Reviewerly/exHarmony huggingface dataset:
Directional Graph Attention Network

In recent years, graph neural networks (GNNs) have become a promising method for analyzing data structured in graph format. By considering connections between entities in a graph, GNNs are able to extract valuable insights. One notable variation of GNN is the graph attention network (GAT), which employs the attention mechanism and has demonstrated promising performance in various applications. However, its ability to incorporate feature information from nodes beyond the immediate neighborhood is limited, leading to degraded performance on heterophilic data. To address this limitation, this thesis proposes a novel attention-based model, namely the Directional Graph Attention Network (DGAT). This model combines the feature-based attention with the global directional information extracted from the graph topology, as inspired by the Directional Graph Network (DGN). A new class of Laplacian matrices is proposed and an existing theoretical result on DGN is extended. This extension bridges a gap in the literature. The experimental results presented in the thesis, based on nine real-world benchmarks and ten synthetic data sets, demonstrate the superiority of the proposed DGAT model compared to the GAT baseline model. Particularly on heterophilic data sets, DGAT showed a notable average increase of approximately 35% in node classification tasks across all heterophilic real-world data sets. In addition, DGAT outperforms GAT by an average margin of around 51% in all ten synthetic data sets with various levels of heterophily.
Research on How to Implement Personalized Education in China

Personalized education is a new idea of higher education reform in the new era. Especially after the popularization of Chinese higher education, there are constant reforms inside schools to increase efficiency by changing the teaching methods of teachers and the learning methods of students. The purpose of this paper is to study how to implement personalized education and what specific aspects can be changed to make the school more personalized. This paper mainly adopts literature analysis and review. Through this paper, people can have a better understanding of the way to develop personalized education. This paper finds that if schools want to realize personalized education, they need to give students more autonomy to choose, change the way of education, and strengthen practical education. In addition, schools also need to create an atmosphere of personalized education, which can motivate students to become more engaged in learning. At the same time, the role of counselors is also an indispensable part of personalized education.
Osteosarcoma Tumor Detection using Different Transfer Learning Models

Abstract The clinical image analysis field has increasingly embraced the utilization of transfer learning models due to their reduced computational complexity and improved accuracy, among other benefits. These models, which are pre-trained and do not require training from scratch, eliminate the need for extensive datasets. While transfer learning models are primarily employed for brain, breast, or lung image analysis, other areas such as bone marrow cell detection or bone cancer detection can also derive advantages from their use, particularly considering the limited availability of large datasets for these specific tasks. This research paper investigates the performance of various transfer learning models for the detection of osteosarcoma tumours, a form of bone cancer predominantly found in the long bones of the body's cells. The dataset comprises histopathology images stained with H\&amp;E, categorized into four groups: Viable Tumor, Non-viable Tumor, Non-Tumor, and Viable Non-viable. The datasets were randomly split into training and test sets, following an 80-20 ratio, with 80% used for training and 20% for testing. Four models were evaluated for comparison: EfficientNetB7, InceptionResNetV2, NasNetLarge, and ResNet50. All these models were pre-trained on ImageNet. According to the results, InceptionResNetV2 achieved the highest accuracy (93.29%), followed by NasNetLarge (90.91%), ResNet50 (89.83%), and EfficientNetB7 (62.77%). InceptionResNetV2 also exhibited the highest precision (0.8658) and recall (0.8658) values among the four models.
Next Steps Towards Better Living Atlas Deployments and Maintenance

The Living Atlases project, facilitated by the Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA), has successfully operated for more than a decade, establishing collaborations with over 30 countries who have implemented ALA components within their respective environments. Over this period, technological advancements and the prevalence of cloud platforms have transformed the landscape of infrastructure management. In this presentation, we will explore innovative approaches to streamline the installation process of ALA, capitalizing on the benefits offered by cloud platforms and cutting-edge technologies. Furthermore, ALA has maintained a strong collaborative partnership with GBIF over the past four years, focusing on data ingestion pipelines and, more recently, engaging in shared user interface (UI) development. These improvements aim to enhance the maintainability of ALA modules, enabling organizations to leverage the advantages provided by cloud-based solutions and novel technologies.
Mean-Field Approximate Optimization Algorithm

The quantum approximate optimization algorithm (QAOA) is suggested as a promising application on early quantum computers. Here a quantum-inspired classical algorithm, the mean-field approximate optimization algorithm (mean-field AOA), is developed by replacement of the quantum evolution of the QAOA with classical spin dynamics through the mean-field approximation. Because of the alternating structure of the QAOA, this classical dynamics can be found exactly for any number of QAOA layers. We benchmark its performance against the QAOA on the Sherrington-Kirkpatrick model and the partition problem, and find that the mean-field AOA outperforms the QAOA in both cases for most instances. Our algorithm can thus serve as a tool to delineate optimization problems that can be solved classically from those that cannot, i.e., we believe that it will help to identify instances where a true quantum advantage can be expected from the QAOA. To quantify quantum fluctuations around the mean-field trajectories, we solve an effective scattering problem in time, which is characterized by a spectrum of time-dependent Lyapunov exponents. These provide an indicator for the hardness of a given optimization problem relative to the mean-field AOA.1 MoreReceived 15 March 2023Accepted 17 August 2023DOI:https://doi.org/10.1103/PRXQuantum.4.030335Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAdiabatic quantum optimizationNonequilibrium fluctuationsOptimization problemsPath integralsQuantum algorithmsPhysical SystemsQuantum spin modelsSpin glassesTechniquesMean field theoryPath-integral Monte CarloPath-integral methodsQuantum Information, Science & TechnologyStatistical Physics & ThermodynamicsCondensed Matter, Materials & Applied Physics
A Full-fledged Commit Message Quality Checker Based on Machine Learning

Commit messages (CMs) are an essential part of version control. By providing important context in regard to what has changed and why, they strongly support software maintenance and evolution. But writing good CMs is difficult and often neglected by developers. So far, there is no tool suitable for practice that automatically assesses how well a CM is written, including its meaning and context. Since this task is challenging, we ask the research question: how well can the CM quality, including semantics and context, be measured with machine learning methods? By considering all rules from the most popular CM quality guideline, creating datasets for those rules, and training and evaluating state-of-the-art machine learning models to check those rules, we can answer the research question with: sufficiently well for practice, with the lowest F$_1$ score of 82.9\%, for the most challenging task. We develop a full-fledged open-source framework that checks all these CM quality rules. It is useful for research, e.g., automatic CM generation, but most importantly for software practitioners to raise the quality of CMs and thus the maintainability and evolution speed of their software.
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization

Recent research indicates that the performance of machine learning models can be improved by aligning the geometry of the latent space with the underlying data structure. Rather than relying solely on Euclidean space, researchers have proposed using hyperbolic and spherical spaces with constant curvature, or combinations thereof, to better model the latent space and enhance model performance. However, little attention has been given to the problem of automatically identifying the optimal latent geometry for the downstream task. We mathematically define this novel formulation and coin it as neural latent geometry search (NLGS). More specifically, we introduce an initial attempt to search for a latent geometry composed of a product of constant curvature model spaces with a small number of query evaluations, under some simplifying assumptions. To accomplish this, we propose a novel notion of distance between candidate latent geometries based on the Gromov-Hausdorff distance from metric geometry. In order to compute the Gromov-Hausdorff distance, we introduce a mapping function that enables the comparison of different manifolds by embedding them in a common high-dimensional ambient space. We then design a graph search space based on the notion of smoothness between latent geometries and employ the calculated distances as an additional inductive bias. Finally, we use Bayesian optimization to search for the optimal latent geometry in a query-efficient manner. This is a general method which can be applied to search for the optimal latent geometry for a variety of models and downstream tasks. We perform experiments on synthetic and real-world datasets to identify the optimal latent geometry for multiple machine learning problems.
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer

Automated segmentation of large volumes of medical images is often plagued by the limited availability of fully annotated data and the diversity of organ surface properties resulting from the use of different acquisition protocols for different patients. In this paper, we introduce a more promising few-shot learning-based method named Region-enhanced Prototypical Transformer (RPT) to mitigate the effects of large intra-class diversity/bias. First, a subdivision strategy is introduced to produce a collection of regional prototypes from the foreground of the support prototype. Second, a self-selection mechanism is proposed to incorporate into the Bias-alleviated Transformer (BaT) block to suppress or remove interferences present in the query prototype and regional support prototypes. By stacking BaT blocks, the proposed RPT can iteratively optimize the generated regional prototypes and finally produce rectified and more accurate global prototypes for Few-Shot Medical Image Segmentation (FSMS). Extensive experiments are conducted on three publicly available medical image datasets, and the obtained results show consistent improvements compared to state-of-the-art FSMS methods. The source code is available at: https://github.com/YazhouZhu19/RPT.
Distributional Data Augmentation Methods for Low Resource Language

Text augmentation is a technique for constructing synthetic data from an under-resourced corpus to improve predictive performance. Synthetic data generation is common in numerous domains. However, recently text augmentation has emerged in natural language processing (NLP) to improve downstream tasks. One of the current state-of-the-art text augmentation techniques is easy data augmentation (EDA), which augments the training data by injecting and replacing synonyms and randomly permuting sentences. One major obstacle with EDA is the need for versatile and complete synonym dictionaries, which cannot be easily found in low-resource languages. To improve the utility of EDA, we propose two extensions, easy distributional data augmentation (EDDA) and type specific similar word replacement (TSSR), which uses semantic word context information and part-of-speech tags for word replacement and augmentation. In an extensive empirical evaluation, we show the utility of the proposed methods, measured by F1 score, on two representative datasets in Swedish as an example of a low-resource language. With the proposed methods, we show that augmented data improve classification performances in low-resource settings.
Fisher's Pioneering work on Discriminant Analysis and its Impact on AI

Fisher opened many new areas in Multivariate Analysis, and the one which we will consider is discriminant analysis. Several papers by Fisher and others followed from his seminal paper in 1936 where he coined the name discrimination function. Historically, his four papers on discriminant analysis during 1936-1940 connect to the contemporaneous pioneering work of Hotelling and Mahalanobis. We revisit the famous iris data which Fisher used in his 1936 paper and in particular, test the hypothesis of multivariate normality for the data which he assumed. Fisher constructed his genetic discriminant motivated by this application and we provide a deeper insight into this construction; however, this construction has not been well understood as far as we know. We also indicate how the subject has developed along with the computer revolution, noting newer methods to carry out discriminant analysis, such as kernel classifiers, classification trees, support vector machines, neural networks, and deep learning. Overall, with computational power, the whole subject of Multivariate Analysis has changed its emphasis but the impact of this Fisher's pioneering work continues as an integral part of supervised learning in Artificial Intelligence.
Correlations in Disordered Solvable Tensor Network States

Solvable matrix product and projected entangled pair states evolved by dual and ternary-unitary quantum circuits have analytically accessible correlation functions. Here, we investigate the influence of disorder. Specifically, we compute the average behavior of a physically motivated two-point equal-time correlation function with respect to random disordered solvable tensor network states arising from the Haar measure on the unitary group. By employing the Weingarten calculus, we provide an exact analytical expression for the average of the $k$th moment of the correlation function. The complexity of the expression scales with $k!$ and is independent of the complexity of the underlying tensor network state. Our result implies that the correlation function vanishes on average, while its covariance is nonzero.
Towards Robust Model Watermark via Reducing Parametric Vulnerability

Deep neural networks are valuable assets considering their commercial benefits and huge demands for costly annotation and computation resources. To protect the copyright of DNNs, backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model by embedding a specific backdoor behavior before releasing it. The defenders (usually the model owners) can identify whether a suspicious third-party model is ``stolen'' from them based on the presence of the behavior. Unfortunately, these watermarks are proven to be vulnerable to removal attacks even like fine-tuning. To further explore this vulnerability, we investigate the parameter space and find there exist many watermark-removed models in the vicinity of the watermarked one, which may be easily used by removal attacks. Inspired by this finding, we propose a mini-max formulation to find these watermark-removed models and recover their watermark behavior. Extensive experiments demonstrate that our method improves the robustness of the model watermarking against parametric changes and numerous watermark-removal attacks. The codes for reproducing our main experiments are available at \url{https://github.com/GuanhaoGan/robust-model-watermarking}.
DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices

Recent years have witnessed the great success of vision transformer (ViT), which has achieved state-of-the-art performance on multiple computer vision benchmarks. However, ViT models suffer from vast amounts of parameters and high computation cost, leading to difficult deployment on resource-constrained edge devices. Existing solutions mostly compress ViT models to a compact model but still cannot achieve real-time inference. To tackle this issue, we propose to explore the divisibility of transformer structure, and decompose the large ViT into multiple small models for collaborative inference at edge devices. Our objective is to achieve fast and energy-efficient collaborative inference while maintaining comparable accuracy compared with large ViTs. To this end, we first propose a collaborative inference framework termed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeViT</b> to facilitate edge deployment by decomposing large ViTs. Subsequently, we design a decomposition-and-ensemble algorithm based on knowledge distillation, termed DEKD, to fuse multiple small decomposed models while dramatically reducing communication overheads, and handle heterogeneous models by developing a feature matching module to promote the imitations of decomposed models from the large ViT. Extensive experiments for three representative ViT backbones on four widely-used datasets demonstrate our method achieves efficient collaborative inference for ViTs and outperforms existing lightweight ViTs, striking a good trade-off between efficiency and accuracy. For example, our DeViTs improves end-to-end latency by 2.89× with only 1.65% accuracy sacrifice using CIFAR-100 compared to the large ViT, ViT-L/16, on the GPU server. DeDeiTs surpasses the recent efficient ViT, MobileViT-S, by 3.54% in accuracy on ImageNet-1K, while running 1.72× faster and requiring 55.28% lower energy consumption on the edge device.
Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation

Generative Adversarial Networks (GANs) are an arrange of two neural networks -- the generator and the discriminator -- that are jointly trained to generate artificial data, such as images, from random inputs. The quality of these generated images has recently reached such levels that can often lead both machines and humans into mistaking fake for real examples. However, the process performed by the generator of the GAN has some limitations when we want to condition the network to generate images from subcategories of a specific class. Some recent approaches tackle this \textit{conditional generation} by introducing extra information prior to the training process, such as image semantic segmentation or textual descriptions. While successful, these techniques still require defining beforehand the desired subcategories and collecting large labeled image datasets representing them to train the GAN from scratch. In this paper we present a novel and alternative method for guiding generic non-conditional GANs to behave as conditional GANs. Instead of re-training the GAN, our approach adds into the mix an encoder network to generate the high-dimensional random input vectors that are fed to the generator network of a non-conditional GAN to make it generate images from a specific subcategory. In our experiments, when compared to training a conditional GAN from scratch, our guided GAN is able to generate artificial images of perceived quality comparable to that of non-conditional GANs after training the encoder on just a few hundreds of images, which substantially accelerates the process and enables adding new subcategories seamlessly.
Reversible adversarial steganography for security enhancement

In adaptive steganography, existing universal stego post-processing methods can enhance security, but suffer from incorrect extraction of messages and undesired visual defects, especially in flat regions. To address this issue, a reversible adversarial steganography method is proposed by modifying the LSB or 2ndLSB of stego-images, which has promising visual quality and security. To that end, the content-adaptive adversarial perturbations are first generated, which consider image texture with noise residual features to counter deep learning-based steganalyzers. Then, a data compression strategy of adversarial perturbations is designed by applying lossless run-length encoding based on the sparse nature of non-zero elements in the perturbations to reduce the perturbation’s quantity. Finally, reversible data hiding based on ternary coding is applied to embed and extract stego images with compressed adversarial perturbations. Extensive experimental results demonstrate that the proposed method can effectively enhance security and visual quality compared with state-of-the-art methods.
Data sharing and re-use in the traumatic stress field: An international survey of trauma researchers

Background: The FAIR data principles aim to make scientific data more Findable, Accessible, Interoperable, and Reusable. In the field of traumatic stress research, FAIR data practices can help accelerate scientific advances to improve clinical practice and can reduce participant burden. Previous studies have identified factors that influence data sharing and re-use among scientists, such as normative pressure, perceived career benefit, scholarly altruism, and availability of data repositories. No prior study has examined researcher views and practices regarding data sharing and re-use in the traumatic stress field.Objective: To investigate the perspectives and practices of traumatic stress researchers around the world concerning data sharing, re-use, and the implementation of FAIR data principles in order to inform development of a FAIR Data Toolkit for traumatic stress researchers.Method: A total of 222 researchers from 28 countries participated in an online survey available in seven languages, assessing their views on data sharing and re-use, current practices, and potential facilitators and barriers to adopting FAIR data principles.Results: The majority of participants held a positive outlook towards data sharing and re-use, endorsing strong scholarly altruism, ethical considerations supporting data sharing, and perceiving data re-use as advantageous for improving research quality and advancing the field. Results were largely consistent with prior surveys of scientists across a wide range of disciplines. A significant proportion of respondents reported instances of data sharing and re-use, but gold standard practices such as formally depositing data in established repositories were reported as infrequent. The study identifies potential barriers such as time constraints, funding, and familiarity with FAIR principles.Conclusions: These results carry crucial implications for promoting change and devising a FAIR Data Toolkit tailored for traumatic stress researchers, emphasizing aspects such as study planning, data preservation, metadata standardization, endorsing data re-use, and establishing metrics to assess scientific and societal impact.