Reviewerly/exHarmony huggingface dataset:
Multilevel triplet deep learning model for person re-identification

Person re-identification (Re-ID) is a typical computer vision problem which matches pedestrians from different cameras. It remains challenging to cope with the variation in light, the change of human pose and view point difference. Many existing person re-identification methods may have difficulty in matching pedestrians when their pictures are similar in appearance or there is object occlusion in pictures. The main problem with these existing methods is that the detail and global features of the images are not well combined. In this paper, we improved the performance of deep CNN network with the proposed Multilevel feature extraction strategy and built a novel Multilevel triplet deep learning model corresponding to our method. The Multilevel feature extraction strategy focuses on combining fine, shallow layer information with coarse, deeper layer information by extracting fusion feature maps from different layers for a better representation of pedestrians. The Multilevel triplet deep learning model (MT-net) provides an end-to-end training and testing plain for our feature extraction strategy. The experiment on the benchmark datasets validated that our multilevel triplet deep learning model had better performance comparing with many state-of-the-art person re-identification methods.
Contrast in Haze Removal: Configurable Contrast Enhancement Model Based on Dark Channel Prior

Conventional haze-removal methods are designed to adjust the contrast and saturation, and in so doing enhance the quality of the reconstructed image. Unfortunately, the removal of haze in this manner can shift the luminance away from its ideal value. In other words, haze removal involves a tradeoff between luminance and contrast. We reformulated the problem of haze removal as a luminance reconstruction scheme, in which an energy term is used to achieve a favorable tradeoff between luminance and contrast. The proposed method bases the luminance values for the reconstructed image on statistical analysis of haze-free images, thereby achieving contrast values superior to those obtained using other methods for a given brightness level. We also developed a novel module for the estimation of atmospheric light using the color constancy method. This module was shown to outperform existing methods, particularly when noise is taken into account. The proposed framework requires only 0.55 s to process a one-megapixel image. Experimental results demonstrate that the proposed haze-removal framework conforms to our theory of contrast.
Extracting Privileged Information for Enhancing Classifier Learning

The accuracy of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), e.g., attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. Moreover, due to the limitations of personal knowledge, manually labeled PI may not be rich enough. To address these issues, we propose to enhance classifier learning by exploring PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data and obtain much richer PI. In detail, we treat each selected PI as a subcategory and learn one classifier for each subcategory independently. The classifiers for all subcategories are integrated together to form a more powerful category classifier. Particularly, we propose a novel instancelevel multi-instance learning (MIL) model to simultaneously select a subset of training images from each subcategory and learn the optimal SVM classifiers based on the selected images. Extensive experiments on four benchmark datasets demonstrate the superiority of our proposed approach.
Speckle Suppression Based on Weighted Nuclear Norm Minimization and Grey Theory

Coherent imaging systems are greatly affected by speckle noise, which makes visual analysis and features extraction a difficult task. In this paper, we propose a speckle suppression algorithm based on weighted nuclear norm minimization (WNNM) and Grey theory. First, we use logarithmic transformation to the noisy images such that the speckle noise is transformed into additive noise. Second, by matching the local blocks based on Grey theory, we will get approximate low-rank matrices grouped by the similar blocks of the reference patches. We then estimate the noise variance of the noisy images with the wavelet transform. Finally, we use WNNM method to denoise the image. The results show that our algorithm not only effectively improves the visual effect of the denoised image and preserves the local structure of the image better but also improves the objective indexes values of the denoised image.
A Novel High-Capacity Reversible Data Hiding Scheme for Encrypted JPEG Bitstreams

As cloud storage becomes more common, concerns about the invasion of privacy are increasing. When images are stored in an encrypted form in the public cloud, reversible data hiding in the encrypted domain can be applied to embed additional data within the encrypted images for ease of management. Most existing works focus on uncompressed images and are not applicable to JPEG images, which are widely used throughout the Internet. Therefore, in this paper, a novel reversible data hiding scheme for encrypted JPEG bitstreams is proposed. First, an effective method of bitstream-based JPEG image encryption is employed to encrypt plaintext JPEG images. Then, we present a reversible data hiding technique for encrypted JPEG images based on invariant zero-run length in the zero-run value pairs. In the cloud, additional data, such as labels, timestamps, origins, and authentication messages, are directly embedded into the encrypted JPEG images with our proposed data hiding technique. From the marked encrypted JPEG images, the extraction of the hidden data and the recovery of the original images can be done independently. Extensive experiments performed on several typical images and three well-known image databases show that the proposed scheme can achieve much higher embedding capacity than that of most recent schemes, and the file sizes of the marked encrypted JPEG images are well preserved compared to those of the related methods. In addition, we provide further analysis to show that the proposed scheme has good format compatibility and low computational complexity.
High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping

In this paper, we propose a high-speed three-dimensional (3-D) shape measurement technique for dynamic scenes using geometry-constraint-based number-theoretical phase unwrapping. As a classical algorithm for temporal phase unwrapping (TPU), the number-theoretical approach is suitable for the binary defocusing fringe projection system since it can retrieve an absolute phase without using low-frequency fringe patterns. However, the conventional number-theoretical TPU approach cannot provide sufficient stability to unwrap a high-frequency phase since it requires the two fringe frequencies to be coprime within the global range of the projector coordinate. In contrast, using low-frequency fringe patterns tends to make phase unwrapping more reliable, but at the expense of the measurement precision. By introducing depth constraint into the traditional number-theoretical TPU, we only need to eliminate the phase ambiguity of each pixel within a small period range defined by the depth range, which means that our method just requires the two fringe frequencies to be coprime within the local period range instead of the conventional global range. Due to the reduction of fringe order candidates and the unambiguous phase range, the reliability of phase unwrapping can be significantly improved compared with the traditional number-theoretical TPU approach even when high-frequency fringe patterns are used. The proposed method has been successfully implemented on a high-frame-rate fringe projection system, achieving high-precision, robust, and absolute 3-D shape measurement at 3333 frames per second.
Differentially Private Hypothesis Transfer Learning

In recent years, the focus of machine learning has been shifting to the paradigm of transfer learning where the data distribution in the target domain differs from that in the source domain. This is a prevalent setting in real-world classification problems and numerous well-established theoretical results in the classical supervised learning paradigm will break down under this setting. In addition, the increasing privacy protection awareness restricts access to source domain samples and poses new challenges for the development of privacy-preserving transfer learning algorithms. In this paper, we propose a novel differentially private multiple-source hypothesis transfer learning method for logistic regression. The target learner operates on differentially private hypotheses and importance weighting information from the sources to construct informative Gaussian priors for its logistic regression model. By leveraging a publicly available auxiliary data set, the importance weighting information can be used to determine the relationship between the source domain and the target domain without leaking source data privacy. Our approach provides a robust performance boost even when high quality labeled samples are extremely scarce in the target data set. The extensive experiments on two real-world data sets confirm the performance improvement of our approach over several baselines. Data related to this paper is available at: http://qwone.com/~jason/20Newsgroups/ and https://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html .
Immersive Virtual Reality (IVR) in Higher Education: Development and Implementation

University educators anticipate better engagement with students through the adoption of the three-dimensional visualization made possible by immersive virtual reality (IVR). Materials can be captured in 360° video for viewing through smartphones bracketed in head-mounted displays (HMDs) with motion sensors. Alternatively, materials can be viewed on notebook computers and tablets to offer some degree of VR experience. The paper reports on the first two undergraduate courses that have adopted both VR and IVR modes for classroom learning: ‘Pharmacology and Therapeutics’ and ‘Understanding Ecotourism’. The 360° videos have undergone a complete cycle of design, development, implementation and evaluation. These video captures can transcend physical boundaries in both clinical cases simulating a hospital ward and natural countryside landmarks. With VR and IVR embedded in the classroom, students expressed greater learning satisfaction while experiencing more opportunities to rehearse professional skills and explore historical artefacts with deeper cultural understanding.
ShadowGAN: Shadow synthesis for virtual objects with conditional adversarial networks

We introduce ShadowGAN, a generative adversarial network (GAN) for synthesizing shadows for virtual objects inserted in images. Given a target image containing several existing objects with shadows, and an input source object with a specified insertion position, the network generates a realistic shadow for the source object. The shadow is synthesized by a generator; using the proposed local adversarial and global adversarial discriminators, the synthetic shadow’s appearance is locally realistic in shape, and globally consistent with other objects’ shadows in terms of shadow direction and area. To overcome the lack of training data, we produced training samples based on public 3D models and rendering technology. Experimental results from a user study show that the synthetic shadowed results look natural and authentic.
Mobile robot path planning in dynamic environment based on cuckoo optimization algorithm

Nowadays, the usage of autonomous mobile robots that fulfill various activities in enormous number of applications without human’s interference in a dynamic environment are thriving. A dynamic environment is the robot’s environment which is comprised of some static obstacles as well as several movable obstacles that their quantity and location change randomly through the time. Efficient path planning is one the significant necessities of these kind of robots to do their tasks effectively. Mobile robot path planning in a dynamic environment is finding a shortest possible path from an arbitrary starting point toward a desired goal point which needs to be safe (obstacle avoidance) and smooth as well as possible. To achieve this target, simultaneously satisfying a collection of certain constraints including the shortest, smooth, and collision free path is required. Therefore, this issue can be considered as an optimization problem, consequently solved via optimization algorithms. In this article, a new method based on cuckoo optimization algorithm is proposed for solving the mobile robot path planning problem in a dynamic environment. Furthermore, to diminish the computational complexity, the feature vector is also optimized (i.e. reduced in dimension) via a new proposed technique. The simulation results show the performance of proposed algorithm in finding a short, safe, smooth, and collision free path in different environment conditions.
A decisive content based image retrieval approach for feature fusion in visual and textual images

Image content analysis plays a dynamic role in various computer vision applications. These contents can be either visual (i.e. color, shape, texture) or the textual (i.e. text appearing within images). Both the contents involve fundamental characteristics of an image and thus can be an enormous asset for any intelligent application. For content based image retrieval (CBIR) systems, most of the art methods are either annotated text based or the visual search based. Due to high demand of multitasking, there is a great need of a system that can combine visual as well as textual features. Consequently, this work proposes a decisive CBIR approach that combines visual and textual features to retrieve similar images. Firstly, the method classifies the query image as textual and non-textual. If any text appears within the image then the query image is classified as textual, and the text is detected and formed as Bag of Textual words. If the query image is classified as non-textual, the visual salient features are extracted and formed as Bag of Visual words. Next, the method fuses the visual and textual features, and top similar images are retrieved based on the fused feature vector. It supports three modes of retrieval: Image query, Keywords, and a combination of both. The experimental results on four datasets show the efficiency and accuracy of the proposed approach for visual and textual images.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELS

Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems.This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks.Multilayer Perceptron (MLP) was developed as a baseline for other networks results.Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files.Dataset was divided to 50% positive reviews and 50% negative reviews.The data was initially pre-processed using Word2Vec and word embedding was applied accordingly.The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks.CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively.Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
Asymmetric Protocols for Scalable High-Rate Measurement-Device-Independent Quantum Key Distribution Networks

Measurement-device-independent quantum key distribution (MDI-QKD) can eliminate detector side channels and prevent all attacks on detectors. The future of MDI-QKD is a quantum network that provides service to many users over untrusted relay nodes. In a real quantum network, the losses of various channels are different and users are added and deleted over time. To adapt to these features, we propose a type of protocol that allows users to independently choose their optimal intensity settings to compensate for different channel losses. Such a protocol enables a scalable high-rate MDI-QKD network that can easily be applied for channels of different losses and allows users to be dynamically added or deleted at any time without affecting the performance of existing users.7 MoreReceived 14 April 2018Revised 11 July 2019DOI:https://doi.org/10.1103/PhysRevX.9.041012Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum cryptographyQuantum networksQuantum Information
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach

While neural machine translation (NMT) has achieved remarkable success, NMT systems are prone to make word omission errors.In this work, we propose a contrastive learning approach to reducing word omission errors in NMT.The basic idea is to enable the NMT model to assign a higher probability to a ground-truth translation and a lower probability to an erroneous translation, which is automatically constructed from the ground-truth translation by omitting words.We design different types of negative examples depending on the number of omitted words, word frequency, and part of speech.Experiments on Chinese-to-English, German-to-English, and Russian-to-English translation tasks show that our approach is effective in reducing word omission errors and achieves better translation performance than three baseline methods.
Unsupervised Paraphrasing without Translation

Paraphrasing exemplifies the ability to abstract semantic content from surface forms.Recent work on automatic paraphrasing is dominated by methods leveraging Machine Translation (MT) as an intermediate step.This contrasts with humans, who can paraphrase without being bilingual.This work proposes to learn paraphrasing models from an unlabeled monolingual corpus only.To that end, we propose a residual variant of vector-quantized variational auto-encoder.We compare with MT-based approaches on paraphrase identification, generation, and training augmentation.Monolingual paraphrasing outperforms unsupervised translation in all settings.Comparisons with supervised translation are more mixed: monolingual paraphrasing is interesting for identification and augmentation; supervised translation is superior for generation.
Deep Dominance - How to Properly Compare Deep Neural Models

Comparing between Deep Neural Network (DNN) models based on their performance on unseen data is crucial for the progress of the NLP field.However, these models have a large number of hyper-parameters and, being non-convex, their convergence point depends on the random values chosen at initialization and during training.Proper DNN comparison hence requires a comparison between their empirical score distributions on unseen data, rather than between single evaluation scores as is standard for more simple, convex models.In this paper, we propose to adapt to this problem a recently proposed test for the Almost Stochastic Dominance relation between two distributions.We define the criteria for a high quality comparison method between DNNs, and show, both theoretically and through analysis of extensive experimental results with leading DNN models for sequence tagging tasks, that the proposed test meets all criteria while previously proposed methods fail to do so.We hope the test we propose here will set a new working practice in the NLP community. 1
User Authentication Based on Mouse Dynamics Using Deep Neural Networks: A Comprehensive Study

Recently conducted research demonstrated the potential use of mouse dynamics as a behavioral biometric for user authentication systems. However, the state-of-the-art methods in this field rely on classical machine learning methods that necessitate the design of hand crafted mouse features for feature extraction. To simplify the feature extraction process, we leverage various deep learning architectures for mouse movement sequences classification, including convolutional networks, recurrent networks, and a hybrid model which combines convolutional and recurrent layers. It is known that the training of these networks with random initialization of weights on small datasets will produce models that perform poorly. Therefore, we consider a two-dimensional convolutional neural network that allows transfer learning, which is a domain adaptation technique effective for learning on small datasets. Although employing such architecture may seem counterintuitive, since the temporal information is discarded from the input data, the architecture has outperformed all the other deep architectures investigated, as well as a classical machine learning method. In order to understand the features learned, we adopt the layer-wise relevance propagation (LRP) algorithm to compute relevance scores for each part of the mouse curves. In addition, the models are measured for their usability and effectiveness in realistic scenarios.
A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues

Discourse structures are beneficial for various NLP tasks such as dialogue understanding, question answering, sentiment analysis, and so on. This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues. The proposed model aims to construct a discourse dependency tree by predicting dependency relations and constructing the discourse structure jointly and alternately. It makes a sequential scan of the Elementary DiscourseUnits(EDUs)1 in a dialogue. For each EDU, the model decides to which previous EDU the current one should link and what the corresponding relation type is. The predicted link and relation type are then used to build the discourse structure incrementally with a structured encoder. During link prediction and relation classification, the model utilizes not only local information that represents the concerned EDUs, but also global information that encodes the EDU sequence and the discourse structure that is already built at the current step. Experiments show that the proposed model outperforms all the state-of-the-art baselines.