Reviewerly/exHarmony huggingface dataset:
Domain Robustness in Neural Machine Translation

Translating text that diverges from the training domain is a key challenge for machine translation. Domain robustness---the generalization of models to unseen test domains---is low for both statistical (SMT) and neural machine translation (NMT). In this paper, we study the performance of SMT and NMT models on out-of-domain test sets. We find that in unknown domains, SMT and NMT suffer from very different problems: SMT systems are mostly adequate but not fluent, while NMT systems are mostly fluent, but not adequate. For NMT, we identify such hallucinations (translations that are fluent but unrelated to the source) as a key reason for low domain robustness. To mitigate this problem, we empirically compare methods that are reported to improve adequacy or in-domain robustness in terms of their effectiveness at improving domain robustness. In experiments on German to English OPUS data, and German to Romansh (a low-resource setting) we find that several methods improve domain robustness. While those methods do lead to higher BLEU scores overall, they only slightly increase the adequacy of translations compared to SMT.
Query-Specific Knowledge Summarization with Entity Evolutionary Networks

Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge summarization. For example, given a query "computer vision'' on a CS literature corpus, rather than returning a list of relevant entities like "cnn'', "imagenet'' and "svm'', we are interested in the connections among them, and furthermore, the evolution patterns of such connections along particular ordinal dimensions such as time. Particularly, we hope to provide structural knowledge relevant to the query, such as "svm'' is related to "imagenet'' but not "cnn''. Moreover, we aim to model the changing trends of the connections, such as "cnn'' becomes highly related to "imagenet'' after 2010, which enables the tracking of knowledge evolutions. In this work, to facilitate such a novel insightful search system, we propose SetEvolve, which is a unified framework based on nonparanomal graphical models for evolutionary network construction from large text corpora. Systematic experiments on synthetic data and insightful case studies on real-world corpora demonstrate the utility of SetEvolve.
Improving Traversability Estimation Through Autonomous Robot Experimentation

The ability to have unmanned ground vehicles navigate unmapped off-road terrain has high impact potential in application areas ranging from supply and logistics, to search and rescue, to planetary exploration. To achieve this, robots must be able to estimate the traversability of the terrain they are facing, in order to be able to plan a safe path through rugged terrain. In the work described here, we pursue the idea of fine-tuning a generic visual recognition network to our task and to new environments, but without requiring any manually labelled data. Instead, we present an autonomous data collection method that allows the robot to derive ground truth labels by attempting to traverse a scene and using localization to decide if the traversal was successful. We then present and experimentally evaluate two deep learning architectures that can be used to adapt a pre-trained network to a new environment. We prove that the networks successfully adapt to their new task and environment from a relatively small dataset.
Anomaly Detection Based on Fixed and Wearable Sensors in Assisted Living Environments

The increasing number of physical devices connected to Internet enables accelerated development of advanced Internet of Things (IoT) applications able to improve our quality of life. One of such applications is Ambient Assisted Living (AAL). Its main goal is to determine the wellness of older people, people with disabilities, or people with acute or chronic pathologies living independently in their homes in order to initiate emergency response actions if necessary. Wellness of people can be inferred by monitoring activities of daily living (ADLs) and detecting possible anomalies. This paper proposes a novel method based on artificial neural networks (ANNs) for ADL identification using fixed and wearable sensor readings. Fixed sensors' readings are suitable for user localization in AAL, while wearable sensors' readings may estimate user body position. Besides identifying ADLs, the synergy of readings from these different sensor types proved to be valuable for anomaly detection. For this purpose, identified activities based on fixed sensor readings are matched with wearable sensors' readings in order to determine anomalous situation that might indicate medical or similar emergency.
Visible Infrared Cross-Modality Person Re-Identification Network Based on Adaptive Pedestrian Alignment

Cross-modality person re-identification between the visible domain and infrared domain is important but extremely challenging for night-time surveillance. Besides the cross-modality discrepancies caused by different camera spectrums, visible infrared person re-identification (VI-REID) still suffers from much pedestrian misalignment as well as the variations caused by different camera viewpoints and various pedestrian pose deformations like traditional person re-identification. In this paper, we propose a multi-path adaptive pedestrian alignment network (MAPAN) to learn discriminative feature representations. The multi-path network learns features directly from the data in an end-to-end manner and aligns the pedestrians adaptively without any additional manual annotations. To alleviate the intra-modality discrepancies caused by image misalignment, we combine the aligned visible image features with the original visible image features and enhance the attention of the network towards pedestrians, resulting in significant improvements in distinguishability of the learning features. To mitigate the cross-modality discrepancies between the visible domain and the infrared domain, the discriminative features of the two modalities are mapped to the same feature embedding space, and the identity loss as well as triplet loss is incorporated as the overall loss. Extensive experiments demonstrate the superior performance of proposed method compared to the state-of-the-arts.
3D Object Detection Algorithm for Panoramic Images With Multi-Scale Convolutional Neural Network

This paper addresses the challenge of 3D object detection from a single panoramic image under severe deformation. The advent of the two-stage approach has impelled significant progress in 3D object detection. However, most available methods only can localize region proposals by a single-scale architecture network, which are sensitive to deformation and distortion. To address this issue, we propose a multi-scale convolutional neural network (MSCNN) to estimate the 3D pose of an object. To be specific, the proposed MSCNN consists of three steps for effectively detecting the distorted object on the panoramic images. The MSCNN contains the CycleGAN network that converts rectilinear images into panoramas, a fused framework that improves both accuracy and speed for object detection, and an adversarial spatial transformer network (ASTN) that extracts the deformation features of the object on panoramic images. Additionally, we recover the 3D pose of the object using a coordinate projection and a 3D bounding box. Extensive experiments demonstrate that the proposed method can achieve a 3D detection accuracy of 38.7% in high-resolution panoramic images, which is higher than the current state-of-the-art algorithm of 5.2%. Moreover, the speed of detection is only about 0.6 seconds per image, which is six times faster than Faster R-CNN (COCO). The code will be available at https://github.com/Yanhui-He.
A blockchain based Trusted Persistent Identifier system for Big Data in Science

Abstract A stable reference of Internet resources is crucial not only to identify a resource in a trustworthy and certified way but also to guarantee continuous access to it over time. The current practice in scientific publication as the use of a Persistent Identifier (PID) like a DOI or Handle, is becoming attractive also for the datasets. In fact, in the era of Big Data, the aspects of replicability and verification of the scientific result are paramount. In this paper we verify the functional feasibility of permissioned blockchain technology as a tool to implement a Trustworthy Persistent Identifier (T-PID) system for datasets in the scientific domain.
State gradients for analyzing memory in LSTM language models

Gradients can be used to train neural networks, but they can also be used to interpret them. We investigate how well the inputs of RNNs are remembered by their state by calculating ‘state gradients’, and applying SVD on the gradient matrix to reveal which directions in embedding space are remembered and to what extent. Our method can be applied to any RNN and reveals which properties in the embedding space influence the state space, without the need to know and label the properties beforehand. In this paper, we propose a normalization method that alleviates the influence of variance in embedding space on the state gradients and show the effectiveness of our method on a synthetic dataset. Additionally, the influence of several training settings on the RNN memory is investigated, and a more fine-grained analysis based on POS and word types shows that LSTM language models learn to model linguistic intuitions. Our code and datasets are publicly available.
Improved Accuracy Test Method for Mobile Eye Tracking in Usability Scenarios

Eye tracking has been used in usability testing for many years to gain objective measurements to inform label, instruction, and product design. With many different testing environments, possible participants, hardware, and software, key metrics can vary greatly. These metrics can also vary for different studies, so a standardized test method, metrics, and calculation method are proposed in this study. The Tobii Pro Glasses 2 is a mobile eye tracker that does not significantly affect user mobility compared to other eye-trackers. This study aims to build a testing method, which can modify to better fit the varying conditions found in usability testing. This study was performed with Tobii Pro Glasses 2; however, this test method can be used with any mobile eye tracking units. Even with poor testing conditions, this test method results in reliable metrics which can be utilized to inform expectation and decisions with eye tracking. These methods are recommended to be performed prior to eye tracking testing to determine testing-specific performance.
Psychological approach to foreign language teaching

This article deals with psychological mechanisms in professionally-oriented approach to learning foreign languages in non-linguistic faculties of universities. This approach is very actual as it develops students’ ability to speak foreign languages in specific professional, business, academic fields and situations, taking into account the characteristics of professional thinking. Psychological approach to foreign language teaching must be closely connected with culture oriented method as the leading one and recognized as a priority in the focus of the education renewal. In the article it is proved that cultural background is of great importance. A dry language course can teach the learners to say what is correct and acceptable only. But the learners also need to know what is appropriate for the target society and what is not. This can only be achieved if they learn about psychology, culture and traditions while learning foreign language. Methods: Mind – maps, pie charts, dramatization, projects, role-playing to decide on which aspects of the culture the learners mostly need to learn about. The most important aspects of the English culture are defined and put into order of importance. Conclusions and Recommendations: suggesting ways of integrating psychology culture into the foreign language syllabus.
An Effective Framework Using Spatial Correlation and Extreme Learning Machine for Moving Cast Shadow Detection

Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, we present a novel moving cast-shadow detection framework based on the extreme learning machine (ELM) to efficiently distinguish shadow points from the foreground object. First, according to the physical model of shadows, pixel-level features of different channels in different color spaces and region-level features derived from the spatial correlation of neighboring pixels are extracted from the foreground. Second, an ELM-based classification model is developed by labelled shadow and un-shadow points, which is able to rapidly distinguish the points in the new input whether they belong to shadows or not. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on two publicly common datasets including 13 different scenes demonstrate that the performance of the proposed framework is superior to representative state-of-the-art methods.
Password meters: inaccurate advice offered inconsistently?

Although we are continually offered the promise of passwords being eradicated they continue to be used extensively on the majority of devices, sites and services. 1, 2 At the same time, they remain a significant point of weakness – particularly in view of the way in which users choose and manage them. For example, in 2017, Verizon's ‘Data Breach Investigations Report’ suggested that 81% of hacking-related breaches had “leveraged either stolen and/or weak passwords” (with 62% of breaches overall having featured hacking). 3
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to $50.7\%$ AP without introducing any overhead. The code is available at https://github.com/sfzhang15/ATSS
TrustedEVoting (TeV) a Secure, Anonymous and Verifiable Blockchain-Based e-Voting Framework

Research and commercial approaches on electronic voting (e-Voting) exist and the recently emerged blockchain technology has given this research area yet another boost. However, one may claim that the actual deployment of e-Voting is at its infancy, mainly due to election integrity issues such as vote non-tampering, voter anonymity, and vote verifiability (even though the last two may seem contradictory). The integration of cryptography and blockchain technology in a single framework could provide a solid foundation for secure, anonymous and still verifiable electronic voting. This paper presents the TrustedEVoting (TeV) framework, a blockchain-based approach, that wraps not only the required key elements for secure and verifiable e-Voting but also additional features such as support for re-voting, post-election vote check and voting channel preference. The last one is important as the transition from traditional physical voting to e-Voting is not immediate and TeV framework makes provision for a multiple-channel approach for voting.
Ontology-Driven News Classification with Aethalides

The ever-increasing amount of Web information offered to news readers (e.g., news analysts) stimulates the need for news selection, so that informed decisions can be made with up-to-date knowledge. Hermes is an ontology-based framework for building news personalization services. It uses an ontology crafted from available news sources, allowing users to select and filter interesting concepts from a domain ontology. The Aethalides framework enhances the Hermes framework by enabling news classification through lexicographic and semantic properties. For this, Aethalides applies word sense disambiguation and ontology learning methods to news items. When tested on a set of news items on finance and politics, the Aethalides implementation yields a precision and recall of 74.4% and 49.4%, respectively, yielding an F0.5-measure of 67.6% when valuing precision more than recall.
Spatio-spectral networks for color-texture analysis

Texture is one of the most-studied visual attributes for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on grayscale images and discarding the color information. Therefore this work proposes a new method for color texture analysis considering all color channels in a more thorough approach. It consists of modeling color images as directed complex networks that we named Spatio-Spectral Network (SSN). Its topology includes within-channel connections that cover spatial patterns of individual color channels, while between-channel connections tackle spectral properties of channel pairs in an opponent fashion. Image descriptors are obtained through topological characterization of the modeled network in a multiscale approach with radially symmetric neighboring. Experiments with four datasets cover several aspects of color-texture analysis, and results demonstrate that SSN overcomes all the compared literature methods, including known deep convolutional networks. It also has the most stable performance between datasets, achieving 98.5(± 1.1) of average accuracy against 97.1(± 1.3) of MCND and 96.8(± 3.2) of AlexNet. Additionally, an experiment verifies the performance of the methods under different color spaces, showing that SSN presents the highest performance and robustness.
Latent Words Recurrent Neural Network Language Models for Automatic Speech Recognition

This paper demonstrates latent word recurrent neural network language models (LW-RNN-LMs) for enhancing automatic speech recognition (ASR). LW-RNN-LMs are constructed so as to pick up advantages in both recurrent neural network language models (RNN-LMs) and latent word language models (LW-LMs). The RNN-LMs can capture long-range context information and offer strong performance, and the LW-LMs are robust for out-of-domain tasks based on the latent word space modeling. However, the RNN-LMs cannot explicitly capture hidden relationships behind observed words since a concept of a latent variable space is not present. In addition, the LW-LMs cannot take into account long-range relationships between latent words. Our idea is to combine RNN-LM and LW-LM so as to compensate individual disadvantages. The LW-RNN-LMs can support both a latent variable space modeling as well as LW-LMs and a long-range relationship modeling as well as RNN-LMs at the same time. From the viewpoint of RNN-LMs, LW-RNN-LM can be considered as a soft class RNN-LM with a vast latent variable space. In contrast, from the viewpoint of LW-LMs, LW-RNN-LM can be considered as an LW-LM that uses the RNN structure for latent variable modeling instead of an n-gram structure. This paper also details a parameter inference method and two kinds of implementation methods, an n-gram approximation and a Viterbi approximation, for introducing the LW-LM to ASR. Our experiments show effectiveness of LW-RNN-LMs on a perplexity evaluation for the Penn Treebank corpus and an ASR evaluation for Japanese spontaneous speech tasks.
Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants

Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. Detecting and analyzing user stances from massive opinion-oriented twitter posts provide enormous opportunities to journalists, governments, companies, and other organizations. Most of the prior studies have explored the traditional deep learning models, e.g., long short-term memory (LSTM) and gated recurrent unit (GRU) for detecting stance in tweets. However, compared to these traditional approaches, recently proposed densely connected bidirectional LSTM and nested LSTMs architectures effectively address the vanishing-gradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural network model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higher-level tweet representations. Results of extensive experiments on single and multi-target benchmark stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.
JSSignature: eliminating third-party-hosted JavaScript infection threats using digital signatures

Today, third-party JavaScript resources are an indispensable part of the web platform. More than 88% of the world’s top websites include at least one JavaScript resource from a remote host. However, there is a great security risk behind using a third-party JavaScript resource, if an attacker can infect one of these remote JavaScript resources all websites those have included the script would be at risk. In this paper, we present JSSignature, an entirely at the client-side pure JavaScript framework in order to validate third-party JavaScript resources using a digital signature. Therefore, all included JavaScript resources are checked against the integrity, authentication and non-repudiation risks before the execution. In contrary to existing methods, JSSignature protects web pages regardless of third-party resource infection nature while it does not set any restrictions on trusted JavaScript providers. This approach has an acceptable one-time performance overhead and is an easily deployable add-in. We have validated the proposed solution by applying tests on an implemented version ( https://iasbs.ac.ir/~ansari/jssignature/demo.html ). A pre-published version of this paper is available at arXiv website ( https://arxiv.org/pdf/1812.03939.pdf ).
Navigation and obstacle avoidance: a case study using Pepper robot

In this paper we present a novel strategy and implementation of autonomous navigation for the Pepper robot. The proposed solution is modular and relies on the proper integration of existing Pepper functionalities. The human interacts with the robot using voice and/or gesture commands to setup the robot functionalities. An example use case demonstrates the ability of the robot to navigate in office environment and grasp an object to place in a target location. The robot is also able to avoid obstacles while navigating.