Reviewerly/exHarmony huggingface dataset:
PET image super-resolution using generative adversarial networks

The intrinsically low spatial resolution of positron emission tomography (PET) leads to image quality degradation and inaccurate image-based quantitation. Recently developed supervised super-resolution (SR) approaches are of great relevance to PET but require paired low- and high-resolution images for training, which are usually unavailable for clinical datasets. In this paper, we present a self-supervised SR (SSSR) technique for PET based on dual generative adversarial networks (GANs), which precludes the need for paired training data, ensuring wider applicability and adoptability. The SSSR network receives as inputs a low-resolution PET image, a high-resolution anatomical magnetic resonance (MR) image, spatial information (axial and radial coordinates), and a high-dimensional feature set extracted from an auxiliary CNN which is separately-trained in a supervised manner using paired simulation datasets. The network is trained using a loss function which includes two adversarial loss terms, a cycle consistency term, and a total variation penalty on the SR image. We validate the SSSR technique using a clinical neuroimaging dataset. We demonstrate that SSSR is promising in terms of image quality, peak signal-to-noise ratio, structural similarity index, contrast-to-noise ratio, and an additional no-reference metric developed specifically for SR image quality assessment. Comparisons with other SSSR variants suggest that its high performance is largely attributable to simulation guidance.
Random forest regression for improved mapping of solar irradiance at high latitudes

Datasets from meteorological reanalyses and retrievals from satellites are the available sources of large-scale information about solar radiation. However, both the reanalyses and the satellite-based estimates can be severely biased, especially in high latitude regions. In this study, surface solar irradiance estimates from the ECMWF Reanalysis 5 (ERA5) and the Cloud, Albedo, Radiation dataset Edition 2 (CLARA-A2) were used as input to a random forest regression (RFR) model to construct a novel dataset with higher accuracy and precision than the input datasets. For daily averages of global horizontal irradiance (GHI) at Norwegian sites, CLARA-A2 and ERA5 respectively produced a root mean squared deviation (RMSD) of 17.9 Wm−2 and 27.1 Wm−2, a mean absolute deviation (MAD) of 11.9 Wm−2 and 17.5 Wm−2, and a bias of −1.5 Wm−2 and 4.3 Wm−2. In contrast, the proposed regression model provided an RMSD of 16.2 Wm−2, an MAD of 10.8 Wm−2, and a bias of 0.0 Wm−2. This shows that the RFR model is both accurate and precise, and significantly reduces both dispersion and bias in the new dataset with respect to the constituent sources. A sky-stratification analysis was performed and it was found that the proposed model provides better estimates under all sky conditions with particular improvements in intermediate-cloudy conditions. The proposed regression model was also tested on five Swedish locations and it was found to improve surface solar irradiance estimates to a similar degree as for the Norwegian locations, thus proving its consistency under similar climatic conditions.
Adversarial Generation of Handwritten Text Images Conditioned on Sequences

We propose a system based on Generative Adversarial Networks (GAN) to produce synthetic images of handwritten words. We use bidirectional LSTM recurrent layers to get an embedding of the word to be rendered, and we feed it to the generator network. We also modify the standard GAN by adding an auxiliary network for text recognition. The system is then trained with a balanced combination of an adversarial loss and a CTC loss. Together, these extensions to GAN enable to control the textual content of the generated word images yielding realistic-looking images on both French and Arabic languages. State-of-the-art offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partially satisfy this requirement, we could use those synthetic images to increase the amount of training data. We show that integrating generated images into the existing training data of a text recognition system can slightly enhance its performance.
Better Bootstrapping for Approximate Homomorphic Encryption

After Cheon et al. (Asiacrypt’ 17) proposed an approximate homomorphic encryption scheme, HEAAN, for operations between encrypted real (or complex) numbers, the scheme is widely used in a variety of fields with needs on privacy-preserving in data analysis. After that, a bootstrapping method for HEAAN is proposed by Cheon et al. (Eurocrypt’ 18) with modulus reduction being replaced by a sine function. In this paper, we generalize the Full-RNS variant of HEAAN proposed by Cheon et al. (SAC, 19) to reduce the number of temporary moduli used in key-switching. As a result, our scheme can support more depth computations without bootstrapping while ensuring the same level of security. We also propose a new polynomial approximation method to evaluate a sine function in an encrypted state, which is specialized for the bootstrapping for HEAAN. Our method considers a ratio between the size of a plaintext and the size of a ciphertext modulus. Consequently, it requires a smaller number of non-scalar multiplications, which is about half of the Chebyshev method. With our variant of the Full-RNS scheme and a new sine evaluation method, we firstly implement bootstrapping for a Full-RNS variant of approximate homomorphic encryption scheme. Our method enables bootstrapping for a plaintext in the space $${\mathbb {C}}^{16384}$$ to be completed in 52 s while preserving 11 bit precision of each slot.
Realizing an Internet of Secure Things: A Survey on Issues and Enabling Technologies

Since the term first coined in 1999, the Internet of Things (IoT) has gained significant momentum in connecting physical objects to the Internet and facilitating machine-to-human and machine-to-machine communications. By offering the capability to connect and integrate both digital and physical entities, IoT becomes an important paradigm that enables a whole new class of applications and services. Security is one of the most challenging issues that need to be addressed before these IoT applications and services can be fully embraced. In this survey, we investigate the major research efforts over the period of 2013-2019 that address IoT security and privacy issues. We provide extensive discussions on securing cloud-based IoT solutions. The main focus of these discussions is on securing the information in transit between IoT devices and IoT applications, where most of the data processing and modelling tasks take place. These discussions include all security aspects and challenges facing the data in transit. Specifically, a number of common attacks that target IoT solutions are first discussed, while presenting the main challenges of IoT security (e.g., the resource limitation of IoT devices, which hinder the ability of such devices to do expensive computations for securing the data). Then we present the main security requirements needed by IoT systems, which include access control, integrity, and authentication. We review recent research work in providing security and privacy services, which delegate the expensive computations to an edge or cloud, to cope with the low computations restrictions in IoT devices. Open research issues and possible research directions in securing cloud-based IoT systems are discussed, while proposing some possible solutions.
Learning Similarity Conditions Without Explicit Supervision

Many real-world tasks require models to compare images along multiple similarity conditions (e.g. similarity in color, category or shape). Existing methods often reason about these complex similarity relationships by learning condition-aware embeddings. While such embeddings aid models in learning different notions of similarity, they also limit their capability to generalize to unseen categories since they require explicit labels at test time. To address this deficiency, we propose an approach that jointly learns representations for the different similarity conditions and their contributions as a latent variable without explicit supervision. Comprehensive experiments across three datasets, Polyvore-Outfits, Maryland-Polyvore and UT-Zappos50k, demonstrate the effectiveness of our approach: our model outperforms the state-of-the-art methods, even those that are strongly supervised with pre-defined similarity conditions, on fill-in-the-blank, outfit compatibility prediction and triplet prediction tasks. Finally, we show that our model learns different visually-relevant semantic sub-spaces that allow it to generalize well to unseen categories.
Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion

Recent work has shown that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion. We term this architecture Compositional Convolutional Neural Network. In particular, we propose to replace the fully connected classification head of a DCNN with a differentiable compositional model. The generative nature of the compositional model enables it to localize occluders and subsequently focus on the non-occluded parts of the object. We conduct classification experiments on artificially occluded images as well as real images of partially occluded objects from the MS-COCO dataset. The results show that DCNNs do not classify occluded objects robustly, even when trained with data that is strongly augmented with partial occlusions. Our proposed model outperforms standard DCNNs by a large margin at classifying partially occluded objects, even when it has not been exposed to occluded objects during training. Additional experiments demonstrate that CompositionalNets can also localize the occluders accurately, despite being trained with class labels only. The code and data used in this work are publicly available.
Out-of-Domain Detection for Natural Language Understanding in Dialog Systems

Natural Language Understanding (NLU) is a vital component of dialogue systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in practical applications, since the acceptance of the OOD input that is unsupported by the current system may lead to catastrophic failure. However, most existing OOD detection methods rely heavily on manually labeled OOD samples and cannot take full advantage of unlabeled data. This limits the feasibility of these models in practical applications. In this paper, we propose a novel model to generate high-quality pseudo OOD samples that are akin to IN-Domain (IND) input utterances and thereby improves the performance of OOD detection. To this end, an autoencoder is trained to map an input utterance into a latent code. Moreover, the codes of IND and OOD samples are trained to be indistinguishable by utilizing a generative adversarial network. To provide more supervision signals, an auxiliary classifier is introduced to regularize the generated OOD samples to have indistinguishable intent labels. Experiments show that these pseudo OOD samples generated by our model can be used to effectively improve OOD detection in NLU. Besides, we also demonstrate that the effectiveness of these pseudo OOD data can be further improved by efficiently utilizing unlabeled data.
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors

In reinforcement learning (RL), function approximation errors are known to easily lead to the Q-value overestimations, thus greatly reducing policy performance. This paper presents a distributional soft actor-critic (DSAC) algorithm, which is an off-policy RL method for continuous control setting, to improve the policy performance by mitigating Q-value overestimations. We first discover in theory that learning a distribution function of state-action returns can effectively mitigate Q-value overestimations because it is capable of adaptively adjusting the update stepsize of the Q-value function. Then, a distributional soft policy iteration (DSPI) framework is developed by embedding the return distribution function into maximum entropy RL. Finally, we present a deep off-policy actor-critic variant of DSPI, called DSAC, which directly learns a continuous return distribution by keeping the variance of the state-action returns within a reasonable range to address exploding and vanishing gradient problems. We evaluate DSAC on the suite of MuJoCo continuous control tasks, achieving the state-of-the-art performance.
Anomalous Sound Detection Based on Interpolation Deep Neural Network

As the labor force decreases, the demand for labor-saving automatic anomalous sound detection technology that conducts maintenance of industrial equipment has grown. Conventional approaches detect anomalies based on the reconstruction errors of an autoencoder. However, when the target machine sound is non-stationary, a reconstruction error tends to be large independent of an anomaly, and its variations increased because of the difficulty of predicting the edge frames. To solve the issue, we propose an approach to anomalous detection in which the model utilizes multiple frames of a spectrogram whose center frame is removed as an input, and it predicts an interpolation of the removed frame as an output. Rather than predicting the edge frames, the proposed approach makes the reconstruction error consistent with the anomaly. Experimental results showed that the proposed approach achieved 27% improvement based on the standard AUC score, especially against non-stationary machinery sounds.
Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment

Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion AssessmentVictoria L. Mango1, Mary Sun2, Ralph T. Wynn2 and Richard Ha2Audio Available | Share
GuardHealth: Blockchain empowered secure data management and Graph Convolutional Network enabled anomaly detection in smart healthcare

The paradox between the dramatic development of medical data privacy demand and years of bureaucratic regulation has slowed innovation for electronic medical records (EMRs). We are at a historical point for such innovation to prompt patients data autonomy. In this paper, we propose GuardHealth: an efficient, secure and decentralized Blockchain system for data privacy preserving and sharing. GuardHealth manages confidentiality, authentication, data preserving and data sharing when handling sensitive information. We exploit consortium Blockchain and smart contract to achieve secure data storage and sharing, which prevents data sharing without permission. A trust model is utilized for precisely managing trust of users with the implementation of the state-of-art Graph Neural Network (GNN) for malicious node detection. Security analysis and experiment results show that the proposed scheme is applicable for smart healthcare system.
Unobtrusive Health Monitoring in Private Spaces: The Smart Home

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking.
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels

We consider a wireless communication system that consists of a transmitter, a receiver, and an adversary. The transmitter transmits signals with different modulation types, while the receiver classifies its received signals to modulation types using a deep learning-based classifier. In the meantime, the adversary makes over-the-air transmissions that are received as superimposed with the transmitter’s signals to fool the classifier at the receiver into making errors. While this evasion attack has received growing interest recently, the channel effects from the adversary to the receiver have been ignored so far such that the previous attack mechanisms cannot be applied under realistic channel effects. In this paper, we present how to launch a realistic evasion attack by considering channels from the adversary to the receiver. Our results show that modulation classification is vulnerable to an adversarial attack over a wireless channel that is modeled as Rayleigh fading with path loss and shadowing. We present various adversarial attacks with respect to availability of information about channel, transmitter input, and classifier architecture. First, we present two types of adversarial attacks, namely a targeted attack (with minimum power) and non-targeted attack that aims to change the classification to a target label or to any other label other than the true label, respectively. Both are white-box attacks that are transmitter input-specific and use channel information. Then we introduce an algorithm to generate adversarial attacks using limited channel information where the adversary only knows the channel distribution. Finally, we present a black-box universal adversarial perturbation (UAP) attack where the adversary has limited knowledge about both channel and transmitter input. By accounting for different levels of information availability, we show the vulnerability of modulation classifier to over-the-air adversarial attacks.
Embedding and approximation theorems for echo state networks

Echo State Networks (ESNs) are a class of single-layer recurrent neural networks that have enjoyed recent attention. In this paper we prove that a suitable ESN, trained on a series of measurements of an invertible dynamical system, induces a C1 map from the dynamical system’s phase space to the ESN’s reservoir space. We call this the Echo State Map. We then prove that the Echo State Map is generically an embedding with positive probability. Under additional mild assumptions, we further conjecture that the Echo State Map is almost surely an embedding. For sufficiently large, and specially structured, but still randomly generated ESNs, we prove that there exists a linear readout layer that allows the ESN to predict the next observation of a dynamical system arbitrarily well. Consequently, if the dynamical system under observation is structurally stable then the trained ESN will exhibit dynamics that are topologically conjugate to the future behaviour of the observed dynamical system. Our theoretical results connect the theory of ESNs to the delay-embedding literature for dynamical systems, and are supported by numerical evidence from simulations of the traditional Lorenz equations. The simulations confirm that, from a one dimensional observation function, an ESN can accurately infer a range of geometric and topological features of the dynamics such as the eigenvalues of equilibrium points, Lyapunov exponents and homology groups.
A Heterogeneous Graph Neural Model for Cold-start Recommendation

The users' historical interactions usually contain their interests and purchase habits based on which personalised recommendations can be made. However, such user interactions are often sparse, leading to the well-known cold-start problem when a user has no or very few interactions. In this paper, we propose a new recommendation model, named Heterogeneous Graph Neural Recommender (HGNR), to tackle the cold-start problem while ensuring effective recommendations for all users. Our HGNR model learns users and items' embeddings by using the Graph Convolutional Network based on a heterogeneous graph, which is constructed from user-item interactions, social links and semantic links predicted from the social network and textual reviews. Our extensive empirical experiments on three public datasets demonstrate that HGNR significantly outperforms competitive baselines in terms of the Normalised Discounted Cumulative Gain and Hit Ratio measures.
Understanding risk of bubbles in cryptocurrencies

As cryptocurrencies emerged only recently, they are subject to only very limited financial regulations. In this paper we study which variables can predict bubbles in the prices of eight major cryptocurrencies, focusing on uncertainty measures as predictors. We detect multiple bubble periods for all eight cryptocurrencies, particularly in 2017 and early 2018. We find that higher volatility, trading volume and transactions are positively associated with the presence of bubbles across cryptocurrencies. Regarding the uncertainty variables, the VIX-index consistently demonstrates negative relationships with bubble occurrence, while the EPU-index mostly exhibits positive associations with bubbles. These results may assist authorities in designing appropriate regulations.