Reviewerly/exHarmony huggingface dataset:
Multi-View Spatial Attention Embedding for Vehicle Re-Identification

Vehicle Re-Identification (Re-ID) is a challenging vision task mainly because the appearance of a vehicle varies dramatically under different viewpoints. Moreover, different vehicles with the same model and color commonly show similar appearance, thus are hard to be distinguished. To alleviate negative effects of viewpoint variance, we design a multi-view branch network where each branch learns a viewpoint-specific feature without parameter sharing. Being able to focus on a limited range of viewpoints, this viewpoint-specific feature performs substantially better than the general feature learned by an uniform network. To further differentiate visually similar vehicles, we strengthen the discriminative power on their subtle local differences by introducing a spatial attention model into each feature learning branch. The multi-view feature learning and spatial attention learning compose our neural network architecture, which is trained end to end with the softmax loss and triplet loss, respectively. We evaluate our methods on two large vehicle Re-ID datasets, i.e., VehicleID and VeRi-776, respectively. Extensive experiments show that our methods achieve promising performance. For example, we achieve mAP accuracy of 76.78% and 72.53% on VehicleID and VeRi-776 dataset respectively, substantially better than current state-of-the art.
Optical image encryption algorithm based on hyper-chaos and public-key cryptography

Aiming at the problem of secure transmission and distribution of complex keys in image encryption system based on optical transformation, an innovative optical image encryption algorithm is proposed based on hyper-chaos and public key cryptography. Firstly, two hyper-chaotic random phase masks are constructed by using the four-wing and Chen 4D hyper-chaotic system. Then, with the hyper-chaotic phase masks, the original image is encrypted by double random phase encoding in the Fresnel domain. Finally, the public key cryptosystem is used to allocate and manage the initial values, system parameters and Fresnel diffraction system parameters of the hyper-chaotic system to achieve asymmetric encryption of images. In the process of decryption, the receiver obtains hyper-chaotic random phase masks by decrypting the private keys in the public key cryptosystem, and decrypts the encrypted image by optical or digital means. The experimental results show that the proposed algorithm has a high key sensitivity, uniform and flat statistical distribution characteristics, and can resist the noise attacks with intensity coefficient of 0.1. The asymmetric image encryption and decryption time only needs 0.0245 s and 0.072 s, respectively. Through the fusion of hyper-chaos and public key cryptosystem, the key space is increased, the key volume is reduced, the computational complexity is reduced, and the algorithm operation efficiency is improved.
Image captioning using DenseNet network and adaptive attention

Considering the image captioning problem, it is difficult to correctly extract the global features of the images. At the same time, most attention methods force each word to correspond to the image region, ignoring the phenomenon that words such as "the" in the description text cannot correspond to the image region. To address these problems, an adaptive attention model with a visual sentinel is proposed in this paper. In the encoding phase, the model introduces DenseNet to extract the global features of the image. At the same time, on each time axis, the sentinel gate is set by the adaptive attention mechanism to decide whether to use the image feature information for word generation. In the decoding phase, the long short-term memory (LSTM) network is applied as a language generation model for image captioning tasks to improve the quality of image caption generation. Experiments on the Flickr30k and COCO datasets indicate that the proposed model exhibits significant improvement in terms ofthe BLEU and METEOR evaluation criteria.
Multi‐needle Localization with Attention U‐Net in US‐guided HDR Prostate Brachytherapy

Purpose Ultrasound (US)‐guided high dose rate (HDR) prostate brachytherapy requests the clinicians to place HDR needles (catheters) into the prostate gland under transrectal US (TRUS) guidance in the operating room. The quality of the subsequent radiation treatment plan is largely dictated by the needle placements, which varies upon the experience level of the clinicians and the procedure protocols. Real‐time plan dose distribution, if available, could be a vital tool to provide more subjective assessment of the needle placements, hence potentially improving the radiation plan quality and the treatment outcome. However, due to low signal‐to‐noise ratio (SNR) in US imaging, real‐time multi‐needle segmentation in 3D TRUS, which is the major obstacle for real‐time dose mapping, has not been realized to date. In this study, we propose a deep learning‐based method that enables accurate and real‐time digitization of the multiple needles in the 3D TRUS images of HDR prostate brachytherapy. Methods A deep learning model based on the U‐Net architecture was developed to segment multiple needles in the 3D TRUS images. Attention gates were considered in our model to improve the prediction on the small needle points. Furthermore, the spatial continuity of needles was encoded into our model with total variation (TV) regularization. The combined network was trained on 3D TRUS patches with the deep supervision strategy, where the binary needle annotation images were provided as ground truth. The trained network was then used to localize and segment the HDR needles for a new patient's TRUS images. We evaluated our proposed method based on the needle shaft and tip errors against manually defined ground truth and compared our method with other state‐of‐art methods (U‐Net and deeply supervised attention U‐Net). Results Our method detected 96% needles of 339 needles from 23 HDR prostate brachytherapy patients with 0.290 ± 0.236 mm at shaft error and 0.442 ± 0.831 mm at tip error. For shaft localization, our method resulted in 96% localizations with less than 0.8 mm error (needle diameter is 1.67 mm), while for tip localization, our method resulted in 75% needles with 0 mm error and 21% needles with 2 mm error (TRUS image slice thickness is 2 mm). No significant difference is observed ( P = 0.83) on tip localization between our results with the ground truth. Compared with U‐Net and deeply supervised attention U‐Net, the proposed method delivers a significant improvement on both shaft error and tip error ( P &lt; 0.05). Conclusions We proposed a new segmentation method to precisely localize the tips and shafts of multiple needles in 3D TRUS images of HDR prostate brachytherapy. The 3D rendering of the needles could help clinicians to evaluate the needle placements. It paves the way for the development of real‐time plan dose assessment tools that can further elevate the quality and outcome of HDR prostate brachytherapy.
Graph Embedding Multi-Kernel Metric Learning for Image Set Classification With Grassmannian Manifold-Valued Features

In the domain of video-based image set classification, a considerable advance has been made by modeling a sequence of video frames (image set) as a linear subspace, which typically resides on a Grassmannian manifold. As a consequence of the large intra-class variations of the video data, there are two open challenges for the modeling task: how to establish appropriate image set models to encode these variations, and how to effectively measure the similarity between any two image sets. As a possible way to tackle these issues, this paper presents a graph embedding multi-kernel metric learning (GEMKML) algorithm for image set classification. The proposed GEMKML implements set modeling, feature extraction, and classification in two steps. Firstly, the proposed framework constructs a novel cascaded feature learning architecture on Grassmannian manifold with the aim of producing more effective Grassmannian manifold-valued feature representations. To make a better use of these learned features, a graph embedding multi-kernel metric learning scheme is then devised to map them into a lower-dimensional Euclidean space, where the inter-class distances are maximized and the intra-class distances are minimized. We evaluate the proposed GEMKML on five different visual classification tasks using widely adopted datasets. The extensive classification results confirm its superiority over the state-of-the-art methods.
Critical literacy for a posthuman world: When people read, and become, with machines

Abstract Computational objects (eg, algorithms, bots, surveillance technology and data) have become increasingly present in our daily lives and are consequential for our changing relations to texts, multimodality and identity. Yet, our current theories of literacy, and especially the prevalence of mediational and representational perspectives, are inadequate to account for these changing relations. What are the implications for critical literacy education when it takes seriously computational agents that interact, produce and process texts? While such work is only beginning in education, scholars in other fields are increasingly writing about how AI and algorithmic mediation are changing the landscape of online intra‐action, and business strategies and tactics for working with AI are advancing far ahead of critical literacy education. Drawing on our own and others’ research into non‐human actors online, and building on posthuman theories of networks, heterogeneous actants and the assemblage, in this conceptual paper, we sketch some of the forms of critical consciousness that media education might provide in this new mixed landscape. Practitioner Notes What is already known about this topic AI is a hot topic in education and in public discourse, but critical literacy theories have not sufficiently accounted for how AI and computational agents change what it means to be “critically literate.” Technology is an important force in shaping (and is also shaped by) literacy practices and identity. Corporate actors have an enormous influence on the texts we read and write, but this influence is often hidden. What this paper adds We bridge between critical literacy studies and posthumanist theory to conceptualize critical posthuman literacy. We argue for re‐imagining what texts, multimodality and identity are and do in the age of AI. We pose new questions of our texts and ourselves, informed by posthuman critical literacy. Implications for practice and/or policy Today’s readers and composers must be able to identify and interrogate networks of computational and human agents that permeate literacy practices. Beyond identifying and understanding computational agents, posthuman critical literacy necessitates that people can actively build more ethical assemblages with computational agents.
Location-Aware and Budget-Constrained Service Deployment for Composite Applications in Multi-Cloud Environment

Enterprise application providers are increasingly moving their workloads to the cloud for technical and economic benefits. Multi-cloud environment makes it possible to orchestrate multiple cloud resources. With the increasing number of available cloud resources provided by multiple cloud providers at different locations with different prices, application providers face the challenge to select proper cloud resources to deploy their applications in the form of a workflow of component service units. Existing studies usually consider minimizing execution time or/and deployment cost. From the perspective of application providers, however, they also pay huge attention to application response time, including particularly network latency between deployed services and users. Meanwhile, application deployment is often subject to stringent budgetary control to ensure financial viability. This article studies a new type of composite application deployment problem that jointly considers both the performance optimization and budget control in multi-cloud at the global scale. To find solutions with minimal response time without running into the risk of over-spending, we propose a hybrid GA-based approach, featuring new design of domain-tailored service clustering, repair algorithm, solution representation, population initialization, and genetic operators. Extensive experiments using the real-world dataset demonstrate that our proposed hybrid GA approach outperforms some recently proposed approaches.
PathSRGAN: Multi-Supervised Super-Resolution for Cytopathological Images Using Generative Adversarial Network

In the cytopathology screening of cervical cancer, high-resolution digital cytopathological slides are critical for the interpretation of lesion cells. However, the acquisition of high-resolution digital slides requires high-end imaging equipment and long scanning time. In the study, we propose a GAN-based progressive multisupervised super-resolution model called PathSRGAN (pathology super-resolution GAN) to learn the mapping of real low-resolution and high-resolution cytopathological images. With respect to the characteristics of cytopathological images, we design a new two-stage generator architecture with two supervision terms. The generator of the first stage corresponds to a densely-connected U-Net and achieves 4×to 10× super resolution. The generator of the second stage corresponds to a residual-in-residual DenseBlock and achieves 10× to 20× super resolution. The designed generator alleviates the difficulty in learning the mapping from 4x images to 20× images caused by the great numerical aperture difference and generates high quality high-resolution images. We conduct a series of comparison experiments and demonstrate the superiority of PathSRGAN to mainstream CNN-based and GAN-based super-resolution methods in cytopathological images. Simultaneously, the reconstructed high-resolution images by PathSRGAN improve the accuracy of computer-aided diagnosis tasks effectively. It is anticipated that the study
DWT-SISA: a secure and effective discrete wavelet transform-based secret image sharing with authentication

Recently, Secret Image Sharing (SIS) has attracted more and more researchers’ attention, in which a secret image is divided into several shadows for sharing. In practice, the produced shadows are meaningless, and are easy to be attacked if that shadows are directly used in transmission. In order to overcome the shortcoming, the shadows would be embedded into the cover image so as to be transmitted to participants securely. The embedding methods of most schemes are based on Least Significant Bit (LSB) replacement in the spatial domain, however, the embedded data can be easily detected by steganalysis algorithm. Moreover, the embedding capacity, visual quality and security need to be improved. In this paper, a secure and effective Discrete Wavelet Transform-based Secret Image Sharing with Authentication (DWT-SISA) is proposed. In DWT-SISA, the carrier has a better embedding capacity and a better visual quality after embedding. Furthermore, for the sake of solving the issue of cheater prevention, more authentication bits are embedded into the carrier. The experimental results and theoretical analysis show that the proposed scheme is secure and effective.
Survive and Thrive: A Stochastic Game for DDoS Attacks in Bitcoin Mining Pools

Mining pools have become dominant in today's bitcoin mining network, where miners can pool their powers together for reduced variance of block mining and steadier stream of potential income. Along with the continuous evolvement of mining pools are the increasingly intense competitions among them. Recent empirical studies have shown that the distributed denial-of-service (DDoS) attack is one of the most common ways for competing mining pools to sabotage the rivals and earn illegitimate rewards. Existing efforts have been made on using static game models to analyze the interactions between mining pools, and derive the Nash Equilibrium and optimal attacking strategies in a one-time static context. To better understand the impact of such DDoS attacks, in this paper, we take a starkly different approach, and for the first time address the dynamics in mining pool attacks. Specifically, we start by formulating the interactive competition among mining pools as a general-sum stochastic game. Then we propose an efficient Nash learning algorithm to obtain the near optimal attacking strategy that maximizes the expected long-term utility. Our theoretical analysis and extensive experimental results both show that the proposed strategy outperforms the baseline myopic learning algorithm, which only aims at maximizing the revenue in the current time stage. These findings, together with our proposed stochastic game model and learning algorithm, are expected to provide more practical guidelines for mining pools to survive and thrive in the highly-competitive bitcoin ecosystem.
Self-adapting cloud services orchestration for fulfilling intensive sensory data-driven IoT workflows

Cloud computing has been adopted to support among others the storage and processing of complex Internet of Things (IoT) workflows handling sensory streamed time-series data. IoT workflow is often composed following a set of procedures which makes it hard to self-adapt, self-configure to react to runtime environment changes. Therefore, declarative data-driven workflow composition will provision self-learning and self-configurable workflows such as those of IoT. This paper proposes a comprehensive architecture to support end-to-end workflow management processes including declarative specification and composition, configuration deployment, orchestration, execution, adaptation, and quality enforcement. The later provision runtime intelligence for IoT workflow orchestration; this is achieved through the automated monitoring and analysis of runtime cloud resource orchestration, the monitoring of workflows tasks execution, as well as through cloud resource utilization prediction and workflow adaptation. In addition, it supports other intelligent features that include: (1) integration of edge computing (sensor edge) for local data processing which is very crucial for life-critical IoT workflows, (2) data compression for fast data transmission, and data storage adaptation, and (3) customization of data reporting and visualization. All these features have been evaluated through a set of experiments that proved a significant gain in terms of workflow execution time, cost and optimum usage of cloud resources compared to baseline adaptation strategy.
Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter

North China is one of the country’s most important socio-economic centers, but its severe air pollution is a huge concern. In this region, precisely forecasting the daily photovoltaic power generation in winter is essential to improve equipment utilization rate and mitigate effects of power system on the environment. Considering the climatic characteristics of North China, the winter days are divided into three classifications. A forecasting model based on random forest algorithm is then designed for each classification. To evaluate its performance, the proposed model and three other methods are separately used to forecast the daily power generation at the Zhonghe PV station, which is located in the center of North China. Empirical results show that, because of its ability to reduce the risk of overfitting by balancing decision trees, the proposed model obtains mean absolute percentage errors as low as 2.83% and 3.89% for clear and cloudy days, respectively. For days in which weather conditions are unusual, forecasting errors are relatively large. On these days, enlarging training samples, performing subdivision, and imposing manual intervention can improve the forecasting precision. Generally, the proposed model is better than the other three methods for nearly all error evaluation indicators in each classification.
Feature distillation network for aspect-based sentiment analysis

A proliferation of user-generated content on the web is fueling research into sentiment analysis for improved extraction of human emotional information. Aspect-based sentiment analysis (ABSA) is currently at the focus, which seeks to predict the sentiment of certain aspects in text. The primary challenge is to recognize the relevant contexts for different aspects. Most prior approaches combining recurrent neural networks and attention mechanisms inevitably introduce extraneous noise and diminish prediction accuracy. Furthermore, the sentiment of some context words varies with different aspects and cannot be inferred from themselves alone, which is another challenge that prevents attention mechanisms from performing properly. In this study, we propose a feature distillation network (FDN) for reducing noise and distilling aspect-relevant sentiment features. A novel double-gate mechanism is designed to implement the interactions between aspects and their corresponding contexts at a fine granularity. We introduce a contextual nonlinear projection layer before the double-gate mechanism to generate aspect-specific word representations, which enables the double-gate mechanism to accurately distinguish between sentiment features of the same context word that corresponds to the different aspects. Experiments show that the FDN achieves state-of-the-art performance and improves accuracy from 1.0 percent to 2.0 percent on all benchmarks for ABSA task.
Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking

In recent years, discriminative correlation filter (DCF) based algorithms have significantly advanced the state of the art in visual object tracking. The key to the success of DCF is an efficient discriminative regression model trained with powerful multi-cue features, including both hand-crafted and deep neural network features. However, the tracking performance is hindered by their inability to respond adequately to abrupt target appearance variations. This issue is posed by the limited representation capability of fixed image features. In this work, we set out to rectify this shortcoming by proposing a complementary representation of a visual content. Specifically, we propose the use of a collaborative representation between successive frames to extract the dynamic appearance information from a target with rapid appearance changes, which results in suppressing the undesirable impact of the background. The resulting collaborative representation coefficients are combined with the original feature maps using a spatially regularised DCF framework for performance boosting. The experimental results on several benchmarking datasets demonstrate the effectiveness and robustness of the proposed method, as compared with a number of state-of-the-art tracking algorithms.