Reviewerly/exHarmony huggingface dataset:
A Comprehensive Survey of Multilingual Neural Machine Translation

We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer learning). MNMT is more promising and interesting than its statistical machine translation counterpart because end-to-end modeling and distributed representations open new avenues for research on machine translation. Many approaches have been proposed in order to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and hence deserve further exploration. In this paper, we present an in-depth survey of existing literature on MNMT. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Wherever possible we address the strengths and weaknesses of several techniques by comparing them with each other. We also discuss the future directions that MNMT research might take. This paper is aimed towards both, beginners and experts in NMT. We hope this paper will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.
Towards Hands-Free Visual Dialog Interactive Recommendation

With the recent advances of multimodal interactive recommendations, the users are able to express their preference by natural language feedback to the item images, to find the desired items. However, the existing systems either retrieve only one item or require the user to specify (e.g., by click or touch) the commented items from a list of recommendations in each user interaction. As a result, the users are not hands-free and the recommendations may be impractical. We propose a hands-free visual dialog recommender system to interactively recommend a list of items. At each time, the system shows a list of items with visual appearance. The user can comment on the list in natural language, to describe the desired features they further want. With these multimodal data, the system chooses another list of items to recommend. To understand the user preference from these multimodal data, we develop neural network models which identify the described items among the list and further predict the desired attributes. To achieve efficient interactive recommendations, we leverage the inferred user preference and further develop a novel bandit algorithm. Specifically, to avoid the system exploring more than needed, the desired attributes are utilized to reduce the exploration space. More importantly, to achieve sample efficient learning in this hands-free setting, we derive additional samples from the user's relative preference expressed in natural language and design a pairwise logistic loss in bandit learning. Our bandit model is jointly updated by the pairwise logistic loss on the additional samples derived from natural language feedback and the traditional logistic loss. The empirical results show that the probability of finding the desired items by our system is about 3 times as high as that by the traditional interactive recommenders, after a few user interactions.
Achieving Privacy-Preserving Subset Aggregation in Fog-Enhanced IoT

Fog-enhanced IoT (Internet of Things) is a fast-growing technology in which many firms and industries are currently investing to develop their own real-time and low latency scenarios. Compared with the traditional IoT, fog-enhanced IoT can offer a higher level of efficiency and stronger security by providing local data pre-processing, filtering, and forwarding mechanisms. However, fog-enhanced IoT faces some security and privacy challenges, since fog nodes are deployed at the network edge and may not be fully trustable. In this paper, we present a new privacy-preserving subset aggregation scheme, called PPSA, in fog-enhanced IoT scenarios, that enables a query user to gain the sum of data from a subset of IoT devices. To identify the subset, inner product similarity of the normalized vectors in the query user side and each IoT device is securely computed. If the inner product is greater than the user's specified threshold, IoT device's data will be privately aggregated to form the final response. To successfully launch privacy-preserving subset aggregation in the proposed scheme, we employ the Paillier homomorphic encryption to encrypt user's attribute vector, similarity threshold, IoT end-devices' data, as well as the intermediate results. To the best of our knowledge, this work is the first one to address the privacy-preserving subset aggregation in fog-enhanced IoT. We analyze and extensively evaluate the efficiency and security of the proposed PPSA scheme, and the detailed analysis and results indicate that our proposed PPSA scheme can practically achieve privacy-preserving subset aggregation with significant communication and computational cost saving.
Kalman tracking algorithm of ping-pong robot based on fuzzy real-time image

The moving target tracking is a very important in computer vision research topic and is widely used, it is of great research significance, has been widely used, and involved in image recognition, production automation, intelligent pattern recognition, artificial intelligence, weather information, a nd other fields, but in some high speed movement, such as target under complex background target trajectory when not much target tracking is still more difficult. This paper mainly studies the Kalman tracking algorithm of ping-pong robot based on fuzzy real-time image. For table tennis high-speed motion blurred images, air resistance and the camera imaging distortion caused by factors such as the error problem, puts forward an adaptive measurement covariance discrete Kalman trajectory estimation algorithm. The algorithm with dynamic adjustment of measuring the size of the covariance, has realized the accurate tracking of the target motion trajectory, and further laid the groundwork for table tennis balls prediction and hitting arm. The experimental results show that the algorithm can effectively overcome the interference of measurement noise and data loss and give excellent tracking results when the image acquisition rate is higher than 70 frames /s and the table tennis speed is higher than 5 m/s.
Study on how to improve visibility of transparent display for augmented reality under various environment conditions

In this paper, a psychophysical investigation to improve a visibility of a transparent display is presented. A new illuminance measurement method for the transparent display, named eye illuminance, is proposed. Through a psychophysical experiment, it is found that the eye illuminance is strongly related with the visibility of the transparent display regardless of its background condition. This paper finds out the optimum emission luminance range of the transparent display under various illuminant conditions. Also, the contrast ratio for visibility is analyzed and it is found that a higher contrast ratio is not needed to provide a visually better image under a brighter ambient environment. In conclusion, our findings will contribute to an auto brightness control technology to improve the visibility of the transparent display for augmented reality devices.
Discrete and Continuous Action Representation for Practical RL in Video Games

While most current research in Reinforcement Learning (RL) focuses on improving the performance of the algorithms in controlled environments, the use of RL under constraints like those met in the video game industry is rarely studied. Operating under such constraints, we propose Hybrid SAC, an extension of the Soft Actor-Critic algorithm able to handle discrete, continuous and parameterized actions in a principled way. We show that Hybrid SAC can successfully solve a highspeed driving task in one of our games, and is competitive with the state-of-the-art on parameterized actions benchmark tasks. We also explore the impact of using normalizing flows to enrich the expressiveness of the policy at minimal computational cost, and identify a potential undesired effect of SAC when used with normalizing flows, that may be addressed by optimizing a different objective.
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons

Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address this problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing. In the light of Bayesian uncertainty estimation and noise-tolerant adversarial training, PC-GAN can estimate attribute rating efficiently and demonstrate robust performance in noise resistance. Through extensive experiments, we show both qualitatively and quantitatively that PC-GAN performs comparably with fully-supervised methods and outperforms unsupervised baselines. Code and Supplementary can be found on the project website*.
Using Eye Tracked Virtual Reality to Classify Understanding of Vocabulary in Recall Tasks

In recent years, augmented and virtual reality (AR/VR) have started to take a foothold in markets such as training and education. Although AR and VR have tremendous potential, current interfaces and applications are still limited in their ability to recognize context, user understanding, and intention, which can limit the options for customized individual user support and the ease of automation. This paper addresses the problem of automatically recognizing whether or not a user has an understanding of a certain term, which is directly applicable to AR/VR interfaces for language and concept learning. To do so, we first designed an interactive word recall task in VR that required non-native English speakers to assess their knowledge of English words, many of which were difficult or uncommon. Using an eye tracker integrated into the VR Display, we collected a variety of eye movement metrics that might correspond to the user's knowledge or memory of a particular word. Through experimentation, we show that both eye movement and pupil radius have a high correlation to user memory, and that several other metrics can also be used to help classify the state of word understanding. This allowed us to build a support vector machine (SVM) that can predict a user's knowledge with an accuracy of 62% in the general case and and 75% for easy versus medium words, which was tested using cross-fold validation. We discuss these results in the context of in-situ learning applications.
The AzAs (Antizionist Antisemitism) Scale: Measuring Antisemitism as Expressed in Relation to Israel and Its Supporters

This paper reports on the development and testing of the AzAs (Antizionist Antisemitism) scale: a six-item questionnaire instrument for measuring antisemitic attitudes as articulated in the language of hostility to Israel and its supporters. It is important to be able to recognize and measure this kind of antisemitism because it is often embedded within ostensibly democratic discourse. The identification of this antisemitism is frequently contested even by those who are in broad agreement on the recognition of older forms of antisemitism. The scale contains a balance of protrait and contrait items, and achieved a satisfactory level of internal consistency when piloted on a sample of US-based respondents recruited through the Mechanical Turk crowdsourcing market (N = 122). There appears to be no relationship between scores on the scale and the subjective political position of respondents (as measured on a left-right self-report scale). We suggest that the AzAs scale will be of general use in measuring antizionist antisemitism because (a) it collects several familiar and demonstrably antisemitic ideas expressed in relation to Israel and its supporters and (b) it exhibits good psychometric properties.
Feature Deformation Meta-Networks in Image Captioning of Novel Objects

This paper studies the task of image captioning with novel objects, which only exist in testing images. Intrinsically, this task can reflect the generalization ability of models in understanding and captioning the semantic meanings of visual concepts and objects unseen in training set, sharing the similarity to one/zero-shot learning. The critical difficulty thus comes from that no paired images and sentences of the novel objects can be used to help train the captioning model. Inspired by recent work (Chen et al. 2019b) that boosts one-shot learning by learning to generate various image deformations, we propose learning meta-networks for deforming features for novel object captioning. To this end, we introduce the feature deformation meta-networks (FDM-net), which is trained on source data, and learn to adapt to the novel object features detected by the auxiliary detection model. FDM-net includes two sub-nets: feature deformation, and scene graph sentence reconstruction, which produce the augmented image features and corresponding sentences, respectively. Thus, rather than directly deforming images, FDM-net can efficiently and dynamically enlarge the paired images and texts by learning to deform image features. Extensive experiments are conducted on the widely used novel object captioning dataset, and the results show the effectiveness of our FDM-net. Ablation study and qualitative visualization further give insights of our model.
Translucent Answer Predictions in Multi-Hop Reading Comprehension

Research on the task of Reading Comprehension style Question Answering (RCQA) has gained momentum in recent years due to the emergence of human annotated datasets and associated leaderboards, for example CoQA, HotpotQA, SQuAD, TriviaQA, etc. While state-of-the-art has advanced considerably, there is still ample opportunity to advance it further on some important variants of the RCQA task. In this paper, we propose a novel deep neural architecture, called TAP (Translucent Answer Prediction), to identify answers and evidence (in the form of supporting facts) in an RCQA task requiring multi-hop reasoning. TAP comprises two loosely coupled networks – Local and Global Interaction eXtractor (LoGIX) and Answer Predictor (AP). LoGIX predicts supporting facts, whereas AP consumes these predicted supporting facts to predict the answer span. The novel design of LoGIX is inspired by two key design desiderata – local context and global interaction– that we identified by analyzing examples of multi-hop RCQA task. The loose coupling between LoGIX and the AP reveals the set of sentences used by the AP in predicting an answer. Therefore, answer predictions of TAP can be interpreted in a translucent manner. TAP offers state-of-the-art performance on the HotpotQA (Yang et al. 2018) dataset – an apt dataset for multi-hop RCQA task – as it occupies Rank-1 on its leaderboard (https://hotpotqa.github.io/) at the time of submission.
Test-retest of automated segmentation with different motion correction strategies: A comparison of prospective versus retrospective methods

Test-retest of automated image segmentation algorithms (FSL FAST, FSL FIRST, and FREESURFER) are computed on magnetic resonance images from 12 unsedated children aged 9.4±2.6 years ([min,max] ​= ​[6.5 years, 13.8 years]) using different approaches to motion correction (prospective versus retrospective). The prospective technique, PROMO MPRAGE, dynamically estimates motion using specially acquired navigator images and adjusts the remaining acquisition accordingly, whereas the retrospective technique, MPnRAGE, uses a self-navigation property to retrospectively estimate and account for motion during image reconstruction. To increase the likelihood and range of motions, participants heads were not stabilized with padding during repeated scans. When motion was negligible both techniques had similar performance. When motion was not negligible, the automated image segmentation and anatomical labeling software tools showed the most consistent performance with the retrospectively corrected MPnRAGE technique (≥80% volume overlaps for 15 of 16 regions for FIRST and FREESURFER, with greater than 90% volume overlaps for 12 regions with FIRST and 11 regions with FREESURFER). Prospectively corrected MPRAGE with linear view-ordering also demonstrated lower performance than MPnRAGE without retrospective motion correction.
Job Recommender Systems: A Survey

From the last two decades internet based recruiting platforms have become a primary channel in most companies for recruiting talents. Such portals decrease the advertisement cost, but they suffer from information overload problem. Job portals using traditional information retrieval techniques such as Boolean search methods are typically using simple word matching algorithms. The main issue of these portals is their inability to understand the complexity of matching between candidates' desires and organizations' requirements. Hence, a vast amount of deserving candidates misses the opportunity to get an appropriate job. The recent recommender systems have achieved success in e-commerce applications. In order to improve the functionality of e-recruitment process, many recommendation systems approaches have been proposed. In this paper, we present a survey of existing recommendation approaches that have been used for building the personalized recommendation systems for job seekers as well as recruiters. Also we have identified the challenges in building a job recruitment system as compared to recommendation systems in other domain.
Deep learning for population size history inference: design, comparison and combination with approximate Bayesian computation

A bstract For the past decades, simulation-based likelihood-free inference methods have enabled researchers to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong opportunity to tackle tasks that current methods hardly solve. However, high data dimensionality forces most methods to summarize large genomic datasets into a relatively small number of handcrafted features (summary statistics). Here we propose an alternative to summary statistics, based on the automatic extraction of relevant information using deep learning techniques. Specifically, we design artificial neural networks (ANNs) that take as input single nucleotide polymorphic sites (SNPs) found in individuals sampled from a single population and infer the past effective population size history. First, we provide guidelines to construct artificial neural networks that comply with the intrinsic properties of SNP data such as invariance to permutation of haplotypes, long scale interactions between SNPs and variable genomic length. Thanks to a Bayesian hyperparameter optimization procedure, we evaluate the performance of multiple networks and compare them to well established methods like Approximate Bayesian Computation (ABC). Even without the expert knowledge of summary statistics, our approach compares fairly well to an ABC based on handcrafted features. Furthermore we show that combining deep learning and ABC can improve performance while taking advantage of both frameworks. Finally, we apply our approach to reconstruct the effective population size history of cattle breed populations.
Modern libraries practices for teaching information literacy

Information literacy as a set of techniques and methods to work with diverse types of information is a necessary condition for s person to exist in the modern world, because it helps to develop personal, professional and educational skills. The concept of information literacy firstly appeared in the United States in the 1970th. It was a response to the development of information society. Up to now the idea has not lost its relevance, due to the intensive growth of information technologies, an increase in information volumes and the emergence of new educational paradigms. Since the 1990th libraries began to actively develop programs and implement methods of increasing information literacy, systematically train users in rational methods of search, analytical and synthetic information processing, extraction, evaluation and transformation, as well as in the grounds of information security. The purpose of this article is to identify current advanced practices of libraries on various training forms to develop users’ information literacy, taking into account the increase in information flows and the emergence of large amounts of misinformation, the expansion of information types, the capabilities of education technologies, including online, library directions of work activities. To get information use was made of the Scopus database (the selection criterion: journal publications from 2015 to 2019 on the topic “information literacy”). The publication analysis has shown that libraries choose various ways and methods to teach information literacy their users: embedding programs in the educational process (academic libraries); through game, interactive forms (public libraries), individual methods of work of subject librarians with readers, as well as online instructions that allow formation of skills for effective search and information processing in any format.
Mixed reality based respiratory liver tumor puncture navigation

Abstract This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation (RFA). Our system contains an optical see-through head-mounted display device (OST-HMD), Microsoft HoloLens for perfectly overlaying the virtual information on the patient, and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical scene. Compared with traditional navigation method with CT, our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking system. In addition, to alleviate the difficulty during needle placement induced by respiratory motion, we reconstruct the patient-specific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver tumors. The proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE, which has the potential to be applied in clinical RFA guidance.
Organisational Memory and its Impact on Competitive Strategies in IT Companies in Jordan

This study aims at measuring the impact of organisational memory (OM) on competitive strategies in information technology (IT) companies in King Hussein Business Park (KHBP) in Amman, the capital of Jordan. KHBP comprises of 20 companies, which will form the research population. A random sample was chosen with the sampling unit constituting of 63 out of originally 74 senior employees. The study adopted a descriptive analytical approach and a structured questionnaire was used as a study tool for the purpose of measuring the variables of the study. The outcomes of the study show that OM and its processes (knowledge acquisition, retention, and retrieval) have an impact on both types of competitive strategies (differentiation and lower cost strategies) in Jordan's IT companies. The study presented key recommendations such as: enhancing the role of OM in IT companies given its clear impact on competitive strategies; carrying out additional studies on OM to look deeper into its dimensions, processes, justifications, hindrances, and strategies.