Reviewerly/exHarmony huggingface dataset:
NICT’s Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task

This paper presents the NICT's participation in the WMT19 unsupervised news translation task.We participated in the unsupervised translation direction: German-Czech.Our primary submission to the task is the result of a simple combination of our unsupervised neural and statistical machine translation systems.Our system is ranked first for the German-to-Czech translation task, using only the data provided by the organizers ("constraint"), according to both BLEU-cased and human evaluation.We also performed contrastive experiments with other language pairs, namely, English-Gujarati and English-Kazakh, to better assess the effectiveness of unsupervised machine translation in for distant language pairs and in truly low-resource conditions.
Secure halftone image steganography with minimizing the distortion on pair swapping

In halftone image data hiding, pixel pairs containing master pixels and slave pixels are common operating units. In most previous researches, master pixels are selected at a set of pseudo-random locations, which degrade the image quality. In this paper, a secure halftone image steganographic scheme based on pair swapping is presented, which aims at minimizing the embedding distortions. Different from most previous researches, there is no master-slave relationship in the proposed scheme and the steganographic performance depends on the selection of pixel pairs instead of slave pixels. Based on a human visual system (HVS) model of halftone images, the superiority of pair swapping is proved and vertical swapping is further demonstrated to be the optimal way to improve visual quality among all pair swapping strategies. Finally, a statistical model is developed to predict the vertical pair pattern by considering its neighboring region, based on which, a distortion measurement is proposed to evaluate the embedding distortions on both vision and statistics. To play the advantage of the distortion measurement, syndrome-trellis code (STC) is employed to minimize the embedding distortions. Experimental results show that the proposed steganographic scheme achieves high statistical security with high embedding capacity without degrading the visual quality.
The impact of columnar file formats on SQL‐on‐hadoop engine performance: A study on ORC and Parquet

Summary Columnar file formats provide an efficient way to store data to be queried by SQL‐on‐Hadoop engines. Related works consider the performance of processing engine and file format together, which makes it impossible to predict their individual impact. In this work, we propose an alternative approach: by executing each file format on the same processing engine, we compare the different file formats as well as their different parameter settings. We apply our strategy to two processing engines, Hive and SparkSQL, and evaluate the performance of two columnar file formats, ORC and Parquet. We use BigBench (TPCx‐BB), a standardized application‐level benchmark for Big Data scenarios. Our experiments confirm that the file format selection and its configuration significantly affect the overall performance. We show that ORC generally performs better on Hive, whereas Parquet achieves best performance with SparkSQL. Using ZLIB compression brings up to 60.2% improvement with ORC, while Parquet achieves up to 7% improvement with Snappy. Exceptions are the queries involving text processing, which do not benefit from using any compression.
ResAttr-GAN: Unpaired Deep Residual Attributes Learning for Multi-Domain Face Image Translation

Facial attributes edit can be seen as an image-to-image translation problem, whose goal is to transfer images from the source domain to the target domain.Specially, facial attributes edit aims at changing some semantic attributes of a given face image while keeping the contents of unrelated area unchanged.The great challenge for this problem lies on the lacking of paired data, i.e. we do not have paired face images that only differ on particular attributes.Moreover, to train a good attributes editing model, there always needs a great amount of train data which labeled by hand.If the train data amount was reduced, then the editing performance would decrease accordingly.Strong intelligent systems should be able to learn knowledge from less data samples (similar idea with few-shot learning).To mitigate this limitation, in this paper, we proposed a Siamese-Network based residual attributes learning model to learn the attributes difference in the high-level latent space.Compared to existing models that perform attributes editing based on an attributes classifier, the proposed deep residual attributes learning model utilized relatively weaker information of attribute differences for face image translation.Sufficient qualitative and quantitative experiments conducted on CelebA dataset proved the effectiveness of our proposed method, moreover, we also adopt the proposed residual attributes learning model in two state-of-the-art models under different data usage percentage to show the effectiveness of the proposed model on boosting attribute editing performance under limited data usage.The experiment results proved that the proposed method can improve data utilization efficiency and thus can boost the editing performance when the train data was limited.
Practical Byte-Granular Memory Blacklisting using Califorms

Recent rapid strides in memory safety tools and hardware have improved software quality and security. While coarse-grained memory safety has improved, achieving memory safety at the granularity of individual objects remains a challenge due to high performance overheads usually between ~1.7x--2.2x. In this paper, we present a novel idea called Califorms, and associated program observations, to obtain a low overhead security solution for practical, byte-granular memory safety.
Effect of augmented reality environments on cognitive load: pedagogical effect, instructional design, motivation and interaction interfaces

The aim of this study was to explain the relationship between cognitive load and the effects of augmented reality (AR) learning environments. To achieve this aim, firstly, the studies of systematic literature reviews on the potential and limitations of AR learning environments were examined. Afterwards, the effect of AR was categorized in terms of (1) pedagogical effect, (2) instructional design, (3) motivation and (4) interaction interfaces. Finally, the relationship between cognitive load and the emerging categories related to the potential and limitations of AR was explained and recommendations were presented. From a pedagogical point of view, AR helps to reduce extraneous cognitive load and to increase germane cognitive load. On the other hand, the effect of AR systems, which are difficult to use and complex in terms of instructional design, on cognitive load was revealed. Some teaching methods and design principles that can be effective as solutions were presented. In addition, the effects of motivational stimuli on the prevention or extension of cognitive capacity among students were described. Finally, the potential and limitations of AR interaction interfaces on cognitive load were explained. The results of this research provide important clues for AR developers and instructional designers in terms of reducing cognitive load and the elimination of working memory limitations.
Pretraining to Recognize PICO Elements from Randomized Controlled Trial Literature.

PICO (Population/problem, Intervention, Comparison, and Outcome) is widely adopted for formulating clinical questions to retrieve evidence from the literature. It plays a crucial role in Evidence-Based Medicine (EBM). This paper contributes a scalable deep learning method to extract PICO statements from RCT articles. It was trained on a small set of richly annotated PubMed abstracts using an LSTM-CRF model. By initializing our model with pretrained parameters from a large related corpus, we improved the model performance significantly with a minimal feature set. Our method has advantages in minimizing the need for laborious feature handcrafting and in avoiding the need for large shared annotated data by reusing related corpora in pretraining with a deep neural network.
Biased Sampling Potentially Guided Intelligent Bidirectional RRT<sup>∗</sup> Algorithm for UAV Path Planning in 3D Environment

During the last decade, Rapidly-exploring Random Tree star (RRT ∗ ) algorithm based on sampling has been widely used in the field of unmanned aerial vehicle (UAV) path planning for its probabilistically complete and asymptotically optimal characteristics. However, the convergence rate of RRT ∗ as well as B-RRT ∗ and IB-RRT ∗ is slow for these algorithms perform pure exploration. To overcome the weaknesses above, Biased Sampling Potentially Guided Intelligent Bidirectional RRT ∗ (BPIB-RRT ∗ ) algorithm is proposed in this paper, which combines the bidirectional artificial potential field method with the idea of bidirectional biased sampling. The proposed algorithm flexibly adjusts the sampling space, greatly reduces the invalid spatial sampling, and improves the convergence rate. Moreover, the deeply theoretical analysis of the proposed BPIB-RRT ∗ algorithm is given regarding its probabilistic completeness, asymptotic optimality, and computational complexity. Finally, compared to the latest UAV path planning algorithms, simulation comparisons are demonstrated to show the superiority of our proposed BPIB-RRT ∗ algorithm.
A Robust Cubature Kalman Filter with Abnormal Observations Identification Using the Mahalanobis Distance Criterion for Vehicular INS/GNSS Integration

INS/GNSS (inertial navigation system/global navigation satellite system) integration is a promising solution of vehicle navigation for intelligent transportation systems. However, the observation of GNSS inevitably involves uncertainty due to the vulnerability to signal blockage in many urban/suburban areas, leading to the degraded navigation performance for INS/GNSS integration. This paper develops a novel robust CKF with scaling factor by combining the emerging cubature Kalman filter (CKF) with the concept of Mahalanobis distance criterion to address the above problem involved in nonlinear INS/GNSS integration. It establishes a theory of abnormal observations identification using the Mahalanobis distance criterion. Subsequently, a robust factor (scaling factor), which is calculated via the Mahalanobis distance criterion, is introduced into the standard CKF to inflate the observation noise covariance, resulting in a decreased filtering gain in the presence of abnormal observations. The proposed robust CKF can effectively resist the influence of abnormal observations on navigation solution and thus improves the robustness of CKF for vehicular INS/GNSS integration. Simulation and experimental results have demonstrated the effectiveness of the proposed robust CKF for vehicular navigation with INS/GNSS integration.
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework

Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks. There are two main paradigms for learning such representations: (1) alignment, which maps different independently trained monolingual representations into a shared space, and (2) joint training, which directly learns unified multilingual representations using monolingual and cross-lingual objectives jointly. In this paper, we first conduct direct comparisons of representations learned using both of these methods across diverse cross-lingual tasks. Our empirical results reveal a set of pros and cons for both methods, and show that the relative performance of alignment versus joint training is task-dependent. Stemming from this analysis, we propose a simple and novel framework that combines these two previously mutually-exclusive approaches. Extensive experiments on various tasks demonstrate that our proposed framework alleviates limitations of both approaches, and outperforms existing methods on the MUSE bilingual lexicon induction (BLI) benchmark. We further show that our proposed framework can generalize to contextualized representations and achieves state-of-the-art results on the CoNLL cross-lingual NER benchmark.
Classification of instagram fake users using supervised machine learning algorithms

On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior, and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity. Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users.
Towards Real-Time Eyeblink Detection in the Wild: Dataset, Theory and Practices

Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing. Despite previous efforts, most of existing focus on addressing the eyeblink detection problem under constrained indoor conditions with relative consistent subject and environment setup. Nevertheless, towards practical applications, eyeblink detection in the wild is highly preferred, and of greater challenges. In this paper, we shed the light to this research topic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673 eyeblink video samples (i.e., 381 positives, and 292 negatives) is first established. These samples are captured from the unconstrained movies, with the dramatic variation on face attribute, head pose, illumination condition, imaging configuration, etc. Then, we formulate eyeblink detection task as a binary spatial-temporal pattern recognition problem. After locating and tracking human eyes using SeetaFace engine and KCF (Kernelized Correlation Filters) tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to verify eyeblink. A feature extraction approach that reveals the appearance and motion characteristics simultaneously is also proposed. The experiments on HUST-LEBW reveal the superiority and efficiency of our approach. The comparisons with the existing state-of-the-art methods validate the advantages of our manner for eyeblink detection in the wild.
Neural Empirical Bayes

We unify $\textit{kernel density estimation}$ and $\textit{empirical Bayes}$ and address a set of problems in unsupervised learning with a geometric interpretation of those methods, rooted in the $\textit{concentration of measure}$ phenomenon. Kernel density is viewed symbolically as $X\rightharpoonup Y$ where the random variable $X$ is smoothed to $Y= X+N(0,\sigma^2 I_d)$, and empirical Bayes is the machinery to denoise in a least-squares sense, which we express as $X \leftharpoondown Y$. A learning objective is derived by combining these two, symbolically captured by $X \rightleftharpoons Y$. Crucially, instead of using the original nonparametric estimators, we parametrize $\textit{the energy function}$ with a neural network denoted by $\phi$; at optimality, $\nabla \phi \approx -\nabla \log f$ where $f$ is the density of $Y$. The optimization problem is abstracted as interactions of high-dimensional spheres which emerge due to the concentration of isotropic gaussians. We introduce two algorithmic frameworks based on this machinery: (i) a walk-jump sampling scheme that combines Langevin MCMC (walks) and empirical Bayes (jumps), and (ii) a probabilistic framework for $\textit{associative memory}$, called NEBULA, defined a la Hopfield by the $\textit{gradient flow}$ of the learned energy to a set of attractors. We finish the paper by reporting the emergence of very rich creative memories as attractors of NEBULA for highly-overlapping spheres.
Learning and Development of Mental Models during Interactions with Driving Automation: A Simulator Study

Higher level cognitive processes such as learning and mental models play a fundamental role in the success of automated driving, as technology can only be as good as our understanding and expectations of it. The present study investigated the development of these processes during interactions with driving automation. In a driving simulator study, N=52 participants completed several transitions between manual and Society of Automotive Engineers (SAE) levels 2 and 3 automated driving. Self-reported learning progress and mental model development were assessed via questionnaires. In parallel, eye-tracking data were collected as a behavioral measure of higher level cognitive functions. The results demonstrated that self-reported learning and gaze behavior followed a power-law function; the power-law functions showed task specific parameter manifestations. The evolution of the mental models of the level 2 and level 3 human-machine interface continued up to the fifth contact, indicating a long lasting process. For researchers and practitioners, the present study implies that accurate mental models require up to 5 repeated interactions. Furthermore, learning progress with driving automation can be captured through gaze behavior.
Self-Tuning fuzzy controller for sun-tracker system using Gray Wolf Optimization (GWO) technique

The demand of electric power consumption is increasing very rapidly worldwide and to fulfill this requirement, solar energy is one of the most viable solution as renewable energy source. Photovoltaic (PV) cell based sun-tracker system (STS) produces maximum current when sunlight vertically incident on its surface. Hence, there is a need of optimized continuous axis position control of STS to achieve maximum output current. This task can be done on the basis of the fuzzy control system. Usually, in the traditional fuzzy control system (FCS), tuning of designed fuzzy parameter is done by trial and error method. However, this type of FCS parameter tuning approach may or may not give optimal solution. Thus, in presented work, an optimal tuning technique with Takagi, Sugeno and Kang (TSK) fuzzy controller (TFC) using Gray Wolf Optimization (GWO) for STS has been proposed. In order to validate the proposed work, different objective functions have been employed to carry out fuzzy controller parameter optimization. A comparative analysis has been performed on the basis of three parameters: settling time, maximum-overshoot and optimal fuzzy parameter on different constrain set. The results obtained with the GWO optimization algorithm were also compared with other popular population algorithms, i.e. Whale Optimization Technique (WOT) and Particle Swarm Optimization (PSO) algorithms.
Swoosh! Rattle! Thump! - Actions that Sound

Action dataFig. 1: Using Tilt-Bot, we collect 15,000 interactions on 60 different objects by tilting them in a tray.When sufficiently tilted, the object slides across the tray and hits the walls of the tray.This generates sound, which is captured by four contact microphones mounted on each side of the tray.An overhead camera records visual (RGB+Depth) information, while the robotic arm applies the tilting actions through end-effector rotations.Abstract-Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world.In robotics, we have seen tremendous progress in using visual and tactile perception; however, we have often ignored a key sense: sound.This is primarily due to the lack of data that captures the interplay of action and sound.In this work, we perform the first large-scale study of the interactions between sound and robotic action.To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot.By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information.Using this data, we explore the synergies between sound and action and present three key insights.First, sound is indicative of finegrained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench.Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied to the object.Finally, object representations derived from audio embeddings are indicative of implicit physical properties.We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings.Project videos and data are at this url
A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

Given the perpetual surging of cloud services’ requests, energy consumption of cloud data centers with their related CO2 emissions still represents major issues. Efficient use of cloud’s resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud’s energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud’s resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra- and inter-cloud network’s resources to be considered. These resources are mainly provisioned through two well-known technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.