Reviewerly/exHarmony huggingface dataset:
Image encryption based on a single‐round dictionary and chaotic sequences in cloud computing

Summary With the increasing popularity of multimedia technology and the prevalence of various smart electronic devices, severe security problems have recently arisen in information and communication systems. The image maintenance can be treated as a typical example of cloud storage outsourcing as images require much more storage space than text documents. An image encryption method with high efficiency is crucial to preserve the privacy of sensitive and critical images in cloud‐edge communications. Compressive sensing (CS) is capable of reconstructing compressible or sparse signals using fewer measurements than traditional methods when the support of the signals is unobtainable in advance. Encryption methods based on CS decrease resources demands during signal acquisition and protect the image data to avoid theft of confidential information by malicious users. Moreover, the CS‐based encryption method can achieve the encryption and the compression of an image simultaneously. Recently, a considerable amount of work using CS theory has been reported. However, most of the existing methods result in recovered images with unsatisfactory quality and employ a massive measurement matrix as the encryption key. This paper proposes an encryption scheme for images on cloud based on a single‐round dictionary and chaotic sequences. Both the experimental and analytical results verify the proposed scheme's effectiveness, high level of security, and substantial image quality improvement after decryption.
GrandSLAm

The microservice architecture has dramatically reduced user effort in adopting and maintaining servers by providing a catalog of functions as services that can be used as building blocks to construct applications. This has enabled datacenter operators to look at managing datacenter hosting microservices quite differently from traditional infrastructures. Such a paradigm shift calls for a need to rethink resource management strategies employed in such execution environments. We observe that the visibility enabled by a microservices execution framework can be exploited to achieve high throughput and resource utilization while still meeting Service Level Agreements, especially in multi-tenant execution scenarios.
DeepSigns

Deep Learning (DL) models have created a paradigm shift in our ability to comprehend raw data in various important fields, ranging from intelligence warfare and healthcare to autonomous transportation and automated manufacturing. A practical concern, in the rush to adopt DL models as a service, is protecting the models against Intellectual Property (IP) infringement. DL models are commonly built by allocating substantial computational resources that process vast amounts of proprietary training data. The resulting models are therefore considered to be an IP of the model builder and need to be protected to preserve the owner's competitive advantage. We propose DeepSigns, the first end-to-end IP protection framework that enables developers to systematically insert digital watermarks in the target DL model before distributing the model. DeepSigns is encapsulated as a high-level wrapper that can be leveraged within common deep learning frameworks including TensorFlow and PyTorch. The libraries in DeepSigns work by dynamically learning the Probability Density Function (pdf) of activation maps obtained in different layers of a DL model. DeepSigns uses the low probabilistic regions within the model to gradually embed the owner's signature (watermark) during DL training while minimally affecting the overall accuracy and training overhead. DeepSigns can demonstrably withstand various removal and transformation attacks, including model pruning, model fine-tuning, and watermark overwriting. We evaluate DeepSigns performance on a wide variety of DL architectures including wide residual convolution neural networks, multi-layer perceptrons, and long short-term memory models. Our extensive evaluations corroborate DeepSigns' effectiveness and applicability. We further provide a highly-optimized accompanying API to facilitate training watermarked neural networks with a training overhead as low as 2.2%.
A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: A case study in the Yangtze Delta, China

It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China. The data were classified into 31 separate and four integrated industry types by five different machine learning approaches. Multinomial naive Bayesian (NB) methods achieved an accuracy of 87% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 260,000 enterprises. The relationship between the different industry classes and measurements of soil cadmium (Cd) and mercury (Hg) concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of Cd, elevated concentrations also occurred in some areas because of excessive fertilization and coal mining. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites.
Behavioural Biometrics in VR

Every person is unique, with individual behavioural characteristics: how one moves, coordinates, and uses their body. In this paper we investigate body motion as behavioural biometrics for virtual reality. In particular, we look into which behaviour is suitable to identify a user. This is valuable in situations where multiple people use a virtual reality environment in parallel, for example in the context of authentication or to adapt the VR environment to users' preferences. We present a user study (N=22) where people perform controlled VR tasks (pointing, grabbing, walking, typing), monitoring their head, hand, and eye motion data over two sessions. These body segments can be arbitrarily combined into body relations, and we found that these movements and their combination lead to characteristic behavioural patterns. We present an extensive analysis of which motion/relation is useful to identify users in which tasks using classification methods. Our findings are beneficial for researchers and practitioners alike who aim to build novel adaptive and secure user interfaces in virtual reality.
An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain

In this paper, a novel image watermarking method is proposed which is based on discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD).First, in the embedding process, the host image is decomposed into a number of sub-bands through multilevel DWT, and the resulting coefficients of which are then used as the input for HD.The watermark is operated on the SVD at the same time.The watermark is finally embedded into the host image by the scaling factor.Fruit fly optimization algorithm, one of the natural-inspired optimization algorithms is devoted to find the scaling factor through the proposed objective evaluation function.The proposed method is compared to other research works under various spoof attacks, such as the filter, noise, JPEG compression, JPEG2000 compression, and sharpening attacks.The experimental results show that the proposed image watermarking method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.
PEPSI : Fast Image Inpainting With Parallel Decoding Network

Recently, a generative adversarial network (GAN)-based method employing the coarse-to-fine network with the contextual attention module (CAM) has shown outstanding results in image inpainting. However, this method requires numerous computational resources due to its two-stage process for feature encoding. To solve this problem, in this paper, we present a novel network structure, called PEPSI: parallel extended-decoder path for semantic inpainting. PEPSI can reduce the number of convolution operations by adopting a structure consisting of a single shared encoding network and a parallel decoding network with coarse and inpainting paths. The coarse path produces a preliminary inpainting result with which the encoding network is trained to predict features for the CAM. At the same time, the inpainting path creates a higher-quality inpainting result using refined features reconstructed by the CAM. PEPSI not only reduces the number of convolution operation almost by half as compared to the conventional coarse-to-fine networks but also exhibits superior performance to other models in terms of testing time and qualitative scores.
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios

Great progress has been made on estimating disparity maps from stereo images. However, with the limited stereo data available in the existing datasets and unstable ranging precision of current stereo methods, industry-level stereo matching in autonomous driving remains challenging. In this paper, we construct a novel large-scale stereo dataset named DrivingStereo. It contains over 180k images covering a diverse set of driving scenarios, which is hundreds of times larger than the KITTI Stereo dataset. High-quality labels of disparity are produced by a model-guided filtering strategy from multi-frame LiDAR points. For better evaluations, we present two new metrics for stereo matching in the driving scenes, i.e. a distance-aware metric and a semantic-aware metric. Extensive experiments show that compared with the models trained on FlyingThings3D or Cityscapes, the models trained on our DrivingStereo achieve higher generalization accuracy in real-world driving scenes, while the proposed metrics better evaluate the stereo methods on all-range distances and across different classes. Our dataset and code are available at https://drivingstereo-dataset.github.io.
NL2Type: Inferring JavaScript Function Types from Natural Language Information

JavaScript is dynamically typed and hence lacks the type safety of statically typed languages, leading to suboptimal IDE support, difficult to understand APIs, and unexpected runtime behavior. Several gradual type systems have been proposed, e.g., Flow and TypeScript, but they rely on developers to annotate code with types. This paper presents NL2Type, a learning-based approach for predicting likely type signatures of JavaScript functions. The key idea is to exploit natural language information in source code, such as comments, function names, and parameter names, a rich source of knowledge that is typically ignored by type inference algorithms. We formulate the problem of predicting types as a classification problem and train a recurrent, LSTM-based neural model that, after learning from an annotated code base, predicts function types for unannotated code. We evaluate the approach with a corpus of 162,673 JavaScript files from real-world projects. NL2Type predicts types with a precision of 84.1% and a recall of 78.9% when considering only the top-most suggestion, and with a precision of 95.5% and a recall of 89.6% when considering the top-5 suggestions. The approach outperforms both JSNice, a state-of-the-art approach that analyzes implementations of functions instead of natural language information, and DeepTyper, a recent type prediction approach that is also based on deep learning. Beyond predicting types, NL2Type serves as a consistency checker for existing type annotations. We show that it discovers 39 inconsistencies that deserve developer attention (from a manual analysis of 50 warnings), most of which are due to incorrect type annotations.
Multi-Temporal Ultra Dense Memory Network for Video Super-Resolution

Video super-resolution (SR) aims to reconstruct the corresponding high-resolution (HR) frames from consecutive low-resolution (LR) frames. It is crucial for video SR to harness both inter-frame temporal correlations and intra-frame spatial correlations among frames. Previous video SR methods based on convolutional neural network (CNN) mostly adopt a single-channel structure and a single memory module, so they are unable to fully exploit inter-frame temporal correlations specific for video. To this end, this paper proposes a multi-temporal ultra-dense memory (MTUDM) network for video super-resolution. Particularly, we embed convolutional long-short-term memory (ConvLSTM) into ultra-dense residual block (UDRB) to construct an ultra-dense memory block (UDMB) for extracting and retaining spatio-temporal correlations. This design also reduces the layer depth by expanding the width, thus avoiding training difficulties, such as gradient exploding and vanishing under a large model. We further adopt multi-temporal information fusion (MTIF) strategy to merge the extracted temporal feature maps in consecutive frames, improving the accuracy without requiring much extra computational cost. The experimental results on extensive public datasets demonstrate that our method outperforms the state-of-the-art methods by a large margin.
Vehicle Re-Identification Using Quadruple Directional Deep Learning Features

In order to resist the adverse effect of viewpoint variations, we design quadruple directional deep learning networks to extract quadruple directional deep learning features (QD-DLF) of vehicle images for improving vehicle re-identification performance. The quadruple directional deep learning networks are of similar overall architecture, including the same basic deep learning architecture but different directional feature pooling layers. Specifically, the same basic deep learning architecture that is a shortly and densely connected convolutional neural network is utilized to extract the basic feature maps of an input square vehicle image in the first stage. Then, the quadruple directional deep learning networks utilize different directional pooling layers, i.e., horizontal average pooling layer, vertical average pooling layer, diagonal average pooling layer, and anti-diagonal average pooling layer, to compress the basic feature maps into horizontal, vertical, diagonal, and anti-diagonal directional feature maps, respectively. Finally, these directional feature maps are spatially normalized and concatenated together as a quadruple directional deep learning feature for vehicle re-identification. The extensive experiments on both VeRi and VehicleID databases show that the proposed QD-DLF approach outperforms multiple state-of-the-art vehicle re-identification methods.
Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy

Recently, large-scale few-shot learning (FSL) becomes topical. It is discovered that, for a large-scale FSL problem with 1,000 classes in the source domain, a strong baseline emerges, that is, simply training a deep feature embedding model using the aggregated source classes and performing nearest neighbor (NN) search using the learned features on the target classes. The state-of-the-art large-scale FSL methods struggle to beat this baseline, indicating intrinsic limitations on scalability. To overcome the challenge, we propose a novel large-scale FSL model by learning transferable visual features with the class hierarchy which encodes the semantic relations between source and target classes. Extensive experiments show that the proposed model significantly outperforms not only the NN baseline but also the state-of-the-art alternatives. Furthermore, we show that the proposed model can be easily extended to the large-scale zero-shot learning (ZSL) problem and also achieves the state-of-the-art results.
CAPER

Despite recent intensive research, existing blockchain systems do not adequately address all the characteristics of distributed applications. In particular, distributed applications collaborate with each other following service level agreements (SLAs) to provide different services. While collaboration between applications, e.g., cross-application transactions, should be visible to all applications, the internal data of each application, e.g, internal transactions, might be confidential . In this paper, we introduce CAPER , a permissioned blockchain system to support both internal and cross-application transactions of collaborating distributed applications. In CAPER, the blockchain ledger is formed as a directed acyclic graph where each application accesses and maintains only its own view of the ledger including its internal and all cross-application transactions. CAPER also introduces three consensus protocols to globally order cross-application transactions between applications with different internal consensus protocols. The experimental results reveal the efficiency of CAPER in terms of performance and scalability.
Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images—Role of Multiscale Decision Aggregation and Data Augmentation

Visual inspection of histopathology images of stained biopsy tissue by expert pathologists is the standard method for grading of prostate cancer (PCa). However, this process is time-consuming and subject to high inter-observer variability. Machine learning-based methods have the potential to improve efficient throughput of large volumes of slides while decreasing variability, but they are not easy to develop because they require substantial amounts of labeled training data. In this paper, we propose a deep learning-based classification technique and data augmentation methods for accurate grading of PCa in histopathology images in the presence of limited data. Our method combines the predictions of three separate convolutional neural networks (CNNs) that work with different patch sizes. This enables our method to take advantage of the greater amount of contextual information in larger patches as well as greater quantity of smaller patches in the labeled training data. The predictions produced by the three CNNs are combined using a logistic regression model, which is trained separately after the CNN training. To effectively train our models, we propose new data augmentation methods and empirically study their effects on the classification accuracy. The proposed method achieves an accuracy of 92% in classifying cancerous patches versus benign patches and an accuracy of 86% in classifying low-grade (i.e., Gleason grade 3) from high-grade (i.e., Gleason grades 4 and 5) patches. The agreement level of our automatic grading method with expert pathologists is within the range of agreement between pathologists. Our experiments indicate that data augmentation is necessary for achieving expert-level performance with deep learning-based methods. A combination of image-space augmentation and feature-space augmentation leads to the best results. Our study shows that well-designed and properly trained deep learning models can achieve PCa Gleason grading accuracy that is comparable to an expert pathologist.
RFAL: Adversarial Learning for RF Transmitter Identification and Classification

Recent advances in wireless technologies have led to several autonomous deployments of such networks. As nodes across distributed networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and predict the RF signals and associated parameters that characterize the RF environment. However, in the presence of adversaries, malicious activities such as jamming and spoofing are inevitable, making most machine learning techniques ineffective in such environments. In this paper we propose the Radio Frequency Adversarial Learning (RFAL) framework for building a robust system to identify rogue RF transmitters by designing and implementing a generative adversarial net (GAN). We hope to exploit transmitter specific “signatures” like the in-phase (I) and quadrature (Q) imbalance (i.e., the I/Q imbalance) present in all transmitters for this task, by learning feature representations using a deep neural network that uses the I/Q data from received signals as input. After detection and elimination of the adversarial transmitters RFAL further uses this learned feature embedding as “fingerprints” for categorizing the trusted transmitters. More specifically, we implement a generative model that learns the sample space of the I/Q values of known transmitters and uses the learned representation to generate signals that imitate the transmissions of these transmitters. We program 8 universal software radio peripheral (USRP) software defined radios (SDRs) as trusted transmitters and collect “over-the-air” raw I/Q data from them using a Realtek Software Defined Radio (RTL-SDR), in a laboratory setting. We also implement a discriminator model that discriminates between the trusted transmitters and the counterfeit ones with 99.9% accuracy and is trained in the GAN framework using data from the generator. Finally, after elimination of the adversarial transmitters, the trusted transmitters are classified using a convolutional neural network (CNN), a fully connected deep neural network (DNN) and a recurrent neural network (RNN) to demonstrate building of an end-to-end robust transmitter identification system with RFAL. Experimental results reveal that the CNN, DNN, and RNN are able to correctly distinguish between the 8 trusted transmitters with 81.6%, 94.6% and 97% accuracy respectively. We also show that better “trusted transmission” classification accuracy is achieved for all three types of neural networks when data from two different types of transmitters (different manufacturers) are used rather than when using the same type of transmitter (same manufacturer).