Reviewerly/exHarmony huggingface dataset:
HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions

MobileNets, a class of top-performing convolutional neural network architectures in terms of accuracy and efficiency trade-off, are increasingly used in many resource-aware vision applications. In this paper, we present Harmonious Bottleneck on two Orthogonal dimensions (HBO), a novel architecture unit, specially tailored to boost the accuracy of extremely lightweight MobileNets at the level of less than 40 MFLOPs. Unlike existing bottleneck designs that mainly focus on exploring the interdependencies among the channels of either groupwise or depthwise convolutional features, our HBO improves bottleneck representation while maintaining similar complexity via jointly encoding the feature interdependencies across both spatial and channel dimensions. It has two reciprocal components, namely spatial contraction-expansion and channel expansion-contraction, nested in a bilaterally symmetric structure. The combination of two interdependent transformations performing on orthogonal dimensions of feature maps enhances the representation and generalization ability of our proposed module, guaranteeing compelling performance with limited computational resource and power. By replacing the original bottlenecks in MobileNetV2 backbone with HBO modules, we construct HBONets which are evaluated on ImageNet classification, PASCAL VOC object detection and Market-1501 person re-identification. Extensive experiments show that with the severe constraint of computational budget our models outperform MobileNetV2 counterparts by remarkable margins of at most 6.6%, 6.3% and 5.0% on the above benchmarks respectively. Code and pretrained models are available at https://github.com/d-li14/HBONet.
A Camera Shooting Resilient Watermarking Scheme for Underpainting Documents

This paper designs a novel underpainting based camera shooting resilient (CSR) document watermarking algorithm for dealing with the leak source tracking problem. By applying such algorithm, we can extract the authentication watermark information from the candid photographs. The watermarked underpainting contains three significant properties. 1) Inconspicuousness. The watermarked underpainting is inconspicuous and it will not easily be maliciously attacked. 2) Robustness. We propose DCT-based watermark embedding algorithm and distortion compensation based extracting algorithm, which make the watermark robust to camera shooting process. 3) Autocorrelation. We design the flip-based method to arrange the watermarked underpainting. So that a complete watermark region can be accurately located even if part of the document is recorded. Compared with previous watermarking algorithms, the proposed scheme guaranteed content independent embedding as well as the robustness to the camera shooting process. Besides, the proposed scheme satisfies the accuracy of extraction even when the captured document is incomplete.
Learning Across Tasks and Domains

Recent works have proven that many relevant visual tasks are closely related one to another. Yet, this connection is seldom deployed in practice due to the lack of practical methodologies to transfer learned concepts across different training processes. In this work, we introduce a novel adaptation framework that can operate across both task and domains. Our framework learns to transfer knowledge across tasks in a fully supervised domain (e.g., synthetic data) and use this knowledge on a different domain where we have only partial supervision (e.g., real data). Our proposal is complementary to existing domain adaptation techniques and extends them to cross tasks scenarios providing additional performance gains. We prove the effectiveness of our framework across two challenging tasks (i.e., monocular depth estimation and semantic segmentation) and four different domains (Synthia, Carla, Kitti, and Cityscapes).
Unlocking Social Media and User Generated Content as a Data Source for Knowledge Management

The pervasiveness of social media and user-generated content has triggered an exponential increase in global data. However, due to collection and extraction challenges, data in embedded comments, reviews and testimonials are largely inaccessible to a knowledge management system. This article describes a KM framework for the end-to-end knowledge management and value extraction from such content. This framework embodies solutions to unlock the potential of UGC as a rich, real-time data source. Three contributions are described in this article. First, a method for automatically navigating webpages to expose UGC for collection is presented. This is evaluated using browser emulation integrated with automated collection. Second, a method for collecting data without any a priori knowledge of the sites is introduced. Finally, a new testbed is developed to reflect the current state of internet sites and shared publicly to encourage future research. The discussion benchmarks the new algorithm alongside existing techniques, providing evidence of the increased amount of UGC data extracted.
Smart Contracts and Internet of Things: A Qualitative Content Analysis using the Technology-Organization-Environment Framework to Identify Key-Determinants

The spread and success of Internet of Things (IoT) is based on the rapidly growing number of applications, and smart contracts may play a pivotal role in IoT. In this paper, we (i) identify the key-determinants of smart contracts in IoT, and (ii) analyze the opportunities and challenges from a management viewpoint. We performed a qualitative content analysis, and used the structure of the Technology-Organization-Environment framework as categorization scheme. Out of four expert-interviews we were able to extract 84 statements, which provided the basis for the identification of 13 key-determinants for the integration of smart contracts and IoT. Furthermore, the findings revealed that the combination of the two technological concepts promises significant opportunities, however, some technical and environmental challenges need to be overcome.
Deep Neural Network-Based Robust Ship Detection Under Different Weather Conditions

Deep learning-based object detection has recently received significant attention among scholars and practitioners. However, the acquired images often suffer from visual quality degradation under severe weather conditions, which could lead to negative effects on object detection in practical applications. Most previous studies proposed to implement object detection based on the assumption that image restoration techniques (e.g., image dehazing and low-light image enhancement, etc.) could improve visual quality while boosting detection accuracy. In contrast, we assumed that the image restoration techniques (i.e., image preprocessing) may also degrade the fine image details resulting in failing to promote object detection performance. In this work, according to the physical imaging process under severe weather conditions, we directly proposed to synthetically generate the degraded images with training labels to enlarge the original training datasets, which commonly contain only clear natural images under normal weather conditions. The advanced YOLOv3 model was then trained and tested on the enlarged dataset which contain both synthetic and realistic ship images generated under different weather conditions. Experiments have been conducted to compare the proposed method with other competing methods which implement training model only with clear images and testing model with (or without) image preprocessing. Results illustrated that our model could achieve superior detection performance under different conditions.
LA-VITON: A Network for Looking-Attractive Virtual Try-On

In this paper, we propose an image-based virtual try-on network, LA-VITON, which allows the generation of high fidelity try-on images that preserves both the overall appearance and the characteristics of clothing items. The proposed network consists of two modules: Geometric Matching Module (GMM) and Try-On Module (TOM). To warp in-shop clothing item to the desired image of a person with high accuracy in GMM, grid interval consistency loss and an occlusion handling technique are proposed. Grid interval consistency loss regularizes transformation to prevent distortion of patterns in clothes and an occlusion handling technique encourages proper warping despite target bodies are covered by hair or arms. The following TOM synthesizes the final try-on image of the target person seamlessly with the warped clothes from GMM. Extensive experiments on fashion datasets show that the proposed method outperforms the state-of-the-art methods.
National-scale development and calibration of empirical models for predicting daily global solar radiation in China

Accurate global solar radiation (Rs) information is pivotal to the design and management of solar energy systems. Nevertheless, the expensive devices for Rs measurements make Rs data always unavailable in many regions around the world. The empirical models that predict Rs using other widely available climatic variables are feasible alternatives when Rs measurements are unavailable. However, the parameters of empirical models are site-specific and always need local calibration. In this context, the present study firstly developed a novel empirical model for accurately predicting Rs at the national scale in China, and then compared the new model with nineteen locally calibrated empirical models that have been largely reported in prior studies, including seven sunshine-based, nine temperature-based, and three complex empirical models. Daily Rs and other meteorological data during 1994–2016 from 96 weather stations in China were used to calibrate/develop and assess the models. The results showed that the newly proposed C4 model generally offered the best prediction accuracy among the models, with average MAE of 1.69 MJ m−2 d−1, RRMSE of 16.2% and NS of 0.895, which can be recommended as the optimal model for predicting Rs in China. The models reviewed and developed in this study improved the prediction accuracy of Rs, which can provide crucial information for the design and implementation of solar photovoltaic and thermal systems.
Safe Policies for Reinforcement Learning via Primal-Dual Methods

In this article, we study the design of controllers in the context of stochastic optimal control under the assumption that the model of the system is not available. This is, we aim to control a Markov decision process of which we do not know the transition probabilities, but we have access to sample trajectories through experience. We define safety as the agent remaining in a desired safe set with high probability during the operation time. The drawbacks of this formulation are twofold. The problem is nonconvex and computing the gradients of the constraints with respect to the policies is prohibitive. Hence, we propose an ergodic relaxation of the constraints with the following advantages. 1) The safety guarantees are maintained in the case of episodic tasks and they hold until a given time horizon for continuing tasks. 2) The constrained optimization problem despite its nonconvexity has arbitrarily small duality gap if the parametrization of the controller is rich enough. 3) The gradients of the Lagrangian associated with the safe learning problem can be computed using standard reinforcement learning results and stochastic approximation tools. Leveraging these advantages, we exploit primal-dual algorithms to find policies that are safe and optimal. We test the proposed approach in a navigation task in a continuous domain. The numerical results show that our algorithm is capable of dynamically adapting the policy to the environment and the required safety levels.
TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications

Ball trajectory data are one of the most fundamental and useful information in the evaluation of players' performance and analysis of game strategies. It is still challenging to recognize and position a high-speed and tiny ball accurately from an ordinary video. In this paper, we develop a deep learning network, called TrackNet, to track the tennis ball from broadcast videos in which the ball images are small, blurry, and sometimes with afterimage tracks or even invisible. The proposed heatmap-based deep learning network is trained to not only recognize the ball image from a single frame but also learn flying patterns from consecutive frames. The network is evaluated on the video of the men's singles final at the 2017 Summer Universiade, which is available on YouTube. The precision, recall, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F1$</tex> -measure reach 99.7%, 97.3%, and 98.5%, respectively. To prevent overfitting, 9 additional videos are partially labeled together with a subset from the previous dataset to implement 10-fold cross-validation, and the precision, recall, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> -measure are 95.3%, 75.7%, and 84.3%, respectively. The source code and dataset are available at https://nol.cs.nctu.edu.tw:234/open-source/TrackNet/.
Lattice-Based Revocable (Hierarchical) IBE with Decryption Key Exposure Resistance

Revocable identity-based encryption (RIBE) is an extension of IBE that supports a key revocation mechanism, which is an indispensable feature for practical cryptographic schemes. Due to this extra feature, RIBE is often required to satisfy a strong security notion unique to the revocation setting called decryption key exposure resistance (DKER). Additionally, hierarchal IBE (HIBE) is another orthogonal extension of IBE that supports key delegation functionalities allowing for scalable deployments of cryptographic schemes. So far, R(H)IBE constructions with DKER are only known from bilinear maps, where all constructions rely heavily on the so-called key re-randomization property to achieve the DKER and/or hierarchal feature. Since lattice-based schemes seem to be inherently ill-fit with the key re-randomization property, no construction of lattice-based R(H)IBE schemes with DKER are known. In this paper, we propose the first lattice-based RHIBE scheme with DKER without relying on the key re-randomization property, departing from all the previously known methods. We start our work by providing a generic construction of RIBE schemes with DKER, which uses as building blocks any two-level standard HIBE scheme and (weak) RIBE scheme without DKER. Based on previous lattice-based RIBE constructions without DKER, our result implies the first lattice-based RIBE scheme with DKER. Then, building on top of our generic construction, we construct the first lattice-based RHIBE scheme with DKER, by further exploiting the algebraic structure of lattices. To this end, we prepare a new tool called the level conversion keys, which enables us to achieve the hierarchal feature without relying on the key re-randomization property.
An integrity verification scheme of cloud storage for internet-of-things mobile terminal devices

Because mobile cloud computing has many advantages such as large storage capacity, low cost and scalability, most data owners prefer to store their data on cloud servers to share with other users. However, the shared data in remote cloud servers is out of data owner's control and exposes to lots of security problems such as data integrity. So far, more and more auditing schemes have been proposed to verify data integrity, but in most of them, blocks are stored in plain text and leaked the privacy of data. In addition, data owner is in charge of generating block tags, which no doubt increases computation burden of data owner. In this paper, we firstly propose a secure and lightweight integrity verification scheme for Internet of Things (IoT) mobile terminal devices. Secondly, we design a data sharing method for data owner to share cloud data with authorized users. Finally, we redesign Merkle Hash Tree that can preserve the information of block node fresh and improve security in integrity verification of the shared data. As a result, the integrity verification scheme guarantees privacy and efficiency of sharing data on cloud and achieves lightweight operations of data owners.
Meaningful Encryption: Generating Visually Meaningful Encrypted Images by Compressive Sensing and Reversible Color Transformation

Recently, compressive sensing (CS) and visual security (VS) have caught researchers attention in information security field.However, the measurement matrix is often reused in CS, which makes it vulnerable to chosen plaintext attack (CPA).In addition, when generating meaningful cipher images, the size of the carrier image is usually not less than the size of the plain image.In order to overcome these drawbacks, a new visually secure image encryption scheme using CS and reversible color transformation is proposed.The algorithm consists of two stages: compression and embedding.In the first stage, chaotic sequence is used to generate different structurally random matrices.When CS is performed, a random number is added during the process of sampling.By choosing different random numbers, different measurement matrices can be used to compress and encrypt the same image in different order.In the second stage, block pairing, color transformation and block replacement are employed to obtain a meaningful image.Different from the block replacement between two similar images, this paper first attempts to replace the block of the carrier image with a compressed noise-like image block.Thus, the carrier image can be smaller than the plain image, which saves the bandwidth of transmission.Both theoretical analysis and experimental results show that the proposed encryption scheme has good encryption performance, can effectively resist common attacks, and is suitable for meaningful image encryption.
A cross-dataset deep learning-based classifier for people fall detection and identification

Abstract Background and Objective: Fall detection is an important problem for vulnerable sectors of the population such as elderly people, who frequently live alone. Note that a fall can be very dangerous for them if they cannot ask for help. Hence, in those situations, an automatic system that detected and informed to emergency services about the fall and subject identity could help to save lives. This way, they would know not only when but also who to help. Thus, our objective is to develop a new approach, based on deep learning, for fall detection and people identification that can be used in different datasets without any fine-tuning of the model parameters. Methods: We present a dataset-independent deep learning-based model that, by employing a multi-task learning approach, uses raw inertial information as input to solve simultaneously two tasks: fall detection and subject identification. By this way, our approach is able to automatically learn the best representations without any constraint introduced by the pre-processed features. Results: Our cross-dataset classifier is able to detect falls with more than a 98% of accuracy in four datasets recorded under different conditions (i.e. accelerometer device, sampling rate, sequence length, age of the subjects, etc.). Moreover, the number of false positives is very low – on average less than 1.6% – establishing a new state-of-the-art. Finally, our classifier is also capable of correctly identifying people with an average accuracy of 79.6%. Conclusions: The presented approach performs both tasks (fall detection and people identification) by using a single model and achieving real-time execution. The obtained results allow us to assert that a single model can be used for both fall detection and people identification under different conditions, easing its real implementation, as it is not necessary to train the model for new subjects.
GLoSH: Global-Local Spherical Harmonics for Intrinsic Image Decomposition

Traditional intrinsic image decomposition focuses on decomposing images into reflectance and shading, leaving surfaces normals and lighting entangled in shading. In this work, we propose a Global-Local Spherical Harmonics (GLoSH) lighting model to improve the lighting component, and jointly predict reflectance and surface normals. The global SH models the holistic lighting while local SH account for the spatial variation of lighting. Also, a novel non-negative lighting constraint is proposed to encourage the estimated SH to be physically meaningful. To seamlessly reflect the GLoSH model, we design a coarse-to-fine network structure. The coarse network predicts global SH, reflectance and normals, and the fine network predicts their local residuals. Lacking labels for reflectance and lighting, we apply synthetic data for model pre-training and fine-tune the model with real data in a self-supervised way. Compared to the state-of-the-art methods only targeting normals or reflectance and shading, our method recovers all components and achieves consistently better results on three real datasets, IIW, SAW and NYUv2.
Time Series Prediction Using Sparse Autoencoder and High-Order Fuzzy Cognitive Maps

The problem of time series prediction based on fuzzy cognitive maps (FCMs) is unresolved. Although many methods have been proposed to cope with this issue, the performance of these methods is far from satisfactory. Traditional FCM-based predictors have three limitations. First, current feature extraction operators are incapable of learning good representations of original time series. Second, current methods use just the output of FCMs to predict the next value; they do not directly utilize the important information of the latent features. Third, current FCM-based predictors optimize each component individually, thereby leading to low prediction accuracy. For example, these methods first optimize the feature extraction operator and then learn the FCMs from the latent features; they do not simultaneously optimize the whole prediction model. In this article, we develop a framework based on a sparse autoencoder (SAE) and a high-order FCM (HFCM) to address the time series prediction problem; we refer this framework as SAE-FCM. To overcome the first limitation of current methods, an SAE is employed to extract features from original time series. Unlike current FCM-based predictors, our method combines the output of both the SAE and the HFCM to calculate the predicted value, thereby overcoming the second limitation of traditional FCM-based predictors. In an application of the idea of “fine tuning” in deep learning, the weights of SAE-FCM can be updated by the batch gradient descent method if the prediction errors are great. Thus, we can optimize SAE-FCM as a whole and overcome the third limitation. We validate the performance of SAE-FCM on ten datasets. Compared with the experimental results obtained by using state-of-the-art methods, the experimental results obtained by using SAE-FCM demonstrate the effectiveness of our method. Extensive experiments also show that SAE-FCM can effectively overcome the above limitations.