Reviewerly/exHarmony huggingface dataset:
IENet: Internal and External Patch Matching ConvNet for Web Image Guided Denoising

From the non-local self-similarity (NSS)-based image denoising to the convolutional-network (ConvNet)-based image denoising, the denoising performance has been greatly improved. However, it is still not clear how to utilize similar web images to guide image denoising using ConvNet. This paper proposes a novel ConvNet for image denoising to explore both internal (NSS) and external correlations when external similar images are available. Since external similar images may be taken with different viewpoints, focal lengths, and may contain different objects, it is difficult to directly explore external correlations at image level using ConvNet. Therefore, we propose an internal and external patch matching ConvNet (IENet), whose inputs are similar patch cubes extracted from the noisy input and its external similar images. We design three different network structures, namely early-fusion, middle-fusion, and late-fusion of the internal and external cubes to fully combine the strengths of internal and external correlations. The experimental results demonstrate that the proposed method achieves the best denoising results compared with the seven state-of-the-art denoising methods. In specific, the proposed method outperforms the state-of-the-art web image guided denoising method by more than 1 dB on average, which further demonstrates the superiority of the proposed IENet-based filtering over the hand-crafted filtering methods.
An edge driven wavelet frame model for image restoration

Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images. In this paper, we introduce a new edge driven wavelet frame model for image restoration by approximating images as piecewise smooth functions. With an implicit representation of image singularities sets, the proposed model inflicts different strength of regularization on smooth and singular image regions and edges. The proposed edge driven model is robust to both image approximation and singularity estimation. The implicit formulation also enables an asymptotic analysis of the proposed models and a rigorous connection between the discrete model and a general continuous variational model. Finally, numerical results on image inpainting and deblurring show that the proposed model is compared favorably against several popular image restoration models.
Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation

Spoken question answering (SQA) is challenging due to complex reasoning on top of the spoken documents. The recent studies have also shown the catastrophic impact of automatic speech recognition (ASR) errors on SQA. Therefore, this work proposes to mitigate the ASR errors by aligning the mismatch between ASR hypotheses and their corresponding reference transcriptions. An adversarial model is applied to this domain adaptation task, which forces the model to learn domain-invariant features the QA model can effectively utilize in order to improve the SQA results. The experiments successfully demonstrate the effectiveness of our proposed model, and the results are better than the previous best model by 2% EM score.
Preserving Geo-Indistinguishability of the Primary User in Dynamic Spectrum Sharing

Dynamic spectrum sharing (DSS) has been taken as an encouraging method to address the increasing demand for wireless spectrum resources. Without privacy protection in location, however, the primary user will hesitate to share spectrum with secondary users. In this paper, we present LpriDSS, the first scheme for a spectrum administrator to select spectrum-sharing secondary users while implementing the geo-indistinguishability of primary user. First, we formulate the secondary users selection without privacy in DSS system as an auction problem and demonstrate the location inference attacks based on the winner sets in detail. Then, a ranking metric to characterize the administrators’ preference for secondary users is defined. Moreover, a probability of each secondary user being selected as a winner is calculated and used during the process of secondary user selection. Finally, a truthful payment computation is designed according to that probability. Thorough theoretical analysis and simulation studies show that LpriDSS can simultaneously achieve geo-indistinguishability, approximate social welfare maximization, and truthfulness.
Minimum-Entropy Couplings and Their Applications

Given two discrete random variables X and Y, with probability distributions p = (p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , ..., p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) and q = (q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , ..., q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> ), respectively, let us denote by C(p, q) the set of all couplings of p and q, that is, the set of all bivariate probability distributions that have p and q as marginals. In this paper, we study the problem of finding a joint probability distribution in C(p, q) of minimum entropy (equivalently, a coupling that maximizes the mutual information between X and Y), and we discuss several situations where the need for this kind of optimization naturally arises. Since the optimization problem is known to be NP-hard, we give an efficient algorithm to find a joint probability distribution in C(p, q) with entropy exceeding the minimum possible at most by 1 bit, thus providing an approximation algorithm with an additive gap of at most 1 bit. Leveraging on this algorithm, we extend our result to the problem of finding a minimum-entropy joint distribution of arbitrary k ≥ 2 discrete random variables X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , ..., X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> , consistent with the known k marginal distributions of the individual random variables X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , ..., X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> . In this case, our algorithm has an additive gap of at most log k from optimum. We also discuss several related applications of our findings and extensions of our results to entropies different from the Shannon entropy.
DADS

We present a novel scheme called Decentralized Attestation for Device Swarms (DADS), which is, to the best of our knowledge, the first to accomplish decentralized attestation in device swarms. Device swarms are smart, mobile, and interconnected devices that operate in large numbers and are likely to be part of emerging applications in Cyber-Physical Systems (CPS) and Industrial Internet of Things (IIoTs). Swarm devices process and exchange safety, privacy, and mission-critical information. Thus, it is important to have a good code verification technique that scales to device swarms and establishes trust among collaborating devices. DADS has several advantages over current state-of-the-art swarm attestation techniques: It is decentralized, has no single point of failure, and can handle changing topologies after nodes are compromised. DADS assures system resilience to node compromise/failure while guaranteeing only devices that execute genuine code remain part of the group. We conduct performance measurements of communication, computation, memory, and energy using the TrustLite embedded systems architecture in OMNeT++ simulation environment. We show that the proposed approach can significantly reduce communication cost and is very efficient in terms of computation, memory, and energy requirements. We also analyze security and show that DADS is very effective and robust against various attacks.
Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics

As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, suggesting that extensive labor-consuming research and derivation is required. In this article, we propose a novel image tampering detection method based on deep multi-scale discriminative networks (MSD-Nets). The multi-scale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality ( QF 1) is higher than the second one ( QF 2). A special network in this module is designed to distinguish the small statistical difference between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area is located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with state-of-the-art approaches.
FCN based preprocessing for exemplar-based face sketch synthesis

Most of the current exemplar-based face sketch synthesis approaches directly synthesize face sketches from face photos. However, due to the great difference between face photos and sketches, as well as the cluttered backgrounds in photo images, there tends to be some noise, deformation and missing parts on the synthesized face sketches by most of the exemplar-based methods. Besides, most exemplar-based methods exist a common problem: they only produce satisfactory results when training and test samples originate from the same dataset. To address these issues, in this paper we propose a simple but effective method which consists of two stages: the preprocessing stage and the sketch synthesis stage. In the preprocessing stage, we first design a fully convolutional neural network for preprocessing (pFCN). To fit the preprocessing task, the pFCN is trained by an L1 based total loss function, which is simple yet could enhance the facial features. Then the full-size photo is fed to the well-trained pFCN to generate the feature map, which we call a semi-sketch since it bridges the discrepancy between photo and sketch. At the sketch synthesis stage, the semi-sketches and an existing exemplar-based method are employed to synthesize the final sketches. Extensive experiments on public face sketch datasets verify that the proposed two-stage method improves the sketch synthesis quality of the state-of-the-art exemplar-based methods in terms of both recognition accuracy and perceptual quality. In addition, the experiments on cross-dataset indicate that the proposed method provides a new means for strengthening the generalization ability of the exemplar-based method.
Cross-Domain Visual Exploration of Academic Corpora via the Latent Meaning of User-Authored Keywords

Nowadays, scholars dedicate a substantial amount of their work to the querying and browsing of increasingly large collections of research papers on the Internet.In parallel, the recent surge of novel interdisciplinary approaches in science requires scholars to acquire competencies in new fields for which they may lack the necessary vocabulary to formulate adequate queries.This problem, together with the issue of information overload, poses new challenges in the fields of natural language processing (NLP) and visualization design that call for a rapid response from the scientific community.In this respect, we report on a novel visualization scheme that enables the exploration of research paper collections via the analysis of semantic proximity relationships found in author-assigned keywords.Our proposal replaces traditional string queries with a bag-of-words (BoW) extracted from a user-generated auxiliary corpus that captures the intentionality of the research.Continuing along the lines established by other authors in the fields of literature-based discovery (LBD), NLP, and visual analytics (VA), we combine novel advances in the fields of NLP with visual network analysis techniques to offer scholars a perspective of the target corpus that better fits their research interests.To highlight the advantages of our proposal, we conduct two experiments employing a collection of visualization research papers and an auxiliary cross-domain BoW.Here, we showcase how our visualization can be used to maximize the effectiveness of a browsing session by enhancing the language acquisition task, which allows for effectively extracting knowledge that is in line with the users' previous expectations.
Heats: Heterogeneity-and Energy-Aware Task-Based Scheduling

Cloud providers usually offer diverse types of hardware for their users. Customers exploit this option to deploy cloud instances featuring GPUs, FPGAs, architectures other than x86 (e.g., ARM, IBM Power8), or featuring certain specific extensions (e.g., Intel SGX). We consider in this work the instances used by customers to deploy containers, nowadays the de facto standard for micro-services, or to execute computing tasks. In doing so, the underlying container orchestrator (e.g., Kubernetes) should be designed so as to take into account and exploit this hardware diversity. In addition, besides the feature range provided by different machines, there is an often overlooked diversity in the energy requirements introduced by hardware heterogeneity, which is simply ignored by default container orchestrator's placement strategies. We introduce Heats, a new task-oriented and energy-aware orchestrator for containerized applications targeting heterogeneous clusters. Heats allows customers to trade performance vs. energy requirements. Our system first learns the performance and energy features of the physical hosts. Then, it monitors the execution of tasks on the hosts and opportunistically migrates them onto different cluster nodes to match the customer-required deployment trade-offs. Our Heats prototype is implemented within Google's Kubernetes. The evaluation with synthetic traces in our cluster indicate that our approach can yield considerable energy savings (up to 8.5%) and only marginally affect the overall runtime of deployed tasks (by at most 7%). Heats is released as open-source.
A Pattern and Calibration Method for Single- Pattern Structured Light System

Structured light is a widely employed approach for profile measurement in active stereo vision. Recent methods have developed learning-based single pattern structured light systems and unstructured light stereo vision systems. However, their methods require additional cameras or a diffractive optical element. In addition, the calibration methods of the learning-based structured light systems require a large number of images, while the stereo vision method cannot detect the blocked region. Classically, a digital projector is treated as a pseudocamera, and the stripe pattern is employed to measure the depth. However, an additional pattern is employed to calibrate the camera-projector system in the stripe-based method. In this paper, 24 reliable features and a novel calibration algorithm are proposed. With the proposed calibration algorithm, the stripe patterns are calibrated without additional patterns, and with the proposed RGB features, the number of encoded columns is enlarged significantly. Experiments demonstrate that the proposed method is accurate and robust.
Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model

Giving or recommending appropriate content based on the quality of experience is the most important and challenging issue in recommender systems. As collaborative filtering (CF) is one of the most prominent and popular techniques used for recommender systems, we propose a new clusteringbased CF (CBCF) method using an incentivized/penalized user (IPU) model only with the ratings given by users, which is thus easy to implement. We aim to design such a simple clustering-based approach with no further prior information while improving the recommendation accuracy. To be precise, the purpose of CBCF with the IPU model is to improve recommendation performance such as precision, recall, and F1 score by carefully exploiting different preferences among users. Specifically, we formulate a constrained optimization problem in which we aim to maximize the recall (or equivalently F1 score) for a given precision. To this end, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient. Afterward, we give each item an incentive/penalty according to the preference tendency by users within the same cluster. Our experimental results show a significant performance improvement over the baseline CF scheme without clustering in terms of recall or F1 score for a given precision.
Unsupervised Polyglot Text-to-speech

We present a TTS neural network that is able to produce speech in multiple languages. The proposed network is able to transfer a voice, which was presented as a sample in a source language, into one of several target languages. Training is done without using matching or parallel data, i.e., without samples of the same speaker in multiple languages, making the method much more applicable. The conversion is based on learning a polyglot network that has multiple per-language sub-networks and adding loss terms that preserve the speaker's identity in multiple languages. We evaluate the proposed polyglot neural network for three languages with a total of more than 400 speakers and demonstrate convincing conversion capabilities.
Variational Learning of Beta-Liouville Hidden Markov Models for Infrared Action Recognition

Infrared (IR) images are characterized by a lower sensitivity to lighting conditions than the visible spectrum. This opens the door to relatively untapped research potential of automatic recognition systems that are robust to shadows and variability in illumination levels or appearance. IR action recognition (AR) is one such application. It remains a fairly unexplored domain in IR. As such, in this paper, we propose the use of hidden Markov models (HMM) for IR AR. We also derive the mathematical model for the variational learning of Beta-Liouville (BL) HMMs. Next, we present the results of the proposed model on the Infrared Action Recognition (InfAR) dataset. To the best of our knowledge, this is the first application of HMMs to AR in the IR domain, and the first application of the BL HMMs to AR. Experimental results demonstrate promising results using different features extracted from the InfAR dataset.
Bent Vectorial Functions, Codes and Designs

Bent functions, or equivalently, Hadamard difference sets in the elementary Abelian group (GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m</sup> ), +), have been employed to construct symmetric and quasi-symmetric designs having the symmetric difference property. The main objective of this paper is to use bent vectorial functions for a construction of a two-parameter family of binary linear codes that do not satisfy the conditions of the Assmus-Mattson theorem, but nevertheless hold 2-designs. A new coding-theoretic characterization of bent vectorial functions is presented.
The Distortion-Rate Function of Sampled Wiener Processes

We consider the recovery of a continuous-time Wiener process from a quantized or a lossy compressed version of its uniform samples under limited bitrate and sampling rate. We derive a closed-form expression for the optimal tradeoff among sampling rate, bitrate, and quadratic distortion in this setting. This expression is given in terms of a reverse waterfilling formula over the asymptotic spectral distribution of a sequence of finite-rank operators associated with the optimal estimator of the Wiener process from its samples. We show that the ratio between this expression and the standard distortion rate function of the Wiener process, describing the optimal tradeoff between bitrate and distortion without a sampling constraint, is only a function of the number of bits per sample. We also consider a sub-optimal lossy compression scheme in which the continuous-time process is estimated from the output of an encoder that is optimal with respect to the discrete-time samples. We show that the latter is strictly greater than the distortion under optimal encoding but only by at most 3%. We, therefore, conclude that near optimal performance is attained even if the encoder is unaware of the continuous-time origin of the samples.
Smart-bot Technology: Conversational Agents Role in Maternal Healthcare Support

There is a need for a system that respond to various queries in real-time, to advice and inform expectant mothers during their pregnancy. Several smart services have been deployed, bundled with health information systems, and other digital services. While such solutions better services in the healthcare settings, they may not be available to the masses in the rural. Besides they rarely dispense this information precisely and or accurately. A new digital ecosystem, represented by chatbots seem to offer promising solution by embodying the function of a virtual healthcare expert, who is always available to provide information in the required precision. Powered by AI and machine learning algorithms, chatbots are forecasted to bring forth accuracy, precision and availability of information when used. This paper discusses the need to develop chatbots to be integrated in smart phones; intended to provide support to to-be mothers during their journey in pregnancy.
Anonymous identity authentication mechanism for hybrid architecture in mobile crowd sensing networks

Summary In the context of mobile crowd sensing (MCS), the security and privacy protection is an important research topic. We investigate an anonymous identity authentication mechanism for MCS networks in this manuscript. To solve the defects of single authentication system, we firstly analyze the design requirements and put forward a hybrid authentication architecture by combining public key infrastructure (PKI) and combined public key (CPK). To ensure key security, we propose a CPK algorithm based on an elliptic curve cryptosystem (ECC) to generate user's key pair and meet the security requirements of users. Particularly, we enrich the Doley‐Yao attacker model in an authentication environment and introduce the concept of “pseudonym set” into the authentication interaction process to solve the large‐scale key management. Finally, we evaluate the proposed anonymous identity authentication mechanism by function verification and performance analysis. Substantial experimental results demonstrate the effectiveness of our solutions that contribute to privacy protection for the process of authentication by the means of pseudonym.