Reviewerly/exHarmony huggingface dataset:
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

The unprecedented success of deep neural networks in many applications has made these networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs). We introduce the concept of Universal Litmus Patterns (ULPs), which enable one to reveal backdoor attacks by feeding these universal patterns to the network and analyzing the output (i.e., classifying the network as `clean' or `corrupted'). This detection is fast because it requires only a few forward passes through a CNN. We demonstrate the effectiveness of ULPs for detecting backdoor attacks on thousands of networks with different architectures trained on four benchmark datasets, namely the German Traffic Sign Recognition Benchmark (GTSRB), MNIST, CIFAR10, and Tiny-ImageNet. The codes and train/test models for this paper can be found here: https://umbcvision.github.io/Universal-Litmus-Patterns/.
Evading Deepfake-Image Detectors with White- and Black-Box Attacks

It is now possible to synthesize highly realistic images of people who do not exist. Such content has, for example, been implicated in the creation of fraudulent socialmedia profiles responsible for dis-information campaigns. Significant efforts are, therefore, being deployed to detect synthetically-generated content. One popular forensic approach trains a neural network to distinguish real from synthetic content.We show that such forensic classifiers are vulnerable to a range of attacks that reduce the classifier to near- 0% accuracy. We develop five attack case studies on a state- of-the-art classifier that achieves an area under the ROC curve (AUC) of 0.95 on almost all existing image generators, when only trained on one generator. With full access to the classifier, we can flip the lowest bit of each pixel in an image to reduce the classifier's AUC to 0.0005; perturb 1% of the image area to reduce the classifier's AUC to 0.08; or add a single noise pattern in the synthesizer's latent space to reduce the classifier's AUC to 0.17. We also develop a black-box attack that, with no access to the target classifier, reduces the AUC to 0.22. These attacks reveal significant vulnerabilities of certain image-forensic classifiers.
Superconducting Parametric Amplifiers: The State of the Art in Josephson Parametric Amplifiers

In superconducting quantum computing, qubit state information is conveyed via low-power microwave fields. As such, ultralow-noise microwave amplification plays a central role in measuring these fields to quickly and accurately infer the qubit state. To this end, parametric amplification, once a well-known concept for low-noise amplifiers during the 1960s and 1970s, has become the leading technology for enabling highly efficient microwave measurements of these quantum circuits. While current research efforts have elements in common with much earlier work, there are some important differences that distinguish this new generation of amplifiers. Here, we try to give an overview of what this excitement is about, how these devices work, and where there are opportunities for improvement.
Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition

Facial Expression Recognition (FER) is a challenging task that improves natural human-computer interaction. This paper focuses on automatic FER on a single in-the-wild (ITW) image. ITW images suffer real problems of pose, direction, and input resolution. In this study, we propose a pyramid with super-resolution (PSR) network architecture to solve the ITW FER task. We also introduce a prior distribution label smoothing (PDLS) loss function that applies the additional prior knowledge of the confusion about each expression in the FER task. Experiments on the three most popular ITW FER datasets showed that our approach outperforms all the state-of-the-art methods.
An improved Jaya optimization algorithm with Lévy flight

Recent advances in metaheuristics have shown the advantages of using the Lévy distribution, which models a kind of random walk (named “Lévy flight”) with occasional “big” steps. This characteristic makes Lévy flight especially useful for performing large “jumps” that allow the search to escape from a local optimum and restart in a different region of the search space. In this paper, we investigate this idea by applying Lévy flight to Jaya, a simple yet effective Swarm Intelligence optimization algorithm recently proposed in the literature. We perform experiments on the CEC 2014 benchmark as well as five industrial optimization problems taken from the CEC 2011 benchmark, and compare the performance of the proposed Lévy flight Jaya Algorithm (LJA) against several state-of-the-art algorithms for continuous optimization. Our numerical results show that, although both Jaya and LJA are in general less efficient than the most advanced algorithms on the CEC 2014 benchmark, LJA largely outperforms the original Jaya algorithm in most cases, and is also highly competitive on the tested industrial problems.
A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images

The visual analysis of histopathological images is the gold standard for diagnosing breast cancer, yet a strenuous and an intricate task that requires years of pathologist training. Therefore, automating this task using computer-aided diagnosis (CAD) is highly expected. This paper proposes a new transfer learning-based approach to automated classiﬁcation of breast cancer from histopathological images, including magniﬁcation dependent (MD) and magniﬁcation independent (MI) binary and eight-class classiﬁcations. We apply the deep neural network ResNet-18 to this problem, which is pre-trained on ImageNet, a large dataset of common images. We then design our transfer learning method to refine the network on histopathological images. Our transfer learning method is based on block-wise fine-tuning strategy; in which we make the last two residual blocks of the deep network model more domain-speciﬁc to our target data. It substantially helps to avoid over-ﬁtting and speed up the training. Furthermore, we strengthen the adaptability of the proposed approach by using global contrast normalization (GCN) based on the target’s data values and three-fold data augmentation on training data. The experimental results of MD and MI binary and eight-class classiﬁcations on the publicly available BreaKHis dataset demonstrate that our approach is promising and effective, outperforming recent state-of-the-art MD and MI counterparts by a fair margin.
Permissioned blockchain frameworks in the industry: A comparison

Permissioned and private blockchain platforms are increasingly used in today’s industry. This paper provides a comprehensive and comparative study of the 5 major frameworks (Fabric, Ethereum, Quorum, MultiChain and R3 Corda) with regard to the community activities, performance, scalability, privacy and adoption criteria. Based on a literature review, this study shows that even if Fabric is promising, the final selection of a framework for a specific case-study is always a trade-off. Finally, lessons learnt are given for industrial practitioners and researchers.
A new fractional one dimensional chaotic map and its application in high-speed image encryption

Chaos theory has been widely used in the design of image encryption schemes. Some low-dimensional chaotic maps have been proved to be easily predictable because of their small chaotic space. On the other hand, high-dimensional chaotic maps have a larger chaotic space. However, their structures are too complicated, and consequently, they are not suitable for real-time image encryption. Motivated by this, we propose a new fractional one-dimensional chaotic map with a large chaotic space. The proposed map has a simple structure and a high chaotic behavior in an extensive range of its control parameters values. Several chaos theoretical tools and tests have been carried out to analyze and prove the proposed map’s high chaotic behavior. Moreover, we use the proposed map in the design of a novel real-time image encryption scheme. In this new scheme, we combine the substitution and permutation stages to simultaneously modify both of the pixels’ positions and values. The merge of these two stages and the use of the new simple one-dimensional chaotic map significantly increase the proposed scheme’s security and speed. Besides, the simulation and experimental analysis prove that the proposed scheme has high performances.
Identifying Causes of and Solutions for Cybersickness in Immersive Technology: Reformulation of a Research and Development Agenda

Immersive technologies, such as virtual and augmented reality, initially failed to live up to expectations, but have improved greatly, with many new head-worn displays and associated applications being released over the past few years. Unfortunately, ‘cybersickness’ remains as a common user problem that must be overcome if mass adoption is to be realized. This article evaluates the state of research on this problem, identifies challenges that must be addressed, and formulates an updated cybersickness research and development (R&D) agenda. The new agenda recommends prioritizing creation of powerful, lightweight, and untethered head-worn displays, reduction of visual latencies, standardization of symptom and aftereffect measurement, development of improved countermeasures, and improved understanding of the magnitude of the problem and its implications for job performance. Some of these priorities are unresolved problems from the original agenda which should get increased attention now that immersive technologies are proliferating widely. If the resulting R&D agenda is carefully executed, it should render cybersickness a challenge of the past and accelerate mass adoption of immersive technologies to enhance training, performance, and recreation.
Demystifying COVID-19 Digital Contact Tracing: A Survey on Frameworks and Mobile Apps

The coronavirus pandemic is a new reality, and it severely affects the modus vivendi of the international community. In this context, governments are rushing to devise or embrace novel surveillance mechanisms and monitoring systems to fight the outbreak. The development of digital tracing apps, which among others are aimed at automatising and globalising the prompt alerting of individuals at risk in a privacy-preserving manner, is a prominent example of this ongoing effort. Very promptly, a number of digital contact tracing architectures have been sprouted, followed by relevant app implementations adopted by governments worldwide. Bluetooth, specifically its Low Energy (BLE) power-conserving variant, has emerged as the most promising short-range wireless network technology to implement the contact tracing service. This work offers the first to our knowledge full-fledged review of the most concrete contact tracing architectures proposed so far in a global scale. This endeavour does not only embrace the diverse types of architectures and systems, namely, centralised, decentralised, or hybrid, but also equally addresses the client side, i.e., the apps that have been already deployed in Europe by each country. There is also a full-spectrum adversary model section, which does not only amalgamate the previous work in the topic but also brings new insights and angles to contemplate upon.
Characterizing serverless platforms with serverlessbench

Serverless computing promises auto-scalability and cost-efficiency (in "pay-as-you-go" manner) for high-productive software development. Because of its virtue, serverless computing has motivated increasingly new applications and services in the cloud. This, however, also presents new challenges including how to efficiently design high-performance serverless platforms and how to efficiently program on the platforms.
Rise of the machines: towards a conceptual service-robot research framework for the hospitality and tourism industry

Purpose This study aims to introduce a conceptual framework for service robot (SR) acceptance by customers, employees and policymakers – a framework to help determine corporate decision-making on SR workplace integration. Design/methodology/approach This study reviews SR adoption literature within the tourism and hospitality industry. These SRs may have some level of artificial intelligence capability and possibly anthropomorphic (e.g. humanoid) or zoomorphic (e.g. animal-like) features, contingent on task and design choices. The study then identifies factors that potentially influence employee and consumer acceptance and experiences of SR, as well as policy and compliance factors and all elements of the corporate decision-making process concerning SR adoption in the hospitality setting. Findings This paper reviews the obstacles and benefits of SR adoption in the hospitality industry based on employee, consumer and public policy considerations. Research limitations/implications SRs are increasingly deployed within hospitality and tourism settings. Future studies should further explore the value-adding functions of SRs implemented in existing hospitality operations. Practical implications Hospitality and tourism industry practitioners should integrate the dimensions in the conceptual framework to make fully informed decisions on SR adoption. Originality/value To the best of the authors’ knowledge, this is one of the first studies to provide a holistic SR adoption framework offering theoretical and practical implications for ongoing SR research and implementation.
Generating high-fidelity synthetic patient data for assessing machine learning healthcare software

There is a growing demand for the uptake of modern artificial intelligence technologies within healthcare systems. Many of these technologies exploit historical patient health data to build powerful predictive models that can be used to improve diagnosis and understanding of disease. However, there are many issues concerning patient privacy that need to be accounted for in order to enable this data to be better harnessed by all sectors. One approach that could offer a method of circumventing privacy issues is the creation of realistic synthetic data sets that capture as many of the complexities of the original data set (distributions, non-linear relationships, and noise) but that does not actually include any real patient data. While previous research has explored models for generating synthetic data sets, here we explore the integration of resampling, probabilistic graphical modelling, latent variable identification, and outlier analysis for producing realistic synthetic data based on UK primary care patient data. In particular, we focus on handling missingness, complex interactions between variables, and the resulting sensitivity analysis statistics from machine learning classifiers, while quantifying the risks of patient re-identification from synthetic datapoints. We show that, through our approach of integrating outlier analysis with graphical modelling and resampling, we can achieve synthetic data sets that are not significantly different from original ground truth data in terms of feature distributions, feature dependencies, and sensitivity analysis statistics when inferring machine learning classifiers. What is more, the risk of generating synthetic data that is identical or very similar to real patients is shown to be low.
Consensus-Aware Visual-Semantic Embedding for Image-Text Matching

Image-text matching plays a central role in bridging vision and language. Most existing approaches only rely on the image-text instance pair to learn their representations, thereby exploiting their matching relationships and making the corresponding alignments. Such approaches only exploit the superficial associations contained in the instance pairwise data, with no consideration of any external commonsense knowledge, which may hinder their capabilities to reason the higher-level relationships between image and text. In this paper, we propose a Consensus-aware Visual-Semantic Embedding (CVSE) model to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching. Specifically, the consensus information is exploited by computing the statistical co-occurrence correlations between the semantic concepts from the image captioning corpus and deploying the constructed concept correlation graph to yield the consensus-aware concept (CAC) representations. Afterwards, CVSE learns the associations and alignments between image and text based on the exploited consensus as well as the instance-level representations for both modalities. Extensive experiments conducted on two public datasets verify that the exploited consensus makes significant contributions to constructing more meaningful visual-semantic embeddings, with the superior performances over the state-of-the-art approaches on the bidirectional image and text retrieval task. Our code of this paper is available at: https://github.com/BruceW91/CVSE .
Innovating the cultural heritage museum service model through virtual reality and augmented reality: the effects on the overall visitor experience and satisfaction

Although museum visitor experience and satisfaction have received growing attention in academic research, the impact of new elements of the museum service model, including AR and VR, on visitor experience and satisfaction remains an unexplored area of investigation. This paper aims to fill this gap in the literature by investigating how the innovation of museum service aspects (exhibition content, VR and AR, general organisation, and reception staff) can enhance visitor experiences and satisfaction, opening spaces for innovating museum service models. A quantitative survey methodology combining correlation analysis, Importance-Performance Analysis (IPA), descriptive statistics, and cluster analysis was conducted on a random sample of 739 museum visitors experiencing ‘The Ara It Was’ project at the Pacis Museum in Rome. Discussion and conclusions contribute to the debate on the disruptive power of AR and VR and its impact on service model innovation for cultural heritage museums. The paper proposes theoretical advances, has managerial implications for future improvements in museum management, and opens Museum 4.0 academic debate.
Learning temporal coherence via self-supervision for GAN-based video generation

Our work explores temporal self-supervision for GAN-based video generation tasks. While adversarial training successfully yields generative models for a variety of areas, temporal relationships in the generated data are much less explored. Natural temporal changes are crucial for sequential generation tasks, e.g. video super-resolution and unpaired video translation. For the former, state-of-the-art methods often favor simpler norm losses such as $L^2$ over adversarial training. However, their averaging nature easily leads to temporally smooth results with an undesirable lack of spatial detail. For unpaired video translation, existing approaches modify the generator networks to form spatio-temporal cycle consistencies. In contrast, we focus on improving learning objectives and propose a temporally self-supervised algorithm. For both tasks, we show that temporal adversarial learning is key to achieving temporally coherent solutions without sacrificing spatial detail. We also propose a novel Ping-Pong loss to improve the long-term temporal consistency. It effectively prevents recurrent networks from accumulating artifacts temporally without depressing detailed features. Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution. A series of user studies confirm the rankings computed with these metrics. Code, data, models, and results are provided at https://github.com/thunil/TecoGAN. The project page https://ge.in.tum.de/publications/2019-tecogan-chu/ contains supplemental materials.
BGNN4VD: Constructing Bidirectional Graph Neural-Network for Vulnerability Detection

Previous studies have shown that existing deep learning-based approaches can significantly improve the performance of vulnerability detection. They represent code in various forms and mine vulnerability features with deep learning models. However, the differences of code representation forms and deep learning models make various approaches still have some limitations. In practice, their false-positive rate (FPR) and false-negative rate (FNR) are still high. To address the limitations of existing deep learning-based vulnerability detection approaches, we propose BGNN4VD (Bidirectional Graph Neural Network for Vulnerability Detection), a vulnerability detection approach by constructing a Bidirectional Graph Neural-Network (BGNN). In Phase 1, we extract the syntax and semantic information of source code through abstract syntax tree (AST), control flow graph (CFG), and data flow graph (DFG). Then in Phase 2, we use vectorized source code as input to Bidirectional Graph Neural-Network (BGNN). In Phase 3, we learn the different features between vulnerable code and non-vulnerable code by introducing backward edges on the basis of traditional Graph Neural-Network (GNN). Finally in Phase 4, a Convolutional Neural-Network (CNN) is used to further extract features and detect vulnerabilities through a classifier. We evaluate BGNN4VD on four popular C/C++ projects from NVD and GitHub, and compare it with four state-of-the-art (Flawfinder, RATS, SySeVR, and VUDDY) vulnerab ility detection approaches. Experiment results show that, when compared these baselines, BGNN4VD achieves 4.9%, 11.0%, and 8.4% improvement in F1-measure, accuracy and precision, respectively. The proposed BGNN4VD achieves a higher precision and accuracy than the state-of-the-art methods. In addition, when applied on the latest vulnerabilities reported by CVE, BGNN4VD can still achieve a precision at 45.1%, which demonstrates the feasibility of BGNN4VD in practical application.
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking

Single Image Deraining (SID) is a relatively new and still challenging topic in emerging vision applications, and most of the recently emerged deraining methods use the supervised manner depending on the ground-truth (i.e., using paired data). However, in practice it is rather common to encounter unpaired images in real deraining task. In such cases, how to remove the rain streaks in an unsupervised way will be a challenging task due to lack of constraints between images and hence suffering from low-quality restoration results. In this paper, we therefore explore the unsupervised SID issue using unpaired data, and propose a new unsupervised framework termed DerainCycleGAN for single image rain removal and generation, which can fully utilize the constrained transfer learning ability and circulatory structures of CycleGAN. In addition, we design an unsupervised rain attentive detector (UARD) for enhancing the rain information detection by paying attention to both rainy and rain-free images. Besides, we also contribute a new synthetic way of generating the rain streak information, which is different from the previous ones. Specifically, since the generated rain streaks have diverse shapes and directions, existing derianing methods trained on the generated rainy image by this way can perform much better for processing real rainy images. Extensive experimental results on synthetic and real datasets show that our DerainCycleGAN is superior to current unsupervised and semi-supervised methods, and is also highly competitive to the fully-supervised ones.