Reviewerly/exHarmony huggingface dataset:
Multi-view X-Ray R-CNN

Motivated by the detection of prohibited objects in carry-on luggage as a part of avionic security screening, we develop a CNN-based object detection approach for multi-view X-ray image data. Our contributions are two-fold. First, we introduce a novel multi-view pooling layer to perform a 3D aggregation of 2D CNN-features extracted from each view. To that end, our pooling layer exploits the known geometry of the imaging system to ensure geometric consistency of the feature aggregation. Second, we introduce an end-to-end trainable multi-view detection pipeline based on Faster R-CNN, which derives the region proposals and performs the final classification in 3D using these aggregated multi-view features. Our approach shows significant accuracy gains compared to single-view detection while even being more efficient than performing single-view detection in each view.
Acceleration of RED via vector extrapolation

Models play an important role in inverse problems, serving as the prior for representing the original signal to be recovered. REgularization by Denoising (RED) is a recently introduced general framework for constructing such priors using state-of-the-art denoising algorithms. Using RED, solving inverse problems is shown to amount to an iterated denoising process. However, as the complexity of denoising algorithms is generally high, this might lead to an overall slow algorithm. In this paper, we suggest an accelerated technique based on vector extrapolation (VE) to speed-up existing RED solvers. Numerical experiments validate the obtained gain by VE, leading to substantial savings in computations compared with the original fixed-point method.
Affine Monotonic and Risk-Sensitive Models in Dynamic Programming

In this paper, we consider a broad class of infinite horizon discrete-time optimal control models that involve a nonnegative cost function and an affine mapping in their dynamic programming equation. They include as special cases several classical models, such as stochastic undiscounted nonnegative cost problems, stochastic multiplicative cost problems, and risk-sensitive problems with exponential cost. We focus on the case where the state space is finite and the control space has some compactness properties, and we emphasize shortest path-type models. We assume that the affine mapping has a semicontractive character, whereby for some policies it is a contraction, whereas for others it is not. In one line of analysis, we impose assumptions guaranteeing that the noncontractive policies cannot be optimal. Under these assumptions, we prove strong results that resemble those for discounted Markovian decision problems, such as the uniqueness of solution of Bellman's equation, and the validity of forms of value and policy iteration. In the absence of these assumptions, the results are weaker and unusual in character: the optimal cost function need not be a solution of Bellman's equation, and may not be found by value or policy iteration. Instead the optimal cost function over just the contractive policies is the largest solution of Bellman's equation, and can be computed by a variety of algorithms.
Accurate Hand Keypoint Localization on Mobile Devices

We present a novel approach for 2D hand keypoint localization from regular color input. The proposed approach relies on an appropriately designed Convolutional Neural Network (CNN) that computes a set of heatmaps, one per hand keypoint of interest. Extensive experiments with the proposed method compare it against state of the art approaches and demonstrate its accuracy and computational performance on standard, publicly available datasets. The obtained results demonstrate that the proposed method matches or outperforms the competing methods in accuracy, but clearly outperforms them in computational efficiency, making it a suitable building block for applications that require hand keypoint estimation on mobile devices.
On Cycles of Pairing-Friendly Elliptic Curves

A cycle of elliptic curves is a list of elliptic curves over finite fields such that the number of points on one curve is equal to the size of the field of definition of the next, in a cyclic way. We study cycles of elliptic curves in which every curve is pairing-friendly. These have recently found notable applications in pairing-based cryptography, for instance, in improving the scalability of distributed ledger technologies. We construct a new cycle of length 4 consisting of MNT curves, and characterize all the possibilities for cycles consisting of MNT curves. We rule out cycles of length 2 for particular choices of small embedding degrees. We show that long cycles cannot be constructed from families of curves with the same complex multiplication discriminant, and that cycles of composite order elliptic curves cannot exist. We show that there are no cycles consisting of curves from only the Freeman or Barreto--Naehrig families.
Tunneling on Wheeler Graphs

Baier (CPM 2018) describes tunneling as a technique to further exploit redundancies in the Burrows-Wheeler Transform. In this paper we show how to retain indexed text searching on the resulting structure and generalize the concept to Wheeler graphs.
A DenseNet Based Approach for Multi-frame In-loop Filter in HEVC

High efficiency video coding (HEVC) has brought outperforming efficiency for video compression. To reduce the compression artifacts of HEVC, we propose a DenseNet based approach as the in-loop filter of HEVC, which leverages multiple adjacent frames to enhance the quality of each encoded frame. Specifically, the higher-quality frames are found by a reference frame selector (RFS). Then, a deep neural network for multi-frame in-loop filter (named MIF-Net) is developed to enhance the quality of each encoded frame by utilizing the spatial information of this frame and the temporal information of its neighboring higher-quality frames. The MIF-Net is built on the recently developed DenseNet, benefiting from the improved generalization capacity and computational efficiency. Finally, experimental results verify the effectiveness of our multi-frame in-loop filter, outperforming the HM baseline and other state-of-the-art approaches.
Emerging applications of reversible data hiding

Reversible data hiding (RDH) is one special type of information hiding, by which the host sequence as well as the embedded data can be both restored from the marked sequence without loss. Beside media annotation and integrity authentication, recently some scholars begin to apply RDH in many other fields innovatively. In this paper, we summarize these emerging applications, including steganography, adversarial example, visual transformation, image processing, and give out the general frameworks to make these operations reversible. As far as we are concerned, this is the first paper to summarize the extended applications of RDH.
Scene video text tracking based on hybrid deep text detection and layout constraint

Video text in real-world scenes often carries rich high-level semantic information and plays an ever-increasingly important role in the content-based video analysis and retrieval. Therefore, the scene video text detection and tracking are important prerequisites of numerous multimedia applications. However, the performance of most existing tracking methods is not satisfactory due to frequent mis-detections, unexpected camera motion and similar appearances between text regions. To address these problems, we propose a new video text tracking approach based on hybrid deep text detection and layout constraint. Firstly, a deep text detection network that combines the advantages of object detection and semantic segmentation in a hybrid way is proposed to locate possible text candidates in individual frames. Then, text trajectories are derived from consecutive frames with a novel data association method, which effectively exploits the layout constraint of text regions in large camera motion. By utilizing the layout constraint, the ambiguities caused by similar text regions are effectively reduced. We conduct experiments on four benchmark datasets, i.e., ICDAR 2015, MSRA-TD 500, USTB-SV1K and Minetto, to evaluate the proposed method. The experimental results demonstrate the effectiveness and superiority of the proposed approach.
Single shot active learning using pseudo annotators

Standard active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried. This random labeling enables standard active learning algorithms to also exhibit the exploratory behavior needed for single shot active learning. The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples. Experiments on real-world datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.
Feature preserving GAN and multi-scale feature enhancement for domain adaption person Re-identification

The performance of person Re-identification (Re-ID) model depends much on its training dataset, and drops significantly when the detector is applied to a new scene due to the large variations between the source training dataset and the target scene. In this paper, we proposed multi-scale Feature Enhancement(MFE) Re-ID model and Feature Preserving Generative Adversarial Network (FPGAN) for cross-domain person Re-ID task. Here, MFE Re-ID model provides a strong baseline model for cross-domain person Re-ID task, and FPGAN bridges the domain gap to improve the performance of Re-ID on target scene. In the MFE Re-ID model, person semantic feaure maps, extracted from backbone of segmentation model, enhance person body region’s multi-scale feature responce. This operation could capture multi-scale robust discriminative visual factors related to person. In FPGAN, we translate the labeled images from source to target domain in an unsupervised manner, and learn a transfer function to preserve the person perceptual information of source images and ensure the transferred person images show similar styles with the target dataset. Extensive experiments demonstrate that combining FPGAN and MFE Re-ID model could achieve state-of-the-art results in cross-domain Re-ID task on DukeMTMC-reID and Market-1501 datasets. Besides, MFE Re-ID model could achieve state-of-the-art results in supervised Re-ID task. All source codes and models will be released for comparative study.
Ancient Painting to Natural Image: A New Solution for Painting Processing

Collecting a large-scale and well-annotated dataset for image processing has become a common practice in computer vision. However, in the ancient painting area, this task is not practical as the number of paintings is limited and their style is greatly diverse. We, therefore, propose a novel solution for the problems that come with ancient painting processing. This is to use domain transfer to convert ancient paintings to photo-realistic natural images. By doing so, the "ancient painting processing problems" become "natural image processing problems" and models trained on natural images can be directly applied to the transferred paintings. Specifically, we focus on Chinese ancient flower, bird and landscape paintings in this work. A novel Domain Style Transfer Network (DSTN) is proposed to transfer ancient paintings to natural images which employ a compound loss to ensure that the transferred paintings still maintain the color composition and content of the input paintings. The experiment results show that the transferred paintings generated by the DSTN have a better performance in both the human perceptual test and other image processing tasks than other state-of-arts methods, indicating the authenticity of the transferred paintings and the superiority of the proposed method.
Symmetries and similarities of planar algebraic curves using harmonic polynomials

We present novel, deterministic, efficient algorithms to compute the symmetries of a planar algebraic curve, implicitly defined, and to check whether or not two given implicit planar algebraic curves are similar, i.e. equal up to a similarity transformation. Both algorithms are based on the fact, well-known in Harmonic Analysis, that the Laplacian commutes with orthogonal transformations, and on efficient algorithms to find the symmetries∕similarities of a harmonic algebraic curve∕two given harmonic algebraic curves. In fact, we show that, except for some special cases, the problem can be reduced to the harmonic case.
The Sound of Rebellion: Voting Dissent and Legislative Speech in the UK House of Commons

Using data on the content of debate associated with votes in the UK House of Commons from 1992–2015, this article examines how government party MPs employ language in legislative speech when they vote against the party line. We find a robust statistical association between dissent on votes and the use of first‐person pronouns, simpler language, and giving longer speeches. Using a random forest algorithm for classification, we find that these language covariates are predictive of rebellion. The use of simpler, first‐person language has implications for political representation and offers new insight into how MPs use votes to distinguish themselves from their party, perhaps reflecting attempts to connect with constituents.
Bidirectional Quaternion Long Short-term Memory Recurrent Neural Networks for Speech Recognition

Recurrent neural networks (RNN) are at the core of modern automatic speech recognition (ASR) systems. In particular, long short-term memory (LSTM) recurrent neural networks have achieved state-of-the-art results in many speech recognition tasks, due to their efficient representation of long and short term dependencies in sequences of inter-dependent features. Nonetheless, internal dependencies within the element composing multidimensional features are weakly considered by traditional real-valued representations. We propose a novel quaternion long short-term memory (QL-STM) recurrent neural network that takes into account both the external relations between the features composing a sequence, and these internal latent structural dependencies with the quaternion algebra. QLSTMs are compared to LSTMs during a memory copy-task and a realistic application of speech recognition on the Wall Street Journal (WSJ) dataset. QLSTM reaches better performances during the two experiments with up to 2.8 times less learning parameters, leading to a more expressive representation of the information.
FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming over HTTP

This paper proposes and evaluates a novel algorithm for streaming video over HTTP. The problem is formulated as a non-convex optimization problem which is constrained by the predicted available bandwidth, chunk deadlines, available video rates, and buffer occupancy. The objective is to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among different chunks for the minimum re-buffering time. We propose FastScan, a low complexity algorithm that solves the problem. The online adaptations for dynamic bandwidth environments are proposed with imperfect available bandwidth prediction. The results of experiments driven by variable bit rate (VBR) encoded video, video platform system (dash.js), and cellular bandwidth traces of a public dataset reveal the robustness of the online version of FastScan algorithm and demonstrate its significant performance improvement as compared to the considered state-of-the-art video streaming algorithms. For example, on an experiment conducted over 100 real cellular available bandwidth traces of a public dataset that spans different available bandwidth regimes, our proposed algorithm (FastScan) achieves the minimum re-buffering (stall) time and the maximum average playback rate in every single trace as compared to Bola, Festive, BBA, RB, FastMPC, and Pensieve algorithms.
A Framework Towards Domain Specific Video Summarization

In the light of exponentially increasing video content, video summarization has attracted a lot of attention recently due to its ability to optimize time and storage. Characteristics of a good summary of a video depend on the particular domain under question. We propose a novel framework for domain specific video summarization. Given a video of a particular domain, our system can produce a summary based on what is important for that domain in addition to possessing other desired characteristics like representation, coverage, diversity etc. as suitable to that domain. Past related work has focused either on using supervised approaches for ranking the snippets to produce summary or on using unsupervised approaches of generating the summary as a subset of snippets with the above characteristics. We look at the joint problem of learning domain specific importance of segments as well as the desired summary characteristic for that domain. Our studies show that the more efficient way of incorporating domain specific relevance into a summary is by obtaining ratings of shots as opposed to binary inclusion/exclusion information. We also argue that ratings can be seen as unified representation of all possible ground truth summaries of a video, taking us one step closer in dealing with challenges associated with multiple ground truth summaries of a video. We also propose a novel evaluation measure which is more naturally suited in assessing the quality of video summary for the task at hand than F1 like measures. It leverages the ratings information and is richer in appropriately modeling desirable and undesirable characteristics of a summary. Lastly, we release a gold standard dataset for furthering research in domain specific video summarization, which to our knowledge is the first dataset with long videos across several domains with rating annotations. We conduct extensive experiments to demonstrate the benefits of our proposed solution.
Optimality of orders one to three and beyond: Characterization and evaluation complexity in constrained nonconvex optimization

Necessary conditions for high-order optimality in smooth nonlinear constrained optimization are explored and their inherent intricacy discussed. A two-phase minimization algorithm is proposed which can achieve approximate first-, second- and third-order criticality and its evaluation complexity is analyzed as a function of the choice (among existing methods) of an inner algorithm for solving subproblems in each of the two phases. The relation between high-order criticality and penalization techniques is finally considered, showing that standard algorithmic approaches will fail if approximate constrained high-order critical points are sought.
Multi-source Multi-level Attention Networks for Visual Question Answering

In recent years, Visual Question Answering (VQA) has attracted increasing attention due to its requirement on cross-modal understanding and reasoning of vision and language. VQA is proposed to automatically answer natural language questions with reference to a given image. VQA is challenging, because the reasoning process on a visual domain needs a full understanding of the spatial relationship, semantic concepts, as well as the common sense for a real image. However, most existing approaches jointly embed the abstract low-level visual features and high-level question features to infer answers. These works have limited reasoning ability due to the lack of modeling of the rich spatial context of regions, high-level semantics of images, and knowledge across multiple sources. To solve the challenges, we propose multi-source multi-level attention networks for visual question answering that can benefit both spatial inferences by visual attention on context-aware region representation and reasoning by semantic attention on concepts as well as external knowledge. Indeed, we learn to reason on image representation by question-guided attention at different levels across multiple sources, including region and concept level representation from image source as well as sentence level representation from the external knowledge base. First, we encode region-based middle-level outputs from Convolutional Neural Networks (CNNs) into spatially embedded representation by a multi-directional two-dimensional recurrent neural network and, further, locate the answer-related regions by Multiple Layer Perceptron as visual attention. Second, we generate semantic concepts from high-level semantics in CNNs and select those question-related concepts as concept attention. Third, we query semantic knowledge from the general knowledge base by concepts and selected question-related knowledge as knowledge attention. Finally, we jointly optimize visual attention, concept attention, knowledge attention, and question embedding by a softmax classifier to infer the final answer. Extensive experiments show the proposed approach achieved significant improvement on two very challenging VQA datasets.
Understanding and Information in the Work of Visual Artists

To better account for information behavior in everyday life, the field must more fully explore information phenomena in the lifeworld, that is, information experience. This article shows that one way to do this is through the concept of understanding. Visual art is identified as an illuminating domain for an initial foray into such research. This article presents findings from a phenomenology‐of‐practice study of the information behavior of visual artists. Seven local artists documented their experiences creating self‐portraits, and semistructured follow‐up interviews were conducted. The findings show how these participants built understanding with information in their work of creating individual self‐portraits. These understandings fall into two categories: of the self and of the artistic process. Many forms of information, traditional and novel, contribute to these understandings; examples of the latter include memories, the lived environment, profound experiences, and online browsing. These findings extend the literature on artists' information behavior, connect everyday information behavior to information experience, and illustrate a method for studying understanding empirically in information science. The article closes by discussing the meaning of these findings for the future of information science, suggesting that the kinds of information employed by artists might be recognized and appreciated more widely throughout society.
Data Discovery and Anomaly Detection Using Atypicality: Theory

A central question in the era of big data is what to do with the enormous amount of information. One possibility is to characterize it through statistics, e.g., averages, or classify it using machine learning, in order to understand the general structure of the overall data. The perspective in this paper is the opposite, namely that most of the value in the information in some applications is in the parts that deviate from the average, that are unusual, atypical. We define what we mean by atypical in an axiomatic way as data that can be encoded with fewer bits in itself rather than using the code for the typical data. We show that this definition has good theoretical properties. We then develop an implementation based on universal source coding, and apply this to a number of real world data sets.