Reviewerly/exHarmony huggingface dataset:
Counteracting the dynamic degradation of high-dimensional digital chaotic systems via a stochastic jump mechanism

To analyze the periodic distribution and state-mapping structures of high-dimensional digital chaotic systems in fixed-point arithmetic domain. A chaotic cycle-finding algorithm (CCFA) can be designed to precisely calculate the period length of the orbit of digital chaos under different finite calculation accuracy. Furthermore, to test the versatility and effectiveness of our proposed algorithm, the periodic distribution and security of the digital Sprott D system are analyzed in detail. Numerical simulation results show that the chaotic system have potential security risks, such as short period and low sequence complexity. In addition, we put forward a new resistance method based on automatic cycle detection and random jump of chaotic trajectories for the objective of enhancing the dynamical properties of high-dimensional digital chaotic systems. This scheme can construct a new chaotic model with many desirable properties, such as long period, high state-space utilization, low autocorrelation, and good ergodicity.
Single underwater image enhancement using integrated variational model

Underwater images often suffer from various degradations such as blurring, fog, low contrast, and color distortion because the light is absorbed and scattered when traveling through water. To solve critical issues, we establish a novel framework combining variational methods and pyramid technology to improve image quality in the frequency domain. Two novel variational models, the adaptive variational contrast enhancement (AVCE) model and the total Laplacian model, are designed with the aim of enhancing the contrast of foreground and preserving texture features at different scales. In order to solve these two models efficiently, we also exploit two optimal algorithms based on gradient descent method (GDM) and alternating direction method of multipliers (ADMM). In addition, fast Fourier transform (FFT) is applied for further accelerating the calculation procedure. Extensive experiments demonstrate that our approach achieves good performance on contrast enhancement, color correction, and texture enlargement for underwater images. Qualitative and quantitative comparisons further validate the superiority of our proposed method. In the quantitative comparisons, the proposed method achieves 1.6170, 0.6484, 0.6333, 0.0332, 4.0355, and 1.6843 scores in terms of underwater image quality measures (UIQM), underwater color image quality evaluation (UCIQE), cumulative probability of blur detection (CPBD), Energy, Entropy, and Contrast metrics, and obtains an average of 10% improvement compared with several state-of-the-art methods. The code is available online at: https://github.com/Hou-Guojia/UIE-IVM.
Youths as targets: factors of online hate speech victimization among adolescents and young adults

Abstract A significant number of adolescents and young adults are targeted by online hate speech. The effect of such hateful utterances can involve severe psychological harm, especially for youths who have to master developmental tasks. Therefore, drawing on criminology’s routine activity theory, this study investigates the factors that help explain why youths become victimized through online hate speech. We conducted a national quota-based quantitative online survey that was representative of adolescent and young adult online users (N = 1,180). In the results, we identified six latent profiles of young targets with overall high or low online hate speech victimization, victimization due to gender, migration background, religion, or political engagement on behalf of the queer community. While relative subjective deprivation, political participation, and lower digital media literacy positively predicted overall victimization through online hate speech, being targeted was more likely for members of the aforementioned social groups and those showing political engagement.
“Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks

One unintended consequence of the Electronic Health Records (EHR) implementation is the overuse of content-importing technology, such as copy-and-paste, that creates "bloated" notes containing large amounts of textual redundancy. Despite the rising interest in applying machine learning models to learn from real-patient data, it is unclear how the phenomenon of note bloat might affect the Natural Language Processing (NLP) models derived from these notes. Therefore, in this work we examine the impact of redundancy on deep learning-based NLP models, considering four clinical prediction tasks using a publicly available EHR database. We applied two deduplication methods to the hospital notes, identifying large quantities of redundancy, and found that removing the redundancy usually has little negative impact on downstream performances, and can in certain circumstances assist models to achieve significantly better results. We also showed it is possible to attack model predictions by simply adding note duplicates, causing changes of correct predictions made by trained models into wrong predictions. In conclusion, we demonstrated that EHR text redundancy substantively affects NLP models for clinical prediction tasks, showing that the awareness of clinical contexts and robust modeling methods are important to create effective and reliable NLP systems in healthcare contexts.
NIMBLE

Emerging Metaverse applications demand reliable, accurate, and photorealistic reproductions of human hands to perform sophisticated operations as if in the physical world. While real human hand represents one of the most intricate coordination between bones, muscle, tendon, and skin, state-of-the-art techniques unanimously focus on modeling only the skeleton of the hand. In this paper, we present NIMBLE, a novel parametric hand model that includes the missing key components, bringing 3D hand model to a new level of realism. We first annotate muscles, bones and skins on the recent Magnetic Resonance Imaging hand (MRI-Hand) dataset [Li et al. 2021] and then register a volumetric template hand onto individual poses and subjects within the dataset. NIMBLE consists of 20 bones as triangular meshes, 7 muscle groups as tetrahedral meshes, and a skin mesh. Via iterative shape registration and parameter learning, it further produces shape blend shapes, pose blend shapes, and a joint regressor. We demonstrate applying NIMBLE to modeling, rendering, and visual inference tasks. By enforcing the inner bones and muscles to match anatomic and kinematic rules, NIMBLE can animate 3D hands to new poses at unprecedented realism. To model the appearance of skin, we further construct a photometric HandStage to acquire high-quality textures and normal maps to model wrinkles and palm print. Finally, NIMBLE also benefits learning-based hand pose and shape estimation by either synthesizing rich data or acting directly as a differentiable layer in the inference network.
Robust convolutional neural networks against adversarial attacks on medical images

Convolutional neural networks (CNNs) have been widely applied to medical images. However, medical images are vulnerable to adversarial attacks by perturbations that are undetectable to human experts. This poses significant security risks and challenges to CNN-based applications in clinic practice. In this work, we quantify the scale of adversarial perturbation imperceptible to clinical practitioners and investigate the cause of the vulnerability in CNNs. Specifically, we discover that noise (i.e., irrelevant or corrupted discriminative information) in medical images might be a key contributor to performance deterioration of CNNs against adversarial perturbations, as noisy features are learned unconsciously by CNNs in feature representations and magnified by adversarial perturbations. In response, we propose a novel defense method by embedding sparsity denoising operators in CNNs for improved robustness. Tested with various state-of-the-art attacking methods on two distinct medical image modalities, we demonstrate that the proposed method can successfully defend against those unnoticeable adversarial attacks by retaining as much as over 90% of its original performance. We believe our findings are critical for improving and deploying CNN-based medical applications in real-world scenarios.
Feature selection for online streaming high-dimensional data: A state-of-the-art review

Knowledge discovery for data streaming requires online feature selection to reduce the complexity of real-world datasets and significantly improve the learning process. This is achieved by selecting highly relevant subsets and minimising irrelevant and redundant features. However, researchers have difficulties in addressing various forms of data. The goal of this article is to present a state-of-the-art review of feature subset selection based on the data form for the high-dimensional data used in online streaming. Through a systematic literature review assessing journal and conference papers from the past five years, detailed discussions on traditional feature selection and online feature selection were presented. Subsequently, a taxonomy of the challenges related to OFS provides a comprehensive review of state-of-the-art OFS and the benchmark methods. Several data forms were identified based on the extensive review: group stream, multi-label, capricious, imbalance, and feature drift. Using critical analysis, the evaluation metrics of online feature selection methods were compared from the perspectives of threshold initialisation, accuracy, high dimensionality, running time, relevancy, and redundancy for the optimal feature subset. An online feature selection framework was derived to illustrate the relationship between the application area, data form, online feature selection methods, evaluation metrics, and tools. Finally, the findings and potential directions for future research were thoroughly discussed. It is suggested that future researchers explore the derived framework and aim to advance each method.
The familiarity hypothesis: Explaining the behavior of deep open set methods

In many object recognition applications, the set of possible categories is an open set, and the deployed recognition system will encounter novel objects belonging to categories unseen during training. Detecting such "novel category" objects is usually formulated as an anomaly detection problem. Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. Instead, methods based on the computed logits of visual object classifiers give state-of-the-art performance. This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty. This distinction is important, because familiarity-based detection will fail in many situations where novelty is present. For example when an image contains both a novel object and a familiar one, the familiarity score will be high, so the novel object will not be noticed. The paper reviews evidence from the literature and presents additional evidence from our own experiments that provide strong support for this hypothesis. The paper concludes with a discussion of whether familiarity-based detection is an inevitable consequence of representation learning.
Exploring Set Similarity for Dense Self-supervised Representation Learning

By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks. However, the pixel-level correspondence tends to be noisy because of many similar misleading pixels, e.g., backgrounds. To address this issue, in this paper, we propose to explore set similarity (SetSim) for dense self-supervised representation learning. We generalize pixel-wise similarity learning to set-wise one to improve the robustness because sets contain more semantic and structure information. Specifically, by resorting to attentional features of views, we establish the corresponding set, thus filtering out noisy backgrounds that may cause incorrect correspondences. Meanwhile, these at-tentional features can keep the coherence of the same image across different views to alleviate semantic inconsistency. We further search the cross-view nearest neighbours of sets and employ the structured neighbourhood information to enhance the robustness. Empirical evaluations demonstrate that SetSim surpasses or is on par with state-of-the-art meth-ods on object detection, keypoint detection, instance segmen-tation, and semantic segmentation.
Narrative medical visualization to communicate disease data

This paper explores narrative techniques combined with medical visualizations to tell data-driven stories about diseases for a general audience. The field of medical illustration uses narrative visualization through hand-crafted techniques to promote health literacy. However, data-driven narrative visualization has rarely been applied to medical data. We derived a template for creating stories about diseases and applied it to three selected diseases to demonstrate how narrative techniques could support visual communication and facilitate understanding of medical data. One of our main considerations is how interactive 3D anatomical models can be integrated into the story and whether this leads to compelling stories in which the users feel involved. A between-subject study with 90 participants suggests that the combination of a carefully designed narrative structure, the constant involvement of a specific patient, high-qualitative visualizations combined with easy-to-use interactions, are critical for an understandable story about diseases that would be remembered by participants.
A survey on privacy inference attacks and defenses in cloud-based Deep Neural Network

Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.
Transfer Learning for Human Activity Recognition Using Representational Analysis of Neural Networks

Human activity recognition (HAR) has increased in recent years due to its applications in mobile health monitoring, activity recognition, and patient rehabilitation. The typical approach is training a HAR classifier offline with known users and then using the same classifier for new users. However, the accuracy for new users can be low with this approach if their activity patterns are different than those in the training data. At the same time, training from scratch for new users is not feasible for mobile applications due to the high computational cost and training time. To address this issue, we propose a HAR transfer learning framework with two components. First, a representational analysis reveals common features that can transfer across users and user-specific features that need to be customized. Using this insight, we transfer the reusable portion of the offline classifier to new users and fine-tune only the rest. Our experiments with five datasets show up to 43% accuracy improvement and 66% training time reduction when compared to the baseline without using transfer learning. Furthermore, measurements on the hardware platform reveal that the power and energy consumption decreased by 43% and 68%, respectively, while achieving the same or higher accuracy as training from scratch. Our code is released for reproducibility. 1
Balancing data privacy and usability in the federal statistical system

The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs.
Complex entropy based encryption and decryption technique for securing medical images

During medical picture transmission, the most pressing concern is security. Medical images must be encrypted since they are extremely sensitive. Watermarking, digital fingerprinting/signature, and encoding are some of the available image security techniques. Images and movies, for example, must be highly encrypted and decoded without losing any content information. Medical photos, for example, require extra protection, and protecting medical images is a critical issue when medical images and related patient information are transferred over public networks. This research work proposes a visual encryption strategy to secure medical pictures before being transmitted or stored in the cloud. This technique makes such pictures of unauthorized people unavailable and also maintains confidentiality, a prime safety requirement. The process made use of a pixel shuffling-based encryption technique and a secret key created from the image. In this research, we encrypted the medical image using modified Arnold Map Encryption and generated secret key values. Therefore, the image is encrypted, and henceforth it is decrypted as well. So this work gave us the encrypted image and decrypted image/original image as well. The modified Arnold Map Encryption tries to add more randomness, thus increasing the entropy of the image and thus makes it harder to decrypt. The modified Arnold Map Encryption is also compared to other algorithms such as Hyper Chaotic, Secure Hash Algorithm-13 (SHA-13), Ten Logistic Maps, Bakers Map, HenonMap, Cross Chaos Map, and 2D Logistic Map and shows better results in terms of encryption speed and Number of Pixel Change Rate (NPCR) value.
Evolving Error Feedback Fuzzy Model for Improved Robustness Under Measurement Noise

In this article, we propose a new variant of evolving fuzzy model for regression problems, which is based on error feedback integration in order to compensate measurement noise and to achieve more robust predictions; thus, it is termed as evolving error feedback fuzzy model. Thereby, we define an autoregressive-based noise model, localized per rule for being able to model different possible noise behaviors in different parts of the input space. Its predictions are added to the real fuzzy model predictions for noise correction purposes. This additive aspect leads to an extended version of the recursive fuzzily weighted least squares estimator (RFWLS-N = RFWLS with noise compensation), inheriting all its favorable convergence properties. For rule antecedent learning, we perform an evolving clustering process with the integration of a new forgetting strategy to be able to achieve more flexible updates in order to account for possible drifts. Therefore, a specific sample weighting concept is designed, with which different forgetting types can be achieved, ranging from (slow) logarithmic forgetting to (uniformly) linear forgetting and to (fast) exponential forgetting. The integration of the weights into the updates of rule centers and inverse covariance matrices is underlined with a theoretical analysis of their sample-wise contributions in the updates. Results on several noise-affected real-world datasets and system identification problems showed significantly improved model performances, compared to related evolving fuzzy methods, to the conventional RFWLS estimator and to the recursive correntropy approach. Antecedent forgetting helped us to improve the error trends in the case of abrupt drifts, while it never worsened the trends in the case of no drift.
A Differential Evolution-Enhanced Position-Transitional Approach to Latent Factor Analysis

High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications. A Position-transitional Latent Factor Analysis (PLFA) model can accurately and efficiently represent an HiDS matrix. However, its involved latent factors are optimized by particle-swarm-optimization-incorporated stochastic gradient descent with the specific gradient direction step-by-step, which may cause a suboptimal solution. To address this issue, this paper proposes a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> equential- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> roup- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ifferential- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> volution (SGDE) algorithm to refine the latent factors optimized by a PLFA model, thereby achieving a highly-accurate SGDE-PLFA model to HiDS matrices. The main idea of SGDE-PLFA is two-fold. First, it divides the obtained latent factors by PLFA into row and column sub-groups to ensure the efficient low-dimensional optimization. Second, it initializes and optimizes these sub-group latent factors with our improved Differential Evolution algorithm to search more accurate solutions nearby the suboptimum. As demonstrated by the extensive experiments on four HiDS matrices, an SGDE-PLFA model outperforms the state-of-the-art models in representing HiDS matrices.