Reviewerly/exHarmony huggingface dataset:
User-Driven Synthetic Dataset Generation With Quantifiable Differential Privacy

Recently, releasing data to a third party for secondary analysis has become a trend of service computing. However, data owners are concerned that such a move may expose individuals’ records, which is in violation of regulations such as the European Union's General Data Protection Regulation. Differential privacy has been proposed as a possible solution to the aforementioned problem. The privacy budget <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> in differential privacy is for theoretical interpretation, but in practice, its application in measuring the risk of data disclosure has not been well studied, especially with sampling-based synthetic datasets. Moreover, datasets released by data owners with quantifiable privacy levels and the explicit utility for these datasets have yet to be well developed. In this paper, we present an intuitive approach for defining the privacy level (i.e., data hit rate and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -level) and utility level (i.e., basic statistics and a series of data mining models), and the privacy budget <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> is quantified for evaluating the risk and utility of private data. In addition, we propose two user-driven synthetic dataset hunting methods to generate a synthetic dataset with the specified privacy objective, enabling the data owner (e.g., the government and financial companies) to understand the possible privacy risk and thereby release datasets with confirmed privacy level. To the best of our knowledge, this is the first method that allows data providers to automatically generate synthetic datasets with a quantifiable privacy level for the service of open data.
ARWGAN: Attention-Guided Robust Image Watermarking Model Based on GAN

In the existing deep learning based watermarking models, extracted image features for fusing with watermark are not abundant enough and more critically, essential features are not highlighted to be learned with the purpose of robust watermarking, both of which limit the watermarking performance. To solve those two drawbacks, this paper proposes an attention-guided robust image watermarking model based on generative adversarial network (ARWGAN). To acquire a great deal of representational image features, a feature fusion module (FFM) is devised to learn shallow and deep features effectively for multi-layer fusion with watermark, and meanwhile, reuse of those features by the dense connection enhances robustness. To alleviate image distortion caused by embedding watermark, an attention module (AM) is deployed to compute the attention mask by mining the global features of the original image. Specifically, with the guidance of the attention mask, image features representing inconspicuous regions and texture regions are enhanced for embedding the high strength of watermark, and simultaneously other features are suppressed to improve the watermarking performance. Furthermore, the noise sub-network is adopted for robustness enhancement by simulating various image attacks in iterative training. The discriminator is used to distinguish the encoded image from the original image for improving watermarking invisibility continuously. Experimental results demonstrate that ARWGAN is superior to the existing state-of-the-art watermarking models, and ablation experiments prove the effectiveness of the FFM and the AM. The code is avaliable in https://github.com/river-huang/ARWGAN.
Stereo Visual Inertial Pose Estimation Based on Feedforward and Feedbacks

In this article, we present a stereo visual inertial pose estimation method based on feedforward and feedbacks. Compared to the widely used filter-based or optimization-based approaches, the proposed method only stores the most recent pose and measurements and thus can achieve fast processing. A gradient decreased feedback, a roll-pitch feedforward, and a bias estimation feedback, are introduced to fuse the vision and the inertial measurements. This system, which is called feedforward and feedback based visual inertial system (FVIS), is evaluated on the popular European robotics challenge micro aerial vehicle (EuRoC MAV) dataset. FVIS achieves high accuracy and robustness with respect to existing visual inertial simultaneous localization and mapping (SLAM) approaches. FVIS has also been implemented and tested on a unmanned aerial vehicle (UAV) platform. The source code developed during this study is available publicly.
Multivariate Variance-Based Genetic Ensemble Learning for Satellite Anomaly Detection

Proactive diagnosis of spacecraft issues and response to conceivable hazards has attracted considerable interest. Hidden anomalies in satellites can cause overall system degradation. In multivariate time series anomaly detection (AD), several sensors report to a fusion center at each timestamp. Each of these sensors measures a specific feature of the satellites. In this paper, we first leverage the eigenvalues of covariance metrics of a multivariate time series to determine the anomaly score at each timestamp. Then, the anomaly scores are fed to various machine learning models. After that, we employ a Multivariate Variance-based Genetic Ensemble (MVVGE) learning method to ensemble the results of several models based on their corresponding performance. More specifically, we ensemble different Neural Network (NN), Random Forest (RF), and Linear Regression (LR) models based on their associated variance to catch the anomalies of three datasets, i.e., two satellite datasets: i) Soil Moisture Active Passive (SMAP), ii) Mars Science Laboratory rover (MSL), and Server Machine Dataset (SMD). Given that establishing the model confidence region is the bottleneck of our approach, we use an approximated version of the Bayesian NNs (BNN)s for acquiring confidence intervals of NN-based models. For RF and LR confidence intervals, we use an empirical method and bootstrapping, respectively. Simulation results confirm the superiority of our proposed approach compared to other methods.
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses

Adversarial training (AT) is considered to be one of the most reliable defenses against adversarial attacks. However, models trained with AT sacrifice standard accuracy and do not generalize well to unseen attacks. Recent works show generalization improvement with adversarial samples under unseen threat models such as on-manifold threat model or neural perceptual threat model. However, the former requires exact manifold information while the latter requires algorithm relaxation. Motivated by these considerations, we propose a novel threat model called Joint Space Threat Model (JSTM), which exploits the underlying manifold information with Normalizing Flow, ensuring that the exact manifold assumption holds. Under JSTM, we develop novel adversarial attacks and defenses. Specifically, we propose the Robust Mixup strategy in which we maximize the adversity of the interpolated images and gain robustness and prevent overfitting. Our experiments show that Interpolated Joint Space Adversarial Training (IJSAT) achieves good performance in standard accuracy, robustness, and generalization. IJSAT is also flexible and can be used as a data augmentation method to improve standard accuracy and combined with many existing AT approaches to improve robustness. We demonstrate the effectiveness of our approach on three benchmark datasets, CIFAR-10/100, OM-ImageNet and CIFAR-10-C.
Integrating quantitative and qualitative analysis to evaluate digital applications in museums

Analyzing the effectiveness of a digital museum application is generally a complex process. This is due to the interaction between a digital tool capable of a limited number of narration paths and a human being whose multifaceted reactions are uniquely affected by many cultural, psychological, and emotional elements. The classical approaches in the literature can be divided into two main areas: qualitative methods, usually based on interviews at the end of the experience for assessing the cognitive effect of the digital interaction, and quantitative approaches, generally more oriented with the assessment of the subjects’ emotional reactions. Among the quantitative methods, we can mention those dealing with measurements of physiological parameters during the experience, such as ECG, EEG, temperature, etc. This paper proposes a novel approach based on integrating a qualitative process using a two-stage interview, with the quantitative analysis of the user's keystrokes during his digital interaction with the application. The results show how the quantitative component can decouple the interview answers from the interviewer's possible interferences, simultaneously delivering: i) a more transparent assessment of the user's learning experience and ii) mapping of the application's least effective sections. This provides crucial input for improving the design of digital applications for museums.
Active consistency network for multi-source domain generalization in brain tumor segmentation

Recent advances in deep learning for brain tumor segmentation demonstrate good performance when the training and test data share the same distribution. However, medical images from different medical institutions often face distribution shifts in clinical applications. To deal with domain shift in multi-source domain generalization, we propose Active Consistency Network (ACN) with a single encoder and dual decoders to learn domain invariant features. A causal feature learning strategy and uncertainty-based refinery strategy are proposed to obtain reliable soft labels of reliable samples (Active Consistency Labels, ACL). Active consistency labels remove the noisy and unreliable samples for model consistency. Causality-based Random Masking (CRM) is proposed to obtain more stable causal features by adversarial learning. Causal features help achieve stable uncertainty estimation. Besides, two decoders are supervised by the active consistency labels learned from each other's decoders, which can help decoders learn domain invariant features. Experimental results show that ACN utilizes causal feature learning and active consistency labels improving segmentation performance on multi-institutional brain tumor segmentation and achieving state-of-the-art performance compared to other domain generalization methods.
Adversarially Robust Fairness-Aware Regression

Fairness and robustness are critical elements of trustworthy machine learning systems that need to be addressed. Using a minimax framework, in this paper, we aim to design an adversarially robust fair regression model that achieves optimal performance in the presence of an attacker who is able to perform a rank-one attack on the dataset. By solving the proposed nonsmooth nonconvex-nonconcave minimax problem, the optimal adversary as well as the robust fairness-aware regression model are obtained. Based on two real-world datasets, numerical results illustrate that the proposed adversarially robust fair model has better performance on the poisoned dataset than other fair machine learning models in both prediction accuracy and group-based fairness measure.
Class-Incremental Learning Based on Big Dataset Pre-Trained Models

Deep neural networks have shown excellent performance in the field of pattern classification and are widely used. However, real-world data are often cannot be obtained at once, and the knowledge of old classes will be heavily forgotten when training new classes of data on the network, which is called catastrophic forgetting. Therefore, the incremental learning method to solve this problem came into being. In this paper, we propose a class-incremental learning method based on a big data pre-trained model, which makes full use of the large amount of public knowledge in the pre-trained model’s front network to reduce the forgetting problem of the network in subsequent classification tasks. On the basis of our previous incremental learning method based on PEDCC, we discuss the effects of different pre-trained models, training strategy, training hyperparameters, etc. PEDCC-Loss is used to constrain the cosine distance between the latent feature and the pre-defined class center, and finally the joint prediction is determined by multiple network prediction results. The algorithm in this paper is verified on the CIFAR100, Tiny ImageNet, and FaceScrub datasets with and without partial retention of old samples, and achieves the best results compared to the previous typical class-incremental learning methods. The performance in coarse-grained datasets even exceeds the accuracy of non-incremental learning without pre-trained model. Code is available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/byBinWen/Class-Incremental-Learning-Based-on-Big-Dataset-Pre-trained-Models</uri> .
Contact-Prioritized Planning of Impact-Resilient Aerial Robots With an Integrated Compliant Arm

This article develops an impact-resilient aerial robot (s-ARQ) equipped with a compliant arm to sense contacts and reduce collision impact and featuring a real-time contact force estimator and a nonlinear motion controller to handle collisions while performing aggressive maneuvers and stabilize from high-speed wall collisions. Further, a new collision-inclusive planning method that aims to prioritize contacts to facilitate aerial robot navigation in cluttered environments is proposed. A range of simulated and physical experiments demonstrate key benefits of the robot and the contact-prioritized (CP) planner. Experimental results show that the compliant robot has only a 4% weight increase but around 40% impact reduction in drop tests and wall collision tests. s-ARQ can handle collisions while performing aggressive maneuvers and stabilize from high-speed wall collisions at 3.0 m/s with a success rate of 100%. Our proposed compliant robot and CP planning method can accelerate computation time while having shorter trajectory time and larger clearances compared to A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\ast$</tex-math></inline-formula> and RRT <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\ast$</tex-math></inline-formula> planners with velocity constraints. Online planning tests in partially known environments further demonstrate the preliminary feasibility of our method to apply in practical use cases.
Colliding Bodies Optimization With Deep Belief Network Based Robust Pedestrian Detection

Pedestrian detection is a significant research topic in the computer vision (CV) domain for a longer period. Recently, deep learning (DL) and specifically convolutional neural network (CNN) exhibit significant improvement in the computer vision tasks such as object detection, segmentation, image classification, etc. With this motivation, this study develops a novel Colliding Bodies Optimization with Deep Learning based Robust Pedestrian Detection (CBODL-RPD) model. The goal of the CBODL-RPD approach is to identify the occurrence of pedestrians and non-pedestrians via object detection process. For object detection process, YOLO v4 with Adagrad optimizer is applied. In addition, the CBODL-RPD technique employs SqueezeNet model to generate feature vectors, and the hyperparameter tuning process is performed via the CBO algorithm. At last, deep belief network (DBN) model is applied for accurate pedestrian detection. A comprehensive experimental analysis is made to demonstrate the significant pedestrian detection results of the CBODL-RPD technique. The comparative outcome study reported the improved outcomes of the CBODL-RPD method over other recent methods.
DASTSiam: Spatio‐temporal fusion and discriminative enhancement for Siamese visual tracking

Abstract The use of deep neural networks has revolutionised object tracking tasks, and Siamese trackers have emerged as a prominent technique for this purpose. Existing Siamese trackers use a fixed template or template updating technique, but it is prone to overfitting, lacks the capacity to exploit global temporal sequences, and cannot utilise multi‐layer features. As a result, it is challenging to deal with dramatic appearance changes in complicated scenarios. Siamese trackers also struggle to learn background information, which impairs their discriminative ability. Hence, two transformer‐based modules, the Spatio‐Temporal Fusion (ST) module and the Discriminative Enhancement (DE) module, are proposed to improve the performance of Siamese trackers. The ST module leverages cross‐attention to accumulate global temporal cues and generates an attention matrix with ST similarity to enhance the template's adaptability to changes in target appearance. The DE module associates semantically similar points from the template and search area, thereby generating a learnable discriminative mask to enhance the discriminative ability of the Siamese trackers. In addition, a Multi‐Layer ST module (ST + ML) was constructed, which can be integrated into Siamese trackers based on multi‐layer cross‐correlation for further improvement. The authors evaluate the proposed modules on four public datasets and show comparative performance compared to existing Siamese trackers.
Detection of homophobia and transphobia in YouTube comments

Abstract Users of online platforms have negative effects on their mental health as a direct result of the spread of abusive content across social media networks. Homophobia are terms that refer to the fear, hatred, discomfort, or suspicion of or toward those who identify as homosexual or bisexual. Transphobia is fear, hatred, discomfort toward those who are transgenders. Homophobia/transphobia speechs are a sort of offensive language that can be summed up as hate speech directed toward LGBTQ+ persons, and it has become an increasing concern in recent years. The homophobia and transphobia found online are a serious societal issue that can make online platforms toxic and unwelcoming to LGBTQ+ individuals and hinder the eradication of equality, diversity, and inclusion. We present a new dataset for online homophobia and transphobia detection that has been annotated by experts, which will enable homophobic and transphobic content to be automatically recognized. The dataset includes 15,141 annotated comments written in English, Tamil, and both Tamil and English. Additionally, we provide the outcomes of our benchmark system in a variety of machine learning models. For the purpose of developing benchmark systems, we conducted a number of experiments utilizing a variety of cutting-edge machine and deep learning models. Furthermore, we discuss our shared task conducted at LTEDI-ACL 2022 workshop to improve the research in homophobia and transphobia detection. It garnered 10 systems for the Tamil language, 13 systems for the English language, and 11 systems for the combination of Tamil and English languages. The best systems for Tamil, English, and Tamil–English each received an average macro F1 score of 0.570, 0.870, and 0.610, respectively.
Performing photonic nonlinear computations by linear operations in a high-dimensional space

Abstract As photonic linear computations are diverse and easy to realize while photonic nonlinear computations are relatively limited and difficult, we propose a novel way to perform photonic nonlinear computations by linear operations in a high-dimensional space, which can achieve many nonlinear functions different from existing optical methods. As a practical application, the arbitrary binary nonlinear computations between two Boolean signals are demonstrated to implement a programmable logic array. In the experiment, by programming the high-dimensional photonic matrix multiplier, we execute fourteen different logic operations with only one fixed nonlinear operation. Then the combined logic functions of half-adder and comparator are demonstrated at 10 Gbit/s. Compared with current methods, the proposed scheme simplifies the devices and the nonlinear operations for programmable logic computing. More importantly, nonlinear realization assisted by space transformation offers a new solution for optical digital computing and enriches the diversity of photonic nonlinear computing.
Towards an Improved Taxonomy of Attacks Related to Digital Identities and Identity Management Systems

Digital transformation with the adoption of cloud technologies, outsourcing, and working-from-home possibilities permits flexibility for organizations and persons. At the same time, it makes it more difficult to secure the IT infrastructure as the IT team needs to keep track of who is accessing what data from where and when on which device. With these changes, identity management as a key element of security becomes more important. Identity management relates to the technologies and policies for the identification, authentication, and authorization of users (humans and devices) in computer networks. Due to the diversity of identity management (i.e., models, protocols, and implementations), different requirements, problems, and attack vectors need to be taken into account. In order to secure identity management systems with their identities, a systematic approach is required. In this article, we propose the improved framework Taxonomy for Identity Management related to Attacks (TaxIdMA). The purpose of TaxIdMA is to classify existing attacks, attack vectors, and vulnerabilities associated with system identities, identity management systems, and end-user identities. In addition, the background of these attacks can be described in a structured and systematic way. The taxonomy is applied to the Internet of Things and self-sovereign identities. It is enhanced by a description language for threat intelligence sharing. Last but not least, TaxIdMA is evaluated and improved based on expert interviews, statistics, and discussions. This step enables broader applicability and level of detail at the same time. The combination of TaxIdMA, which allows a structured way to outline attacks and is applicable to different scenarios, and a description language for threat intelligence helps to improve the security identity management systems and processes.
Olivine chemistry as a petrogenetic indicator for origin and formation conditions of Sargaz-Abshur ultramafic-mafic intrusion, SE Baft, Kerman

Olivine chemistry as a petrogenetic indicator for origin and formation conditions of Sargaz-Abshur ultramafic-mafic intrusion, SE Baft, Kerman