Reviewerly/exHarmony huggingface dataset:
Building Damage Assessment From Post-Hurricane Imageries Using Unsupervised Domain Adaptation With Enhanced Feature Discrimination

Rapid damage assessment after hurricane disasters is crucial for initiating effective emergency response actions. Unsupervised domain adaptation (UDA), which improves the classification accuracy of the target domain with leveraging abundant labeled data of the source domain, exhibits the potential to solve the issue of lacking labeled data in the task of damage building assessment. However, the application of UDA to building damage detection remains a challenge due to the complexity of post-hurricane imageries. The image characteristics of the source domain differ from those of the target domain, and the similarity of interclass samples is high, thereby degrading the transfer performance of UDA. We propose Duplex Alignment Networks with enhanced feature discrimination, which consists of a pair of generative adversarial networks (GANs) and a dedicated classifier, to address this issue. Our approach can be highlighted in two aspects: 1) a pair of GANs are used to eliminate the discrepancy between the source and the target domains by the alignments at the feature and pixel levels and 2) a dedicated classifier is integrated into the second discriminator to improve the discrimination of the extracted features of the target domain, which can alleviate performance deterioration caused by the high similarity of interclass samples. The proposed approach is validated by two challenging transfer tasks by using the data sets of Hurricanes Sandy, Maria, and Irma. Average accuracy rates of 85.1% (Hurricane Sandy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> Maria) and 98.3% (Hurricane Sandy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> Irma) are achieved, thereby leading to superior performance compared with the state-of-the-art methods.
Non-Linear-Echo Based Anti-Collusion Mechanism for Audio Signals

Collusion attacks are considered to be challenging attacks in audio copyright protection. The traditional watermarking algorithms cannot identify the traitors when other attacks, such as desynchronization attacks, are applied with a collusion attack. Instead of tracing the traitors, in this paper we aim to tackle collusion attacks by removing the commercial value from the colluded copy, which will demotivate the attackers from launching collusion attacks. Since the commercial value of an audio signal is directly reflected by its perceptual quality, we propose a novel non-linear-echo generation (NLEG) based algorithm to significantly degrade the perceptual quality of the colluded copy by embedding a time delay sequence into the host signal. The proposed NLEG is also designed to be resilient to common signal processing attacks and desynchronization attacks. Furthermore, the proposed NLEG can be combined with other digital watermarking techniques to enhance its performance on protecting the copyright information. Experimental results show the validity of the proposed NLEG.
Real-Time Gaze-Aware Cognitive Support System for Security Surveillance

Security surveillance entails many cognitive challenges (e.g., task interruption, vigilance decrements, cognitive overload). To help surveillance operators overcome these difficulties and perform more efficient visual search, gaze-based intelligent systems can be developed. The present study aimed at testing the impact of the Scantracker system—which pinpointed neglected cameras while detecting and correcting attentional tunneling and vigilance decrease—on human scanning behavior and surveillance performance. Participants took part in a surveillance simulation, monitoring cameras and searching for ongoing incidents, and half of them was supported by the Scantracker. Although behavioral surveillance performance was not improved, participants supported by the Scantracker showed more efficient gaze-based measures of surveillance. Moreover, some of these measures were associated with performance, suggesting that scan pattern improvements might lead indirectly to more efficient incident detection. Overall, these results speak to the potential of using gaze- aware intelligent systems to support surveillance operators.
Multi-Sensor Complex Network Data Fusion Under the Condition of Uncertainty of Coupling Occurrence Probability

Based on the time and location information of nodes, multi-sensor complex networks can achieve high-speed and low-delay exchange of control and sensor data between sensor nodes to ensure the accuracy and real-time performance of the entire detection and control system. However, complex multi-sensor networks face limitations in terms of computing, storage, and network resources. After grouping the sensor data of nodes, this paper employs an adaptive weighted data fusion method to complete the data fusion processing in the network. This approach can significantly reduce the data redundancy in the wireless sensor network, save a large amount of storage resources, and lower the network bandwidth occupation, with high efficiency and good scalability. The least square (LS) fitting is performed by two sets of temperature and humidity data obtained by weighted fusion at the cluster head node. Experimental results indicate that the fusion of two-dimensional data transmitted to the base station or control center can further diminish the amount of data transmission. In addition, after the adaptive weighted data fusion of the two-dimensional data at the cluster head node, the energy consumption of the cluster head node without the fusion and the cluster head node after the fusion is compared. The results demonstrate that the energy consumption of the cluster head node is notably abridged, the energy of the cluster head node is saved to a greater extent, and the life cycle of the network is prolonged.
PerfNetRT: Platform-Aware Performance Modeling for Optimized Deep Neural Networks

As deep learning techniques based on artificial neural networks have been widely applied to diverse application domains, the delivered performance of such deep learning models on the target hardware platforms should be taken into account during the system design process in order to meet the application-specific timing requirements. Specifically, there are neural network optimization frameworks available for boosting the execution efficiency of a trained model on the vendor-specific hardware platforms, e.g., OpenVINO for Intel hardware and TensorRT for NVIDIA GPUs, and it is important that system designers have access to the estimated performance of the optimized models running on the specific hardware so as to make better design decisions. In this work, we have developed PerfNetRT to facilitate the design making process by offering the estimated inference time of a trained model that is optimized for the NVIDIA GPU using TensorRT. Our preliminary results show that PerfNetRT is able to produce accurate estimates of the inference time for the popular models, including LeNet, AlexNet and VGG16, which are optimized with TensorRT running on NVIDIA GTX 1080Ti.
Designated-ciphertext searchable encryption

Public-key encryption with keyword search (PEKS), proposed by Boneh et al. (2004), allows users to search encrypted keywords without losing data privacy. Although extensive studies have been conducted on this topic, only a few have focused on insider-keyword-guessing attacks (IKGA) that can reveal a user's sensitive information. In particular, after receiving a trapdoor used to search ciphertext from a user, a malicious insider (e.g., a server) can randomly encrypt possible keywords using the user's public key, and then test whether the trapdoor corresponds to the selected keyword. This paper introduces a new concept called designated-ciphertext searchable encryption (DCSE), which provides the same desired functionality as a PEKS scheme and prevents IKGA. Each trapdoor in DCSE is designated to a specific ciphertext, and thus malicious insiders cannot perform IKGA. We further propose a generic DCSE construction that employs identity-based encryption and a key encapsulation mechanism. We provide formal proofs to demonstrate that the generic construction satisfies the security requirements. Moreover, we provide a lattice-based instantiation whose security is based on NTRU and ring-learning with errors assumptions; the proposed scheme is thus considered to be resistant to the quantum-computing attacks.
Authorship Verification of Literary Texts with Rhythm Features

The paper is devoted to the authorship verification of literary texts of 19th-21st centuries using rhythm features and statistical analysis of these features. The authors presented algorithms that fully automatically find lexico-grammatical figures in raw texts. Then the authors calculated rhythm features basing on the frequency of the appearance and the structure of these figures. The experiments showed that many English, Russian, French, and Spanish authors were successfully verified with their rhythm features, the best F-measure for the AdaBoost classifier achieved 88-96 %. Besides, rhythm features were clearly visualized in boxplots and heatmaps that allowed to compare the text rhythm in a whole for several authors and languages simultaneously.
Scalable Scalable Vector Graphics: Automatic Translation of Interactive SVGs to a Multithread VDOM for Fast Rendering

The dominant markup language for Web visualizations-Scalable Vector Graphics (SVG)-is comparatively easy to learn, and is open, accessible, customizable via CSS, and searchable via the DOM, with easy interaction handling and debugging. Because these attributes allow visualization creators to focus on design on implementation details, tools built on top of SVG, such as D3.js, are essential to the visualization community. However, slow SVG rendering can limit designs by effectively capping the number of on-screen data points, and this can force visualization creators to switch to Canvas or WebGL. These are less flexible (e.g., no search or styling via CSS), and harder to learn. We introduce Scalable Scalable Vector Graphics (SSVG) to reduce these limitations and allow complex and smooth visualizations to be created with SVG. SSVG automatically translates interactive SVG visualizations into a dynamic virtual DOM (VDOM) to bypass the browser's slow 'to specification' rendering by intercepting JavaScript function calls. De-coupling the SVG visualization specification from SVG rendering, and obtaining a dynamic VDOM, creates flexibility and opportunity for visualization system research. SSVG uses this flexibility to free up the main thread for more interactivity and renders the visualization with Canvas or WebGL on a web worker. Together, these concepts create a drop-in JavaScript library which can improve rendering performance by 3-9× with only one line of code added. To demonstrate applicability, we describe the use of SSVG on multiple example visualizations including published visualization research. A free copy of this article, collected data, and source code are available as open science at osf.io/ge8wp.
Reconstruction of Partially Occluded Facial Image for Classification

The recent literature indicates that face recognition and facial expression classification has achieved a high accuracy on benchmark datasets with a large number of face images in the wild. On the other hand, unlike the purpose of recognizing as many people as possible, real applications for families or companies usually aim to recognize a small group of people as accurate as possible. In case of the face is partially occluded, convolutional solutions always simply put images with occlusions into the training dataset and hope the convolution neural network learn a model robust to partial occlusion. These processes not only increase the burden of learning, but also affect the model to identify normal images without occlusions. In this paper, we investigate the research problem of facial image reconstruction to discard the influence of partial occlusion. Based on the phenomenon that human faces are roughly symmetrical, we propose to reconstruct the facial information of occluded areas with the intact half face. Specifically, occlusion on the left‐half face is reconstructed with a linear combination of features on the right‐half face and vice versa. The process is modeled by keeping row sparsity for the coefficient matrix with l 2,1 ‐norm regularization while minimizing the reconstruction error. An alternative iterative algorithm is proposed to solve the optimization problem. To validate the effectiveness of the reconstruction, we fine‐tune a pretrained model, AlexNet, training on normal face images and test with various occluded images. Extensive experimental results show that our method has improved the classification performance effectively. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking

With the wide application of blockchain in the financial field, the rise of various types of cybercrimes has brought great challenges to the security of blockchain. In order to better understand this emerging market and explore more efficient countermeasures for effective supervision, it is imperative to track transactions on blockchain-based systems. Due to the openness of Ethereum, we can easily access the publicly available transaction records, model them as a complex network, and further study the problem of transaction tracking via link prediction, which provides a deeper understanding of Ethereum transactions from a network perspective. Specifically, we introduce an embedding based link prediction framework that is composed of temporal-amount snapshot multigraph (TASMG) and present temporal-amount walk (TAW). By taking the realistic rules and features of transaction networks into consideration, we propose TASMG to model Ethereum transaction records as a temporal-amount network and then present TAW to effectively embed accounts via their transaction records, which integrates temporal and amount information of the proposed network. Experimental results demonstrate the superiority of the proposed framework in learning more informative representations and could be an effective method for transaction tracking.
Measurement-driven multi-target tracking filter under the framework of labeled random finite set

Multiple clusters of particles characterized in terms of fixed-position and fixed-distribution are utilized for target capture, target state estimation, and track maintenance in traditional particle filters. It is often unsustainable in target acquisition and track maintenance since the prior information of target is hard to acquire. In addition, the obtained measurement information may be fuzzy (interval measurement) in practical engineering applications. This is also a challenge in traditional particle filters. In view of these two problems, a novel measurement-driven AParticle-δ-GLMB-IM filter is proposed. The AParticle-δ-GLMB-IM filter uses RLF to determine the affiliation between the survival targets and the interval measurements. A newborn label or a survival label will be given to the newborn particles driven by the same single interval measurement. Furthermore, this paper presents a new box particle application named ABox-δ-GLMB filter. Numerical experiments verify the effectiveness and superiority of the proposed methods.
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP

Estimating a dense and accurate depth map is the key requirement for autonomous driving and robotics. Recent advances in deep learning have allowed depth estimation in full resolution from a single image. Despite this impressive result, many deep-learning-based monocular depth estimation (MDE) algorithms have failed to keep their accuracy yielding a meter-level estimation error. In many robotics applications, accurate but sparse measurements are readily available from Light Detection and Ranging (LiDAR). Although they are highly accurate, the sparsity limits full resolution depth map reconstruction. Targeting the problem of dense and accurate depth map recovery, this paper introduces the fusion of these two modalities as a depth completion (DC) problem by dividing the role of depth inference and depth regression. Utilizing the state-of-the-art MDE and our Gaussian process (GP) based depth-regression method, we propose a general solution that can flexibly work with various MDE modules by enhancing its depth with sparse range measurements. To overcome the major limitation of GP, we adopt Kernel Interpolation for Scalable Structured (KISS)-GP and mitigate the computational complexity from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to O(N). Our experiments demonstrate that the accuracy and robustness of our method outperform state-of-the-art unsupervised methods for sparse and biased measurements.
Instance-Dependent Positive and Unlabeled Learning with Labeling Bias Estimation

This paper studies instance-dependent Positive and Unlabeled (PU) classification, where whether a positive example will be labeled (indicated by s) is not only related to the class label y, but also depends on the observation x. Therefore, the labeling probability on positive examples is not uniform as previous works assumed, but is biased to some simple or critical data points. To depict the above dependency relationship, a graphical model is built in this paper which further leads to a maximization problem on the induced likelihood function regarding P(s,y|x). By utilizing the well-known EM and Adam optimization techniques, the labeling probability of any positive example P(s=1|y=1,x) as well as the classifier induced by P(y|x) can be acquired. Theoretically, we prove that the critical solution always exists, and is locally unique for linear model if some sufficient conditions are met. Moreover, we upper bound the generalization error for both linear logistic and non-linear network instantiations of our algorithm. Empirically, we compare our method with state-of-the-art instance-independent and instance-dependent PU algorithms on a wide range of synthetic, benchmark and real-world datasets, and the experimental results firmly demonstrate the advantage of the proposed method over the existing PU approaches.
VAE-GAN Based Zero-shot Outlier Detection

Outlier detection is one of the main fields in machine learning and it has been growing rapidly due to its wide range of applications. In the last few years, deep learning-based methods have outperformed machine learning and handcrafted outlier detection techniques, and our method is no different. We present a new twist to generative models which leverages variational autoencoders as a source for uniform distributions which can be used to separate the inliers from the outliers. Both the generative and adversarial parts of the model are used to obtain three main losses (Reconstruction loss, KL-divergence, Discriminative loss) which in return are wrapped with a one-class SVM which is used to make the predictions. We evaluated our method against several datasets both for images and tabular data and it has shown great results for the zero-shot outlier detection problem and was able to easily generalize it for supervised outlier detection tasks on which the performance has increased. For comparison, we evaluated our method against several of the common outlier detection techniques such as DBSCAN-based outlier detection, GMM, K-means and one class SVM directly, and we have outperformed all of them on all datasets.
G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling

In the realms of computer vision, it is evident that deep neural networks perform better in a supervised setting with a large amount of labeled data. The representations learned with supervision are not only of high quality but also helps the model in enhancing its accuracy. However, the collection and annotation of a large dataset are costly and time-consuming. To avoid the same, there has been a lot of research going on in the field of unsupervised visual representation learning especially in a self-supervised setting. Amongst the recent advancements in self-supervised methods for visual recognition, in SimCLR Chen et al. shows that good quality representations can indeed be learned without explicit supervision. In SimCLR, the authors maximize the similarity of augmentations of the same image and minimize the similarity of augmentations of different images. A linear classifier trained with the representations learned using this approach yields 76.5% top-1 accuracy on the ImageNet ILSVRC-2012 dataset. In this work, we propose that, with the normalized temperature-scaled cross-entropy ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{NT}-\boldsymbol{Xent}$</tex> ) loss function (as used in SimCLR), it is beneficial to not have images of the same category in the same batch. In an unsupervised setting, the information of images pertaining to the same category is missing. We use the latent space representation of a denoising autoencoder trained on the unlabeled dataset and cluster them with k-means to obtain pseudo labels. With this apriori information we batch images, where no two images from the same category are to be found. We report comparable performance enhancements on the CIFAR10 dataset and a subset of the ImageNet dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/thunderInfy/imagenet-5-categories. We refer to our method as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{G}-\boldsymbol{SimCLR}$</tex> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Code available at https://github.com/ariG23498/G-SimCLR.
Aerial-DeepSearch: Distributed Multi-Agent Deep Reinforcement Learning for Search Missions

Search and Rescue (SAR) is an important part of several applications of national and social interest. Existing solutions for search missions in both terrestrial and aerial domains are mostly limited to single agent and specific environments; however, search missions can significantly benefit from the use of multiple agents that can quickly adapt to new environments. In this paper, we propose a framework based on Multi-Agent Deep Reinforcement Learning (MADRL) that realizes the actor-critic framework in a distributed manner for coordinating multiple Unmanned Aerial Vehicles (UAVs) in the exploration of unknown regions. One of the original aspects of our work is that the actors represent simulated or actual UAVs exploring the environment in parallel instead of traditional computer threads. Also, we propose addition of Long Short Term Memory (LSTM) neural network layers to the actor and critic architectures to handle imperfect communication and partial observability scenarios. The proposed approach has been evaluated in a grid world and has been compared against other competing algorithms such as Multi-Agent Q-Learning, Multi-Agent Deep Q-Learning to show its advantages. More generally, our approach could be extended to image-based/continuous action space environments as well.