Reviewerly/exHarmony huggingface dataset:
A Deep Multi-View Framework for Anomaly Detection on Attributed Networks

The explosion of modeling complex systems using attributed networks boosts the research on anomaly detection in such networks, which can be applied in various high-impact domains. Many existing attempts, however, do not seriously tackle the inherent multi-view property in attribute space but concatenate multiple views into a single feature vector, which inevitably ignores the incompatibility between heterogeneous views caused by their own statistical properties. Actually, the distinct but complementary information brought by multi-view data promises the potential for more effective anomaly detection than the efforts only based on single-view data. Furthermore, the abnormal patterns naturally behave diversely in different views, which coincides with people’s desire to discover specific abnormality according to their preferences for views (attributes). Most existing methods cannot adapt to people’s requirements as they fail to consider the idiosyncrasy of user preferences. Therefore, we propose a multi-view framework <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Alarm</small> to incorporate user preferences into anomaly detection and simultaneously tackle heterogeneous attribute characteristics through multiple graph encoders and a well-designed aggregator that supports self-learning and user-guided learning. Experiments on synthetic and real-world datasets, e.g., Disney, Books, and Enron, corroborate the improvement of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Alarm</small> in detection accuracy evaluated by the AUC metric and its effectiveness in supporting user-oriented anomaly detection.
KASLR: Break It, Fix It, Repeat

In this paper, we analyze the hardware-based Meltdown mitigations in recent Intel microarchitectures, revealing that illegally accessed data is only zeroed out. Hence, while non-present loads stall the CPU, illegal loads are still executed. We present EchoLoad, a novel technique to distinguish load stalls from transiently executed loads. EchoLoad allows detecting physically-backed addresses from unprivileged applications, breaking KASLR in 40's on the newest Meltdown- and MDS-resistant Cascade Lake microarchitecture. As EchoLoad only relies on memory loads, it runs in highly-restricted environments, e.g., SGX or JavaScript, making it the first JavaScript-based KASLR break. Based on EchoLoad, we demonstrate the first proof-of-concept Meltdown attack from JavaScript on systems that are still broadly not patched against Meltdown, i.e., 32-bit x86 OSs. We propose FLARE, a generic mitigation against known microarchitectural KASLR breaks with negligible overhead. By mapping unused kernel addresses to a reserved page and mirroring neighboring permission bits, we make used and unused kernel memory indistinguishable, i.e., a uniform behavior across the entire kernel address space, mitigating the root cause behind microarchitectural KASLR breaks. With incomplete hardware mitigations, we propose to deploy FLARE even on recent CPUs.
ODOB: One Drone One Block-based Lightweight Blockchain Architecture for Internet of Drones

The Internet of Drones (IoD) is an environment, which facilitates the autonomous and hybrid (semi-autonomous) operations of drones. Due to this advantage, the drones have paved their way into every segment (almost) across the world. Whatever be the application (warfare, surveillance, photography, rescue, delivery, etc), the transmission of data (to and fro) occur between the drones and the other infrastructure over wireless channels. While on the move, this data (operational or service-related) is vulnerable to several security risks and attacks. Hence, the maintenance of the confidentiality, integrity, and authenticity of the data is the primary goal in the IoD environment. Recently, the blockchain, a distributed ledger-based technology is being popularly adapted to store the data immutably, making it a possible solution to handle the above-raised issues. But, there are several challenges with conventional blockchain architecture, which make it difficult to adapt it in its current form. Firstly, the blockchain works by building trust in the trust-less environment by using a consensus mechanism. Secondly, the blockchain inherently involves a large number of network communication operations to synchronize the peer-to-peer network. Even more, the bigger the blockchain grows, the more is the data flowing across the network and so does the challenges. Therefore, to overcome these problems, ODOB: One Drone One Block-based Lightweight Blockchain Architecture for IoD is proposed. ODOB decouples the data part (or block ledger) from the block header to form a distributed architecture. This architecture couple the drones with an individual amendable block (each drone can access only their own block), thereby making it simple, trustworthy, and lightweight. ODOB is evaluated for several parameters and the results obtained favor the proposed architecture.
Improving Hamiltonian encodings with the Gray code

Due to the limitations of present-day quantum hardware, it is especially critical to design algorithms that make the best possible use of available resources. When simulating quantum many-body systems on a quantum computer, straightforward encodings that transform many-body Hamiltonians into qubit Hamiltonians use $N$ of the available basis states of an $N$-qubit system, whereas $2^N$ are in theory available. We explore an efficient encoding that uses the entire set of basis states, where terms in the Hamiltonian are mapped to qubit operators with a Hamiltonian that acts on the basis states in Gray code order. This encoding is applied to the commonly-studied problem of finding the ground state energy of a deuteron with a simulated variational quantum eigensolver (VQE). It is compared to a standard "one-hot" encoding, and various trade-offs that arise are analyzed. The energy distribution of VQE solutions has smaller variance than the one obtained by the one-hot encoding even in the presence of simulated hardware noise, despite an increase in the number of measurements. The reduced number of qubits and a shorter-depth variational ansatz enables the encoding of larger problems on current-generation machines. This encoding also demonstrates improvements for simulating time evolution of the same system, producing circuits for the evolution operators with reduced depth and roughly half the number of gates compared to a one-hot encoding.
Knowledge Distillation for Semantic Segmentation Using Channel and Spatial Correlations and Adaptive Cross Entropy

In this paper, we propose an efficient knowledge distillation method to train light networks using heavy networks for semantic segmentation. Most semantic segmentation networks that exhibit good accuracy are based on computationally expensive networks. These networks are not suitable for mobile applications using vision sensors, because computational resources are limited in these environments. In this view, knowledge distillation, which transfers knowledge from heavy networks acting as teachers to light networks as students, is suitable methodology. Although previous knowledge distillation approaches have been proven to improve the performance of student networks, most methods have some limitations. First, they tend to use only the spatial correlation of feature maps and ignore the relational information of their channels. Second, they can transfer false knowledge when the results of the teacher networks are not perfect. To address these two problems, we propose two loss functions: a channel and spatial correlation (CSC) loss function and an adaptive cross entropy (ACE) loss function. The former computes the full relationship of both the channel and spatial information in the feature map, and the latter adaptively exploits one-hot encodings using the ground truth labels and the probability maps predicted by the teacher network. To evaluate our method, we conduct experiments on scene parsing datasets: Cityscapes and Camvid. Our method presents significantly better performance than previous methods.
CariGAN: Caricature generation through weakly paired adversarial learning

Caricature generation is an interesting yet challenging task. The primary goal is to generate a plausible caricature with reasonable exaggerations given a face image. Conventional caricature generation approaches mainly use low-level geometric transformations such as image warping to generate exaggerated images, which lack richness and diversity in terms of content and style. The recent progress in generative adversarial networks (GANs) makes it possible to learn an image-to-image transformation from data so as to generate diverse images. However, directly applying GAN-based models to this task leads to unsatisfactory results due to the large variance in the caricature distribution. Moreover, conventional models typically require pixel-wisely paired training data which largely limits their usage scenarios. In this paper, we model caricature generation as a weakly paired image-to-image translation task, and propose CariGAN to address these issues. Specifically, to enforce reasonable exaggeration and facial deformation, manually annotated caricature facial landmarks are used as an additional condition to constrain the generated image. Furthermore, an image fusion mechanism is designed to encourage our model to focus on the key facial parts so that more vivid details in these regions can be generated. Finally, a diversity loss is proposed to encourage the model to produce diverse results. Extensive experiments on a large-scale “WebCaricature” dataset show that the proposed CariGAN can generate more visually plausible caricatures with larger diversity compared with the state-of-the-art models.
A novel image scaling based reversible watermarking scheme for secure medical image transmission

Reversible watermarking is an active research area in the field of data security. In this manuscript, a reversible watermarking scheme able for electronic patient record (EPR) transmission and medical image authentication is introduced. The proposed scheme computes an adaptive authentication code from the image to be watermarked. Further, the watermark (authentication code and EPR) are embedded into the medical image through a novel image scaling up operation. While scaling up an original image with M×M pixels by a factor of 2, 3M2 number of pixel intensity values in the scaled-up image needs to be approximated by considering the original pixel values. In this work, a set of rules is defined to insert authentication code and EPR data through the copying of neighborhood pixels as the missing pixel intensity values in the new scaled-up image. When the neighborhood pixels have the same intensity value, the adaptive pixel copy operation will not be successful to insert the watermark. In such cases, the bit in the least significant position uses for the watermarking process. The standard medical images downloaded OsriX dataset is used for experimental study. The results obtained show that the new scheme introduced in this manuscript surpasses the existing reversible watermarking. The scheme introduced in this paper can be used as part of an automated system where EPR data needs to be transmitted along with the medical images for better healthcare services.
Automated prediction of visual complexity of web pages: Tools and evaluations

Understanding visual complexity as it relates to websites has been an emergent area for many years. However, predicting the visual complexity of a website as perceived by users has been a real challenge. Perception is important because it influences user engagement, dictating if they will find it dull, engaging, or too complex. While others have suggested solutions to certain levels of success, here we propose a simple but accurate model that generates a Visual Complexity Score (VCS) based on common aspects of an HTML Document Object Model (DOM). We created our model based on a statistical analysis of 3300 ratings of 55 users on 30 web pages. We then implemented this prediction model in an open source Eclipse framework called ViCRAM that both predicts and visualises the complexity of web pages in the form of a pixelated heat map. Finally, we evaluated this model and the tool prediction with another user study of 6240 ratings of 104 users on 30 web pages. This study shows that our tool can predict the perceived complexity with a strong correlation to users’ perceived complexity.
ChartSeer: Interactive Steering Exploratory Visual Analysis With Machine Intelligence

During exploratory visual analysis (EVA), analysts need to continually determine which subsequent activities to perform, such as which data variables to explore or how to present data variables visually. Due to the vast combinations of data variables and visual encodings that are possible, it is often challenging to make such decisions. Further, while performing local explorations, analysts often fail to attend to the holistic picture that is emerging from their analysis, leading them to improperly steer their EVA. These issues become even more impactful in the real world analysis scenarios where EVA occurs in multiple asynchronous sessions that could be completed by one or more analysts. To address these challenges, this work proposes ChartSeer, a system that uses machine intelligence to enable analysts to visually monitor the current state of an EVA and effectively identify future activities to perform. ChartSeer utilizes deep learning techniques to characterize analyst-created data charts to generate visual summaries and recommend appropriate charts for further exploration based on user interactions. A case study was first conducted to demonstrate the usage of ChartSeer in practice, followed by a controlled study to compare ChartSeer's performance with a baseline during EVA tasks. The results demonstrated that ChartSeer enables analysts to adequately understand current EVA status and advance their analysis by creating charts with increased coverage and visual encoding diversity.
Self-Supervised Agent Learning for Unsupervised Cross-Domain Person Re-Identification

Unsupervised person re-identification (Re-ID) has better scalability and practicability than supervised Re-ID in the actual deployment. However, it is difficult to learn a discriminative Re-ID model without annotations. To address the above issue, we propose an end-to-end Self-supervised Agent Learning (SAL) algorithm by exploiting a set of agents as a bridge to reduce domain gaps for unsupervised cross-domain person Re-ID. The proposed SAL model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit self-supervised learning for unsupervised person Re-ID. Second, our model has designed three effective learning mechanisms including supervised label learning in source domain, similarity consistency learning in target domain, and self-supervised learning in cross domain, which can learn domain-invariant yet discriminative representations through the principled lens of agent learning by reducing domain discrepancy adaptively. Extensive experimental results on three standard benchmarks demonstrate that the proposed SAL performs favorably against state-of-the-art unsupervised person Re-ID methods.
Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection

Convolutional neural networks (CNNs) have been successfully applied in many computer vision applications [1], especially in image classification tasks, where most of the structures have been designed manually.With the aid of skip connection and dense connection, the depths of the models are becoming "deeper" and the filters of layers are getting "wider" in order to tackle the challenge of large-scale datasets.However, large-scale models in convolutional layers become inefficient due to the redundant channels from input feature maps.In this paper, we aim to automatically optimize the topology of the DenseNet, in which unnecessary convolutional kernels are reduced.To achieve this, we present a training pipeline that generates the network structure using a genetic algorithm.We first propose two encoding methods that can represent the structure of the model using a fixed-length binary string.A three-step based evolutionary process consisting of selection, crossover, and mutation is proposed to optimize the structure.We also present a pretrained weight inheritance method which can largely reduce the total time consumption of the genetic process.Experimental results have demonstrated that our proposed model can achieve comparable accuracy to the state-of-the-art models, across a wide range of image recognition and classification datasets, whilst significantly reducing the number of parameters.
Correlation Networks for Extreme Multi-label Text Classification

This paper develops the Correlation Networks (CorNet) architecture for the extreme multi-label text classification (XMTC) task, where the objective is to tag an input text sequence with the most relevant subset of labels from an extremely large label set. XMTC can be found in many real-world applications, such as document tagging and product annotation. Recently, deep learning models have achieved outstanding performances in XMTC tasks. However, these deep XMTC models ignore the useful correlation information among different labels. CorNet addresses this limitation by adding an extra CorNet module at the prediction layer of a deep model, which is able to learn label correlations, enhance raw label predictions with correlation knowledge and output augmented label predictions. We show that CorNet can be easily integrated with deep XMTC models and generalize effectively across different datasets. We further demonstrate that CorNet can bring significant improvements over the existing deep XMTC models in terms of both performance and convergence rate. The models and datasets are available at: https://github.com/XunGuangxu/CorNet.
Quantum and classical genetic algorithms for multilevel segmentation of medical images: A comparative study

In this paper, we propose a multilevel segmentation methods of medical images based on the classical and quantum genetic algorithms. The Genetic Algorithm (GA) uses a binary coding while the Quantum Genetic Algorithm (QGA) uses the qubit encoding of individuals. The two evolutionary algorithms are employed to maximize efficiently Rényi, Masi and Shannon entropies for the purpose of multi-objects segmentation of medical images. The Particle Swarm Optimization algorithm (PSO) was also used for comparison reasons. The segmentation quality of the nine proposed approaches is assessed by means of the prevailing indices PSNR, SSIM and FSIM. The numerical results and the comparative study were carried out on a sample of twenty medical images. It was shown that the QGA outpaces the GA, and the PSO outperforms significantly the both algorithms in the optimization task. Finally, it was found that the Rényi entropy is more suitable for the purpose of medical image multilevel thresholding.
Redundancy-Free Computation for Graph Neural Networks

Graph Neural Networks (GNNs) are based on repeated aggregations of information from nodes' neighbors in a graph. However, because nodes share many neighbors, a naive implementation leads to repeated and inefficient aggregations and represents significant computational overhead. Here we propose Hierarchically Aggregated computation Graphs(HAGs), a new GNN representation technique that explicitly avoids redundancy by managing intermediate aggregation results hierarchically and eliminates repeated computations and unnecessary data transfers in GNN training and inference. HAGs perform the same computations and give the same models/accuracy as traditional GNNs, but in a much shorter time dueto optimized computations. To identify redundant computations,we introduce an accurate cost function and use a novel search algorithm to find optimized HAGs. Experiments show that the HAG representation significantly outperforms the standard GNN by increasing the end-to-end training throughput by up to 2.8× and reducing the aggregations and data transfers in GNN training byup to 6.3× and 5.6×, with only 0.1% memory overhead. Overall,our results represent an important advancement in speeding-up and scaling-up GNNs without any loss in model predictive performance.
The evaluation of user experience of a human walking and a driving simulation in the virtual reality

Virtual reality (VR) has been implemented in various applications such as gaming, e-learning, rehabilitation, etc. Although many studies have been conducted to evaluate the user experiences (UX) of various applications in VR, there are few studies evaluating the UX of VR in a holistic view. Since user experience means holistic feelings caused by attributes like efficiency, effectiveness, pleasure, and attractiveness, it is necessary to evaluate user experience in many respects, including presence, usability, and workload. Therefore, this study aims to evaluate the UX of VR in four various metrics: presence, workload, usability, and flow. In this study, the user experience factor was confirmed through the previous studies comparing visual display terminals (VDT) and head-mounted displays (HMD). In addition, the UX difference between VDT and HMD in two different situations, walking and driving, is analyzed. The data of 26 participants were analyzed and found that there were significant differences in the four studied metrics related to UX. In the driving situation, the usability value was higher in VDT, whereas they were higher in HMD for walking. Comparing the two contexts in HMD, walking was significantly higher in three metrics: presence, usability, and flow. The results show that HMD is better in terms of user experience than VDT, and HMD is more suitable for walking situations than for driving situations. Through this study, in terms of user experience, it is suitable to replace walking tasks with VR when the experiment in a real-world environment is impossible.
Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.