Reviewerly/exHarmony huggingface dataset:
QuantumHammer

Post-quantum schemes are expected to replace existing public-key schemes within a decade in billions of devices. To facilitate the transition, the US National Institute for Standards and Technology (NIST) is running a standardization process. Multivariate signatures is one of the main categories in NIST's post-quantum cryptography competition. Among the four candidates in this category, the LUOV and Rainbow schemes are based on the Oil and Vinegar scheme, first introduced in 1997 which has withstood over two decades of cryptanalysis. Beyond mathematical security and efficiency, security against side-channel attacks is a major concern in the competition. The current sentiment is that post-quantum schemes may be more resistant to fault-injection attacks due to their large key sizes and the lack of algebraic structure. We show that this is not true. We introduce a novel hybrid attack, QuantumHammer, and demonstrate it on the constant-time implementation of LUOV currently in Round 2 of the NIST post-quantum competition. The QuantumHammer attack is a combination of two attacks, a bit-tracing attack enabled via Rowhammer fault injection and a divide and conquer attack that uses bit-tracing as an oracle. Using bit-tracing, an attacker with access to faulty signatures collected using Rowhammer attack, can recover secret key bits albeit slowly. We employ a divide and conquer attack which exploits the structure in the key generation part of LUOV and solves the system of equations for the secret key more efficiently with few key bits recovered via bit-tracing. We have demonstrated the first successful in-the-wild attack on LUOV recovering all 11K key bits with less than 4 hours of an active Rowhammer attack. The post-processing part is highly parallel and thus can be trivially sped up using modest resources. QuantumHammer does not make any unrealistic assumptions, only requires software co-location (no physical access), and therefore can be used to target shared cloud servers or in other sandboxed environments.
Speeded-Up Robust Features-based image mosaic method for large-scale microscopic hyperspectral pathological imaging

Abstract Microscopic hyperspectral imaging technology has been widely used in pathological analysis as it can obtain both spatial and spectral information of samples. However, most hyperspectral imaging systems can only capture images in a single field of view. Therefore, an image mosaic is one of the most important steps in a large-scale microscopic hyperspectral imaging system. This paper proposes a microscopic hyperspectral image (HSI) mosaic method based on Speeded-Up Robust Features (SURF) and linear synthesis to achieve large-scale HSIs. In contrast to other SURF-based image mosaic methods, the proposed method leverages both image content and coordinate information to improve the accuracy and stability of the image mosaic. In addition, multiple bands of HSIs with different texture information and grayscale are applied in image matching to take full advantage of spatial redundancy. Simultaneously, a blank microscopic HSI screening method is introduced in this paper to pick out a clearer blank image for better preprocessing, i.e. removing interference in the optical path and the interference of dust on slides. Finally, the preprocessed images are synthesized by linear-based synthesis methods due to their simple synthesis structure and better universality. Additionally, a file format, i.e. hyperslide, is defined for large-scale HSIs and can be browsed with HyperViewer software. Experimental results show that the proposed microscopic HSI mosaic method can obtain high-quality large-scale microscopic HSIs of tissue sections.
An Efficient Authenticated Key Exchange from Random Self-reducibility on CSIDH

SIDH and CSIDH are key exchange protocols based on isogenies and conjectured to be quantum-resistant. Since the protocols are similar to the classical Diffie–Hellman, they are vulnerable to the man-in-the-middle attack. A key exchange which is resistant to such an attack is called an authenticated key exchange (AKE), and many isogeny-based AKEs have been proposed. However, the parameter sizes of the existing schemes should be large since they all have relatively large security losses in security proofs. This is partially because the random self-reducibility of isogeny-based decisional problems has not been proved yet. In this paper, we show that the computational problem and the gap problem of CSIDH are random self-reducible. A gap problem is a computational problem given access to the corresponding decision oracle. Moreover, we propose a CSIDH-based AKE with small security loss, following the construction of Cohn-Gordon et al. in CRYPTO 2019, as an application of the random self-reducibility of the gap problem of CSIDH. Our AKE is proved to be the fastest CSIDH-based AKE when we aim at 110-bit security level.
NanoMine: A Knowledge Graph for Nanocomposite Materials Science

Knowledge graphs can be used to help scientists integrate and explore their data in novel ways. NanoMine, built with the Whyis knowledge graph framework, integrates diverse data from over 1,700 polymer nanocomposite experiments. Polymer nanocomposites (polymer materials with nanometer-scale particles embedded in them) exhibit complex changes in their properties depending upon their composition or processing methods. Building an overall theory of how nanoparticles interact with the polymer they are embedded in therefore typically has to rely on an integrated view across hundreds of datasets. Because the NanoMine knowledge graph is able to integrate across many experiments, materials scientists can explore custom visualizations and, with minimal semantic training, produce custom visualizations of their own. NanoMine provides access to experimental results and their provenance in a linked data format that conforms to well-used semantic web ontologies and vocabularies (PROV-O, Schema.org, and the Semanticscience Integrated Ontology). We curated data described by an XML schema into an extensible knowledge graph format that enables users to more easily browse, filter, and visualize nanocomposite materials data. We evaluated NanoMine on the ability for material scientists to produce visualizations that help them explore and understand nanomaterials and assess the diversity of the integrated data. Additionally, NanoMine has been used by the materials science community to produce an integrated view of a journal special issue focusing on data sharing, demonstrating the advantages of sharing data in an interoperable manner.
HOSE-Net: Higher Order Structure Embedded Network for Scene Graph Generation

Scene graph generation aims to produce structured representations for images, which requires to understand the relations between objects. Due to the continuous nature of deep neural networks, the prediction of scene graphs is divided into object detection and relation classification. However, the independent relation classes cannot separate the visual features well. Although some methods organize the visual features into graph structures and use message passing to learn contextual information, they still suffer from drastic intra-class variations and unbalanced data distributions. One important factor is that they learn an unstructured output space that ignores the inherent structures of scene graphs. Accordingly, in this paper, we propose a Higher Order Structure Embedded Network (HOSE-Net) to mitigate this issue. First, we propose a novel structure-aware embedding-to-classifier(SEC) module to incorporate both local and global structural information of relationships into the output space. Specifically, a set of context embeddings are learned via local graph based message passing and then mapped to a global structure based classification space. Second, since learning too many context-specific classification subspaces can suffer from data sparsity issues, we propose a hierarchical semantic aggregation(HSA) module to reduces the number of subspaces by introducing higher order structural information. HSA is also a fast and flexible tool to automatically search a semantic object hierarchy based on relational knowledge graphs. Extensive experiments show that the proposed HOSE-Net achieves the state-of-the-art performance on two popular benchmarks of Visual Genome and VRD.
Importance Driven Continual Learning for Segmentation Across Domains

The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications. However, current neural networks tend to forget previously learned tasks when trained on new ones, i.e., they suffer from Catastrophic Forgetting (CF). The objective of Continual Learning (CL) is to alleviate this problem, which is particularly relevant for medical applications, where it may not be feasible to store and access previously used sensitive patient data. In this work, we propose a Continual Learning approach for brain segmentation, where a single network is consecutively trained on samples from different domains. We build upon an importance driven approach and adapt it for medical image segmentation. Particularly, we introduce a learning rate regularization to prevent the loss of the network’s knowledge. Our results demonstrate that directly restricting the adaptation of important network parameters clearly reduces Catastrophic Forgetting for segmentation across domains. Our code is publicly available on https://github.com/ai-med/MAS-LR.
Resolving overlapping convex objects in silhouette images by concavity analysis and Gaussian process

This paper introduces a novel method for segmentation of clustered partially overlapping convex objects in silhouette images. The proposed method involves three main steps: pre-processing, contour evidence extraction, and contour estimation. Contour evidence extraction starts by recovering contour segments from a binarized image by detecting concave points. After this the contour segments which belong to the same objects are grouped. The grouping is formulated as a combinatorial optimization problem and solved using the branch and bound algorithm. Finally, the full contours of the objects are estimated by a Gaussian process regression method. The experiments on a challenging dataset consisting of nanoparticles demonstrate that the proposed method outperforms three current state-of-art approaches in overlapping convex objects segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have a convex shape.
International Workshop on Cross-Reality (XR) Interaction

Established as separate disciplines, Augmented Reality (AR) and Virtual Reality (VR) have already positioned themselves as strong research disciplines. However, being part of the same Reality-Virtuality continuum, as presented by Paul Milgram, it is possible to envision (i) a smooth transition between systems using different degrees of virtuality or (ii) collaboration between users using different systems with different degrees of virtuality. We refer to these types of systems as cross-reality (XR) systems, which can better fulfil different modalities for a given task or context of use, and potentially enable rich applications in training, education, remote assistance, or emergency response compared to individual closed systems. This workshop will bring together researchers and practitioners that are interested in XR to identify current issues and future directions of research while the long-term goal is to create a strong interdisciplinary research community and foster future development of the discipline and collaborations.
LSRU: A Novel Deep Learning based Hybrid Method to Predict the Workload of Virtual Machines in Cloud Data Center

Task scheduling is a key innovation of cloud computing. However, forecasting resource usage depends on the previous usage of resource. Thus, this type of problem is modeled as a time series prediction problem. Cloud computing platform gives the ability of sharing both hardware and software resources on demand. Because of dynamic change of resource request in data center, it is difficult to predict the future workload precisely. Thus, we propose a novel hybrid-method for improving the accuracy, named LSRU, which is mainly the combination of two models i.e. Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM). Our method LSRU achieves better accuracy in comparison with different statistical models, and LSTM and GRU model separately. LSRU can predict short-time ahead prediction along with long-time ahead prediction with sudden burst of workload. Experimental results show the performance of LSRU compared to other existing models and the visual representation of forecasting workload traces of different types and duration.
Automatically detecting risky scripts in infrastructure code

Infrastructure code supports embedded scripting languages such as Shell and PowerShell to manage the infrastructure resources and conduct life-cycle operations. Risky patterns in the embedded scripts have widespread of negative impacts across the whole infrastructure, causing disastrous consequences. In this paper, we propose an analysis framework, which can automatically extract and compose the embedded scripts from infrastructure code before detecting their risky code patterns with correlated severity levels and negative impacts. We implement SecureCode based on the proposed framework to check infrastructure code supported by Ansible, i.e., Ansible playbooks. We integrate SecureCode with the DevOp pipeline deployed in IBM cloud and test Secure-Code on 45 IBM Services community repositories. Our evaluation shows that SecureCode can efficiently and effectively identify 3419 true issues with 116 false positives in minutes. Among the 3419 true issues, 1691 have high severity levels.
Representation and Experience-Based Learning of Explainable Models for Robot Action Execution

For robots acting in human-centered environments, the ability to improve based on experience is essential for reliable and adaptive operation; however, particularly in the context of robot failure analysis, experience-based improvement is practically useful only if robots are also able to reason about and explain the decisions they make during execution. In this paper, we describe and analyse a representation of execution-specific knowledge that combines (i) a relational model in the form of qualitative attributes that describe the conditions under which actions can be executed successfully and (ii) a continuous model in the form of a Gaussian process that can be used for generating parameters for action execution, but also for evaluating the expected execution success given a particular action parameterisation. The proposed representation is based on prior, modelled knowledge about actions and is combined with a learning process that is supervised by a teacher. We analyse the benefits of this representation in the context of two actions - grasping handles and pulling an object on a table -such that the experiments demonstrate that the joint relational-continuous model allows a robot to improve its execution based on experience, while reducing the severity of failures experienced during execution.
Cross-blockchain protocol for public registries

Purpose The purpose of this paper is to present a concept of the protocol for public registries based on blockchain. New database protocol aims to use the benefits of blockchain technologies and ensure their interoperability. Design/methodology/approach This paper is framed with design science research (DSR). The primary method is exaptation, i.e. adoption of solutions from other fields. The research is looking into existing technologies which are applied here as elements of the protocol: Name-Value Storage (NVS), Berkley DB, RAID protocol, among others. The choice of NVS as a reference technology for creating a database over blockchain is based on the analysis and comparison with two other similar technologies Bigchain and Amazon QLDB. Findings The proposed mechanism allows creating a standard database over a bundle of distributed ledgers. It ensures a blockchain agnostic approach and uses the benefits of various blockchain technologies in one ecosystem. In this scheme, blockchains play the role of journal storages (immutable log), whereas the overlaid database is the indexed storage. The distinctive feature of such a system is that in blockchain, users can perform peer-to-peer transactions directly in the ledger using blockchain native mechanism of user access management with public-key cryptography (blockchain does not require to administrate its database). Originality/value This paper presents a new method of creating a public peer-to-peer database across a bundle of distributed ledgers.
When Visual Disparity Generation Meets Semantic Segmentation: A Mutual Encouragement Approach

Semantic segmentation and depth estimation play important roles in the field of autonomous driving. In recent years, the advantages of Convolutional Neural Networks (CNNs) have allowed these two topics to flourish. However, people always solve these two tasks separately and rarely solve them in a united model. In this paper, we propose a Mutual Encouragement Network (MENet), which includes a semantic segmentation branch and a disparity regression branch, and simultaneously generates semantic map and visual disparity. In the cost volume construction phase, the depth information is embedded in the semantic segmentation branch to increase contextual understanding. Similarly, the semantic information is also included in the disparity regression branch to generate more accurate disparity. Two branches mutually promote each other during training phase and inference phase. We conducted our method on the popular dataset KITTI, and the experimental results show that our method can outperform the state-of-the-art methods on both visual disparity generation and semantic segmentation. In addition, extensive ablation studies also demonstrate that the two tasks in our method can facilitate each other significantly with the proposed approach.
Online Handwritten Arabic Scripts Recognition Using Stroke-Based Class Labeling Scheme

handwriting recognition Two-stage SVMs A B S T R A C TWith the increasing availability of pen-based user interfaces, we often come upon multiple data sets of online handwritten scripts such as letters, words, etc., that are collected based on a viable interface.In this paper, we set forward a new method for online handwritten Arabic scripts recognition.Departing from the assumption that handwritten scripts are encoded as a set of strokes, the proposed approach relies first upon classifying strokes contained on the script and then recognizes the whole script.For stroke classification, an support vector machine (SVM) is trained with stroke features vectors obtained from the Beta-elliptic model and fuzzy elementary perceptual codes to obtain class stroke probabilities.The output of this SVM is combined with spatial relation vectors feeding to a second SVM to provide scripts level recognition.The proposed model has been tested on MAYASTROUN dataset.In order to obtain additional insight into the efficiency of the proposed approach, we performed further experiments on ADAB data set.The experimental results highlight its relevance by comfortably outperforming state-of-art systems.
Asynchronous Remote Key Generation

WebAuthn, forming part of FIDO2, is a W3C standard for strong authentication, which employs digital signatures to authenticate web users whilst preserving their privacy. Owned by users, WebAuthn authenticators generate attested and unlinkable public-key credentials for each web service to authenticate users. Since the loss of authenticators prevents users from accessing web services, usable recovery solutions preserving the original WebAuthn design choices and security objectives are urgently needed. We examine Yubico's recent proposal for recovering from the loss of a WebAuthn authenticator by using a secondary backup authenticator. We analyse the cryptographic core of their proposal by modelling a new primitive, called Asynchronous Remote Key Generation (ARKG), which allows some primary authenticator to generate unlinkable public keys for which the backup authenticator may later recover corresponding private keys. Both processes occur asynchronously without the need for authenticators to export or share secrets, adhering to WebAuthn's attestation requirements. We prove that Yubico's proposal achieves our ARKG security properties under the discrete logarithm and PRF-ODH assumptions in the random oracle model. To prove that recovered private keys can be used securely by other cryptographic schemes, such as digital signatures or encryption schemes, we model compositional security of ARKG using composable games by Brzuska et al. (ACM CCS 2011), extended to the case of arbitrary public-key protocols. As well as being more general, our results show that private keys generated by ARKG may be used securely to produce unforgeable signatures for challenge-response protocols, as used in WebAuthn. We conclude our analysis by discussing concrete instantiations behind Yubico's ARKG protocol, its integration with the WebAuthn standard, performance, and usability aspects.
Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds

Deep neural networks are vulnerable to adversarial attacks. Though various attempts have been made, it is still largely open to fully understand the existence of adversarial samples and thereby develop effective defense strategies. In this paper, we present a new perspective, namely gradient leaking hypothesis, to understand the existence of adversarial examples and to further motivate effective defense strategies. Specifically, we consider the low dimensional manifold structure of natural images, and empirically verify that the leakage of the gradient (w.r.t input) along the (approximately) perpendicular direction to the tangent space of data manifold is a reason for the vulnerability over adversarial attacks. Based on our investigation, we further present a new robust learning algorithm which encourages a larger gradient component in the tangent space of data manifold, suppressing the gradient leaking phenomenon consequently. Experiments on various tasks demonstrate the effectiveness of our algorithm despite its simplicity.
An Optimized Hybrid Algorithm for Blind Watermarking Scheme Using Singular Value Decomposition in RDWT-DCT Domain

Watermarking is a technique which offers more robustness and good imperceptibility. Even if the algorithmic principle is public or the existence of hidden information is known, it is difficult to attacker to destroy the embedded watermark without degrading the watermarked media severely. In existence, non-blind watermarking (NBW) schemes and different combinations of techniques are proposed for watermarking procedure. But these methods failed to provide the good imperceptibility, lack of robustness and embedding capacity standards, respectively. Thus, to overcome this problem, a hybrid algorithm for blind watermarking (BW) is proposed, which utilizes redundant discrete wavelet transform (RDWT), discrete cosine transform (DCT) and singular value decomposition (SVD) together to have the advantages of all three. In addition, optimization of proposed RDWT-DCT-SVD is obtained using a new bio-inspired algorithm called Porcellio Scaber algorithm (PSA). Thus, the visual perception of extracted watermark image also good enough by maintaining the robustness against various image attacks. Experiments are carried out to test the proposed BW algorithm and it is successful under various geometrical and non-geometrical attacks. Finally, the performances of proposed BW using RDWT-DCT-SVD with PSA optimization is compared with various existing BW approaches in terms of various quality metrics like peak signal to noise ratio (PSNR), structural similarity (SSIM) index, root mean square error (RMSE) and normalized correlation coefficient (NCC).