Reviewerly/exHarmony huggingface dataset:
Style transfer strategy for developing a generalizable deep learning application in digital pathology

Despite recent advances in artificial intelligence for medical images, the development of a robust deep learning model for identifying malignancy on pathology slides has been limited by problems related to substantial inter- and intra-institutional heterogeneity attributable to tissue preparation. The paucity of available data aggravates this limitation for relatively rare cancers. Here, using ovarian cancer pathology images, we explored the effect of image-to-image style transfer approaches on diagnostic performance. We leveraged a relatively large public image set for 142 patients with ovarian cancer from The Cancer Image Archive (TCIA) to fine-tune the renowned deep learning model Inception V3 for identifying malignancy on tissue slides. As an external validation, the performance of the developed classifier was tested using a relatively small institutional pathology image set for 32 patients. To reduce deterioration of the performance associated with the inter-institutional heterogeneity of pathology slides, we translated the style of the small image set of the local institution into the large image set style of the TCIA using cycle-consistent generative adversarial networks. Without style transfer, the performance of the classifier was as follows: area under the receiver operating characteristic curve (AUROC) = 0.737 and area under the precision recall curve (AUPRC) = 0.710. After style transfer, AUROC and AUPRC improved to 0.916 and 0.898, respectively. This study provides a case of the successful application of style transfer technology to generalize a deep learning model into small image sets in the field of digital pathology. Researchers at local institutions can select this collaborative system to make their small image sets acceptable to the deep learning model.
Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database

Mature industrial sectors (e.g., aviation) collect their real world failures in incident databases to inform safety improvements. Intelligent systems currently cause real world harms without a collective memory of their failings. As a result, companies repeatedly make the same mistakes in the design, development, and deployment of intelligent systems. A collection of intelligent system failures experienced in the real world (i.e., incidents) is needed to ensure intelligent systems benefit people and society. The AI Incident Database is an incident collection initiated by an industrial/non-profit cooperative to enable AI incident avoidance and mitigation. The database supports a variety of research and development use cases with faceted and full text search on more than 1,000 incident reports archived to date.
Texture Classification in Extreme Scale Variations Using GANet

Research in texture recognition often concentrates on recognizing textures with intraclass variations such as illumination, rotation, viewpoint and small scale changes. In contrast, in real-world applications a change in scale can have a dramatic impact on texture appearance, to the point of changing completely from one texture category to another. As a result, texture variations due to changes in scale are amongst the hardest to handle. In this work we conduct the first study of classifying textures with extreme variations in scale. To address this issue, we first propose and then reduce scale proposals on the basis of dominant texture patterns. Motivated by the challenges posed by this problem, we propose a new GANet network where we use a Genetic Algorithm to change the units in the hidden layers during network training, in order to promote the learning of more informative semantic texture patterns. Finally, we adopt a FVCNN (Fisher Vector pooling of a Convolutional Neural Network filter bank) feature encoder for global texture representation. Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding we are developing a new dataset, the Extreme Scale Variation Textures (ESVaT), to test the performance of our framework. It is demonstrated that the proposed framework significantly outperforms gold-standard texture features by more than 10% on ESVaT. We also test the performance of our proposed approach on the KTHTIPS2b and OS datasets and a further dataset synthetically derived from Forrest, showing superior performance compared to the state of the art.
The social and legislative principles of counteracting ransomware crime

This article aims to analyze the relationship between the threat of ransomware and new effective counteraction principles for law enforcement agencies to utilize.Moreover, it contemplates on how specific behavior of persons can help reduce the threat of being infected with this malicious software.It establishes that certain changes made in society's mentality towards their computer and network systems can significantly reduce the consequent damages of ransomware attacks.The manuscript uses a qualitative research approach and the analysis of variance (ANOVA), including an F-test, which defines major challenges in ransomware.This is the first empirical research piece which uses this type of data and approach for the analysis of current threats in global ransomware security.The article suggests that the main challenge is the systematic growth of ransomware connected to illegal businesses and the inattentive actions of casual users.The research paper proposes the implementation of global ransomware counteraction principles on the base of challenges that are present now and the prospects of rising threats in the future.In addition, the manuscript analyzes the trends of the last 2-years of attacks to find and determine new ways of successfully counteracting it for optimal innovative regional development.
Solving Math Word Problems with Multi-Encoders and Multi-Decoders

Math word problems solving remains a challenging task where potential semantic and mathematical logic need to be mined from natural language.Although previous researches employ the Seq2Seq technique to transform text descriptions into equation expressions, most of them achieve inferior performance due to insufficient consideration in the design of encoder and decoder.Specifically, these models only consider input/output objects as sequences, ignoring the important structural information contained in text descriptions and equation expressions.To overcome those defects, a model with multi-encoders and multi-decoders is proposed in this paper, which combines sequence-based encoder and graph-based encoder to enhance the representation of text descriptions, and generates different equation expressions via sequence-based decoder and tree-based decoder.Experimental results on the dataset Math23K show that our model outperforms existing state-of-the-art methods.
MAGLeak: A Learning-Based Side-Channel Attack for Password Recognition With Multiple Sensors in IIoT Environment

As an emerging technology, industrial Internet of Things (IIoT) connects massive sensors and actuators to empower industrial sectors being smart, autonomous, efficient, and safety. However, due the large number of build-in sensors of IIoT smart devices, the IIoT systems are vulnerable to side-channel attack. In this article, a novel side-channel-based passwords cracking system, namely MAGLeak, is proposed to recognize the victim's passwords by leveraging accelerometer, gyroscope, and magnetometer of IIoT touch-screen smart device. Specifically, an event-driven data collection method is proposed to ensure that the user's keystroke behavior can be reflected accurately by the obtained measurements of three sensors. Moreover, random forest algorithm is leveraged for the recognition module, followed by a data preprocessing process. Extensive experimental results demonstrate that MAGLeak achieves a high recognition accuracy under small training dataset, e.g., achieving recognition accuracy 98% of each single key for 2000 training samples.
Hand Pose Guided 3D Pooling for Word-level Sign Language Recognition

Gestures in American Sign Language (ASL) are characterized by fast, highly articulate motion of upper body, including arm movements with complex hand shapes and facial expressions. In this work, we propose a new method for word-level sign recognition from American Sign Language (ASL) using video. Our method uses both motion and hand shape cues while being robust to variations of execution. We exploit the knowledge of the body pose, estimated from an off-the-shelf pose estimator. Using the pose as a guide, we pool spatio-temporal feature maps from different layers of a 3D convolutional neural network. We train separate classifiers using pose guided pooled features from different resolutions and fuse their prediction scores during test time. This leads to a significant improvement in performance on the WLASL benchmark dataset [25]. The proposed approach achieves 10%, 12%, 9.5% and 6.5% performance gain on WLASL100, WLASL300, WLASL1000, WLASL2000 subsets respectively. To demonstrate the robustness of the pose guided pooling and proposed fusion mechanism, we also evaluate our method by fine tuning the model on another dataset. This yields 10% performance improvement for the proposed method using only 0.4% training data during fine tuning stage.
Experimental realization of nonadiabatic geometric gates with a superconducting Xmon qubit

Geometric phases are only dependent on evolution paths but independent of evolution details so that they possess some intrinsic noise-resilience features. Based on different geometric phases, various quantum gates have been proposed, such as nonadiabatic geometric gates based on nonadiabatic Abelian geometric phases and nonadiabatic holonomic gates based on nonadiabatic non-Abelian geometric phases. Up to now, nonadiabatic holonomic one-qubit gates have been experimentally demonstrated with superconducting transmons, where the three lowest levels are all utilized in operation. However, the second excited state of transmons has a relatively short coherence time, which results in a decreased fidelity of quantum gates. Here, we experimentally realize Abelian-geometric-phase-based nonadiabatic geometric one-qubit gates with a superconducting Xmon qubit. The realization is performed on the two lowest levels of an Xmon qubit and thus avoids the influence from the short coherence time of the second excited state. The experimental result indicates that the average fidelities of single-qubit gates can be up to 99.6% and 99.7% characterized by quantum process tomography and randomized benchmarking.
AttM-CNN: Attention and metric learning based CNN for pornography, age and Child Sexual Abuse (CSA) Detection in images

Nowadays, the growing number of cases of possession and distribution of Child Sexual Abuse (CSA) material pose a significant challenge for Law Enforcement Agencies (LEAs). In this paper, we decompose the automatic CSA detection problem into two simpler ones for which it is feasible to create massive labeled datasets, especially to train deep neural networks: (i) pornographic content detection and (ii) age-group classification of a person as a minor or an adult. We propose a deep CNN architecture with a novel attention mechanism and metric learning, denoted as AttM-CNN, for these tasks. Furthermore, the pornography detection and the age-group classification networks are combined for CSA detection using two different strategies: decision level fusion for binary CSA classification and score level fusion for the re-arrangement of the suspicious images. We also introduce two new datasets: (i) Pornographic-2M, which contains two million pornographic images, and (ii) Juvenile-80k, including 80k manually labeled images with apparent facial age. The experiments conducted for age-group and pornographic classification demonstrate that our approach obtained similar or superior results compared to the state-of-the-art systems on various benchmark datasets for both tasks, respectively. For the evaluation of CSA detection, we created a test dataset comprising one million adult porn, one million non-porn images, and 5,000 real CSA images provided to us by Police Forces. For binary CSA classification, our method obtained an accuracy of 92.72%, which increases the recognition rate by more than 21% compared to a well-known forensic tool, i.e. NuDetective. Furthermore, re-arrangement of the CSA test dataset images showed that 80% of CSA images can be found in the top 8.5% of images in the ranked list created using our approach.
Moving Vehicle Detection for Remote Sensing Video Surveillance with Nonstationary Satellite Platform

With satellite platforms gazing at a target territory, the captured satellite videos exhibit local misalignment and local intensity variation on some stationary objects that can be mistakenly extracted as moving objects and increase false alarm rates. Typical approaches for mitigating the effect of moving cameras in moving object detection (MOD) follow domain transformation technique, where the misalignment between consecutive frames is restricted to the image planar. However, such technique cannot properly handle satellite videos, as the local misalignment on them is caused by the varying projections from the 3D objects on the Earth's surface to 2D image planar. In order to suppress the effect of moving satellite platform in MOD, we propose a Moving-Confidence-Assisted Matrix Decomposition (MCMD) model, where foreground regularization is designed to promote real moving objects and ignore system movements with the assistance of a moving-confidence score estimated from dense optical flows. For solving the convex optimization problem in MCMD, both batch processing and online solutions are developed in this study, by adopting the alternating direction method and the stochastic optimization strategy, respectively. Experimental results on the videos captured by SkySat and Jilin-1 show that MCMD outperforms the state-of-the-art techniques with improved precision by suppressing effect of nonstationary satellite platforms.
Training SAR-ATR Models for Reliable Operation in Open-World Environments

Training deep learning-based synthetic aperture radar automatic target recognition (SAR-ATR) systems for use in an “open-world” operating environment has, thus far proven difficult. Most SAR-ATR systems are designed to achieve maximum accuracy for a limited set of classes, yet ignore the implications of encountering novel target classes during deployment. Even worse, the standard deep learning training objectives fundamentally inherit a closed-world assumption, and provide no guidance for how to handle out-of-distribution (OOD) data. In this work, we develop a novel training procedure called adversarial outlier exposure (AdvOE) to codesign the ATR system for accuracy and OOD detection. Our method introduces a large, diverse, and unlabeled auxiliary training dataset containing samples from the OOD set. The AdvOE objective encourages a deep neural network to learn robust features of the in-distribution training data, while also promoting maximum entropy predictions for adversarially perturbed versions of the OOD data. We experiment with the recent SAMPLE dataset, and find our method nearly doubles the OOD detection performance over the baseline in key settings, and excels when using only synthetic training data. As compared to several other advanced ATR training techniques, AdvOE also affords significant improvements in both classification and detection statistics. Finally, we conduct extensive experiments that measure the effect of OOD set granularity on detection rates; discuss the implications of using different detection algorithms; and develop a novel analysis technique to validate our findings and interpret the OOD detection problem from a new perspective.
Promoting Reality Awareness in Virtual Reality through Proxemics

Head-Mounted Virtual reality (VR) systems provide full-immersive experiences to users and completely isolate them from the outside world, placing them in unsafe situations. Existing research proposed different alert-based solutions to address this. Our work builds on these studies on notification systems for VR environments from a different perspective. We focus on: (i) exploring alert systems to notify VR users about non-immersed bystanders' in socially related, non-critical interaction contexts; (ii) understanding how best to provide awareness of non-immersed bystanders while maintaining presence and immersion within the Virtual Environment(VE). To this end, we developed single and combined alert cues - leveraging proxemics, perception channels, and push/pull approaches and evaluated those via two user studies. Our findings indicate a strong preference towards maintaining immersion and combining audio and visual cues, push and pull notification techniques that evolve dynamically based on proximity.
Prioritized Architecture Sampling with Monto-Carlo Tree Search

One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once. However, current methods select each operation independently without considering previous layers. Besides, the historical information obtained with huge computation costs is usually used only once and then discarded. In this paper, we introduce a sampling strategy based on Monte Carlo tree search (MCTS) with the search space modeled as a Monte Carlo tree (MCT), which captures the dependency among layers. Furthermore, intermediate results are stored in the MCT for future decisions and a better exploration-exploitation balance. Concretely, MCT is updated using the training loss as a reward to the architecture performance; for accurately evaluating the numerous nodes, we propose node communication and hierarchical node selection methods in the training and search stages, respectively, making better uses of the operation rewards and hierarchical information. Moreover, for a fair comparison of different NAS methods, we construct an open-source NAS benchmark of a macro search space evaluated on CIFAR-10, namely NAS-Bench-Macro. Extensive experiments on NAS-Bench-Macro and ImageNet demonstrate that our method significantly improves search efficiency and performance. For example, by only searching 20 architectures, our obtained architecture achieves 78.0% top-1 accuracy with 442M FLOPs on ImageNet. Code (Benchmark) is available at: https://github.com/xiusu/NAS-Bench-Macro.
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification

Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks (GNN), but these approaches are usually vulnerable to parsing errors.To better leverage syntactic information in the face of unavoidable errors, we propose a simple yet effective graph ensemble technique, GraphMerge, to make use of the predictions from different parsers.Instead of assigning one set of model parameters to each dependency tree, we first combine the dependency relations from different parses before applying GNNs over the resulting graph.This allows GNN models to be robust to parse errors at no additional computational cost, and helps avoid overparameterization and overfitting from GNN layer stacking by introducing more connectivity into the ensemble graph.Our experiments on the SemEval 2014 Task 4 and ACL 14 Twitter datasets show that our GraphMerge model not only outperforms models with single dependency tree, but also beats other ensemble models without adding model parameters.
5G-enabled contactless multi-user presence and activity detection for independent assisted living

Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors' knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being.
Zero-Watermarking Algorithm for Medical Image Based on VGG19 Deep Convolution Neural Network

Aiming at the security issues in the storage and transmission of medical images in the medical information system, combined with the special requirements of medical images for the protection of lesion areas, this paper proposes a robust zero-watermarking algorithm for medical images' security based on VGG19. First, the pretrained VGG19 is used to extract deep feature maps of medical images, which are fused into the feature image. Second, the feature image is transformed by Fourier transform, and low-frequency coefficients of the Fourier transform are selected to construct the feature matrix of the medical image. Then, based on the low-frequency part of the feature matrix of the medical image, the mean-perceptual hashing algorithm is used to achieve a set of 64-bit binary perceptual hashing values, which can effectively resist local nonlinear geometric attacks. Finally, the algorithm adopts a watermarking image after scrambling and the 64-bit binary perceptual hashing value to obtain robust zero-watermarking. At the same time, the proposed algorithm utilizes Hermite chaotic neural network to scramble the watermarking image for secondary protection, which enhances the security of the algorithm. Compared with the existing related works, the proposed algorithm is simple to implement and can effectively resist local nonlinear geometric attacks, with good robustness, security, and invisibility.
Security analysis of Pan et al.’s “Public-key authenticated encryption with keyword search achieving both multi-ciphertext and multi-trapdoor indistinguishability”

Recently, Pan et al. proposed the “Public-Key Authenticated Encryption with Keyword Search Achieving both Multi-Ciphertext and Multi-Trapdoor Indistinguishability” in Pan and Li (2021). However, after carefully revisiting the scheme, we found an attack on Pan et al.’s scheme. As a result, the multi-ciphertext indistinguishability of their scheme is totally broken. In addition, we also pointed out a serious mistake in the security proof of multi-trapdoor indistinguishability in Pan’s scheme.