Reviewerly/exHarmony huggingface dataset:
Secure Cloud Data Deduplication with Efficient Re-Encryption

Data deduplication technique has been widely adopted by commercial cloud storage providers, which is both important and necessary in coping with the explosive growth of data. To further protect the security of users&#x2019; sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware encrypted deduplication scheme (REED) is vulnerable to an attack which we call it stub-reserved attack. Furthermore, we propose a secure data deduplication scheme with efficient re-encryption based on the convergent all-or-nothing transform (CAONT) and randomly sampled bits from the Bloom filter. Due to the intrinsic property of one-way hash function, our scheme can resist the stub-reserved attack and guarantee the data privacy of data owners&#x2019; sensitive data. Moreover, instead of re-encrypting the entire package, data owners are only required to re-encrypt a small part of it through the CAONT, thereby effectively reducing the computation overhead of the system. Finally, security analysis and experimental results show that our scheme is secure and efficient in re-encryption.
On the sample complexity of actor-critic method for reinforcement learning with function approximation

Reinforcement learning, mathematically described by Markov Decision Problems, may be approached either through dynamic programming or policy search. Actor-critic algorithms combine the merits of both approaches by alternating between steps to estimate the value function and policy gradient updates. Due to the fact that the updates exhibit correlated noise and biased gradient updates, only the asymptotic behavior of actor-critic is known by connecting its behavior to dynamical systems. This work puts forth a new variant of actor-critic that employs Monte Carlo rollouts during the policy search updates, which results in controllable bias that depends on the number of critic evaluations. As a result, we are able to provide for the first time the convergence rate of actor-critic algorithms when the policy search step employs policy gradient, agnostic to the choice of policy evaluation technique. In particular, we establish conditions under which the sample complexity is comparable to stochastic gradient method for non-convex problems or slower as a result of the critic estimation error, which is the main complexity bottleneck. These results hold in continuous state and action spaces with linear function approximation for the value function. We then specialize these conceptual results to the case where the critic is estimated by Temporal Difference, Gradient Temporal Difference, and Accelerated Gradient Temporal Difference. These learning rates are then corroborated on a navigation problem involving an obstacle and the pendulum problem which provide insight into the interplay between optimization and generalization in reinforcement learning.
Hidden unit specialization in layered neural networks: ReLU vs. sigmoidal activation

By applying concepts from the statistical physics of learning, we study layered neural networks of rectified linear units (ReLU). The comparison with conventional, sigmoidal activation functions is in the center of interest. We compute typical learning curves for large shallow networks with K hidden units in matching student teacher scenarios. The systems undergo phase transitions, i.e. sudden changes of the generalization performance via the process of hidden unit specialization at critical sizes of the training set. Surprisingly, our results show that the training behavior of ReLU networks is qualitatively different from that of networks with sigmoidal activations. In networks with K≥3 sigmoidal hidden units, the transition is discontinuous: Specialized network configurations co-exist and compete with states of poor performance even for very large training sets. On the contrary, the use of ReLU activations results in continuous transitions for all K. For large enough training sets, two competing, differently specialized states display similar generalization abilities, which coincide exactly for large hidden layers in the limit K→∞. Our findings are also confirmed in Monte Carlo simulations of the training processes.
SDIT

Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces.
Unpaired Cross-lingual Image Caption Generation with Self-Supervised Rewards

Generating image descriptions in different languages is essential to satisfy users worldwide. However, it is prohibitively expensive to collect large-scale paired image-caption dataset for every target language which is critical for training descent image captioning models. Previous works tackle the unpaired cross-lingual image captioning problem through a pivot language, which is with the help of paired image-caption data in the pivot language and pivot-to-target machine translation models. However, such language-pivoted approach suffers from inaccuracy brought by the pivot-to-target translation, including disfluency and visual irrelevancy errors. In this paper, we propose to generate cross-lingual image captions with self-supervised rewards in the reinforcement learning framework to alleviate these two types of errors. We employ self-supervision from mono-lingual corpus in the target language to provide fluency reward, and propose a multi-level visual semantic matching model to provide both sentence-level and concept-level visual relevancy rewards. We conduct extensive experiments for unpaired cross-lingual image captioning in both English and Chinese respectively on two widely used image caption corpora. The proposed approach achieves significant performance improvement over state-of-the-art methods.
Enhancing multi-view clustering through common subspace integration by considering both global similarities and local structures

Multi-view clustering seeks to partition objects based on various observations by utilizing cross-views to provide a complementary description of the same objects. It remains challenging to effectively fuse the multi-view data with various dimensions as well as different structures into a new yet highly informative form, thus facilitating adequate assignment of the objects. To tackle the issue, we propose a common subspace integration (CSI) model. The CSI actively learns a common subspace by jointly preserving the local geometry of each view, while incorporating a global partition information to enhance its separability during the learning process. It can be easily generalized to its kernel version, thereby popularizing its general usages. An effective alternative optimization scheme is designed to solve the proposed model. Extensive experiments on six real-world datasets were conducted to demonstrate its superiority by comparing with the twelve state-of-art methods.
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

In this article, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this article, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e., the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon.
SoK: Communication Across Distributed Ledgers

Since the inception of Bitcoin, a plethora of distributed ledgers differing in design and purpose has been created. While by design, blockchains provide no means to securely communicate with external systems, numerous attempts towards trustless cross-chain communication have been proposed over the years. Today, cross-chain communication (CCC) plays a fundamental role in cryptocurrency exchanges, scalability efforts via sharding, extension of existing systems through sidechains, and bootstrapping of new blockchains. Unfortunately, existing proposals are designed ad-hoc for specific use-cases, making it hard to gain confidence in their correctness and composability. We provide the first systematic exposition of cross-chain communication protocols. We formalize the underlying research problem and show that CCC is impossible without a trusted third party, contrary to common beliefs in the blockchain community. With this result in mind, we develop a framework to design new and evaluate existing CCC protocols, focusing on the inherent trust assumptions thereof, and derive a classification covering the field of cross-chain communication to date. We conclude by discussing open challenges for CCC research and the implications of interoperability on the security and privacy of blockchains.
A Management System for Servicing Multi-Organizations on Community Cloud Model in Secure Cloud Environment

The emergence of cloud computing has been growing rapidly in the last decades especially for workflow scheduling.Organizations with the same requirements and needs go to use the community cloud for saving costs.One of the important challenges of using the community cloud is resource allocation and task scheduling.In this paper, we propose a new Management System for servicing Multi-organizations in a Community cloud (MSMC) in a secure cloud environment.The MSMC employs a virtual machine allocation algorithm to organize the community cloud usage among the organizations, where it allocates the available virtual machines according to the use of each organization in an efficient and fair way to execute the submitted applications.Moreover, the MSMC system proposes a new scheduling algorithm, called Ideal Distribution Algorithm (IDA), to schedule the workflow tasks to the virtual machines of the cloud considering both the deadline and cost constraints.Additionally, an enhanced version of the IDA, called Enhanced IDA (EIDA) is proposed to provide load balancing required by the cloud.The simulation experiments show that the system can improve the system ability under deadline constraints and improve the monetary cost.
Enhancing Self-Supervised Monocular Depth Estimation with Traditional Visual Odometry

Estimating depth from a single image represents an attractive alternative to more traditional approaches leveraging multiple cameras. In this field, deep learning yielded outstanding results at the cost of needing large amounts of data labeled with precise depth measurements for training. An issue softened by self-supervised approaches leveraging monocular sequences or stereo pairs in place of expensive ground truth depth annotations. This paper enables to further improve monocular depth estimation by integrating into existing self-supervised networks a geometrical prior. Specifically, we propose a sparsity-invariant autoencoder able to process the output of conventional visual odometry algorithms working in synergy with depth-from-mono networks. Experimental results on the KITTI dataset show that by exploiting the geometrical prior, our proposal: i) outperforms existing approaches in the literature and ii) couples well with both compact and complex depth-from-mono architectures, allowing for its deployment on high-end GPUs as well as on embedded devices (e.g., NVIDIA Jetson TX2).
Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming

This paper presents a novel method for expanding the use of sequential convex programming (SCP) to the domain of optimal guidance and control problems with nonlinear dynamics constraints. SCP is a useful tool in obtaining real-time solutions to direct optimal control, but it is unable to adequately model nonlinear dynamics due to the linearization and discretization required. As nonlinear program solvers are not yet functioning in real-time, a tool is needed to bridge the gap between satisfying the nonlinear dynamics and completing execution fast enough to be useful. Two methods are proposed, SCP with nonlinear dynamics correction (SCPn) and modified SCPn (M-SCPn), which mixes SCP and SCPn to reduce runtime and improve algorithmic robustness. Both methods are proven to generate optimal state and control trajectories that satisfy the nonlinear dynamics. Simulations are presented to validate the efficacy of the methods as compared with SCP.
An Improved Faster R-CNN Based on MSER Decision Criterion for SAR Image Ship Detection in Harbor

SAR ship detection is essential for marine monitoring. Due to the high similarity between the harbor and the ship body on gray and texture features, the traditional methods are unable to achieve effective inshore ship detection. An improved Faster R-CNN based on MSER decision criterion for SAR ship detection in harbor is proposed in this paper. It is a ship detection method based on the combination of feature-based method and pixel-based method. Firstly, Faster R-CNN is used to generate region proposals. Then, replace the threshold decision criterion of Faster R-CNN with the maximum stability extremal region (MSER) method to reassess the generated region proposals with higher scores, aiming at improving the detection rate and reducing the false alarm rate simultaneously. Experimental results based on satellite-borne SAR data illustrate that the proposed method obtains excellent detection performance and low false alarm rate.
PrivDPI

Network middleboxes perform deep packet inspection (DPI) to detect anomalies and suspicious activities in network traffic. However, increasingly these traffic are encrypted and middleboxes can no longer make sense of them. A recent proposal by Sherry et al. (SIGCOMM 2015), named BlindBox, enables the middlebox to perform inspection in a privacy-preserving manner. BlindBox deploys garbled circuit to generate encrypted rules for the purpose of inspecting the encrypted traffic directly. However, the setup latency (which could be 97s on a ruleset of 3,000 as reported) and overhead size incurred by garbled circuit are high. Since communication can only be commenced after the encrypted rules being generated, such delay is intolerable in many real-time applications. In this work, we present PrivDPI, which reduces the setup delay while retaining similar privacy guarantee. Compared to BlindBox, for a ruleset of 3,000, our encrypted rule generation is 288x faster and requires 290,227x smaller overhead for the first session, and is even 1,036x faster and requires 3424,505x smaller overhead over 20 consecutive sessions. The performance gain is based on a new technique for generating encrypted rules as well as the idea of reusing intermediate results generated in previous sessions across subsequent sessions. This is in contrast to Blindbox which performs encrypted rule generation from scratch for every session. Nevertheless, PrivDPI is 6x slower in generating the encrypted traffic tokens, yet in our implementation, the token encryption rate of PrivDPI is more than 17,271 per second which is sufficient for many real-time applications. Moreover, the intermediate values generated in each session can be reused across subsequent sessions for repeated tokens, which could further speedup token encryption. Overall, our experiment shows that PrivDPI is practical and especially suitable for connections with short flows.
Perceptual Deep Depth Super-Resolution

RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common. One can significantly improve the resolution of depth maps by taking advantage of color information; deep learning methods make combining color and depth information particularly easy. However, fusing these two sources of data may lead to a variety of artifacts. If depth maps are used to reconstruct 3D shapes, e.g., for virtual reality applications, the visual quality of upsampled images is particularly important. The main idea of our approach is to measure the quality of depth map upsampling using renderings of resulting 3D surfaces. We demonstrate that a simple visual appearance-based loss, when used with either a trained CNN or simply a deep prior, yields significantly improved 3D shapes, as measured by a number of existing perceptual metrics. We compare this approach with a number of existing optimization and learning-based techniques.
Performance of roof-top PV systems in Germany from 2012 to 2018

The photovoltaic (PV) sector is a central pillar for the global energy transition process which aims to reach the climate change mitigation goals. Roof-mounted systems in particular can make a significant contribution to greenhouse gas-free energy generation. By the end of 2018, more than 1.5 million rooftop systems have been installed in Germany. The yield data measured by monitoring systems and provided by web-based online systems from 2012 to 2018, as well as the configuration information of 23,944 PV systems, are evaluated in this publication. Long-term indicators are derived from the spatial and temporal distribution of the yield data. The yield analysis shows a typical south-north gradient in Germany. 2018 was the year with the highest yield, 2013 the year with the lowest. The annual specific yield varies between 816 kWh/kWp in 2017 at the Baltic Sea and 1049 kWh/kWp in 2018 in Bavaria. If analysis of data evaluation is narrowed to one to two years, a deviation of five percent and more from the long-term yield can be observed. In order to obtain detailed information on the quality of PV systems, at least five years of data are required. The yield data evaluation can be used by PV system operators and owners to identify an undersupply of their PV system and to initiate countermeasures.
FCSR-GAN: Joint Face Completion and Super-Resolution via Multi-Task Learning

Combined variations containing low-resolution and occlusion often present in face images in the wild, e.g., under the scenario of video surveillance. While most of the existing face image recovery approaches can handle only one type of variation per model, in this work, we propose a deep generative adversarial network (FCSR-GAN) for performing joint face completion and face super-resolution via multi-task learning. The generator of FCSR-GAN aims to recover a high-resolution face image without occlusion given an input low-resolution face image with occlusion. The discriminator of FCSR-GAN uses a set of carefully designed losses (an adversarial loss, a perceptual loss, a pixel loss, a smooth loss, a style loss, and a face prior loss) to assure the high quality of the recovered high-resolution face images without occlusion. The whole network of FCSR-GAN can be trained end-to-end using our two-stage training strategy. Experimental results on the public-domain CelebA and Helen databases show that the proposed approach outperforms the state-of-the-art methods in jointly performing face super-resolution (up to 8×) and face completion, and shows good generalization ability in cross-database testing. Our FCSR-GAN is also useful for improving face identification performance when there are low-resolution and occlusion in face images. The code of FCSR-GAN is available at: https://github.com/swordcheng/FCSR-GAN.
Accurate Transmission Estimation for Removing Haze and Noise From a Single Image

Image noise usually causes depth-dependent visual artifacts in single image dehazing. Most existing dehazing methods exploit a two-step strategy in the restoration, which inevitably leads to inaccurate transmission maps and low-quality scene radiance for noisy and hazy inputs. To address these problems, we present a novel variational model for joint recovery of the transmission map and the scene radiance from a single image. In the model, we propose a transmission-aware non-local regularization to avoid noise amplification by adaptively suppressing noise and preserving fine details in the recovered image. Meanwhile, to improve the accuracy of transmission estimation, we introduce a semantic-guided regularization to smooth out the transmission map while keeping depth inconsistency at the boundaries of different objects. Furthermore, we design an alternating scheme to jointly optimize the transmission map and the scene radiance as well as the segmentation map. Extensive experiments on synthetic and real-world data demonstrate that the proposed algorithm performs favorably against state-of-the-art dehazing methods on noisy and hazy images.
Deep Reinforcement Learning for Image Hashing

Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the strong representation power of deep networks. However, most existing deep hashing methods learn a whole set of hashing functions independently, while ignore the correlations between different hashing functions that can promote the retrieval accuracy greatly. Inspired by the sequential decision ability of deep reinforcement learning, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH). Our proposed DRLIH approach models the hashing learning problem as a sequential decision process, which learns each hashing function by correcting the errors imposed by previous ones and promotes retrieval accuracy. To the best of our knowledge, this is the first work to address hashing problem from deep reinforcement learning perspective. The main contributions of our proposed DRLIH approach can be summarized as follows: (1) We propose a deep reinforcement learning hashing network. In the proposed network, we utilize recurrent neural network (RNN) as agents to model the hashing functions, which take actions of projecting images into binary codes sequentially, so that the current hashing function learning can take previous hashing functions' error into account. (2) We propose a sequential learning strategy based on proposed DRLIH. We define the state as a tuple of internal features of RNN's hidden layers and image features, which can reflect history decisions made by the agents. We also propose an action group method to enhance the correlation of hash functions in the same group. Experiments on three widely-used datasets demonstrate the effectiveness of our proposed DRLIH approach.