Reviewerly/exHarmony huggingface dataset:
MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation

Abstract Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT). We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have constructed the traditional MT model using Moses toolkit and have additionally enriched the language model using external data sets. Thereafter, we have ranked the phrase tables using an RNN encoder-decoder module created originally as a part of the GroundHog project of LISA lab.
Robust Boundary Segmentation in Medical Images Using a Consecutive Deep Encoder-Decoder Network

Image segmentation is typically used to locate objects and boundaries.It is essential in many clinical applications, such as the pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.The segmentation task is hampered by fuzzy boundaries, complex backgrounds, and appearances of objects of interest, which vary considerably.The success of the procedure is still highly dependent on the operator's skills and the level of hand-eye coordination.Thus, this paper was strongly motivated by the necessity to obtain an early and accurate diagnosis of a detected object in medical images.In this paper, we propose a new polyp segmentation method based on the architecture of a multiple deep encoder-decoder networks combination called CDED-net.The architecture can not only hold multi-level contextual information by extracting discriminative features at different effective fields-of-view and multiple image scales but also learn rich information features from missing pixels in the training phase.Moreover, the network is also able to capture object boundaries by using multiscale effective decoders.We also propose a novel strategy for improving the method's segmentation performance based on a combination of a boundary-emphasization data augmentation method and a new effective dice loss function.The goal of this strategy is to make our deep learning network available with poorly defined object boundaries, which are caused by the non-specular transition zone between the background and foreground regions.To provide a general view of the proposed method, our network was trained and evaluated on three well-known polyp datasets, CVC-ColonDB, CVC-ClinicDB, and ETIS-Larib PolypDB.Furthermore, we also used the Pedro Hispano Hospital (PH 2 ), ISBI 2016 skin lesion segmentation dataset, and CT healthy abdominal organ segmentation dataset to depict our network's ability.Our results reveal that the CDED-net significantly surpasses the state-of-the-art methods.
Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data

The changing population age structure has a significant influence on the economy, society, and numerous other aspects of a country. This paper has innovatively applied the method of compositional data forecasting for the prediction of population age changes of the young (aged 0–14), the middle-aged (aged 15–64), and the elderly (aged older than 65) in China, India, and Vietnam by 2030 based on data from 1960 to 2016. To select the best-suited forecasting model, an array of data transformation approaches and forecasting models have been extensively employed, and a large number of comparisons have been made between the aforementioned methods. The best-suited model for each country is identified considering the root mean squared error and mean absolute percent error values from the compositional data. As noted in this study, first and foremost, it is predicted that by the year 2030, China will witness the disappearance of population dividend and get mired in an aging problem far more severe than that of India or Vietnam. Second, Vietnam’s trend of change in population age structure resembles that of China, but the country will sustain its good health as a whole. Finally, the working population of India demonstrates a strong rising trend, indicating that the age structure of the Indian population still remains relatively “young”. Meanwhile, the continuous rise in the proportion of elderly population and the gradual leveling off growth of the young population have nevertheless become serious problems in the world. The present paper attempts to offer crucial insights into the Asian population size, labor market and urbanization, and, moreover, provides suggestions for a sustainable global demographic development.
Sellers’ risk perceptions in public and private social media drug markets

From the early use of pagers and cellular phones to the darknet and smartphones, technological developments have facilitated drug deals in various ways, especially by altering time and space boundaries. Traditional drug market literature theorises about how physical markets, within which sellers act according to their risk perceptions and motivation, are led by supply, demand, and enforcement. However, there is an almost absolute research gap in understanding how this relates to digital markets and social media markets in particular. It is expected that the plasticity of technology makes digital markets highly mouldable so that the sellers are able to shape markets according to their use. The aim of the study is to describe and understand drug dealing on social media within the structure of existing markets. We aim to do so by analysing how drug sellers’ risk perceptions and motivations form and are formed by social media technology. We conducted a three-month digital ethnographic study on Facebook and Instagram in the five Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden), as well as 107 semi-structured qualitative interviews with sellers (2/3 of the sample) and buyers (1/3 of the sample) using online markets within the same countries. Drug dealing on social media varies according to the structure of the chosen media and users’ risk perceptions and motivations. Two market forms are suggested: 1) public digital markets (e.g., Facebook groups and Instagram) allow sellers to expand their customer lists, but the risk is quite high, while 2) private digital markets are based on one-on-one communication and demand greater knowledge but are perceived as more secure. Sellers choose which media to use and how to use them based on perceived risk and, therefore, have a significant impact on the formation of social media drug markets.
Customer Reviews Analysis With Deep Neural Networks for E-Commerce Recommender Systems

An essential prerequisite of an effective recommender system is providing helpful information regarding users and items to generate high-quality recommendations. Written customer review is a rich source of information that can offer insights into the recommender system. However, dealing with the customer feedback in text format, as unstructured data, is challenging. In this research, we extract those features from customer reviews and use them for similarity evaluation of the users and ultimately in recommendation generation. To do so, we developed a glossary of features for each product category and evaluated them for removing irrelevant terms using Latent Dirichlet Allocation. Then, we employed a deep neural network to extract deep features from the reviews-characteristics matrix to deal with sparsity, ambiguity, and redundancy. We applied matrix factorization as the collaborative filtering method to provide recommendations. As the experimental results on the Amazon.com dataset demonstrate, our methodology improves the performance of the recommender system by incorporating information from reviews and produces recommendations with higher quality in terms of rating prediction accuracy compared to the baseline methods.
Findings of the First Shared Task on Machine Translation Robustness

We share the findings of the first shared task on improving robustness of Machine Translation (MT).The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models' robustness to noisy input and domain mismatch.We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit 1 and professionally sourced translations.As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc.All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU.We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson's r = 0.94 and 0.95).Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt 2 , which revealed their salient differences in handling challenges in this task.Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g.systems better at producing colloquial expressions received higher score from human judgment.
Implementation of Fall Detection System Based on 3D Skeleton for Deep Learning Technique

In recent years, the fall detection system has become an important topic in the homecare system. Compared with the traditional fall detection algorithm, the method used by neural network is more robust and has higher accuracy. However neural network consumes a large amount of energy due to a huge number of computations, and needs more memory to store parameters as compared to traditional algorithms. In this paper, we propose a fall detection system in combination of the traditional algorithm with the neural network. First, we propose a skeleton information extraction algorithm, which transforms depth information into skeleton information and extracts the important joints related to fall activity. Also we have modified the skeleton-based method with seven highlight feature points. Second, we propose a highly robust deep convolution neural network architecture, which uses a pruning method to reduce parameters and calculations in the network. The low number of parameters and calculations makes the system suitable for the implementation on an embedded system. The experiment results show the high accuracy and robustness on the popular benchmark dataset NTU RGB+D. The proposed system has been implemented on NVIDIA Jetson Tx2 platform with real-time processing.
Generalization in Generation: A closer look at Exposure Bias

Exposure bias refers to the train-test discrepancy that seemingly arises when an autoregressive generative model uses only ground-truth contexts at training time but generated ones at test time.We separate the contributions of the model and the learning framework to clarify the debate on consequences and review proposed counter-measures.In this light, we argue that generalization is the underlying property to address and propose unconditional generation as its fundamental benchmark.Finally, we combine latent variable modeling with a recent formulation of exploration in reinforcement learning to obtain a rigorous handling of true and generated contexts.Results on language modeling and variational sentence auto-encoding confirm the model's generalization capability.
Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm

The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting. The method interleaves sampling and consolidation of the current data interpretation via repetitive hypothesis proposal, fast rejection, and integration of the new hypothesis into the kept instance set by labeling energy minimization. Due to exploring the data progressively, the method has several beneficial properties compared with the state-of-the-art. First, a clear criterion, adopted from RANSAC, controls the termination and stops the algorithm when the probability of finding a new model with a reasonable number of inliers falls below a threshold. Second, Prog-X is an any-time algorithm. Thus, whenever is interrupted, e.g. due to a time limit, the returned instances cover real and, likely, the most dominant ones. The method is superior to the state-of-the-art in terms of accuracy in both synthetic experiments and on publicly available real-world datasets for homography, two-view motion, and motion segmentation.
Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging

The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered. This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches.The training and validation sets comprised randomly selected thyroid and breast nodule images. The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method). A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines. Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves.Among the 4 models, the CNN model using segmented images for classification achieved the best result. For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively. There was no statistically significant difference between the CNN model and the ultrasound guidelines. The combination of the two methods achieved superior diagnostic efficiency compared with their use individually.The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs. The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model. Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems.
Modelling of satellite constellations for trusted node QKD networks

Quantum key distribution from satellites becomes particularly valuable when it can be used on a large network and on-demand to provide a symmetric encryption key to any two nodes. A constellation model is described which enables QKD-derived encryption keys to be established between any two ground stations with low latency. This is achieved through the use of low earth orbit, trusted-node QKD satellites which create a buffer of keys with the ground stations they pass over, and geostationary relay satellites to transfer secure combinations of the keys to the ground stations. Regional and global network models are considered and the use of inter-satellite QKD links for balancing keys is assessed.
What Is the Effect of Using Mobile Augmented Reality in K12 Inquiry-Based Learning?

A systematic review of the potential of implementing augmented reality (AR) in inquiry-based learning was conducted. We considered the purposes, potential advantages, application characteristics and the effects of using AR in inquiry-based learning. The findings reveal that AR, in the context of inquiry-based learning, is mostly implemented successfully to achieve cognitive and, less often, motivational and emotional learning goals. The AR solutions have mainly been applied in the Conceptualization phase and less in the Investigation phase. The affordances of AR in the Orientation, Conclusion and Discussion phases need to be applied in further studies.
Combining PRNU and noiseprint for robust and efficient device source identification

Abstract PRNU-based image processing is a key asset in digital multimedia forensics. It allows for reliable device identification and effective detection and localization of image forgeries, in very general conditions. However, performance impairs significantly in challenging conditions involving low quality and quantity of data. These include working on compressed and cropped images or estimating the camera PRNU pattern based on only a few images. To boost the performance of PRNU-based analyses in such conditions, we propose to leverage the image noiseprint, a recently proposed camera-model fingerprint that has proved effective for several forensic tasks. Numerical experiments on datasets widely used for source identification prove that the proposed method ensures a significant performance improvement in a wide range of challenging situations.
Location Privacy Challenges in Mobile Edge Computing: Classification and Exploration

LPPMs have been well surveyed based on the view of methodologies, attacks, and so on. It has long been ignored that LBS applicability will be sacrificed due to the implementation of LPPMs. Existing and future LPPMs should clearly claim LBS applicability. This is essential for future research on location privacy in the MEC environment because of the diversity development of the LBS. To realize this goal, in this article, we present a classification of LBS by dividing the LBS into three independent dimensions, and we list a brief sample of LBS applicability of typical LPPMs based on our LBS categories. Then we consider even further new trends of LBS in MEC and introduce a new type of LBS named DLBS. We explain that DLBS subverts the existing LPPM problem, and we present a conceptual method to cope with the location privacy threat from DLBS.
Blockchain-based event processing in supply chains—A case study at IKEA

One of the major areas of interest for deployment of blockchains is in the supply chain sector where decentralisation combined with immutability is expected to enhance the transparency and robustness of transaction processing drastically. In this work we contribute to a feasibility study for permissioned blockchains in the context of a major international retail company. First, we characterise the types, relationships, and volumes of events that are created in the life cycle of a product from creation to ownership transfer. We use the created event model and the data from the company operations to identify the load that such an event processing system would have to manage. Then we create a prototype based on Quorum that deals with a significant subset of the events and expose the blockchain-based prototype to variations in load to identify the maximum throughput and average transaction processing time. This system can then form the basis of understanding the bottlenecks, configuration settings and platform choices needed in future potential deployment.
Classification of Handwritten Devanagari Number – An analysis of Pattern Recognition Tool using Neural Network and CNN

This paper majorly concerns the classification of handwritten numerals of Devanagari Script. The major contributions in this paper are 1) Development of a dataset for handwritten numerals similar to MNIST dataset, 2) Analysis of Pattern Recognitions tools based on NN and Convolution Neural Network, 3) Detailed discussion on the results by calculating the Precision, recall and F-measure values and compared with the other dataset available online. The present dataset includes 4,282 handwritten numerals of Devanagari which are collected from people of different ages. In the methodology developed in this paper, all the numerals are extracted from the image. After pre-processing, the images are resized to 30X30 which are later converted to vectors. These vectors with labels are the inputs for the classifiers. ANN classifier is designed by using PRTool and Deep Learning network is designed to make comparison with ANN. Data for training and testing splits into different ratios – 80:20, 70:30, 60:40 and 50:50. This research has achieved accuracy of more than 95%. The results of the dataset generated are compared with the dataset available online.
Multi-Lane Capsule Network for Classifying Images With Complex Background

Capsule Network (CapsNet) is a novel structure for deep neural network, mapping the region of target instance to vectors and matrices rather than scalars.This process is enabled by dynamic routing algorithm which help CapsNet to achieve more robust capacity with fewer parameters than traditional CNNs.However, one drawback of Capsule is that it turns to account for everything in the image, which leads to a poor performance when the backgrounds are too varied to model with a reasonable sized net.We proposed a multi-lane capsule network with strict-squash (MLSCN) to solve this problem.In MLSCN, we designed a novel Capsule based network structure, replaced the Squash function and optimized the implementation of dropout.To validate modification proposed in the paper, we conducted extensive experiments on three public datasets which include MNIST, affNIST and CIFAR10.Ablation experiments were also conducted to analyze contributions of each component of MLSCN.Experimental results show that MLSCN outperforms the original CapsNet in multiple benchmarks.Our model boosts the classification accuracy of CIFAR10 about 17% with negligible parameter increase compare to original CapsNet.Besides, the proposed model achieves an accuracy of 65.37%, while original CapsNet is only 41.62% on MNIST-BC.
End-to-End Neural Word Alignment Outperforms GIZA++

Word alignment was once a core unsupervised learning task in natural language processing because of its essential role in training statistical machine translation (MT) models. Although unnecessary for training neural MT models, word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection. While statistical MT methods have been replaced by neural approaches with superior performance, the twenty-year-old GIZA++ toolkit remains a key component of state-of-the-art word alignment systems. Prior work on neural word alignment has only been able to outperform GIZA++ by using its output during training. We present the first end-to-end neural word alignment method that consistently outperforms GIZA++ on three data sets. Our approach repurposes a Transformer model trained for supervised translation to also serve as an unsupervised word alignment model in a manner that is tightly integrated and does not affect translation quality.
Learning to Update Natural Language Comments Based on Code Changes

We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies.We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications.We train and evaluate our model using a dataset that we collected from commit histories of open-source software projects, with each example consisting of a concurrent update to a method and its corresponding comment.We compare our approach against multiple baselines using both automatic metrics and human evaluation.Results reflect the challenge of this task and that our model outperforms baselines with respect to making edits.