Reviewerly/exHarmony huggingface dataset:
One-to-N &amp; N-to-One: Two Advanced Backdoor Attacks Against Deep Learning Models

In recent years, deep learning models have been widely deployed in various application scenarios. The training processes of deep neural network (DNN) models are time-consuming, and require massive training data and large hardware overhead. These issues have led to the outsourced training procedure, pre-trained models supplied from third parties, or massive training data from untrusted users. However, a few recent researches indicate that, by injecting some well-designed backdoor instances into the training set, the attackers can create a concealed backdoor in the DNN model. In this way, the attacked model still works normally on the benign inputs, but when a backdoor instance is submitted, some specific abnormal behaviors will be triggered. Existing studies all focus on attacking a single target that triggered by a single backdoor (referred to as One-to-One attack), while the backdoor attacks against multiple target classes, and backdoor attacks triggered by multiple backdoors have not been studied yet. In this article, for the first time, we propose two advanced backdoor attacks, the multi-target backdoor attacks and multi-trigger backdoor attacks: 1) One-to-N attack, where the attacker can trigger multiple backdoor targets by controlling the different intensities of the same backdoor; 2) N-to-One attack, where such attack is triggered only when all the <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> backdoors are satisfied. Compared with existing One-to-One attacks, the proposed two backdoor attacks are more flexible, more powerful and more difficult to be detected. Besides, the proposed backdoor attacks can be applied under the weak attack model, where the attacker has no knowledge about the parameters and architectures of the DNN models. Experimental results show that these two attacks can achieve better or similar performances when injecting a much smaller proportion or same proportion of backdoor instances than those existing One-to-One backdoor attacks. The two attack methods can achieve high attack success rates (up to 100 percent in MNIST dataset and 92.22 percent in CIFAR-10 dataset), while the test accuracy of the DNN model has hardly dropped (as low as 0 percent in LeNet-5 model and 0.76 percent in VGG-16 model), thus will not raise administrator’s suspicions. Further, the two attacks are also evaluated on a large and realistic dataset (Youtube Aligned Face dataset), where the maximum attack success rate reaches 90 percent (One-to-N) and 94 percent (N-to-One), and the accuracy degradation of target face recognition model (VGGFace model) is only 0.05 percent. The proposed One-to-N and N-to-One attacks are demonstrated to be effective and stealthy against two state-of-the-art defense methods.
Using Deep learning for image watermarking attack

Digital image watermarking has justified its suitability for copyright protection and copy control of digital images. In the past years, various watermarking schemes were proposed to enhance the fidelity and the robustness of watermarked images against different types of attacks such as additive noise, filtering, and geometric attacks. It is highly important to guarantee a sufficient level of robustness of watermarked images against such type of attacks. Recently, Deep learning and neural networks achieved noticeable development and improvement, especially in image processing, segmentation, and classification. Therefore, in this paper, we studied the effect of a Fully Convolutional Neural Network (FCNN), as a denoising attack, on watermarked images. This deep architecture improves the training process and denoising performance, through which the encoder–decoder remove the noise while preserving the detailed structure of the image. FCNNDA outperforms the other types of attacks because it destroys the watermarks while preserving a good quality of the attacked images. Spread Transform Dither Modulation (STDM) and Spread Spectrum (SS) are used as watermarking schemes to embed the watermarks in the images using several scenarios. This evaluation shows that such type of denoising attack preserves the image quality while breaking the robustness of all evaluated watermarked schemes. It could also be considered a deleterious attack.
Self-supervised Nuclei Segmentation in Histopathological Images Using Attention

Segmentation and accurate localization of nuclei in histopathological images is a very challenging problem, with most existing approaches adopting a supervised strategy. These methods usually rely on manual annotations that require a lot of time and effort from medical experts. In this study, we present a self-supervised approach for segmentation of nuclei for whole slide histopathology images. Our method works on the assumption that the size and texture of nuclei can determine the magnification at which a patch is extracted. We show that the identification of the magnification level for tiles can generate a preliminary self-supervision signal to locate nuclei. We further show that by appropriately constraining our model it is possible to retrieve meaningful segmentation maps as an auxiliary output to the primary magnification identification task. Our experiments show that with standard post-processing, our method can outperform other unsupervised nuclei segmentation approaches and report similar performance with supervised ones on the publicly available MoNuSeg dataset. Our code and models are available online (https://github.com/msahasrabudhe/miccai2020_self_sup_nuclei_seg) to facilitate further research.
Countering hate on social media: Large scale classification of hate and counter speech

Hateful rhetoric is plaguing online discourse, fostering extreme societal movements and possibly giving rise to real-world violence. A potential solution to this growing global problem is citizen-generated counter speech where citizens actively engage with hate speech to restore civil non-polarized discourse. However, its actual effectiveness in curbing the spread of hatred is unknown and hard to quantify. One major obstacle to researching this question is a lack of large labeled data sets for training automated classifiers to identify counter speech. Here we use a unique situation in Germany where self-labeling groups engaged in organized online hate and counter speech. We use an ensemble learning algorithm which pairs a variety of paragraph embeddings with regularized logistic regression functions to classify both hate and counter speech in a corpus of millions of relevant tweets from these two groups. Our pipeline achieves macro F1 scores on out of sample balanced test sets ranging from 0.76 to 0.97—accuracy in line and even exceeding the state of the art. We then use the classifier to discover hate and counter speech in more than 135,000 fully-resolved Twitter conversations occurring from 2013 to 2018 and study their frequency and interaction. Altogether, our results highlight the potential of automated methods to evaluate the impact of coordinated counter speech in stabilizing conversations on social media.
SiENet: Siamese Expansion Network for Image Extrapolation

Different from image inpainting, image outpainting has relative less context in the image center to capture and more content at the image border to predict. Therefore, classical encoder-decoder pipeline of existing methods may not predict the outstretched unknown content perfectly. In this paper, a novel two-stage siamese adversarial model for image extrapolation, named Siamese Expansion Network (SiENet) is proposed. In two stages, a novel border sensitive convolution named adaptive filling convolution is designed for allowing encoder to predict the unknown content, alleviating the burden of decoder. Besides, to introduce prior knowledge to network and reinforce the inferring ability of encoder, siamese adversarial mechanism is designed to enable our network to model the distribution of covered long range feature for that of uncovered image feature. The results on four datasets has demonstrated that our method outperforms existing state-of-the-arts and could produce realistic results.
Joint Bilateral Learning for Real-Time Universal Photorealistic Style Transfer

Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera. Recent approaches, based on deep neural networks, produce impressive results but are either too slow to run at practical resolutions, or still contain objectionable artifacts. We propose a new end-to-end model for photorealistic style transfer that is both fast and inherently generates photorealistic results. The core of our approach is a feed-forward neural network that learns local edge-aware affine transforms that automatically obey the photorealism constraint. When trained on a diverse set of images and a variety of styles, our model can robustly apply style transfer to an arbitrary pair of input images. Compared to the state of the art, our method produces visually superior results and is three orders of magnitude faster, enabling real-time performance at 4K on a mobile phone. We validate our method with ablation and user studies.
Spatiotemporal Attacks for Embodied Agents

Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness. Existing work on adversarial attacks have mainly focused on static scenes; however, it remains unclear whether such attacks are effective against embodied agents, which could navigate and interact with a dynamic environment. In this work, we take the first step to study adversarial attacks for embodied agents. In particular, we generate spatiotemporal perturbations to form 3D adversarial examples, which exploit the interaction history in both the temporal and spatial dimensions. Regarding the temporal dimension, since agents make predictions based on historical observations, we develop a trajectory attention module to explore scene view contributions, which further help localize 3D objects appeared with highest stimuli. By conciliating with clues from the temporal dimension, along the spatial dimension, we adversarially perturb the physical properties (e.g., texture and 3D shape) of the contextual objects that appeared in the most important scene views. Extensive experiments on the EQA-v1 dataset for several embodied tasks in both the white-box and black-box settings have been conducted, which demonstrate that our perturbations have strong attack and generalization abilities (Our code can be found at https://github.com/liuaishan/SpatiotemporalAttack ).
Phishing Detection Using Machine Learning Technique

Today, everyone is highly dependent on the internet. Everyone performed online shopping and online activities such as online Bank, online booking, online recharge and more on internet. Phishing is a type of website threat and phishing is Illegally on the original website Information such as login id, password and information of credit card. This paper proposed an efficient machine learning based phishing detection technique. Overall, experimental results show that the proposed technique, when integrated with the Support vector machine classifier, has the best performance of accurately distinguishing 95.66% of phishing and appropriate websites using only 22.5% of the innovative functionality. The proposed technique exhibits optimistic results when benchmarking with a range of standard phishing datasets of the “University of California Irvine (UCI)” archive. Therefore, proposed technique is preferred and used for phishing detection based on machine learning.
Controlled Secure Direct Communication Protocol via the Three-qubit Partially Entangled Set of States

In this paper, we first re-examine the previous protocol of controlled quantum secure direct communication of Zhang et al.’s scheme, which was found insecure under two kinds of attacks, fake entangled particles attack and disentanglement attack. Then, by changing the party of the preparation of cluster states and using unitary operations, we present an improved protocol which can avoid these two kinds of attacks. Moreover, the protocol is proposed using the three-qubit partially entangled set of states. It is more efficient by only using three particles rather than four or even more to transmit one bit secret information. Given our using state is much easier to prepare for multiqubit states and our protocol needs less measurement resource, it makes this protocol more convenient from an applied point of view.
Parametric-Resonance Entangling Gates with a Tunable Coupler

High-fidelity parametric gates have been demonstrated with superconducting qubits via rf flux modulation of the qubit frequency. The modulation however leads to renormalization of the bare qubit-qubit coupling, thereby reducing the gate speed. Here, we realize a parametric-resonance gate, which is activated by bringing the average frequency of the modulated qubit in resonance with a static-frequency qubit while approximately retaining the bare qubit-qubit coupling. The activation of parametric-resonance gates does not depend on the frequency of modulation, allowing us to choose the modulation frequencies and avoid frequency collisions. Moreover, we show that this approach is compatible with tunable coupler architectures, which reduce always-on residual couplings. Using these techniques, we demonstrate iswap and controlled-$Z$ gates between two qubits coupled via a tunable coupler with average process fidelities as high as $99.3\mathrm{%}$ and $97.9\mathrm{%}$, respectively. The flexibility in activating parametric-resonance gates combined with a tunable coupler architecture provides a pathway for building large-scale quantum computers.
Training quantum embedding kernels on near-term quantum computers

Kernel methods are a cornerstone of classical machine learning. The idea of using quantum computers to compute kernels has recently attracted attention. Quantum embedding kernels (QEKs), constructed by embedding data into the Hilbert space of a quantum computer, are a particular quantum kernel technique that is particularly suitable for noisy intermediate-scale quantum devices. Unfortunately, kernel methods face three major problems: Constructing the kernel matrix has quadratic computational complexity in the number of training samples, choosing the right kernel function is nontrivial, and the effects of noise are unknown. In this work, we addressed the latter two. In particular, we introduced the notion of trainable QEKs, based on the idea of classical model optimization methods. To train the parameters of the QEK, we proposed the use of kernel-target alignment. We verified the feasibility of this method, and showed that for our experimental setup we could reduce the training error significantly. Furthermore, we investigated the effects of device and finite sampling noise, and we evaluated various mitigation techniques numerically on classical hardware. We took the best performing strategy and evaluated it on data from a real quantum processing unit. We found that using this mitigation strategy demonstrated an increased kernel matrix quality.
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders

Previous work has indicated that pretrained Masked Language Models (MLMs) are not effective as universal lexical and sentence encoders off-the-shelf, i.e., without further taskspecific fine-tuning on NLI, sentence similarity, or paraphrasing tasks using annotated task data.In this work, we demonstrate that it is possible to turn MLMs into effective lexical and sentence encoders even without any additional data, relying simply on self-supervision.We propose an extremely simple, fast, and effective contrastive learning technique, termed Mirror-BERT, which converts MLMs (e.g., BERT and RoBERTa) into such encoders in 20-30 seconds with no access to additional external knowledge.Mirror-BERT relies on identical and slightly modified string pairs as positive (i.e., synonymous) fine-tuning examples, and aims to maximise their similarity during "identity fine-tuning".We report huge gains over off-the-shelf MLMs with Mirror-BERT both in lexical-level and in sentencelevel tasks, across different domains and different languages.Notably, in sentence similarity (STS) and question-answer entailment (QNLI) tasks, our self-supervised Mirror-BERT model even matches the performance of the Sentence-BERT models from prior work which rely on annotated task data.Finally, we delve deeper into the inner workings of MLMs, and suggest some evidence on why this simple Mirror-BERT fine-tuning approach can yield effective universal lexical and sentence encoders.
Robust image encryption and zero-watermarking scheme using SCA and modified logistic map

In this work, we first present a modified version of the traditional logistic chaotic map. The proposed version contains an additional parameter that is used to increase the security level of the proposed digital image copyright protection scheme. The latter merges two methods of image copyright protection, namely the image zero-watermarking and image encryption, which provides a high level of security when communicating images via the Internet. Next, we discuss the influence of geometric attacks on the efficiency of the proposed scheme, and then we introduce an efficient solution that can resist such attacks. The proposed solution involves the use of Sine Cosine Algorithm (SCA) with an appropriate algorithm suitable for the correction of geometric attacks (image translation, orientation and its combination) applied to the encrypted image. On the one hand, the simulation results show that the proposed scheme provides a high level of security and can resist various attacks (differential, common image processing, geometric, etc.). On the other hand, the conducted comparison in terms of robustness against geometric attacks clearly demonstrates the superiority of our scheme over recent image encryption ones.
Graph Structure Learning with Variational Information Bottleneck

Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming the observed structure perfectly depicts the accurate and complete relations between nodes. However, graphs in the real-world are inevitably noisy or incomplete, which could even exacerbate the quality of graph representations. In this work, we propose a novel Variational Information Bottleneck guided Graph Structure Learning framework, namely VIB-GSL, in the perspective of information theory. VIB-GSL is the first attempt to advance the Information Bottleneck (IB) principle for graph structure learning, providing a more elegant and universal framework for mining underlying task-relevant relations. VIB-GSL learns an informative and compressive graph structure to distill the actionable information for specific downstream tasks. VIB-GSL deduces a variational approximation for irregular graph data to form a tractable IB objective function, which facilitates training stability. Extensive experimental results demonstrate that the superior effectiveness and robustness of the proposed VIB-GSL.
Augmented and Virtual Reality (AR/VR) for Education and Training in the AEC Industry: A Systematic Review of Research and Applications

With updated equipment and maturing technology, the applications of augmented and virtual reality (AR/VR) technologies in the architecture, engineering, and construction (AEC) industry are receiving increasing attention rapidly. Especially in education and training, an increasing number of researchers have started to implement AR/VR technologies to provide students or trainees with a visual, immersive, and interactive environment. In this article, a systematic review of AR/VR technologies for education and training in the AEC industry is conducted. First of all, through comprehensive analysis, 82 related studies are identified from two databases, namely Scopus and Web of Science. Secondly, the VOSviewer is used to analyze the current status of AR/VR for education and training in the AEC industry. Thirdly, the identified studies are classified into different categories according to their application domains by qualitative analysis. Fourthly, after a further filtering, 17 out of the 82 studies are included in the meta-analysis to quantify the actual impact of AR/VR. The results indicate that there are some limitations in the applications of AR/VR for education and training in the AEC industry. Finally, to further explore the reasons for the existence of limitations, the 82 studies are summarized to analyze the current challenges of AR/VR for education and training in the AEC industry. This study also provides insights into future trends in AR/VR for education and training in the AEC industry.
A new algorithm for software clustering considering the knowledge of dependency between artifacts in the source code

Context: Software systems evolve over time to meet the new requirements of users. These new requirements, usually, are not reflected in the original documents of these software systems. Therefore, the new version of a software system deviates from the original and documented architecture. This way, it will be more difficult to understand it after a while and it will be difficult to make new changes conveniently. Clustering techniques are used to extract the architecture of a software system in order to understand it. An artifact dependency graph (ADG) is often used for clustering, which is extracted from a source code. In the literature, some hierarchical and search-based clustering methods have been presented to extract the software architecture. Hierarchical algorithms have reasonable search time; however, they are not able to find a good architecture. In contrast, search-based algorithms are often better in this regard; however, their time and space limitations make them useless in practice for large-scale software systems. Both hierarchical and search-based clustering methods overlook the existing knowledge in an ADG for clustering. Objective: To overcome the limitations of the existing clustering methods, this paper presents a new deterministic clustering algorithm named Neighborhood tree algorithm. Method: The new algorithm creates a neighborhood tree using available knowledge in an ADG and uses this tree for clustering. Results: Our initial results indicate that the algorithm is better able to extract an acceptable architecture in a reasonable time, compared with hierarchical and search-based algorithms. Conclusions: The proposed clustering algorithm is expected to greatly assist software engineers in extracting meaningful and understandable subsystems from a source code.