Reviewerly/exHarmony huggingface dataset:
Exposing splicing forgery in realistic scenes using deep fusion network

Creating fake pictures becomes more accessible than ever, but tampered images are more harmful because the Internet propagates misleading information so rapidly. Reliable digital forensic tools are therefore strongly needed. Traditional methods based on hand-crafted features are only useful when tampered images meet specific requirements, and the low detection accuracy prevents them from using in realistic scenes. Recently proposed learning-based methods improve the accuracy, but neural networks usually require to be trained on large labeled databases. This is because commonly used deep and narrow neural networks extract high-level visual features and neglect low-level features where there are abundant forensic cues. To solve the problem, we propose a novel neural network which concentrates on learning low-level forensic features and consequently can detect splicing forgery although the network is trained on a small automatically generated splicing dataset. Furthermore, our fusion network can be easily extended to support new forensic hypotheses without any changes in the network structure. The experimental results show that our method achieves state-of-the-art performance on several benchmark datasets and shows superior generalization capability: our fusion network can work very well even it never sees any pictures in test databases. Therefore, our method can detect splicing forgery in realistic scenes.
BR$^2$Net: Defocus Blur Detection Via a Bidirectional Channel Attention Residual Refining Network

Due to the remarkable potential applications, defocus blur detection, which aims to separate blurry regions from an image, has attracted much attention. Although significant progress has been made by many methods, there are still various challenges that hinder the results, e.g., confusing background areas, sensitivity to the scale and missing the boundary details of the defocus blur regions. To solve these issues, in this paper, we propose a deep convolutional neural network (CNN) for defocus blur detection via a Bi-directional Residual Refining network (BR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net). Specifically, a residual learning and refining module (RLRM) is designed to correct the prediction errors in the intermediate defocus blur map. Then, we develop a bidirectional residual feature refining network with two branches by embedding multiple RLRMs into it for recurrently combining and refining the residual features. One branch of the network refines the residual features from the shallow layers to the deep layers, and the other branch refines the residual features from the deep layers to the shallow layers. In such a manner, both the low-level spatial details and highlevel semantic information can be encoded step by step in two directions to suppress background clutter and enhance the detected region details. The outputs of the two branches are fused to generate the final results. In addition, with the observation that different feature channels have different extents of discrimination for detecting blurred regions, we add a channel attention module to each feature extraction layer to select more discriminative features for residual learning. To promote further research on defocus blur detection, we create a new dataset with various challenging images and manually annotate their corresponding pixelwise ground truths. The proposed network is validated on two commonly used defocus blur detection datasets and our newly collected dataset by comparing it with 10 other state-of-the-art methods. Extensive experiments with ablation studies demonstrate that BR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net consistently and significantly outperforms the competitors in terms of both the efficiency and accuracy.
Evolutionary recurrent neural network for image captioning

Automatic architecture search is efficient to discover novel neural networks while it is mostly employed for pure vision or natural language tasks. However, cross-modality tasks are highly emphasized on the associative mechanisms between visual and language models rather than merely convolutional neural network (CNN) or recurrent neural network (RNN) with the best performance. In this work, the intermediary associative connection is approximated to the topological inner structure of RNN cell, which is further evolved by an evolutionary algorithm on the proxy of image captioning task. On the MSCOCO dataset, the proposed algorithm, starting from scratch, discovers more than 100 RNN variants with the performances all above 100 on CIDEr and 31 on BLEU4, and the top performance achieves 101.4 and 32.6 accordingly. Additionally, several unknown interesting patterns as well as many existing powerful structures are found in the generated RNNs. The patterns of operation and connection in the generated architecture are analyzed to understand the language modeling of cross-modality compared with general RNNs.
A Novel OFDM Autoencoder Featuring CNN-Based Channel Estimation for Internet of Vessels

This article proposes a novel orthogonal frequency-division multiplexing (OFDM) autoencoder featuring convolutional neural networks (CNNs)-based channel estimation for marine communications with complex and fast-changing environments. We demonstrate that the proposed OFDM autoencoder system can be generalized to work under various channel environments, different throughputs, while outperforming the traditional OFDM counterparts, especially when working at high throughputs. In addition, since OFDM systems require accurate channel estimations to function properly, this treatise also proposes a new channel estimation algorithm for OFDM systems that combine the power of deep learning (DL) with the philosophy of super-resolution reconstruction, which uses dense convolutional neural networks (Dense-Nets) to reconstruct low-resolution pilot information images into high-resolution full-channel impulse responses (CIRs). The Dense-Net structure has the characteristics of dense connections and feature multiplexing. The simulation results show that under slow fading, the proposed channel estimator (CE) can estimate the CIRs perfectly. Under fast fading, the proposed CE outperforms the existing learning-based algorithms with fewer neural network parameters. Therefore, the proposed novel autoencoder scheme and the powerful CE are potentially attractive approaches for the Internet of Vessels (IoV).
Texception: A Character/Word-Level Deep Learning Model for Phishing URL Detection

Phishing is the starting point for many cyberattacks that threaten the confidentiality, availability and integrity of enterprises' and consumers' data. The URL of a web page that hosts the attack provides a rich source of information to determine the maliciousness of the web server. In this work, we propose a novel deep learning architecture, Texception, that takes a URL as input and predicts whether it belongs to a phishing attack. Architecturally, Texception uses both character-level and word-level information from the incoming URL and does not depend on manually crafted features or feature engineering. This makes it different from classical approaches. In addition, Texception benefits from multiple parallel convolutional layers and can grow deeper or wider. We show that this flexibility enables Texception to generalize better for new URLs. Our results on production data show that Texception is able to significantly outperform a traditional text classification method by increasing the true positive rate by 126.7% at an extremely low false positive rate (0.01%) which is crucial for our model's healthy operation at internet scale.
Crowd Density Estimation Using Fusion of Multi-Layer Features

Crowd counting is very important in many tasks such as video surveillance, traffic monitoring, public security, and urban planning, so it is a very important part of the intelligent transportation system. However, achieving an accurate crowd counting and generating a precise density map are still challenging tasks due to the occlusion, perspective distortion, complex backgrounds, and varying scales. In addition, most of the existing methods focus only on the accuracy of crowd counting without considering the correctness of a density distribution; namely, there are many false negatives and false positives in a generated density map. To address this issue, we propose a novel encoder-decoder Convolution Neural Network (CNN) that fuses the feature maps in both encoding and decoding sub-networks to generate a more reasonable density map and estimate the number of people more accurately. Furthermore, we introduce a new evaluation method named the Patch Absolute Error (PAE) which is more appropriate to measure the accuracy of a density map. The extensive experiments on several existing public crowd counting datasets demonstrate that our approach achieves better performance than the current state-of-the-art methods. Lastly, considering the cross-scene crowd counting in practice, we evaluate our model on some cross-scene datasets. The results show our method has a good performance in cross-scene datasets.
The Rwth Asr System for Ted-Lium Release 2: Improving Hybrid Hmm With Specaugment

We present a complete training pipeline to build a state-of-the-art hybrid HMM-based ASR system on the 2nd release of the TED-LIUM corpus. Data augmentation using SpecAugment is successfully applied to improve performance on top of our best SAT model using i-vectors. By investigating the effect of different maskings, we achieve improvements from SpecAugment on hybrid HMM models without increasing model size and training time. A subsequent sMBR training is applied to fine-tune the final acoustic model, and both LSTM and Transformer language models are trained and evaluated. Our best system achieves a 5.6% WER on the test set, which outperforms the previous state-of-the-art by 27% relative.
An Adaptive Authenticated Data Structure With Privacy-Preserving for Big Data Stream in Cloud

With the rapid development of 5G network, big data and IoT, data in many environments is often continuously and dynamically generated with high growth rates, just like stream. Thus, we call it big data stream, which plays an increasingly important role in all walks of life. However, how to verify its authenticity becomes a challenge when this big data stream in an untrusted environment such as cloud platform, for it faces the problems just like delay-sensitive, unpredictable data size and privacy leaks caused by third-party audits. To solve these problems, we propose a new authenticate data structure named privacy-preserving adaptive trapdoor hash authentication tree (P-ATHAT) by introducing trapdoor hash and BLS signature to the Merkle hash tree. The P-ATHAT scheme realizes real-time verification of data stream and can dynamically expand its structure as the data stream arrives. These characteristics not only shorten the authentication path but also solve the single point failure problem of the conventional authentication trees and enhance the robustness of the scheme. Moreover, we construct a homomorphic verification scheme above tree structure to solve the privacy leakage problem in third-party audit. Finally, security analysis and detailed experimental evaluation are performed on the proposed scheme, both results demonstrate that it is desirable for big data stream authentication and privacy-preserving in practical application.
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-Segmentation

We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks. The key insights of our method are two-fold. First, the estimated dense correspondence fields from semantic matching provide supervision for object co-segmentation by enforcing consistency between the predicted masks from a pair of images. Second, the predicted object masks from object co-segmentation in turn allow us to reduce the adverse effects due to background clutters for improving semantic matching. Our model is end-to-end trainable and does not require supervision from manually annotated correspondences and object masks. We validate the efficacy of our approach on five benchmark datasets: TSS, Internet, PF-PASCAL, PF-WILLOW, and SPair-71k, and show that our algorithm performs favorably against the state-of-the-art methods on both semantic matching and object co-segmentation tasks.
Falco: Fast likelihood‐based collision avoidance with extension to human‐guided navigation

Abstract We propose a planning method to enable fast autonomous flight in cluttered environments. Typically, autonomous navigation through a complex environment requires a continuous search on a graph generated by a k ‐connected grid or a probabilistic scheme. As the vehicle travels, updating the graph with data from onboard sensors is expensive as is the search on the graph especially if the paths must be kinodynamically feasible. We propose to avoid the online search to reduce the computational complexity. Our method models the environment differently in two separate regions. Obstacles are considered to be deterministically known within the sensor range and probabilistically known beyond the sensor range. Instead of searching for the path with the lowest cost (typically the shortest path), the method maximizes the likelihood to reach the goal in determining the immediate next step for navigation. With such a problem formulation, the online method realized by a trajectory library can determine a path within 0.2–0.3 ms using a single central processing unit thread on a modem embedded computer. The method supports two configurations working with and without a prior map. Both configurations can be used to plan toward a goal point. Further, the later can allow human guidance for the navigation through a directional input. In experiments, it enables a lightweight unmanned aerial vehicle to fly at 10 m/s in a cluttered forest environment (see Figure 1 as an example).
Error-Detected State Transfer and Entanglement in a Superconducting Quantum Network

Modular networks are a promising paradigm for increasingly complex quantum devices based on the ability to transfer qubits and generate entanglement between modules. These tasks require a low-loss, high-speed intermodule link that enables extensible network connectivity. Satisfying these demands simultaneously remains an outstanding goal for long-range optical quantum networks as well as modular superconducting processors within a single cryostat. We demonstrate communication and entanglement in a superconducting network with a microwave-actuated beamsplitter transformation between two bosonic qubits, which are housed in separate modules and joined by a demountable coaxial bus resonator. We transfer a qubit in a multi-photon encoding and track photon loss events to improve the fidelity, making it as high as in a single-photon encoding. Furthermore, generating entanglement with two-photon interference and postselection against loss errors produces a Bell state with success probability 79% and fidelity 0.94, halving the error obtained with a single photon. These capabilities demonstrate several promising methods for faithful operations between modules, including novel possibilities for resource-efficient direct gates.
Fast Temporal Video Segmentation Based on Krawtchouk-Tchebichef Moments

With the increasing growth of multimedia data, the current real-world video sharing websites are being huge in repository size, more specifically video databases. This growth necessitates to look for superior techniques in processing video because video contains a lot of useful information. Temporal video segmentation (TVS) is considered essential stage in content-based video indexing and retrieval system. TVS aims to detect boundaries between successive video shots. TVS algorithm design is still challenging because most of the recent methods are unable to achieve fast and robust detection. In this regard, this paper proposes a TVS algorithm with high precision and recall values, and low computation cost for detecting different types of video transitions. The proposed algorithm is based on orthogonal moments which are considered as features to detect transitions. To increase the speed of the TVS algorithm as well as the accuracy, fast block processing and embedded orthogonal polynomial algorithms are utilized to extract features. This utilization will lead to extract multiple local features with low computational cost. Support vector machine (SVM) classifier is used to detect transitions. Specifically, the hard transitions are detected by the trained SVM model. The proposed algorithm has been evaluated on four datasets. In addition, the performance of the proposed algorithm is compared to several state-of-the-art TVS algorithms. Experimental results demonstrated that the proposed algorithm performance improvements in terms of recall, precision, and F1-score are within the ranges (1.31 - 2.58), (1.53 - 4.28), and (1.41 - 3.03), respectively. Moreover, the proposed method shows low computation cost which is 2% of real-time.
Behind the Intents

Code refactorings are of pivotal importance in modern code review. Developers may preserve, revisit, add or undo refactorings through changes' revisions. Their goal is to certify that the driving intent of a code change is properly achieved. Developers' intents behind refactorings may vary from pure structural improvement to facilitating feature additions and bug fixes. However, there is little understanding of the refactoring practices performed by developers during the code review process. It is also unclear whether the developers' intents influence the selection, composition, and evolution of refactorings during the review of a code change. Through mining 1,780 reviewed code changes from 6 systems pertaining to two large open-source communities, we report the first in-depth empirical study on software refactoring during code review. We inspected and classified the developers' intents behind each code change into 7 distinct categories. By analyzing data generated during the complete reviewing process, we observe: (i) how refactorings are selected, composed and evolved throughout each code change, and (ii) how developers' intents are related to these decisions. For instance, our analysis shows developers regularly apply non-trivial sequences of refactorings that crosscut multiple code elements (i.e., widely scattered in the program) to support a single feature addition. Moreover, we observed that new developers' intents commonly emerge during the code review process, influencing how developers select and compose their refactorings to achieve the new and adapted goals. Finally, we provide an enriched dataset that allows researchers to investigate the context and motivations behind refactoring operations during the code review process.
Composite learning sliding mode synchronization of chaotic fractional-order neural networks

In this work, a sliding mode control (SMC) method and a composite learning SMC (CLSMC) method are proposed to solve the synchronization problem of chaotic fractional-order neural networks (FONNs). A sliding mode surface and an adaptive law are constructed to update parameter estimation. The SMC ensures that the synchronization error asymptotically tends to zero under a strict permanent excitation (PE) condition. To reduce its rigor, online recording data together with instantaneous data is used to define a prediction error about the uncertain parameter. Both synchronization error and prediction error are used to construct a composite learning law. The proposed CLSMC method can ensure that the synchronization error asymptotically approaches zero, and it can accurately estimate the uncertain parameter. The above results obtained in the CLSMC method only requires an interval-excitation (IE) condition which can be easily satisfied. Finally, comparative results reveal the control effects of the two proposed methods.
Enhanced generative adversarial network for 3D brain MRI super-resolution

Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data. Generative Adversarial Networks (GAN) have proven to perform well in image recovery tasks. In this work, we followed the GAN framework and developed a generator coupled with discriminator to tackle the task of 3D SISR on T1 brain MRI images. We developed a novel 3D memory-efficient residual-dense block generator (MRDG) that achieves state-of-the-art performance in terms of SSIM (Structural Similarity), PSNR (Peak Signal to Noise Ratio) and NRMSE (Normalized Root Mean Squared Error) metrics. We also designed a pyramid pooling discriminator (PPD) to recover details on different size scales simultaneously. Finally, we introduced model blending, a simple and computational efficient method to balance between image and texture quality in the final output, to the task of SISR on 3D images.
Viewport-Adaptive Scalable Multi-User Virtual Reality Mobile-Edge Streaming

Virtual reality (VR) holds tremendous potential to advance our society, expected to make impact on quality of life, energy conservation, and the economy. To bring us closer to this vision, the present paper investigates a novel communications system that integrates for the first time scalable multi-layer 360° video tiling, viewport-adaptive rate-distortion optimal resource allocation, and VR-centric edge computing and caching, to enable next generation high-quality untethered VR streaming. Our system comprises a collection of 5G small cells that can pool their communication, computing, and storage resources to collectively deliver scalable 360° video content to mobile VR clients at much higher quality. The major contributions of the paper are the rigorous design of multi-layer 360° tiling and related models of statistical user navigation, analysis and optimization of edge-based multi-user VR streaming that integrates viewport adaptation and server cooperation, and base station 360° video packet scheduling. We also explore the possibility of network coded data operation and its implications for the analysis, optimization, and system performance we pursue in this setting. The advances introduced by our framework over the state-of-theart comprise considerable gains in delivered immersion fidelity, featuring much higher 360° viewport peak signal to noise ratio (PSNR) and VR video frame rates and spatial resolutions.