Reviewerly/exHarmony huggingface dataset:
Volumetric feature points integration with bio-structure-informed guidance for deformable multi-modal CT image registration

Objective.Medical image registration represents a fundamental challenge in medical image processing. Specifically, CT-CBCT registration has significant implications in the context of image-guided radiation therapy (IGRT). However, traditional iterative methods often require considerable computational time. Deep learning based methods, especially when dealing with low contrast organs, are frequently entangled in local optimal solutions.Approach.To address these limitations, we introduce a registration method based on volumetric feature points integration with bio-structure-informed guidance. Surface point cloud is generated from segmentation labels during the training stage, with both the surface-registered point pairs and voxel feature point pairs co-guiding the training process, thereby achieving higher registration accuracy.Main results.Our findings have been validated on paired CT-CBCT datasets. In comparison with other deep learning registration methods, our approach has improved the precision by 6%, reaching a state-of-the-art status.Significance.The integration of voxel feature points and bio-structure feature points to guide the training of the medical image registration network has achieved promising results. This provides a meaningful direction for further research in medical image registration and IGRT.
An improved method for blind navigation based on image-to-sound conversion

For a long time, blind or visually impaired people have relied on traditional aids such as canes or guide dogs to assist them in walking, but these aids have significant limitations. In order to help visually impaired individuals walk independently, this article improves the method of mapping images to sound in the literature, making it more applicable to blind navigation. By limiting the range and size of captured images, as well as improving the mapping relationship between captured images and transformed sound, the accuracy of the method has been improved from 84.2% to 88.8%, an increase of nearly 5% compared to the method in the literature, which can better assist in guiding the blind.
Mix Transformer depth-wise separable convolution UNet for breast mass segmentation in mammographic

Mammography is main tests used for breast cancer risk assessment. However, the mass segmentation and classification of mammograms are extremely challenging. To reduce computational costs and the workloads of radiologists, deep neural networks have been widely used in various medical image segmentation and classification tasks. Since there are some common features between these two tasks, a multi-task learning approach to solve both tasks are a promising direction. We propose a mix Transformer depth-wise separable convolution U-Network (MTDUNet) for mass segmentation of mammograms. We introduce depth-wise separable convolutions to replace traditional convolutions and improve the network’s perception of multi-scale features within the receptive field. Additionally, due to the inherent limitations of convolutional networks, we introduce mix transformer to model remote contextual information. We conducted evaluations the proposed GATNet on two publicly available breast mass segmentation datasets. The average Dice similarity coefficients between the MTDUNet results and INBreast and CBIS-DDSM data were 89.90% and 83.63%, respectively. The experimental results indicate that MTDUNet can significantly reduce the spatial complexity of medical image segmentation networks and effectively save computational resources.
A local color transfer method based on optimal transmission

How to transmit better the color distribution of one image to another image is an interesting problem in computer image generation, and the optimal transmission mathematical theory has been applied to the research field of this type of problem. In response to the common problems of color distribution differences or lack of hierarchy between the transmitted result image and the reference image in existing methods, this paper proposes a new color transfer method: first, we segment the foreground and background regions of the reference image and the specify image; Then we use the optimal transmission theory to transfer the colors of the foreground and background regions separately; Finally, we merge and enhance the transmitted regions to obtain the resulting image. The experimental results show that compared to the two image color transfer methods in the literature, the method proposed in this paper can better transfer the color distribution of reference images to specify image, preserve the hierarchical sense of the original specify image, and achieve better visual effects.
Research on foreground subject segmentation algorithm with high similarity of foreground background

One of the advanced algorithms for image foreground segmentation is DIM (Deep Image Matting), which applies convolutional neural networks. However, for complex foreground shapes, fixed convolutional kernels are not the optimal approach. Therefore, Deformable Convolution Networks (DCN) were introduced as a replacement, which partially address the issue of losing edge details in deep layers. However, DCN suffers from the extraction of undesired features, significantly reducing the model’s flexibility. To address this, the bias is reshaped using a balanced refining bias with an adaptive weight matrix, instead of a fixed 9-grid bias. To evaluate the effectiveness of the bias, a simple model called DIM_RDC is designed and trained, which utilizes the balanced refining bias. The model is tested on the Adobe dataset, and the results show that DIM_RDC improves the evaluation metrics of SAD by 1.62% and MSE by 11.05% compared to DIM. Therefore, DIM_RDC demonstrates a certain level of competitiveness over DIM.
A comprehensive assessment method based on improved principal component analysis

With the development of networked combat and integrated combat ideas and technologies, military combat is increasingly emphasizing the system-system confrontation and the effectiveness of the system. In response to the problems faced in the assessment of the effectiveness of weapon and equipment systems, such as the large number of assessment indicators and the diversity of system combat tasks, a comprehensive assessment method for the combat effectiveness of weapon and equipment systems is given. A comprehensive assessment method based on improved principal component analysis is proposed to eliminate the influence of correlation between indicators, while retaining the differences in the importance of each effectiveness indicator. For typical combat tasks, multiple simulations are run, all the information of the original data is retained while eliminating the dimension, and all the effectiveness indicators are integrated to obtain the comprehensive combat effectiveness of the system, which makes the system effectiveness assessment results more reasonable and comprehensive.
SQBA: sequential query-based blackbox attack

Many existing approaches to blackbox adversarial attacks follow attack strategies with predefined priori which are fixed throughout the process. As a result, they often require an excessive number of queries against the victim models to succeed. In this paper, we proposed a new attacking paradigm that better resembles real-world attacks in practical settings, where an agent (i.e., attacker) approaches the attack by taking actions (i.e., perturbations to the source image) through sequential interactions with the environment (i.e., the victim model) to achieve maximum rewards (i.e., the success of attack with the minimum number of queries). Naturally, as any action the agent chooses to take would alter the query image and change the state of the attack, the agent needs to adapt its policy accordingly along the trajectory instead of applying a predefined strategy unanimously. As an instantiation, we propose a “sequential query-based boundary blackbox attack” (SQBA), which learns a policy to adaptively select from a set of candidates attacking methods and then follow the selected method to apply one attack at each step. For demonstration, we restrict the candidate to subspace-based boundary attack methods. We show that the policy can be learned effectively with a variety of approaches, including imitation learning, policy optimization, and an ensemble of both. Extensive experiments on four benchmark datasets (MNIST, CIFAR-10, CelebA, and ImageNet) show that SQBA can significantly reduce the query complexity under different settings compared with baselines while keeping a 100% attack success rate. In addition, we find that the Reinforcement Learning agent as an ensemble of TRPO and BiLSTM performs the best among different agents.
Study on biomechanical characteristics of lower limbs in Tai Chi, Baduanjin, and walking gait based on the AnyBody simulations

To explore the influence factors of different step practices on the human balance ability, and provide data support and theoretical basis for the practice of Tai Chi and Baduanjin. Sports biomechanics parameters of the lunge step support period of Tai Chi group, horse step support period of Baduanjin group and walking gait support period of the beginner group were compared, scanning and collecting knee CT data from the subjects of lunge step test, horse step test and walking gait test, the tibia model was treated with Geomagic Studio, the right tibia of the musculoskeletal model was replaced by mirror image, adding the net force of the knee joint derived from AnyBody to Ansys, after assigning values to materials, setting boundary conditions and adding loads, the equivalent stresses of lunge step, horse step and upright step were analyzed. Compared with the walking gait, in lunge step, the hip flexion angle and knee flexion angle were significantly larger (<i>P</i>&lt;0.01), the hip abduction torque, hip rotation torque and ankle plantar flexion torque were significantly larger (<i>P</i>&lt;0.01); in horse step, the hip abduction angle, hip flexion angle and knee flexion angle were significantly larger (<i>P</i>&lt;0.01). Lunge step exercise is conducive to improving the deep anterior thigh muscle strength, horse step exercise is conducive to improving the deep medial hip muscle strength and shallow posterior calf muscle strength, walking step is conducive to improving the shallow anterior calf muscle strength and ankle dorsal flexion muscle strength.
Design and implementation of intelligent analysis equipment for non-coal mine safety risk based on edge computing

In view of the problems in the field of non-coal mine, such as high pressure of perception data acquisition and transmission, cloud analysis and processing, insufficient ability of active safety risk analysis, and poor timeliness, this paper discusses the necessity of building a non-coal mine safety risk monitoring and early warning system using edge computing technology against the requirements of major disaster risk prevention and control construction in non-coal mine. It analyzes the technical requirements of edge computing for data security and data intelligent analysis of non-coal mine safety risks, propose the edge computing architecture of non-coal mine safety risk. Designed intelligent risk analysis equipment for non-coal mine safety risks, implementing functions such as data collection and aggregation, data association analysis, video intelligent analysis, and data security control. This research improved the level of intelligent warning on the edge of non-coal mine safety risks, with great significance in preventing and extricating major safety production accidents in noncoal mining enterprises.
Improved stacked-denoising auto encoder for tide image denoising

Tide images collected by outdoor cameras are susceptible to complex environmental factors and seriously affect the accuracy of object recognition algorithm. Therefore, an improved Stacked-Denoising Auto Encoder (SDAE) tide image denoising algorithm in complex environment is proposed. In order to increase the number of network layers and find features faster, an SDAE based on convolutional network is proposed. Inside the network architecture of SDAE, layers including input layer, convolutional layer, pooling layer, full connection layer and output convolutional pooling layer are introduced to effectively extract image noise features and reduce network training parameters. The experimental results show that the improved algorithm can effectively remove single noises such as blur, illumination imbalance, rain and fog in complex environment, and the PSNR value and SSIM value of images of various noise types are improved by at least 13% and 33% respectively, and the SSIM value of the denoised rain images is close to 1.
Effect of having and switching multiple avatars on the operator’s right to talk and receive social support

People with communication difficulties encounter several challenges in their daily online interactions, such as a limited right to talk (RoT), insufficient social support (SS), and a low sense of being attended to (SoBA). Computer-mediated technologies are limited in addressing such problems owing to their limited capacity in transferring verbal and nonverbal cues between users. In this study, to address the limited RoT, low SS, and low SoBA challenges, we proposed a robotic video conference system with two teleoperated robot avatars. The proposed system was compared with another robotic video conference system that adopts only one teleoperated robot avatar. In the field experiment, 37 participants took part in two discussion sessions using each system type, where RoT, SS, and SoBA were adopted as the measured indices. The proposed system significantly increased the users' RoT and SS compared with other robotic video conference systems. This study contributes to the literature by demonstrating the effect exerted by the type of robotic video conference adopted on users' feelings about RoT, SS, and SoBA.
Let's Go to the Whiteboard (Again): Perceptions From Software Architects on Whiteboard Architecture Meetings

The whiteboard plays a crucial role in the day-to-day lives of software architects, as they frequently will organize meetings at the whiteboard to discuss a new architecture, some proposed changes to an existing architecture, a mismatch between a prescribed architecture and its code, and more. While much has been studied about software architects, the architectures they produce, and how they produce them, a detailed understanding of these whiteboards meetings is still lacking. In this paper, we contribute a mixed-methods study involving semi-structured interviews and a subsequent survey to understand the perceptions of software architects on whiteboard architecture meetings. We focus on four aspects: (1) why do they hold these meetings, (2) what is the impact of the experience levels of the participants in these meetings, (3) how do the architects document the meetings, and (4) what kinds of changes are made in downstream activities to the work produced after the meetings have concluded? In studying these aspects, we identify eleven observations related to both technical aspects and social aspects of the meetings. These insights have implications for further research, offer concrete advice to practitioners, and suggest ways of educating future software architects.
The effect of icon patterns on user attention and memory

This study explored the impact of icon patterns, position, composition, and congruence on user attention and recognition memory for single-row icon sets. Four patterns of an icon set were experimentally tested: plane congruent (plane-plane-plane, PPP), plane incongruent (line-plane-line, LPL), line congruent (line-line-line, LLL), and line incongruent (plane-line-plane, PLP). For attention, this study found no significant difference between LLL and PPP. Furthermore, the combination of plane-based and line-based icons (LPL) was most suitable for user attention. For recognition memory, icon sets with plane-based targets (PPP or LPL) were more recognizable than those with line-based targets (LLL or PLP), and PLP was the least recognizable. Overall, the LPL pattern best attracted attention and enhanced subsequent memory. For evenly distributed cognition, a PPP pattern was recommended. The findings of this study can provide guidance for designing graphical user interfaces with multiple icons (e.g., navigation bars and tab bars).
Density estimation for toroidal data using semiparametric mixtures

Abstract Toroidal data is an extension of circular data on a torus and plays a critical part in various scientific fields. This article studies the density estimation of multivariate toroidal data based on semiparametric mixtures. One of the major challenges of semiparametric mixture modelling in a multi-dimensional space is that one can not directly maximize the likelihood over the unrestricted component density as it will result in a degenerate estimate with an unbounded likelihood. To overcome this problem, we propose to fix the maximum of the component density, which subsequently bounds the maximum of the mixture and its likelihood function, hence providing a satisfactory density estimate. The product of univariate circular distributions are utilized to form multivariate toroidal densities as candidates for mixture components. Numerical studies show that the mixture-based density estimator is superior in general to the kernel density estimator.
Computing and analyzing decision boundaries from shortest path maps

This paper proposes a methodology for computing, visualizing, and analyzing critical decision boundaries for the selection of shortest paths in a given environment. Decision boundaries are defined as the points in a map from which two or more different shortest paths exist towards a destination. This paper introduces the problem of visualizing their evolution, taking into account moving obstacles, moving goals, and as well multiple goals. The proposed visualizations enable analyzing which paths should be taken and at which departure times, such that a destination can be reached by the shortest possible path when taking into account a moving target or time-varying areas to be avoided. The proposed techniques are also applied to the analysis and improvement of exit placement in a given environment, in order to improve the evacuation flow in emergency situations.
Prediction of Downward Surface Solar Radiation Using Particle Swarm Optimization and Neural Networks

Concerning the impacts of climate change, renewable energy systems are becoming more prominent by providing more environmentally friendly energy. However, these systems are very dependent on meteorological parameters. In this study, it is aimed to forecast hourly downward surface solar radiation (DSSR), which is the main determinant of power output from PV systems, using Particle Swarm Optimization (PSO) and Long Short-Term Memory Recurrent Neural Networks (LSTM). The hourly data of 12 meteorological features in Karaman, Turkey, for the period between January 2020 and December 2021 were obtained from the European Centre for Medium-Range Weather Forecasts’ (ECMWF) ERA5 reanalysis data sets. PSO is utilized to select the most significant features from this data. These selected variables are put into the LSTM model as inputs. The established hybrid forecasting model is examined for 1 hour, 2 hours, 4 hours, and 6 hours ahead. Furthermore, to measure the performance of the forecasting model, the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are calculated. As a result, the lowest error rate is acquired for 1 hour ahead of time horizon with 8496 (W/m2) MSE and 92 (W/m2) RMSE. This study also exhibits that the proposed hybrid model may provide very accurate results to forecast hourly DSSR with selected variables in case it is not possible to measure the DSSR values directly.
Self-supervised learning for clustering of wireless spectrum activity

In recent years, much work has been done on processing of wireless spectrum data involving machine learning techniques in domain-related problems for cognitive radio networks, such as anomaly detection, modulation classification, technology classification and device fingerprinting. Most of the solutions are based on labeled data, created in a controlled manner and processed with supervised learning approaches. However, spectrum data measured in real-world environment is highly nondeterministic, making its labeling a laborious and expensive process, requiring domain expertise, thus being one of the main drawbacks of using supervised learning approaches in this domain. In this paper, we investigate the utilization of self-supervised learning (SSL) for exploring spectrum activities in a real-world unlabeled data. In particular, we assess the performance of SSL models, based on the reference DeepCluster architecture. We carefully consider the current state-of-the-art feature extractors, taking into account the performance and complexity trade-offs. Our findings demonstrate that SSL models achieve superior performance regarding the feature quality and clustering performance compared to baseline feature learning approaches. With SSL models we achieve significant reduction of the feature vectors size by two orders of magnitude, while improving the performance by a factor ranging from 2 to 2.5 across the evaluation metrics, supported by visual assessment. Furthermore, we showcase how adapting the reference SSL architecture to domain-specific data is followed by a substantial reduction in model complexity up to one order of magnitude, without compromising, and in some cases, even improving the clustering performance.
Bridging the Gap Between Reservoirs and Neural Networks

Abstract While reservoir computing is drawing attention, its applications are limited to small tasks. This chapter proposes a solution to this issue by introducing the Hidden-Fold Network, a recursive model with fixed random weights that resembles reservoir computing. The model is constructed based on recent discoveries in neural networks, namely the Strong Lottery Ticket Hypothesis and folding. By pruning an overparameterized model that is randomly initialized, it is possible to find accurate neural networks without the need for weight optimization. It is conjectured that residual networks may contain better subnetwork candidates for inference time when transformed into recurrent architectures, as they may be approximating unrolled shallow recurrent neural networks. This hypothesis is tested in image classification tasks, where subnetworks within the recurrent models are found to be more accurate and parameter-efficient than those within feedforward models, as well as the full models with learned weights.
Reservoir Computing Based on Iterative Function Systems

Abstract Various approaches have been proposed to construct reservoir computing systems. However, the network structure and information processing capacity of these systems are often tied to their individual implementations, which typically become difficult to modify after physical setup. This limitation can hinder performance when the system is required to handle a wide spectrum of prediction tasks. To address this limitation, it is crucial to develop tunable systems that can adapt to a wide range of problem domains. This chapter presents a tunable optical computing method based on the iterative function system (IFS). The tuning capability of IFS provides adjustment of the network structure and optimizes the performance of the optical system. Numerical and experimental results show the tuning capability of the IFS reservoir computing. The relationship between tuning parameters and reservoir properties is discussed. We further investigate the impact of optical feedback on the reservoir properties and present the prediction results.