Reviewerly/exHarmony huggingface dataset:
Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification

Incomplete view information often results in failure cases of the conventional multi-view methods. To address this problem, we propose a Deep Correlated Predictive Subspace Learning (DCPSL) method for incomplete multi-view semi-supervised classification. Specifically, we integrate semi-supervised deep matrix factorization, correlated subspace learning, and multi-view label prediction into a unified framework to jointly learn the deep correlated predictive subspace and multi-view shared and private label predictors. DCPSL is able to learn proper subspace representation that is suitable for class label prediction, which can further improve the performance of classification. Extensive experimental results on various practical datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.
Secure and Efficient ${K}$ Nearest Neighbor Query Over Encrypted Uncertain Data in Cloud-IoT Ecosystem

Uncertain data pervades many fields, including environmental monitoring, the monitoring of animal migrations, and urban warfare. Such uncertain data collected by field devices, such as Internet of Things (IoT) and Internet of Battlefield Things (IoBT) devices, may also be encrypted and outsourced to an untrustworthy third party for storage and data sharing such as a cloud server. However, the properties of uncertain data and the complication of operating over encrypted data make the searching schemes more ineffective. In this article, we design an efficient and safe K nearest neighbor (KNN) query scheme for uncertain data stored in semi-trusted cloud servers. We apply the modified homomorphic encryption, which requires two servers to interact and encrypt the uncertain data, and we use the authorized rank method to compute KNN. We protect the security of the data while simultaneously improving the query efficiency. Our detailed security analysis show that our scheme can realize the goal of concealing both the access and the search patterns. Comprehensive experiments are conducted to demonstrate the scheme's performance.
A Copy-Proof Scheme Based on the Spectral and Spatial Barcoding Channel Models

The traditional two-dimensional (2D) barcode has been employed in anti-counterfeiting systems as a storage media for serial numbers. However, an attack can be initiated by simply copying the 2D barcode and attaching it to a counterfeit product. In this paper, we aim at proposing an authentication scheme with a mobile imaging device for a 2D barcode. This work presents a competitive solution among the 2D barcode authentication schemes that have been verified under mobile imaging conditions. The proposed copy-proof scheme is composed of two sets of features which are extracted by exploiting the characteristics of barcoding channel models. The proposed features identify the intrinsic differences between genuine and counterfeit barcode images in the frequency and spatial domains. An efficient two-stage barcode authentication framework is then proposed by combining the two sets of features in a cascading manner. To evaluate the practicality of the proposed authentication scheme, four databases with different devices (printers, scanners, mobile cameras), barcode sizes, and barcode designs are considered in the experiments. By comparing with the existing texture descriptors and some deep learning-based approaches, it is shown that the proposed scheme has a higher authentication accuracy under various conditions, such as cross-database, cross-size and cross-pattern experiments which study the generalities of a pre-trained model towards challenging conditions commonly found in real-world scenarios. Last but not least, the proposed scheme has been evaluated under some state-of-the-art attack scenarios where the attacker employs several realizations of genuine patterns or the deep learning-based technique to produce a counterfeit copy. The source code and data for producing the results in our experiments are available at https://bit.ly/2FOlJH7.
Quantitative Analysis of Iron and Silicon Concentrations in Iron Ore Concentrate Using Portable X-ray Fluorescence (XRF)

As a technique capable of rapid, nondestructive, and multi-elemental analysis, portable X-ray fluorescence (pXRF) has applications to mineral exploration, environmental evaluation, and archaeological analysis. However, few applications have been conducted in the smelting industry especially when analyzing the metal concentration in ore concentrate samples. This research analyzed the effectiveness of using pXRF in determining the metal concentration in Fe concentrate. For this proof of concept study, Fe ore samples dominated by Fe and Si were collected from the Northeastern University Mineral Processing Laboratory (Shenyang, China) and directly analyzed using pXRF, laboratory-based XRF, and titration methods. The compactness (density) of the ore concentrate was found to have very little effect on pXRF readings. The pXRF readings for Fe and Si were comparative to laboratory-based XRF results. Based on the strong correlations between the pXRF and XRF results (Fe: R 2 &gt; 0.99, Si: R 2 &gt; 0.96), linear calibrations were adopted to improve the accuracy of pXRF readings. Linear regression equations derived from the relations between XRF results and pXRF results of 21 Fe ore concentrate samples were used to calibrate the pXRF, and then validation was performed on five additional samples. Results from this preliminary study suggest that ordinary least squares (OLS) regression improves the accuracy dramatically, especially for Fe with relative errors (REs) decreasing to 0.03%–3.27% from 4.26%–8.32%. Consequently, pXRF shows strong promise for rapid, quantitative analysis of Fe concentration in Fe ore concentrate. Based on the results obtained in this study, a larger, more comprehensive study is warranted to confirm the results obtained.
Hardware Security in IoT era: the Role of Measurements and Instrumentation

Broadly, Internet of Things (IoT) paradigm represents an evolving technology which tries to reach its main goal, where each and every `thing' (i.e. device) can be: (i) connected through a network and (ii) controllable from any remote station. Activity sectors like intelligent manufacturing, farms, e-agriculture, real-time traffic controls, environment monitoring, camera security systems, health-care, etc., are going to be governed by the IoT paradigm as backbone in the near future. In this context, ensuring security during `thing' operation and information exchange becomes a critical task. Therefore, secured IoT applies equally to: (i) IoT device-to-device communication, (ii) IoT sensing/actuating, and (iii) IoT information exchange. In this paper, a brief literature survey regarding the research pointing out the Hardware Security (HS) for IoT is presented. Furthermore, in the paper, a short analysis regarding the existing trends for measurements and instrumentation is reported.
Unsupervised natural image patch learning

A metric for natural image patches is an important tool for analyzing images. An efficient means of learning one is to train a deep network to map an image patch to a vector space, in which the Euclidean distance reflects patch similarity. Previous attempts learned such an embedding in a supervised manner, requiring the availability of many annotated images. In this paper, we present an unsupervised embedding of natural image patches, avoiding the need for annotated images. The key idea is that the similarity of two patches can be learned from the prevalence of their spatial proximity in natural images. Clearly, relying on this simple principle, many spatially nearby pairs are outliers. However, as we show, these outliers do not harm the convergence of the metric learning. We show that our unsupervised embedding approach is more effective than a supervised one or one that uses deep patch representations. Moreover, we show that it naturally lends itself to an efficient self-supervised domain adaptation technique onto a target domain that contains a common foreground object.
Leveraging resource management for efficient performance of Apache Spark

Apache Spark is one of the most widely used open source processing framework for big data, it allows to process large datasets in parallel using a large number of nodes. Often, applications of this framework use resource management systems like YARN, which provide jobs a specific amount of resources for their execution. In addition, a distributed file system such as HDFS stores the data that is to be analyzed by the framework. This design allows sharing cluster resources effectively by running jobs on a single-node cluster or multi-nodes cluster infrastructure. Thus, one challenging issue is to realize effective resource management of these large cluster infrastructures in order to run distributed data analytics in an economically viable way. In this study, we use the Machine Learning library (MLlib) of Spark to implement different machine learning algorithms, then we manage the resources (CPU, memory, and Disk) in order to assess the performance of Apache Spark. In this paper, we present a review of various works that focus on resource management and data processing in Big Data platforms. Furthermore, we perform a scalability analysis using Spark. We analyze the speedup and processing time. We deduce that from a certain number of nodes in the cluster, it is no longer necessary to add additional nodes to improve the speedup and the processing Time. Then, we investigate the tuning of the resource allocation in Spark. We showed that it is not only by allocating all the available resources we get better performance but it depends on how to tune the resource allocation. We propose new managed parameters and we show that they give better total processing time than the default parameters used by Spark. Finally, we study the Persistence of Resilient Distributed Datasets (RDDs) in Spark using machine learning algorithms. We show that one storage level gives the best execution time among all tested storage levels.
Deep learning the semantics of change sequences for query expansion

Summary The overexpansion problem negatively affects the quality of query expansion. To improve the quality of queries for searching code, this paper proposed a DBN‐based algorithm for effective query expansion. The deep belief network (DBN) model is trained on the code sequences and their change sequences, which aims to capture the meaningful terms during the evolution of source code. In contrast to previous studies, the proposed model not only extracts relevant terms to expand a query but also excludes irrelevant terms from the query. It addresses two problems in query expansion, including the overexpansion of the original query and the negative influence of the changed terms in the target source code. Experiments on both artificial queries and real queries show that the proposed algorithm outperforms several query expansion algorithms for code search.
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization

Many Data Augmentation (DA) methods have been proposed for neural machine translation.Existing works measure the superiority of DA methods in terms of their performance on a specific test set, but we find that some DA methods do not exhibit consistent improvements across translation tasks.Based on the observation, this paper makes an initial attempt to answer a fundamental question: what benefits, which are consistent across different methods and tasks, does DA in general obtain?Inspired by recent theoretic advances in deep learning, the paper understands DA from two perspectives towards the generalization ability of a model: input sensitivity and prediction margin, which are defined independent of specific test set thereby may lead to findings with relatively low variance.Extensive experiments show that relatively consistent benefits across five DA methods and four translation tasks are achieved regarding both perspectives.
News2vec: News Network Embedding with Subnode Information

With the development of NLP technologies, news can be automatically categorized and labeled according to a variety of characteristics, at the same time be represented as low dimensional embeddings.However, it lacks a systematic approach that effectively integrates the inherited features and inter-textual knowledge of news to represent the collective information with a dense vector.With the aim of filling this gap, the News2vec model is proposed to allow the distributed representation of news taking into account its associated features.To describe the cross-document linkages between news, a network consisting of news and its attributes is constructed.Moreover, the News2vec model treats the news node as a bag of features by developing the Subnode model.Based on the biased random walk and the skip-gram model, each news feature is mapped to a vector, and the news is thus represented as the sum of its features.This approach offers an easy solution to create embeddings for unseen news nodes based on its attributes.To evaluate our model, dimension reduction plots and correlation heat-maps are created to visualize the news vectors, together with the application of two downstream tasks, the stock movement prediction and news recommendation.By comparing with other established text/sentence embedding models, we show that News2vec achieves state-of-the-art performance on these news-related tasks.
Variational Hierarchical User-based Conversation Model

Generating appropriate conversation responses requires careful modeling of the utterances and speakers together.Some recent approaches to response generation model both the utterances and the speakers, but these approaches tend to generate responses that are overly tailored to the speakers.To overcome this limitation, we propose a new model with a stochastic variable designed to capture the speaker information and deliver it to the conversational context.An important part of this model is the network of speakers in which each speaker is connected to one or more conversational partner, and this network is then used to model the speakers better.To test whether our model generates more appropriate conversation responses, we build a new conversation corpus containing approximately 27,000 speakers and 770,000 conversations.With this corpus, we run experiments of generating conversational responses and compare our model with other state-of-the-art models.By automatic evaluation metrics and human evaluation, we show that our model outperforms other models in generating appropriate responses.An additional advantage of our model is that it generates better responses for various new user scenarios, for example when one of the speakers is a known user in our corpus but the partner is a new user.For replicability, we make available all our code and data 1 .
The AIP-Tohoku System at the BEA-2019 Shared Task

We introduce the AIP-Tohoku grammatical error correction (GEC) system for the BEA-2019 shared task in Track 1 (Restricted Track) and Track 2 (Unrestricted Track) using the same system architecture.Our system comprises two key components: error generation and sentence-level error detection.In particular, GEC with sentence-level grammatical error detection is a novel and versatile approach, and we experimentally demonstrate that it significantly improves the precision of the base model.Our system is ranked 9th in Track 1 and 2nd in Track 2.
Multiple Vehicle Tracking Based on Labeled Multiple Bernoulli Filter Using Pre-Clustered Laser Range Finder Data

Multiple vehicle tracking (MVT) system is a prerequisite to path planning and decision making of self-driving cars as it can provide positions of surrounding vehicles. Most of the available approaches belonging to the so called tracking-by-detection approach inevitably bring detection errors into the tracking result. In this study, we proposed a laser range finder (LRF) based track-before-detect MVT algorithm without detection procedure. Moreover, different from the state of the art in track-before-detect approaches using raw data, we applied a pre-clustering procedure to segment the raw data into disjoint clusters to reduce computation demand. Specifically, a clustering algorithm named iterative nearest point search (INPS) which can even handle the partial occlusion situations that are challenging for traditional clustering algorithms was designed for the pre-clustering procedure. Furthermore, a detailed cluster-to-target measurement model was proposed to describe the difference between cluster and hypothesis vehicle. Finally, we integrated the measurement model into the labeled multi-Bernoulli filter with particle implementation. Simulations and experiments show that the proposed MVT algorithm provides more accurate estimates of vehicle number and position in comparison with conventional methods.
Enhanced Player Interaction Using Motion Controllers for First-Person Shooting Games in Virtual Reality

The main purpose of virtual reality (VR) is to enhance realism and the player experience.To do this, we focus on VR interaction design methods, analyze the existing interaction solutions including both accurate and rough interaction methods, and propose a new method for creating stable and realistic player interactions in a first-person shooter (FPS) game prototype.In this research, we design and modify the existing mapping methods between physical and virtual worlds, and create interfaces such that physical devices correspond to shooting tools in virtual reality.Moreover, we propose and design prototypes of universal interactions that can be implemented in a simple and straightforward way.Proposed interactions allow the player to perform actions similar to those of real shooting, using both hands such as firing, reloading, attaching and grabbing objects.In addition, we develop a gun template with haptic feedback, and a visual collision guide that can optionally be enabled.Then, we evaluate and compare our methods with the existing solutions.We then use these in a VR FPS game prototype and conduct a user study with participants, and the resulting user study proves that the proposed method is more stable, player-friendly and realistic.
Attention module-based spatial–temporal graph convolutional networks for skeleton-based action recognition

Skeleton-based action recognition is a significant direction of human action recognition, because the skeleton contains important information for recognizing action. The spatial–temporal graph convolutional networks (ST-GCN) automatically learn both the temporal and spatial features from the skeleton data and achieve remarkable performance for skeleton-based action recognition. However, ST-GCN just learns local information on a certain neighborhood but does not capture the correlation information between all joints (i.e., global information). Therefore, we need to introduce global information into the ST-GCN. We propose a model of dynamic skeletons called attention module-based-ST-GCN, which solves these problems by adding attention module. The attention module can capture some global information, which brings stronger expressive power and generalization capability. Experimental results on two large-scale datasets, Kinetics and NTU-RGB+D, demonstrate that our model achieves significant improvements over previous representative methods.
WHSP-Net: A Weakly-Supervised Approach for 3D Hand Shape and Pose Recovery from a Single Depth Image

Hand shape and pose recovery is essential for many computer vision applications such as animation of a personalized hand mesh in a virtual environment. Although there are many hand pose estimation methods, only a few deep learning based algorithms target 3D hand shape and pose from a single RGB or depth image. Jointly estimating hand shape and pose is very challenging because none of the existing real benchmarks provides ground truth hand shape. For this reason, we propose a novel weakly-supervised approach for 3D hand shape and pose recovery (named WHSP-Net) from a single depth image by learning shapes from unlabeled real data and labeled synthetic data. To this end, we propose a novel framework which consists of three novel components. The first is the Convolutional Neural Network (CNN) based deep network which produces 3D joints positions from learned 3D bone vectors using a new layer. The second is a novel shape decoder that recovers dense 3D hand mesh from sparse joints. The third is a novel depth synthesizer which reconstructs 2D depth image from 3D hand mesh. The whole pipeline is fine-tuned in an end-to-end manner. We demonstrate that our approach recovers reasonable hand shapes from real world datasets as well as from live stream of depth camera in real-time. Our algorithm outperforms state-of-the-art methods that output more than the joint positions and shows competitive performance on 3D pose estimation task.
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

Hierarchical Reinforcement Learning (HRL) is a promising approach to solving long-horizon problems with sparse and delayed rewards. Many existing HRL algorithms either use pre-trained low-level skills that are unadaptable, or require domain-specific information to define low-level rewards. In this paper, we aim to adapt low-level skills to downstream tasks while maintaining the generality of reward design. We propose an HRL framework which sets auxiliary rewards for low-level skill training based on the advantage function of the high-level policy. This auxiliary reward enables efficient, simultaneous learning of the high-level policy and low-level skills without using task-specific knowledge. In addition, we also theoretically prove that optimizing low-level skills with this auxiliary reward will increase the task return for the joint policy. Experimental results show that our algorithm dramatically outperforms other state-of-the-art HRL methods in Mujoco domains. We also find both low-level and high-level policies trained by our algorithm transferable.
Learning to Update Knowledge Graphs by Reading News

News streams contain rich up-to-date information which can be used to update knowledge graphs (KGs).Most current text-based KG updating methods rely on elaborately designed information extraction systems and carefully crafted rules, which are often domain-specific and hard to maintain or generalize.However, such methods may not pay enough attention to the implicit information that lies underneath texts, thus often suffer from coverage issues.In this paper, we propose a novel graph based neural network method, GUpdater, to tackle these problems 1 .GUpdater is built upon graph neural networks (GNNs) with a text-based attention mechanism to guide the updating message passing through the KG structures.Experiments on a real-world KG updating dataset show that our model can effectively broadcast the news information to the KG structures and perform necessary link-adding or link-deleting operations to ensure the KG up-to-date according to news snippets.
Text Recognition in Images Based on Transformer with Hierarchical Attention

Recognizing text in images has been a hot research topic in computer vision for decades due to its various application. However, the variations in text appearance in term of perspective distortion, text line curvature, text styles, etc., cause great trouble in text recognition. Inspired by the Transformer structure [1] that achieved outstanding performance in many natural language processing related applications, we propose a new Transformer-like structure for text recognition in images, which is referred to as the Hierarchical Attention Transformer Network (HATN). The entire network can be trained end-to-end by using only images and sentence-level annotations. A new hierarchical attention mechanism is proposed to lean the character-level, word-level and sentence-level contexts more efficiently and sufficiently. Extensive experiments on seven public datasets with regular and irregular text arrangements have demonstrated that the proposed HATN can achieve accurate recognition results with high efficiency.