Reviewerly/exHarmony huggingface dataset:
Quantum-Safe Round-Optimal Password Authentication for Mobile Devices

Password authentication is the dominant form of access control for the Web and mobile devices, and its practicality and ubiquity is unlikely to be replaced by other authentication approaches in the foreseeable future. To guarantee the security of data communication and mitigate the problem of password-cracking, a <i>Password Authenticated Key Exchange</i> ( <inline-formula><tex-math notation="LaTeX">$\mathsf {PAKE}$</tex-math></inline-formula> ) system can be deployed between two peer participants. The main drawback of traditional <inline-formula><tex-math notation="LaTeX">$\mathsf {PAKE}$</tex-math></inline-formula> is that passwords are exposed in plaintext when the remote server is compromised. To overcome this limitation, it is recommended by industry standards (such as SRP family RFC 5054, RFC6628, RFC7914, OPAQUE, <i>etc</i> ) to use <i>asymmetric</i> - <inline-formula><tex-math notation="LaTeX">$\mathsf {PAKE}$</tex-math></inline-formula> protocols, which enable the server to store a hash of the user's password with a random salt, providing guarantees that the user's password is never transmitted in plain-text to the server when login. However, most of the existing <i>asymmetric</i> - <inline-formula><tex-math notation="LaTeX">$\mathsf {PAKE}$</tex-math></inline-formula> protocols either are based on traditional hash functions under random oracles, or depend on non-quantum-secure hardness assumptions and become insecure in the quantum era. To bridge the gap between <i>asymmetric</i> - <inline-formula><tex-math notation="LaTeX">$\mathsf {PAKE}$</tex-math></inline-formula> and quantum-security, in this article, we resort to <i>smooth projective hash functions</i> ( <inline-formula><tex-math notation="LaTeX">$\mathsf {SPHF}$</tex-math></inline-formula> ) and <i>commitment</i> -based <i>password-hashing schemes</i> ( <inline-formula><tex-math notation="LaTeX">$\mathsf {PHS}$</tex-math></inline-formula> ) over lattice-based cryptography, and construct an asymmetric <inline-formula><tex-math notation="LaTeX">$\mathsf {PAKE}$</tex-math></inline-formula> protocol secure against quantum attacks. Our construction eliminates the costly non-interactive zero-knowledge (NIZK) method, bypasses assumptions of the random oracle model, and achieves quantum resistance. We also show that our asymmetric- <inline-formula><tex-math notation="LaTeX">$\mathsf {PAKE}$</tex-math></inline-formula> protocol can achieve security and efficiency under the Bellare-Pointcheval-Rogaway (BPR) model. Finally, we develop a prototype implementation of our instantiation and use it to evaluate its performance in realistic settings.
Tree-RNN: Tree structural recurrent neural network for network traffic classification

Network traffic classification plays an important role in network monitoring and network management. With the continuous development of network technology, traditional methods of traffic classification have more limitations in accuracy to deal with encrypted traffic. Fortunately, deep neural network (DNN) is an effective method for handling traffic classification due to its ability to learn inherent data features. However, this method generally classifies network traffic with only the single classifier, which makes it relatively less effective in some classes for the problem of large classification. In this paper, we propose a tree structural recurrent neural network (Tree-RNN), which divides a large classification into small classifications by using the tree structure. A specific classifier is set for each small classification after division. With multiple classifiers employed, Tree-RNN can complement each other in classification performance, and the problem of the single classifier is solved. Since multiple classifiers are all end-to-end frameworks, Tree-RNN can automatically learn the nonlinear relationship between input data and output data without feature extraction. To verify the validity of our model, we compare Tree-RNN with state-of-the-art methods using the ISCX public traffic dataset. Experimental results show that Tree-RNN can achieve higher performance in less training time. The average accuracy of Tree-RNN is 4.88% higher than other state-of-the-art methods, and it has higher average precision and average recall.
Transforming and Projecting Images into Class-Conditional Generative Networks

We present a method for projecting an input image into the space of a class-conditional generative neural network. We propose a method that optimizes for transformation to counteract the model biases in generative neural networks. Specifically, we demonstrate that one can solve for image translation, scale, and global color transformation, during the projection optimization to address the object-center bias and color bias of a Generative Adversarial Network. This projection process poses a difficult optimization problem, and purely gradient-based optimizations fail to find good solutions. We describe a hybrid optimization strategy that finds good projections by estimating transformations and class parameters. We show the effectiveness of our method on real images and further demonstrate how the corresponding projections lead to better editability of these images. The project page and the code is available at https://minyoungg.github.io/GAN-Transform-and-Project/ .
Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation

Although unsupervised domain adaptation methods have been widely adopted across several computer vision tasks, it is more desirable if we can exploit a few labeled data from new domains encountered in a real application. The novel setting of the semi-supervised domain adaptation (SSDA) problem shares the challenges with the domain adaptation problem and the semi-supervised learning problem. However, a recent study shows that conventional domain adaptation and semi-supervised learning methods often result in less effective or negative transfer in the SSDA problem. In order to interpret the observation and address the SSDA problem, in this paper, we raise the intra-domain discrepancy issue within the target domain, which has never been discussed so far. Then, we demonstrate that addressing the intra-domain discrepancy leads to the ultimate goal of the SSDA problem. We propose an SSDA framework that aims to align features via alleviation of the intra-domain discrepancy. Our framework mainly consists of three schemes, i.e., attraction, perturbation, and exploration. First, the attraction scheme globally minimizes the intra-domain discrepancy within the target domain. Second, we demonstrate the incompatibility of the conventional adversarial perturbation methods with SSDA. Then, we present a domain adaptive adversarial perturbation scheme, which perturbs the given target samples in a way that reduces the intra-domain discrepancy. Finally, the exploration scheme locally aligns features in a class-wise manner complementary to the attraction scheme by selectively aligning unlabeled target features complementary to the perturbation scheme. We conduct extensive experiments on domain adaptation benchmark datasets such as DomainNet, Office-Home, and Office. Our method achieves state-of-the-art performances on all datasets.
Coverage Path Planning for Decomposition Reconfigurable Grid-Maps Using Deep Reinforcement Learning Based Travelling Salesman Problem

Optimizing the coverage path planning (CPP) in robotics has become essential to accomplish efficient coverage applications. This work presents a novel approach to solve the CPP problem in large complex environments based on the Travelling Salesman Problem (TSP) and Deep Reinforcement Learning (DRL) leveraging the grid-based maps. The proposed algorithm applies the cellular decomposition methods to decompose the environment and generate the coverage path by recursively solving each decomposed cell formulated as TSP. A solution to TSP is determined by training Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) layers using Reinforcement Learning (RL). We validated the proposed method by systematically benchmarked with other conventional methods in terms of path length, execution time, and overlapping rate under four different map layouts with various obstacle density. The results depict that the proposed method outperforms all considered parameters than the conventional schemes. Moreover, simulation experiments demonstrate that the proposed approach is scalable to the larger grid-maps and guarantees complete coverage with efficiently generated coverage paths.
MobileSal: Extremely Efficient RGB-D Salient Object Detection

The high computational cost of neural networks has prevented recent successes in RGB-D salient object detection (SOD) from benefiting real-world applications. Hence, this article introduces a novel network, MobileSal, which focuses on efficient RGB-D SOD using mobile networks for deep feature extraction. However, mobile networks are less powerful in feature representation than cumbersome networks. To this end, we observe that the depth information of color images can strengthen the feature representation related to SOD if leveraged properly. Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the mobile networks' feature representation capability for RGB-D SOD. IDR is only adopted in the training phase and is omitted during testing, so it is computationally free. Besides, we propose compact pyramid refinement (CPR) for efficient multi-level feature aggregation to derive salient objects with clear boundaries. With IDR and CPR incorporated, MobileSal performs favorably against state-of-the-art methods on six challenging RGB-D SOD datasets with much faster speed (450fps for the input size of 320×320) and fewer parameters (6.5M). The code is released at https://mmcheng.net/mobilesal.
Scale-, Shift-, and Rotation-Invariant Diffractive Optical Networks

Recent research efforts in optical computing have gravitated toward developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/photonics in machine learning applications. Among these endeavors, Diffractive Deep Neural Networks (D2NNs) harness light-matter interaction over a series of trainable surfaces, designed using deep learning, to compute a desired statistical inference task as the light waves propagate from the input plane to the output field-of-view. Although earlier studies have demonstrated the generalization capability of diffractive optical networks to unseen data, achieving, e.g., >98% image classification accuracy for handwritten digits, these previous designs are in general sensitive to the spatial scaling, translation, and rotation of the input objects. Here, we demonstrate a new training strategy for diffractive networks that introduces input object translation, rotation, and/or scaling during the training phase as uniformly distributed random variables to build resilience in their blind inference performance against such object transformations. This training strategy successfully guides the evolution of the diffractive optical network design toward a solution that is scale-, shift-, and rotation-invariant, which is especially important and useful for dynamic machine vision applications in, e.g., autonomous cars, in vivo imaging of biomedical specimen, among others.
OD-LBP: Orthogonal difference-local binary pattern for Face Recognition

Most of the local descriptors exists today are based on the relationship between the neighborhood pixels and the center pixel by considering the 3×3 spatial window. Moving one step forward from the previous methodologies used, this research paper introduces the novel descriptor for Face analysis called as orthogonal difference-Local binary pattern (OD-LBP). In OD-LBP, initially the 3 gray level differences are computed for each orthogonal position (of 2 groups, in 3×3 pixel window) by subtracting the 2 closest neighborhoods and the center pixel from the respective orthogonal value. Then these respective gray level differences are compared with the value produced after employing the novel comparison method. Finally the 1×24 size row vector is produced by concatenating the binary patterns produced from both the orthogonal groups. Eventually the 24 bit row vector is split into three 8 bit binary patterns from which the 3 OD-LBP codes are produced. By employing this concept for each pixel position eventually results in the 3 OD-LBP transformed images. All the 3 transformed images are then divided into 3×3 sub-regions for histogram extraction. The combined sub-regional histograms from all the transformed images are the entire feature size of the OD-LBP descriptor. To achieve the compact feature representation PCA is applied further and then classification is performed by the SVMs. The entire concept is tested on 5 challenging databases which include ORL, GT, JAFFE, MIT-CBCL and Yale. The proposed descriptor (i.e. OD-LBP) achieves very promising results on all 5 databases as it comprehensively outperforms the other 10 state of art descriptors LBP, CS-LBP, 6×6 MB-LBP, OC-LBP, MBP, HOG, ELBP, OS-LTP, WCLBP and RLBP on majority of sub-sets. Numerous states of approaches from the literature are also outclassed by proposed descriptor.
A fusion-domain color image watermarking based on Haar transform and image correction

The leapfrog development of computer technology has greatly enhanced the breadth of information dissemination. As a larger information carrier, color image becomes more and more popular, but the copyright protection problem becomes more and more serious. To solve this problem, this paper proposes a fusion-domain color watermarking based on Haar transform and image correction. Firstly, the maximum energy coefficient of Haar transform is directly obtained in spatial domain. Then, the coefficient is quantified with the help of variable quantization steps to embed the color watermark that encrypted by affine transform. If the watermarked image is processed by geometric attack, then the attacked image can be corrected by using of the geometric properties. Finally, the inverse embedding process is performed to extract the watermark. The performances of the proposed method are shown as follows: 1) all PSNR (Peak Signal-to-Noise Ratio) values are greater than 40 dB; 2) all SSIM (Structural Similarity Index Metric) values are greater than 0.96; 3) most NC (Normalized Cross-correlation) values are more than 0.9; 4) the key space is more than 2432; 5) the maximum embedded capacity is 0.25bpp; 6) the running time is about 6 s. Compared with the related methods, the experimental results show that the proposed method is feasible and has good performance.
Cubature rule-based distributed optimal fusion with identification and prediction of kinematic model error for integrated UAV navigation

Integrated MIMU/GNSS/CNS (micro-electro-mechanical system-based inertial measurement unit/global navigation satellite system/celestial navigation system) is a promising strategy for UAV (unmanned aerial vehicle) navigation. However, given its strong nonlinearity and involvement of kinematic model error, integrated MIMU/GNSS/CNS UAV navigation presents the difficulty in achieving optimal navigation solutions. This paper presents a method of cubature rule-based distributed optimal fusion combined with identification and prediction of kinematic model error to address the above problem in nonlinear integrated MIMU/GNSS/CNS. This method is in a distributed structure to simultaneously process observations from integrated MIMU/GNSS and MIMU/CNS subsystems for the subsequent global fusion. A theory for identification of kinematic model error is established using the concept of Mahalanobis distance. Further, the standard cubature Kalman filter is modified with the model prediction filter to serve as the local filters in integrated MIMU/GNSS and MIMU/CNS subsystems to hinder the disturbance of kinematic model error. Based on the above, an optimal fusion technique is developed to fuse the filtering results of each subsystem for achieving globally optimal state estimation in the sense of mean square error. Simulations and experimental results as well as comparison analysis demonstrate that the proposed distributed optimal fusion method can effectively identify and predict kinematic model error and further achieve globally optimal fusion results, leading to improved performance for integrated MIMU/GNSS/CNS UAV navigation.
Joint entity and relation extraction model based on rich semantics

Extracting entities and relations from unstructured texts has become an important task in the natural language processing (NLP), especially knowledge graphs (KG). However, relation classification (RC) and named entity recognition (NER) tasks are usually considered separately, which lost a lot of associated contextual information. Therefore, a novel end-to-end method based on the attention mechanism integrating convolutional and recurrent neural networks is proposed for joint entity and relation extraction, which can obtain rich semantics and takes full advantage of the associated information between entities and relations without introducing external complicated features. The convolutional operation is employed to obtain character-level and word-level embeddings which are transferred to the multi-head attention mechanism. Then the multi-head attention mechanism can encode contextual semantics and embeddings to obtain efficient semantic representation. Moreover, the rich semantics are encoded to obtain final tag sequence based on recurrent neural networks. Finally, the experiments are performed on NYT10 and NYT11 benchmarks to demonstrate the proposed method. Compared with the current pipelined and joint approaches, the experimental results indicate that the proposed method can obtain state-of-the-art performance in terms of the standard F1-score.
CAT-Net: Compression Artifact Tracing Network for Detection and Localization of Image Splicing

Detecting and localizing image splicing has become essential to fight against malicious forgery. A major challenge to localize spliced areas is to discriminate between authentic and tampered regions with intrinsic properties such as compression artifacts. We propose CAT-Net, an end-to-end fully convolutional neural network including RGB and DCT streams, to learn forensic features of compression artifacts on RGB and DCT domains jointly. Each stream considers multiple resolutions to deal with spliced object's various shapes and sizes. The DCT stream is pretrained on double JPEG detection to utilize JPEG artifacts. The proposed method outperforms state-of-the-art neural networks for localizing spliced regions in JPEG or non-JPEG images.
Postdigital-biodigital: An emerging configuration

This dialogue (trilogue) is an attempt to critically discuss the technoscientific convergence that is taking place with biodigital technologies in the postdigital condition. In this discussion, Sarah Hayes, Petar Jandrić and Michael A. Peters examine the nature of the convergences, their applications for bioeconomic sustainability and associated ecopedagogies. The dialogue paper raises issues of definition and places the technological convergence (‘nano-bio-info-cogno’) – of new systems biology and digital technologies at the nano level – in an evolutionary context to speculate, on the basis of the latest research, future possibilities. The paper also reviews these developments within familiar landscapes of posthumanism and postmodernism, raises the question of political bioeconomy and the role of postdigital education within it.
A Vector-based Representation to Enhance Head Pose Estimation

This paper proposes to use the three vectors in a rotation matrix as the representation in head pose estimation and develops a new neural network based on the characteristic of such representation. We address two potential issues existed in current head pose estimation works: 1. Public datasets for head pose estimation use either Euler angles or quaternions to annotate data samples. However, both of these annotations have the issue of discontinuity and thus could result in some performance issues in neural network training. 2. Most research works report Mean Absolute Error (MAE) of Euler angles as the measurement of performance. We show that MAE may not reflect the actual behavior especially for the cases of profile views. To solve these two problems, we propose a new annotation method which uses three vectors to describe head poses and a new measurement Mean Absolute Error of Vectors (MAEV) to assess the performance. We also train a new neural network to predict the three vectors with the constraints of orthogonality. Our proposed method achieves state-of-the-art results on both AFLW2000 and BIWI datasets. Experiments show our vector-based annotation method can effectively reduce prediction errors for large pose angles.
Applicability of Blockchain smart contracts in securing Internet and IoT: A systematic literature review

In the recent decade, there has been an exponential rise in the development and study of Blockchain Technology space. The driving force behind the interest in Blockchain research has been its key characteristics that provide security, anonymity, and integrity without relying on trusted third party organizations. Initially, Blockchain usage was restricted to cryptocurrencies only, however, with the advent of Ethereum that supports creation and execution of smart contracts (small computer programs that run on Blockchain), applications beyond cryptocurrencies are being developed and explored. Although there has been substantial progress towards improving Blockchain technology with a focus on the smart contracts, however, it was identified that there is a lack of descriptive review on the applicability of smart contracts in securing the Internet and IoT. This paper aims to identify and analyse peer reviewed literature that seeks to use Blockchain smart contracts for securing Internet in general and Internet of Things in particular and presents the systematic analysis of the identified literature. Finally, the paper highlights some challenges and future research directions in the field of Blockchain smart contracts application in securing Internet and IoT.
Edge Blockchain Assisted Lightweight Privacy-Preserving Data Aggregation for Smart Grid

Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.