemisphere by 1° increase for between the latitudes of 1° N and 65° N. While the panel angle is at the highest level in autumn and winter (November-December-January and February) in annual process, the lowest angle is observed in spring and summer (May-June-July-August). Several different mathematical models have been developed for the sample provinces and Northern Hemisphere. While the variable of 12 different models that were developed for provinces is the Declination (δ) coefficient, the variable of 7 different models that were developed Northern Hemisphere is the latitude (Ø). Regional values in literature with estimation results of models were analyzed based on NASA and PVGIS data color scale. There was created a possibility of comparison by aligning all the optimum solar panel angle values of related location via a scale whose values vary by 1 and 10. Moreover, all the models were verified by statistical analysis methods. R2 (determination coefficient) in 19 different estimation equations is pretty close or equal to 1. However, the best among them is Eq. 32 (0.9979) for sample provinces and Eq. (33) (1) for the Northern Hemisphere; developed models are less-than-stellar. Other statistical data of these equations are MBE (−0.0616), RMSE (1.1176), t-sat (0.1830), Bias (1). For Eq. (32); MBE (1.96), RMSE (2.75), t-sat (8.13), MPE % (3.98), MAPE (5.87), SSRE (0.27), and RSE (0.06) for Eq. (33). The statistical analyzes indicate that all regression models are applicable in Turkey and Northern Hemisphere. Developed all correlations are recommended for academic and industrial users.
Variational segmentation of textile composite preforms from X-ray computed tomography

Prediction of the thermo-mechanical behavior of woven composites necessitates a reliable knowledge of their inner structure. A sufficiently accurate description of the fabric geometry could be obtained using X-ray computed microtomography (μCT) at the mesoscopic scale. However, systematic construction of numerical models from μCT remains a difficult task. To address this challenge, we propose a variational segmentation approach that combines μCT with a prior geometric model that is iteratively improved thanks to a heuristic optimization process. The fidelity of the models with respect to the input μCT is evaluated using a measure of similarity including both gray levels and local directions. Our method allowed to build realistic numerical models of woven fabrics that preserve the prescribed weaving pattern, and that are free of interpenetration, which makes them compatible with further numerical simulations. Using our approach, models of complex woven fabrics, but also of woven composites, could be consistently generated from μCT and can serve as reference models, e.g. to analyze in situ tests by providing digital twins.
Automatic extraction of product line architecture and feature models from UML class diagram variants

Software Product Lines (SPLs) are families of related products developed for specific domains. SPLs commonly emerge from existing variants when their individual maintenance and/or evolution become complex. Even though there exists a vast research literature on SPL extraction, the majority of the approaches have only focused on source code, are partially automated, or do not reflect domain constraints. Such limitations can make more difficult the extraction, management, documentation and generation of some important SPL artifacts such as the product line architecture, a fact that can impact negatively the evolution and maintenance of SPLs. To tackle these limitations, this work presents ModelVars2SPL (Model Variants to SPL Core Assets), an automated approach to aid the development of SPLs from existing system variants. The input for ModelVars2SPL is a set of Unified Modeling Language (UML) class diagrams and the list of features they implement. The approach extracts two main assets: (i) Feature Model (FM), which represents the combinations of features, and (ii) a Product Line Architecture (PLA), which represents a global structure of the variants. ModelVars2SPL is composed of four automated steps. We conducted a thorough evaluation of ModelVars2SPL to analyze the artefacts it generates and its performance. The results show that the FMs well-represent the features organization, providing useful information to define and manage commonalities and variabilities. The PLAs show a global structure of current variants, facilitating the understanding of existing implementations of all variants. An advantage of ModelVars2SPL is to exploit the use of UML design models, that is, it is independent of the programming language, and supports the re-engineering process in the design level, allowing practitioners to have a broader view of the SPL.
A new fast search algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based check strategy

The k-nearest neighbor (KNN) algorithm has been widely used in pattern recognition, regression, outlier detection and other data mining areas. However, it suffers from the large distance computation cost, especially when dealing with big data applications. In this paper, we propose a new fast search (FS) algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based (OTI) check strategy. During the procedure of searching exact k-nearest neighbors for any query, the OTI check strategy can eliminate more redundant distance computations for the instances located in the marginal area of neighboring clusters compared with the original TI check strategy. Considering the large space complexity and extra time complexity of OTI, we also propose an efficient optimal triangle-inequality-based (EOTI) check strategy. The experimental results demonstrate that our proposed two algorithms (OTI and EOTI) achieve the best performance compared with other related KNN fast search algorithms, especially in the case of dealing with high-dimensional datasets.
Programmable Integrated Silicon Photonics Waveguide Meshes: Optimized Designs and Control Algorithms

Programmable Integrated Photonics is a recent area of research that aims to integrate a very-large scale of reconfigurable photonic components to enable flexible and versatile photonic integrated circuits. In this paper, we review the state of the art of general-purpose waveguide mesh arrangements with a special focus on those that allow the synthesis of optical feedback loops. Moreover, we propose for the first time, a new design approach to generate waveguide mesh patterns with equally-oriented components. This innovation is of special relevance to improve performance and to mitigate one of the main scalability limitations, the integration density. The paper finalizes with an introduction to control algorithms for waveguide mesh arrangements based on derivative methods and non-derivative methods. These control methods provide a proof for the self-reconfiguration of large-scale waveguide mesh arrangements. In particular, we apply the computational optimization algorithms to program a hexagonal waveguide mesh to emulate a 1 × 8 beamforming network and an optical filter based on an unbalanced MZI design. All in all, the paper comprises recipes to achieve truly practical software-defined photonic integrated circuits.
I-LSH: I/O Efficient c-Approximate Nearest Neighbor Search in High-Dimensional Space

Nearest Neighbor search has been well solved in low-dimensional space, but is challenging in high-dimensional space due to the curse of dimensionality.As a trade-off between efficiency and result accuracy, a variety of c-approximate nearest neighbor (c-ANN) algorithms have been proposed to return a c-approximate NN with confident at least δ.We observe that existing c-ANN search algorithms have some limitations on I/O efficiency when their indexes are resided on the external memory, which is critical for handling large scale high-dimensional data.In this paper, we introduce an incremental search based c-ANN search algorithm, named I-LSH.Unlike the previous LSH methods, which expand the bucket width in an exponential way, I-LSH adopts a more natural search strategy to incrementally access the hash values of the objects.We provide rigorous theoretical analysis to underpin our incremental search strategy.Our comprehensive experiment results show that, compared with state-of-the-art I/O efficient c-ANN techniques, our algorithm can achieve much better I/O efficiency under the same theoretical guarantee.
Learning Large-Scale Bayesian Networks with the <b>sparsebn</b> Package

Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences.Nowadays datasets often have upwards of thousands -sometimes tens or hundreds of thousands -of variables and far fewer samples.To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks.While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions.As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting.Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data.Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle

The principle of the Information Bottleneck (Tishby et al., 1999) is to produce a summary of information X optimized to predict some other relevant information Y .In this paper, we propose a novel approach to unsupervised sentence summarization by mapping the Information Bottleneck principle to a conditional language modelling objective: given a sentence, our approach seeks a compressed sentence that can best predict the next sentence.Our iterative algorithm under the Information Bottleneck objective searches gradually shorter subsequences of the given sentence while maximizing the probability of the next sentence conditioned on the summary.Using only pretrained language models with no direct supervision, our approach can efficiently perform extractive sentence summarization over a large corpus.Building on our unsupervised extractive summarization (BottleSum Ex ), we then present a new approach to self-supervised abstractive summarization (BottleSum Self ), where a transformer-based language model is trained on the output summaries of our unsupervised method.Empirical results demonstrate that our extractive method outperforms other unsupervised models on multiple automatic metrics.In addition, we find that our selfsupervised abstractive model outperforms unsupervised baselines (including our own) by human evaluation along multiple attributes. Autoencoder Information BottleneckHong Kong, a bustling metropolis with a population over 7 million, was once under British Rule.Hong Kong has population over 7 million, was once under British Rule.
Structural Representation Learning for User Alignment Across Social Networks

Aligning users across different social networks has become increasingly studied as an important task to social network analysis. In this paper, we propose a novel representation learning method that mainly exploits social structures for the network alignment. In particular, the proposed network embedding framework models the follower-ship and followee-ship of each user explicitly as input and output context vectors, while preserving the proximity of users with “similar” followers and followees in the embedded space. We incorporate both known and predicted user anchors across the networks as constraints to facilitate the transfer of context information to achieve accurate user alignment. Both network embedding and user alignment are inferred under a unified optimization framework with negative sampling adopted to ensure scalability. Also, variants of the proposed framework, including the incorporation of higher-order structural features, are also explored for further boosting the alignment accuracy. Extensive experiments on large-scale social and academia network datasets demonstrate the efficacy of our proposed model compared with state-of-the-art methods.