Reviewerly/exHarmony huggingface dataset:
Development of a Special Class Curriculum Operation Model for Students with Special Needs in General Schools According to the High School Credit System

Purpose: The purpose of this study was to develop a special class curriculum operation model forstudents with special needs in general schools according to the implementation of the high schoolcredit system policy.&#x0D; Methods: A Delphi survey was conducted targeting a total of eight secondary special teachers andsupervisors who had experience about the credit system to review the validity of the model.&#x0D; Results: Based on the results of a preliminary survey to collect expert opinions on the directionof model construction, first, the core elements to be considered when promoting the high school creditsystem, including career guidance, were included as a propulsion system. Second, regarding the typeof operation, discussions were conducted on the appropriateness of the name depending on the overallmeaning, the clarification of the distinction between subject support type and cooperative teachingtype, and the possibility of opening open type courses that reflect the demand of students withoutdisabilities. The compositions of the model are an overview of the model, presenting the vision,operation goal, and promotion strategy. Moreover, the operation type is inclusion implementation type(subject support type, cooperative teaching type, open type) and special class independent type(independent type, visiting type, association type). As for the propulsion system, content elementsrelated to curriculum organization and operation, career and study design guidance, classes andevaluation, school culture and operation innovation, and credit system creation were developed.&#x0D; Conclusion: Based on the results of the feasibility review of this operation model, implications oforganizing and operating the special class credit system curriculum for students who need specialeducation in general schools were discussed.
Toward a stable and low-resource PLM-based medical diagnostic system via prompt tuning and MoE structure

Machine learning (ML) has been extensively involved in assistant disease diagnosis and prediction systems to emancipate the serious dependence on medical resources and improve healthcare quality. Moreover, with the booming of pre-training language models (PLMs), the application prospect and promotion potential of machine learning methods in the relevant field have been further inspired. PLMs have recently achieved tremendous success in diverse text processing tasks, whereas limited by the significant semantic gap between the pre-training corpus and the structured electronic health records (EHRs), PLMs cannot converge to anticipated disease diagnosis and prediction results. Unfortunately, establishing connections between PLMs and EHRs typically requires the extraction of curated predictor variables from structured EHR resources, which is tedious and labor-intensive, and even discards vast implicit information.In this work, we propose an Input Prompting and Discriminative language model with the Mixture-of-experts framework (IPDM) by promoting the model's capabilities to learn knowledge from heterogeneous information and facilitating the feature-aware ability of the model. Furthermore, leveraging the prompt-tuning mechanism, IPDM can inherit the impacts of the pre-training in downstream tasks exclusively through minor modifications. IPDM remarkably outperforms existing models, proved by experiments on one disease diagnosis task and two disease prediction tasks. Finally, experiments with few-feature and few-sample demonstrate that IPDM achieves significant stability and impressive performance in predicting chronic diseases with unclear early-onset characteristics or sudden diseases with insufficient data, which verifies the superiority of IPDM over existing mainstream methods, and reveals the IPDM can powerfully address the aforementioned challenges via establishing a stable and low-resource medical diagnostic system for various clinical scenarios.
End-to-End Alternating Optimization for Real-World Blind Super Resolution

Blind super-resolution (SR) usually involves two sub-problems: (1) estimating the degradation of the given low-resolution (LR) image; (2) super-resolving the LR image to its high-resolution (HR) counterpart. Both problems are ill-posed due to the information loss in the degrading process. Most previous methods try to solve the two problems independently, but often fall into a dilemma: a good super-resolved HR result requires an accurate degradation estimation, which however, is difficult to be obtained without the help of original HR information. To address this issue, instead of considering these two problems independently, we adopt an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model. Specifically, we design two convolutional neural modules, namely Restorer and Estimator. Restorer restores the SR image based on the estimated degradation, and Estimator estimates the degradation with the help of the restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, both Restorer and Estimator could get benefited from the intermediate results of each other, and make each sub-problem easier. Moreover, Restorer and Estimator are optimized in an end-to-end manner, thus they could get more tolerant of the estimation deviations of each other and cooperate better to achieve more robust and accurate final results. Extensive experiments on both synthetic datasets and real-world images show that the proposed method can largely outperform state-of-the-art methods and produce more visually favorable results.
Linear+: An optimized sequence reordering for robust scout accelerated retrospective motion estimation and correction

Distributed sequence reorderings for 3D multi-shot acquisitions ensure overlap with the central k-space in every shot. This improves the stability of navigator-free retrospective motion estimation but often reduces the robustness of the image reconstruction. In this work, we have developed and optimized a novel sampling strategy (Linear + ) that extends the standard sequential sequence ordering with a small number of additional calibration samples acquired near the k-space center. In simulation and in vivo, Linear + enabled accurate motion parameter estimation and correction across multiple motion patterns while providing improved image homogeneity and spatial resolution compared to a distributed reordering.
Spatial properties of noise in prostate DWI acquired with an endo-rectal coil as critical information for clinical QA

Development of quantitative QC methods for prostate DWI is important in clinical practice, necessitating reliable estimates of spatially heterogenous noise. In data obtained at 3T with the use of an endo-rectal coil, we use raw noise measurements to simulate pure-noise k-space datasets that are post-processed to yield noise maps. Good agreement is found between noise levels measured from noise maps in the central prostate and the estimates of noise produced from the anterior rectal ROIs in high TE / high b-value DWI. Thus, noise can be estimated from an anterior rectum region when the endo-rectal coil is used.
Learned 3D radial trajectories using Image Quality Metrics from Prior Data

3D radial sampling provides high levels of acceleration with insensitivity to motion. Methods of designing the radial projection angles and orders have been focused on improving the sampling uniformity, which is indirectly related to image quality. We propose a flexible framework to optimize the sampling directly based on image reconstruction metrics. We demonstrated the optimized trajectories were able to produce higher quality images in both simulations and scans.
Super-resolution MR Vessel Wall Images Using deep learning

To develop a super-resolution method based on the 3D high-resolution MR vessel wall images for generating high-resolution images from low-resolution, a 3D complex-valued super resolution (CVSR) neural network was proposed, which maintained complex algebraic structure of the original acquired images. CVSR was trained on 20 pairs of data sets and tested on 5 pairs. Ground truth with 0.44 mm were compared with Fourier interpolation method, EDSR with two real-valued channels and CVSR. Evaluations were performed using structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and error map quality metrics. The CVSR achieved the best performance when compared with the other methods.
Interactive cryptographic proofs of quantumness using mid-circuit measurements

The ability to perform measurements in the middle of a quantum circuit is a powerful resource. It underlies a wide range of applications, from remote state preparation to quantum error correction. Here we apply mid-circuit measurements for a particular task: demonstrating quantum computational advantage. The goal of such a demonstration is for a quantum device to perform a computational task that is infeasible for a classical device with comparable resources. In contrast to existing demonstrations, the distinguishing feature of our approach is that the classical verification process is efficient, both in asymptotic complexity and in practice. Furthermore, the classical hardness of performing the task is based upon well-established cryptographic assumptions. Protocols with these features are known as cryptographic proofs of quantumness. Using a trapped-ion quantum computer, we perform mid-circuit measurements by spatially isolating portions of the ion chain via shuttling. This enables us to implement two interactive cryptographic proofs of quantumness, which when suitably scaled to larger systems, promise the efficient verification of quantum computational advantage. Our methods can be applied to a range of interactive quantum protocols. Being able to perform qubit measurements within a quantum circuit and adapt to their outcome broadens the power of quantum computers. These mid-circuit measurements have now been used to implement a cryptographic proof of non-classical behaviour.
Temporally improved volume registration by data driven subvolume interpolation of simultaneous multislice EPI time series

In the context of EPI time series acquisition, fast motion occurring below the Nyquist limit (&amp;lt; 1 TR), present a problem for accurate registration by 3D rigid-body algorithms. A method for temporally upsampling SMS EPI acquisitions is proposed. EPI volumes are splitted into subvolumes according to the temporal acquisition order of SMS and interpolated using a data driven approach based on pairwise affine transforms and linear interpolation. A proof of concept based EPI acquisitions of controlled head choreographies shows that fast motion below the Nyquist rate can be more accurately estimated by our method.
Think outside the box: Exploiting the imaging workflow for Deep Learning based motion estimation and correction

Estimating intrascan subject motion enables the reduction of motion artifacts but often requires further calibration data e.g. from an additional motion-free reference. In this work, we explore how the reuse of supplementary scans in the imaging workflow can be used as motion calibration data. More specifically, the preceding parallel imaging calibration scan is reutilized to support a Deep Learning (DL) approach for estimating motion. Results are presented which indicate that DL, in contrast to a conventional optimization approach, can extract the motion and improve the image quality despite contrast differences between the calibration and imaging scans.
Rotation estimation for cloverleaf navigators using a k-space map simulation

Short &amp;lt;5 ms cloverleaf navigators have been demonstrated for prospective motion correction at a rate of 71 Hz in steady-state FLASH. The original cloverleaf approach required a 12 second k-space mapping pre-scan to reliably deal with out-of-plane rotations. Here we investigate simulation of a k-space map based on a low spatial resolution pre-scan. The approach shows potential for real-time detection of rigid-body rotation in the context of real-time self-correction of the cloverleaf orientation.
3D brain MRI synthesis utilizing 2D SPADE-GAN and 3D CNN architecture

We propose a method to synthesize 3D consistent brain MRI utilizing multiple 2D conditional SPatially-Adaptive (DE)normalization (SPADE) GANs to preserve the spatial information of patient-specific brain anatomy and a 3D CNN based network to improve the image consistency in all directions in a cost-efficient manner. Three individual 2D SPADE-GAN networks are trained across the axial, sagittal and coronal slice directions and their outputs are thereafter used further to train a CNN based 3D image restoration network to combine them into one 3D volume, using real MRI as a reference. The resulting predicted-synthesized 3D brain MRI is evaluated quantitatively and qualitatively.
GROUPING PROVINCES IN INDONESIA BASED ON YOUTH DEVELOPMENT INDICATORS IN 2021

Youth have a vital role in development. Indonesia has an uneven distribution of youth across provinces, with over half concentrated in Java. Many young people are also only sometimes in line with their qualities. Youth development was observed through youth development indicators in 2021. The method used is a multivariate method using cluster analysis. The cluster method applied in this research is hierarchy and partition. Based on internal and stability validity, the hierarchical method for five clusters and the number of clusters is the best. The hierarchical method that has the most significant agglomeration coefficient is complete linkage. There is one province with indicators that are very different from other provinces: Papua as cluster 1. Papua requires massive development in all aspects. Cluster 2 comprises Riau Island, Jakarta, the Special Region of Yogyakarta, Bali, and East Kalimantan. Cluster 3 consists of West Nusa Tenggara, Bengkulu, and Lampung. Cluster 4 consists of West Java, Banten, Central Java, Gorontalo, South Sumatra, and East Java. Meanwhile, cluster 5 consists of the remaining members, with the remaining 19 provinces having the most members.&#x0D; &#x0D; Keywords: Youth Development Indicators, Cluster Analysis, Hierarchy, Partition
Discovering the Common Traits of Cybercrimes in Pakistan Using Associative Classification with Ant Colony Optimization

In the modern world, law enforcement authorities are facing challenges due to the advanced technology used by criminals to commit crimes.Criminals follow specific patterns to carry out their crimes, which can be identified using machine learning and swarm intelligence approaches.This article proposes the use of the Ant Colony Optimization algorithm to create an associative classification of crime data, which can reveal potential relationships between different features and crime types.The experiments conducted in this research show that this approach can discover various associations among the features of crime data and the specific patterns that major crime types depend on.This research can be beneficial in discovering the patterns leading to a specific class of crimes, allowing law enforcement agencies to take proactive measures to prevent them.Experimental results demonstrate that ACO-based associative classification model predicted 10 out of 16 crime types with 90% or more accuracy based on discovery of association among dataset features.Hence, the proposed approach is a viable tool for application in forensic and investigation of crimes.
Full Scale-Aware Balanced High-Resolution Network for Multi-Person Pose Estimation

Scale variation is a major challenge in multi-person pose estimation.In scenes where persons are present at various distances, models tend to perform better on larger-scale persons, while the performance for smaller-scale persons often falls short of expectations.Therefore, effectively balancing the persons of different scales poses a significant challenge.So this paper proposes a new multi-person pose estimation model called FSA Net to improve the model's performance in complex scenes.Our model utilizes High-Resolution Network (HRNet) as the backbone and feeds the outputs of the last stage's four branches into the DCB module.The dilated convolution-based (DCB) module employs a parallel structure that incorporates dilated convolutions with different rates to expand the receptive field of each branch.Subsequently, the attention operation-based (AOB) module performs attention operations at both branch and channel levels to enhance high-frequency features and reduce the influence of noise.Finally, predictions are made using the heatmap representation.The model can recognize images with diverse scales and more complex semantic information.Experimental results demonstrate that FSA Net achieves competitive results on the MSCOCO and MPII datasets, validating the effectiveness of our proposed approach.
Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data

Selecting suitable architecture parameters and training hyperparameters is essential for enhancing machine learning (ML) model performance. Several recent empirical studies conduct large-scale correlational analysis on neural networks (NNs) to search for effective generalization metrics that can guide this type of model selection. Effective metrics are typically expected to correlate strongly with test performance. In this paper, we expand on prior analyses by examining generalization-metric-based model selection with the following objectives: (i) focusing on natural language processing (NLP) tasks, as prior work primarily concentrates on computer vision (CV) tasks; (ii) considering metrics that directly predict test error instead of the generalization gap; (iii) exploring metrics that do not need access to data to compute. From these objectives, we are able to provide the first model selection results on large pretrained Transformers from Huggingface using generalization metrics. Our analyses consider (I) hundreds of Transformers trained in different settings, in which we systematically vary the amount of data, the model size and the optimization hyperparameters, (II) a total of 51 pretrained Transformers from eight families of Huggingface NLP models, including GPT2, BERT, etc., and (III) a total of 28 existing and novel generalization metrics. Despite their niche status, we find that metrics derived from the heavy-tail (HT) perspective are particularly useful in NLP tasks, exhibiting stronger correlations than other, more popular metrics. To further examine these metrics, we extend prior formulations relying on power law (PL) spectral distributions to exponential (EXP) and exponentially-truncated power law (E-TPL) families.
Enhancing Node-Level Adversarial Defenses by Lipschitz Regularization of Graph Neural Networks

Graph neural networks (GNNs) have shown considerable promise for graph-structured data. However, they are also known to be unstable and vulnerable to perturbations and attacks. Recently, the Lipschitz constant has been adopted as a control on the stability of Euclidean neural networks, but calculating the exact constant is also known to be difficult even for very shallow networks. In this paper, we extend the Lipschitz analysis to graphs by providing a systematic scheme for estimating upper bounds of the Lipschitz constants of GNNs. We also derive concrete bounds for widely used GNN architectures including GCN, GraphSAGE and GAT. We then use these Lipschitz bounds for regularized GNN training for improved stability. Our numerical results on Lipschitz regularization of GNNs not only illustrate enhanced test accuracy under random noise, but also show consistent improvement for state-of-the-art defense methods against adversarial attacks.
How does the Memorization of Neural Networks Impact Adversarial Robust Models?

Recent studies suggest that "memorization" is one necessary factor for overparameterized deep neural networks (DNNs) to achieve optimal performance. Specifically, the perfectly fitted DNNs can memorize the labels of many atypical samples, generalize their memorization to correctly classify test atypical samples and enjoy better test performance. While, DNNs which are optimized via adversarial training algorithms can also achieve perfect training performance by memorizing the labels of atypical samples, as well as the adversarially perturbed atypical samples. However, adversarially trained models always suffer from poor generalization, with both relatively low clean accuracy and robustness on the test set. In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples. Based on these two findings, we propose Benign Adversarial Training (BAT) which can facilitate adversarial training to avoid fitting "harmful" atypical samples and fit as more "benign" atypical samples as possible. In our experiments, we validate the effectiveness of BAT, and show that it can achieve better clean accuracy vs. robustness trade-off than baseline methods, in benchmark datasets for image classification.
C-AOI: Contour-based Instance Segmentation for High-Quality Areas-of-Interest in Online Food Delivery Platform

Online food delivery (OFD) services have become popular globally, serving people's daily needs. Precise area-of-interest (AOI) boundaries help OFD platforms determine customers' exact locations, which is crucial for maintaining consistency in delivery difficulty and providing a uniform customer experience within an AOI. Existing AOI generation methods primarily rely on predefined shapes or density-based clustering, which limits the quality of the contours. Recently, Meituan has treated the AOI contours as a binary semantic segmentation problem. Their approach involves a multi-step post-process to address the issues with boundary breaks caused by semantic segmentation models, leading to decreased quality and inefficiency in the learning process. In this paper, we propose a novel method for AOI contour generation called C-AOI (Contour-based Area-of-Interest). C-AOI is an instance segmentation model that focuses on generating high-quality AOI contours. Unlike the former method, which relies on pixel-by-pixel classification, C-AOI starts from the center point of the AOI and regresses the boundary. This approach results in a higher-quality boundary and is less computationally intensive. C-AOI first corrects errors on the contour using a local aggregation mechanism. Then, we propose a novel deforming module called the contour transformer, which captures the global geometry of the object. To enhance the positional relationship among vertices, we introduce a learnable cyclic positional encoding applied to the contour transformer. Finally, to improve the boundary details, we propose the Adaptive Matching Loss (AML) that eliminates over-smoothed boundaries and promotes optimized convergence pathways. Experimental results on real-world datasets collected from Meituan have demonstrated that C-AOI significantly improves the mask and boundary quality compared to Meituan's previous work. Moreover, Its inference speed is comparable to that of E2EC, a state-of-the-art real-time contour-based method. It is noteworthy that C-AOI has been deployed in the Meituan platform for producing AOIs.