Reviewerly/exHarmony huggingface dataset:
APP: A<sup>*</sup> Post-Processing Algorithm for Robots With Bidirectional Shortcut and Path Perturbation

Paths generated by A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> and other graph-search-based planners are widely used in the robotic field. Due to the restricted node-expansion directions, the resulting paths are usually not the shortest. Besides, unnecessary heading changes, or zig-zag patterns, exist even when no obstacle is nearby, which is inconsistent with the human intuition that the path segments should be straight in wide-open space due to the absence of obstacles. This article puts forward a general and systematic post-processing algorithm for A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> and other graph-search-based planners. The A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> post-processing algorithm, called APP, is developed based on the costmap, which is widely used in commercial service robots. First, a bidirectional vertices reduction algorithm is proposed to tackle the asymmetry of the path and the environments. During the forward and backward vertices reduction, a thorough shortcut strategy is put forward to improve the path-shortening performance and avoid unnecessary heading changes. Second, an iterative path perturbation algorithm is adopted to locally reduce the number of unnecessary heading changes and improve the path smoothness. Comparative experiments are then carried out to validate the superiority of the proposed method. Quantitative performance indexes show that APP outperforms the existing methods in planning time, path length as well as the number of unnecessary heading changes. Finally, field navigation experiments are carried out to verify the practicability of APP.
Aspect-Oriented Opinion Alignment Network for Aspect-Based Sentiment Classification

Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkable progress in leveraging attention mechanism to extract opinion words for different aspects. However, a persistent challenge is the effective management of semantic mismatches, which stem from attention mechanisms that fall short in adequately aligning opinions words with their corresponding aspect in multi-aspect sentences. To address this issue, we propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. Specifically, we first introduce a neighboring span enhanced module which highlights various compositions of neighboring words and given aspects. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Extensive experiments on three benchmark datasets demonstrate that our model achieves state-of-the-art results. The source code is available at https://github.com/AONE-NLP/ABSA-AOAN.
Model-Based Offline Policy Optimization with Adversarial Network

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model’s uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately.
Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation

Obtaining labelled data in medical image segmentation is challenging due to the need for pixel-level annotations by experts. Recent works have shown that augmenting the object of interest with deformable transformations can help mitigate this challenge. However, these transformations have been learned globally for the image, limiting their transferability across datasets or applicability in problems where image alignment is difficult. While object-centric augmentations provide a great opportunity to overcome these issues, existing works are only focused on position and random transformations without considering shape variations of the objects. To this end, we propose a novel object-centric data augmentation model that is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image. We demonstrated its effectiveness in improving kidney tumour segmentation when leveraging shape variations learned both from within the same dataset and transferred from external datasets.
Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers

Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems. The training enforces dynamical invariants-such as the Lyapunov exponent spectrum and the fractal dimension-in the systems of interest, enabling longer and more stable forecasts when operating with limited data. The technique is demonstrated in detail using reservoir computing, a specific kind of recurrent neural network. Results are given for the Lorenz 1996 chaotic dynamical system and a spectral quasi-geostrophic model of the atmosphere, both typical test cases for numerical weather prediction.
Understanding Effects of Visual Feedback Delay in AR on Fine Motor Surgical Tasks

Latency is a pervasive issue in various systems that can significantly impact motor performance and user perception. In medical settings, latency can hinder surgeons' ability to quickly correct movements, resulting in an experience that doesn't align with user expectations and standards of care. Despite numerous studies reporting on the negative effects of latency, there is still a gap in understanding how it impacts the use of augmented reality (AR) in medical settings. This study aims to address this gap by examining how latency impacts motor task performance and subjective perceptions, such as cognitive load, on two display types: a monitor display, traditionally used inside an operating room (OR), and a Microsoft HoloLens 2 display. Our findings indicate that both level of latency and display type impact motor performance, and higher latencies on the HoloLens result in relatively poor performance. However, cognitive load was found to be unrelated to display type or latency, but was dependent on the surgeon's training level. Surgeons did not compromise accuracy to gain more speed and were generally well aware of the latency in the system irrespective of their performance on task. Our study provides valuable insights into acceptable thresholds of latency for AR displays and proposes design implications for the successful implementation and use of AR in surgical settings.
FEUSNet: Fourier Embedded U-Shaped Network for Image Denoising

Deep convolution neural networks have proven their powerful ability in comparing many tasks of computer vision due to their strong data learning capacity. In this paper, we propose a novel end-to-end denoising network, termed Fourier embedded U-shaped network (FEUSNet). By analyzing the amplitude spectrum and phase spectrum of Fourier coefficients, we find that low-frequency features of an image are in the former while noise features are in the latter. To make full use of this characteristic, Fourier features are learned and are concatenated as a prior module that is embedded into a U-shaped network to reduce noise while preserving multi-scale fine details. In the experiments, we first present ablation studies on the Fourier coefficients' learning networks and loss function. Then, we compare the proposed FEUSNet with the state-of-the-art denoising methods in quantization and qualification. The experimental results show that our FEUSNet performs well in noise suppression and preserves multi-scale enjoyable structures, even outperforming advanced denoising approaches.
Improved Shark Smell Optimization Algorithm for Human Action Recognition

Human Action Recognition (HAR) in uncontrolled environments targets to recognition of different actions from a video. An effective HAR model can be employed for an application like human-computer interaction, health care, person tracking, and video surveillance. Machine Learning (ML) approaches, specifically, Convolutional Neural Network (CNN) models had been widely used and achieved impressive results through feature fusion. The accuracy and effectiveness of these models continue to be the biggest challenge in this field. In this article, a novel feature optimization algorithm, called improved Shark Smell Optimization (iSSO) is proposed to reduce the redundancy of extracted features. This proposed technique is inspired by the behavior of white sharks, and how they find the best prey in the whole search space. The proposed iSSO algorithm divides the Feature Vector (FV) into subparts, where a search is conducted to find optimal local features from each subpart of FV. Once local optimal features are selected, a global search is conducted to further optimize these features. The proposed iSSO algorithm is employed on nine (9) selected CNN models. These CNN models are selected based on their top-1 and top-5 accuracy in ImageNet competition. To evaluate the model, two publicly available datasets UCF-Sports and Hollywood2 are selected.
A deep supervised transformer U‐shaped full‐resolution residual network for the segmentation of breast ultrasound image

Breast ultrasound (BUS) is an important breast imaging tool. Automatic BUS image segmentation can measure the breast tumor size objectively and reduce doctors' workload. In this article, we proposed a deep supervised transformer U-shaped full-resolution residual network (DSTransUFRRN) to segment BUS images.In the proposed method, a full-resolution residual stream and a deep supervision mechanism were introduced into TransU-Net. The residual stream can keep full resolution features from different levels and enhance features fusion. Then, the deep supervision can suppress gradient dispersion. Moreover, the transformer module can suppress irrelevant features and improve feature extraction process. Two datasets (dataset A and B) were used for training and evaluation. The dataset A included 980 BUS image samples and the dataset B had 163 BUS image samples.Cross-validation was conducted. For the dataset A, the proposed DSTransUFRRN achieved significantly higher Dice (91.04 ± 0.86%) than all compared methods (p < 0.05). For the dataset B, the Dice was lower than that for the dataset A due to the small number of samples, but the Dice of DSTransUFRRN (88.15% ± 2.11%) was significantly higher than that of other compared methods (p < 0.05).In this study, we proposed DSTransUFRRN for BUS image segmentation. The proposed methods achieved significantly higher accuracy than the compared previous methods.
Fine-Tuning a Deconvolution Algorithm to Restore Displacement and Strain Maps Obtained with LSA

Reliably measuring sharp details in displacement and strain maps returned by full-field measurement techniques remains an open question in the photomechanics community. The primary objective of this study is to improve and fine-tune a deconvolution algorithm in order to limit the blur that obscures the details in displacement and strain maps. Checkerboard patterns are used and processed with a spectral method, namely the Localized Spectrum Analysis (LSA), and the raw maps returned by this technique are deconvolved. The influence of various settings on the quality of the results is studied by using synthetic images deformed through a well-vetted reference displacement field. It is shown that linking the size of the analysis window used in LSA on the one hand, and the size of the second derivative kernel employed in the deconvolution algorithm on the other hand, ensures the convergence of the deconvolution algorithm in all cases. This was not the case with the initial version. The ratio between these sizes, which optimizes the metrological performance of LSA followed by deconvolution, is identified. The influence of the sampling density of the checkerboard pattern in the images is also examined. The efficiency of the deconvolution algorithm employed with optimized settings is illustrated with strain maps obtained on two specimens, one in shape memory alloy, and the other in wood. It is shown in this study that deconvolution with optimized settings is an effective tool to enhance small and sharp details in strain maps obtained with LSA.
Multi-Level Security in Healthcare by Integrating Lattice-Based Access Control and Blockchain- Based Smart Contracts System

Access control to patient information has become increasingly important in healthcare systems. It is vital to enhance the security of healthcare systems to avoid data loss despite the various security policies imposed by healthcare management. The issue needs to be resolved with a comprehensive secure framework, which allows users to access data according to their level of confidentiality. This article presents a solution by imposing multi-level security in e-health systems by integrating the Lattice-Based Access Control (LBAC) model and blockchain-based smart contract mechanisms. These mechanisms provide security levels in compliance with data access restrictions among users and resources while maintaining compliance security levels. By using LBAC, you can provide multilevel protection for access control restrictions, whereas smart contracts are used to ensure the transaction process in a decentralized system via an agreement between the parties. A smart contract validates every user and performs the authentication process in the envisioned model, which uses the Ethereum Virtual Machine (EVM). In the blockchain network, the patient’s e-health details are accessed and stored as immutable blocks. Comparing the proposed scheme with existing benchmarking methods reveals that the proposed scheme preserves privacy, maintains transparency, provides an authentication process, maintains data integrity, and provides multilevel access control security. The proposed model performs better than other existing models. As a result, lattice-based access control enhances the security of e-health records.
AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis

Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1–7, low risk of malignancy; 8–9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected (n = 1016) or interval (n = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers (P < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1–7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Mehta in this issue.
3D point cloud global denoising algorithm based on different noise characteristics

Abstract When acquiring object point cloud data by three-dimensional scanning technology, noise is generated due to instrument accuracy and external factors. Existing algorithms rarely consider the characteristics of different noises and different regional noises when solving the point cloud denoising problem, this results in a limited effect on denoising. This paper presents an algorithm for denoising based on the characteristics of different types of noise and different regions in the point cloud. The algorithm includes large-scale noise removal and small-scale noise smoothing. Remove large-scale noise points by the relationship between the local point cloud and the global point cloud. For small-scale noise, the feature regions and non-feature regions are extracted according to the normal cosine information entropy. According to the characteristics of the small-scale noise in two regions, the noise distance distribution and the optimized bilateral filtering are used to deal with the small-scale noise in two regions respectively. Comparison experiments show that our algorithm can effectively remove the noise points mixed in the normal point cloud. The accuracy of large-scale noise removal reaches 99.1%. The proposed algorithm can protect the feature areas from being over-smoothed while smoothing the small-scale noise in non-featured areas.
Illicit Trafficking of Narcotic Drugs, Psychotropic or Potent Substances with the Use of Distance Communication Technologies: New Criminal Ways

Illicit trafficking of narcotic drugs traditionally holds a special place among criminal threats due to the scale of its negative social impact. In the conditions of global informatization, the mechanism of drug trade is transitioning to an active use of contactless means of committing crimes. Information and telecommunication technologies allow offenders to design increasingly sophisticated criminal schemes of drug trafficking whose matrix is made up of deliberate and thoroughly planned actions. The study reported in this article considered typical methods of contactless drug trade through mobile communication tools - pre-order and instant online offer. The author examined changes in the typology of the members of modern organized drug trade criminal groups involved in contactless drug delivery using information and telecommunication technologies. A typical structure of a criminal «store» is presented. According to the author, the proposed research approach to understanding the mechanism of illicit drug trade, which is based on the use of distance communication devices and information-telecommunication technologies, should render more focused the work of law enforcement aimed at searching, identifying and preserving proof on the stages of creating such organized criminal groups, coordinating the actions of their structural elements, forms of finding and recruiting their new members, and a comprehensive system of drug trades IT support.
Dodging DeepFake Detection via Implicit Spatial-Domain Notch Filtering

The current high-fidelity generation and high-precision detection of DeepFake images are at an arms race. We believe that producing DeepFakes that are highly realistic and “detection evasive” can serve the ultimate goal of improving future generation DeepFake detection capabilities. In this paper, we propose a simple yet powerful pipeline to reduce the artifact patterns of fake images without hurting image quality by performing implicit spatial-domain notch filtering. We first demonstrate that frequency-domain notch filtering, although famously shown to be effective in removing periodic noise in the spatial domain, is infeasible for our task at hand due to the manual designs required for the notch filters. We, therefore, resort to a learning-based approach to reproduce the notch filtering effects, but solely in the spatial domain. We adopt a combination of adding overwhelming spatial noise for breaking the periodic noise pattern and deep image filtering to reconstruct the noise-free fake images, and we name our method DeepNotch. Deep image filtering provides a specialized filter for each pixel in the noisy image, producing filtered images with high fidelity compared to their DeepFake counterparts. Moreover, we also use the semantic information of the image to generate an adversarial guidance map to add noise intelligently. Our large-scale evaluation on 3 representative DeepFake detection methods (tested on 16 types of DeepFakes) has demonstrated that our technique significantly reduces the accuracy of these 3 fake image detection methods, 36.79% on average and up to 97.02% in the best case.
A Low-Complexity Hand Gesture Recognition Framework via Dual mmWave FMCW Radar System

In this paper, we propose a novel low-complexity hand gesture recognition framework via a multiple Frequency Modulation Continuous Wave (FMCW) radar-based sensing system. In this considered system, two radars are deployed distributively to acquire motion vectors from different observation perspectives. We first independently extract reflection points of the interested target from different radars by applying the proposed neighboring reflection points detection method after processing the traditional 2-dimensional Fast Fourier Transform (2D-FFT). The obtained sufficient corresponding information of detected reflection points, e.g., distances, velocities, and angle information, can be exploited to synthesize motion velocity vectors to achieve a high signal-to-noise ratio (SNR) performance, which does not require knowledge of the relative position of the two radars. Furthermore, we utilize a long short-term memory (LSTM) network as well as the synthesized motion velocity vectors to classify different gestures, which can achieve a significantly high accuracy of gesture recognition with a 1600-sample data set, e.g., 98.0%. The experimental results also illustrate the robustness of the proposed gesture recognition systems, e.g., changing the environment background and adding new gesture performers.
Postdigital Epistemology

This entry discusses epistemology with respect to the postdigital condition. It provides a necessarily simple overview of traditional and social epistemology, before discussing what postdigital epistemology means in online environments where distortions in knowledge practices and procedures can be difficult to detect and rectify. The procedures are the same as with offline epistemology, requiring research and digital literacy, the motivation to evaluate, verify, evaluate, compare, and contrast, and an open mind about the truth claims of any assertion or image.