Reviewerly/exHarmony huggingface dataset:
BookKD: A novel knowledge distillation for reducing distillation costs by decoupling knowledge generation and learning

Knowledge distillation guides student networks’ training and enhances their performance through excellent teacher networks. However, along with the performance advantages, knowledge distillation also entails a huge computational burden, sometimes tens or even hundreds of times that of traditional training methods. So, this paper designs a book-based knowledge distillation (BookKD) to minimize the costs of knowledge distillation while improving performance. First, a decoupling-based knowledge distillation framework is designed. By decoupling the traditional knowledge distillation process into two independent sub-processes, book-making and book-learning, knowledge distillation can be completed with little resource consumption. Second, a book-making method based on knowledge ensemble and knowledge regularization is developed, which makes books by organizing and processing the knowledge generated by teachers. These books can replace these teachers to provide sufficient knowledge with little distillation costs. Finally, a book-learning method based on entropy dynamic adjustment and label smoothing is designed. The entropy dynamic adjustment optimizes the training loss and mitigates student networks’ difficulty in learning books. Label smoothing alleviates the student network’s over-confidence in ground truth labels, which increases its attention to the class similarity knowledge in books. BookKD is tested on three image classification datasets, CIFAR100, ImageNet and ImageNet100, and an object detection dataset PASCAL VOC 2007. The experiment results indicate the advantages of BookKD in reducing distillation costs and improving distillation performance.
Using Smart Devices in Forming the Foreign Language Competency of Economics Students

This article presents a study on the effectiveness of using smart devices in teaching foreign languages. The research involved conducting an experiment with students (N=195), allowing for a comparison of learning outcomes between groups of those using smart devices in the process of learning a foreign language and those without them. The results demonstrate the benefits of practically using smart devices in foreign language acquisition, significantly improving the quality of instructional material presentation and its absorption by students, enriching the educational process, increasing motivation for language learning, fostering closer collaboration between teachers and students, introducing variation in homework and independent student work, and thereby creating new conditions for self-education and individual learning trajectories. The authors conclude that smart technologies eliminate the limitations inherent in traditional methods, taking teaching and learning beyond the classroom. Implementing smart technologies in students' practical activities in professionally oriented foreign language classes promotes the development of all four language skills – reading, writing, speaking, and listening, improves communicative abilities, and helps foreign language learners adapt to international testing.
WeChat online visual language among Chinese Gen Z: virtual gift, aesthetic identity, and affection language

Introduction As a relatively new and innovative form of digital communication and visual image, emojis, emoticons, and stickers are appreciated, yet criticised. Young people on social media have lost the traditional social etiquette and interpersonal networking skills, which is a challenge in itself. This study provides a better understanding of how young people engage and behave in diverse interpersonal contexts while utilising digital visual language. Methods This study contributes to digital anthography and social media studies, by conducting this study through interviews with 10 Generation Z young people from urban China and a 2-month-long participatory observation of three WeChat group chats. Results By examining how young people use WeChat visual language in relationships with their elders or superiors, equivalent peers and intimate lovers or friends, this study found that emoticons and stickers become virtual gifts, aesthetic identities and the affection language of Generation Z. Discussion This study contends that digital visual languages and new media do not alter human nature or ethical standards but rather provide new avenues for expression and empower human affective communication. Although the core of traditional etiquette is still being passed on and absorbed, its form has changed.
Ditto: Efficient Serverless Analytics with Elastic Parallelism

Serverless computing provides fine-grained resource elasticity for data analytics---a job can flexibly scale its resources for each stage, instead of sticking to a fixed pool of resources throughout its lifetime. Due to different data dependencies and different shuffling overheads caused by intra- and inter-server communication, the best degree of parallelism (DoP) for each stage varies based on runtime conditions.
Displacement Measurement of Shaking Table Experiments Based on Binocular Vision and Subpixel Optimization

Abstract The shaking table experiment is a very useful tool to assess the seismic performance of structural models. However, there are many shortcomings in traditional displacement measurement methods. In this research, a binocular vision algorithm for accurately extracting the subpixel center of the circular target based on elliptic fitting is proposed. Firstly, the images are collected by two high-speed cameras and pre-processed to enhance their brightness and contrast. Then, an improved subpixel optimization method based on the Zernike moments and Canny operator is adopted to get accurate localization of extracted target points’ three-dimension coordinates. After comparing the displacement of target points with laser sensors, the results show that the proposed algorithm achieves high-precision displacement measurements and overcomes the problems that traditional methods hold.
TSDNET: A Tumour Segmentation Network with 3D Direction-Wise Convolution

The segmentation of tumours requires accurate identification and localisation in medical images. With the advent of U-Net and its variants, medical image segmentation has improved by leaps and bounds. Recently, researchers have adapted Transformers for vision tasks by fusing Transformer blocks with the U-Net architecture. Although tumour segmentation results have improved, models have also significantly increased computation costs. To reduce the computation costs while retaining accurate segmentation results, we propose TSDNet, a tumour segmentation network which utilises a novel 3D direction-wise convolution. We conducted experiments on five tumour segmentation datasets and compared the TSDNet model with state-of-the-art tumour segmentation models. Experimental results show that TSDNet performs better at segmenting complex tumour shapes and obtains results at par with ensemble and Transformer models, while also reducing the computation costs.
PLPP: A Pseudo Labeling Post-Processing Strategy for Unsupervised Domain Adaptation

A well known problem in medical imaging is the ability to use an existing model learned on source data, in a new site. This is known as the domain shift problem. We propose a pseudo labels procedure, which was originally introduced for semi-supervised learning, that is suitable for unsupervised domain adaptation (UDA). We iteratively improve the pseudo labels of the target domain data only using the current pseudo labels without involving the labeled source domain data. We applied our method to several medical MRI image segmentation tasks. We show that, by combining our approach as a post-processing step in standard UDA algorithms, we consistently and significantly improve the segmentation results on test images from the target site.
CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement

Tumor region segmentation is an essential task for the quantitative analysis of digital pathology. Recently presented deep neural networks have shown state-of-the-art performance in various image-segmentation tasks. However, because of the unclear boundary between the cancerous and normal regions in pathology images, despite using modern methods, it is difficult to produce satisfactory segmentation results in terms of the reliability and accuracy required for medical data. In this study, we propose an interactive segmentation method that allows users to refine the output of deep neural networks through click-type user interactions. The primary method is to formulate interactive segmentation as an optimization problem that leverages both user-provided click constraints and semantic information in a feature map using a click-guided attention module (CGAM). Unlike other existing methods, CGAM avoids excessive changes in segmentation results, which can lead to the overfitting of user clicks. Another advantage of CGAM is that the model size is independent of input image size. Experimental results on pathology image datasets indicated that our method performs better than existing state-of-the-art methods.
Theorem Proving in Dependently-Typed Higher-Order Logic

Abstract Higher-order logic HOL offers a very simple syntax and semantics for representing and reasoning about typed data structures. But its type system lacks advanced features where types may depend on terms. Dependent type theory offers such a rich type system, but has rather substantial conceptual differences to HOL, as well as comparatively poor proof automation support. We introduce a dependently-typed extension DHOL of HOL that retains the style and conceptual framework of HOL. Moreover, we build a translation from DHOL to HOL and implement it as a preprocessor to a HOL theorem prover, thereby obtaining a theorem prover for DHOL.
Automatically Extracting Information in Medical Dialogue: Expert System and Attention for Labelling

Medical dialogue information extraction is becoming an increasingly significant problem in modern medical care. It is difficult to extract key information from electronic medical records (EMRs) due to their large numbers. Previously, researchers proposed attention-based models for retrieving features from EMRs, but their limitations were reflected in their inability to recognize different categories in medical dialogues. In this paper, we propose a novel model, Expert System and Attention for Labelling (ESAL). We use mixture of experts and pre-trained BERT to retrieve the semantics of different categories, enabling the model to fuse the differences between them. In our experiment, ESAL was applied to a public dataset and the experimental results indicated that ESAL significantly improved the performance of Medical Information Classification.
When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review

Abstract Earth observations (EOs) have successfully been used to train artificial intelligence (AI)-based models in the field of disaster risk reduction (DRR) contributing to tools such as disaster early warning systems. Given the number of in situ and remote (e.g. radiosonde/satellite) monitoring devices, there is a common perception that there are no limits to the availability of EO for immediate use in such AI-based models. However, a mere fraction of EO is actually being used in this way. This topical review draws on use cases, workshop presentations, literature, and consultation with experts from key institutes to explore reasons for this discrepancy. Specifically, it evaluates the types of EO needed to train AI-based models for DRR applications and identifies the main characteristics, possible challenges, and innovative solutions for EO. Finally, it suggests ways to make EO more user ready and to facilitate its uptake in AI for DRR and beyond.
It looks like me, but it isn’t me: On the societal implications of deepfakes

Deepfakes are images or videos generated using deep learning technology to change the original conditions of a piece of media. Potential uses of this technology range from satirical content, depicting public figures in comical scenarios; to generating audio to mimic a specific voice; to inserting the face of an unknowing individual into potentially embarrassing content, for example, a pornographic scene. As deepfake technologies, such as autoencoders and generative adversarial networks (GANs), become more advanced and accessible, deepfakes become easier to create and more believable. This poses some serious threats to individual and institutional safety, as scenes can be modified to alter public perception. Although technology to identify deepfakes does exist, it is essential that these methods progress as rapidly as deepfake technology so that they remain accurate. Additionally, legal consequences for deepfakes are limited, so continued advocacy for increased protection against false content is crucial.
End-To-End Deformable Attention Graph Neural Network for Single-View Liver Mesh Reconstruction

Intensity modulated radiotherapy (IMRT) is one of the most common modalities for treating cancer patients. One of the biggest challenges is precise treatment delivery that accounts for varying motion patterns originating from free-breathing. Currently, image-guided solutions for IMRT is limited to 2D guidance due to the complexity of 3D tracking solutions. We propose a novel end-to-end attention graph neural network model that generates in real-time a triangular shape of the liver based on a reference segmentation obtained at the preoperative phase and a 2D MRI coronal slice taken during the treatment. Graph neural networks work directly with graph data and can capture hidden patterns in non-Euclidean domains. Furthermore, contrary to existing methods, it produces the shape entirely in a mesh structure and correctly infers mesh shape and position based on a surrogate image. We define two on-the-fly approaches to make the correspondence of liver mesh vertices with 2D images obtained during treatment. Furthermore, we introduce a novel task-specific identity loss to constrain the deformation of the liver in the graph neural network to limit phenomenons such as flying vertices or mesh holes. The proposed method achieves results with an average error of 3.06 ± 0.7 mm and Chamfer distance with L2 norm of 63.14 ± 27.28.
Generation of Synthetic 3D Data Using Simulated MR Examinations in Augmented Reality

In the future, medical imaging devices such as computed tomography (CT) and magnetic resonance (MR) are expected to become increasingly autonomous. Therefore, design criteria and workflow of such devices will change substantially. Moreover, sensor data of the system are required to develop scene understanding algorithms to support and guide the user. In this work, we present an approach to generate synthetic 3D point cloud data from a simulated MR examination experienced on the Microsoft Hololens2. The complete workflow of an MR examination using a virtual, autonomous MR scanner is reproduced in an AR scene. The user is recorded by a system of active stereo vision RGBD-cameras while interacting with AR elements. A registration routine of the AR scene and the RGBD-cameras is described, and accuracy measurements are provided. The real point clouds are fused with virtually generated point clouds from the AR scene.
Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations

SplitFed Learning, a combination of Federated and Split Learning (FL and SL), is one of the most recent developments in the decentralized machine learning domain. In SplitFed learning, a model is trained by clients and a server collaboratively. For image segmentation, labels are created at each client independently and, therefore, are subject to clients' bias, inaccuracies, and inconsistencies. In this paper, we propose a data quality-based adaptive averaging strategy for SplitFed learning, called QA-SplitFed, to cope with the variation of annotated ground truth (GT) quality over multiple clients. The proposed method is compared against five state-of-the-art model averaging methods on the task of learning human embryo image segmentation. Our experiments show that all five baseline methods fail to maintain accuracy as the number of corrupted clients increases. QA-SplitFed, however, copes effectively with corruption as long as there is at least one uncorrupted client.
EVIL: Evidential Inference Learning for Trustworthy Semi-Supervised Medical Image Segmentation

Recently, uncertainty-aware methods have attracted increasing attention in semi-supervised medical image segmentation. However, current methods usually suffer from the drawback that it is difficult to balance the computational cost, estimation accuracy, and theoretical support in a unified framework. To alleviate this problem, we introduce the Dempster–Shafer Theory of Evidence (DST) into semi-supervised medical image segmentation, dubbed EVidential Inference Learning (EVIL). EVIL provides a theoretically guaranteed solution to infer accurate uncertainty quantification in a single forward pass. Trustworthy pseudo labels on unlabeled data are generated after uncertainty estimation. The recently proposed consistency regularization-based training paradigm is adopted in our framework, which enforces the consistency on the perturbed predictions to enhance the generalization with few labeled data. Experimental results show that EVIL achieves competitive performance in comparison with several state-of-the-art methods on the public dataset.
ConPrEF: A Context-based Privacy Enforcement Framework for Edge Computing

Edge computing is an emerging computational paradigm where edge nodes provide services to users performing computation-in-place. It allows faster computation, better support for real-time applications and can simplify the implementation of security measures. Concerning individual privacy, a relevant requirement is giving users more control over how their data is used. It is important to check compliance between user privacy preferences and provider privacy policy. However, the typical edge computing application scenario is dynamic, with users in constant motion, changing their location and time at which they connect to the edge node as well as the situation under which they connect. This makes the common notion of privacy preference compliance insufficient. To address this issue, we provide a framework for allowing users to define their privacy preferences according to a rich set of contextual features. We also demonstrate the feasibility of our solution through realistic and synthetic tests.
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation

The development of medical image segmentation using deep learning can significantly support doctors’ diagnoses. Deep learning needs large amounts of data for training, which also requires data augmentation to extend diversity for preventing overfitting. However, the existing methods for data augmentation of medical image segmentation are mainly based on models which need to update parameters and cost extra computing resources. We proposed data augmentation methods designed to train a high-accuracy deep learning network for medical image segmentation. The proposed data augmentation approaches are called KeepMask and KeepMix, which can create medical images by better identifying the boundary of the organ with no more parameters. Our methods achieved better performance and obtained more precise boundaries for medical image segmentation on datasets. The dice coefficient of our methods achieved 94.15% (3.04% higher than baseline) on CHAOS and 74.70% (5.25% higher than baseline) on MSD spleen with Unet.
Conditional Deformable Image Registration with Spatially-Variant and Adaptive Regularization

Deep learning-based image registration approaches have shown competitive performance and run-time advantages compared to conventional image registration methods. However, existing learning-based approaches mostly require to train separate models with respect to different regularization hyperparameters for manual hyperparameter searching and often do not allow spatially-variant regularization. In this work, we propose a learning-based registration approach based on a novel conditional spatially adaptive instance normalization (CSAIN) to address these challenges. The proposed method introduces a spatially-variant regularization and learns its effect of achieving spatially-adaptive regularization by conditioning the registration network on the hyperparameter matrix via CSAIN. This allows varying of spatially adaptive regularization at inference to obtain multiple plausible deformations with a single pre-trained model. Additionally, the proposed method enables automatic hyperparameter optimization to avoid manual hyperparameter searching. Experiments show that our proposed method outperforms the baseline approaches while achieving spatially-variant and adaptive regularization.
QIn: Enabling Formal Methods to Deal with Quantum Circuits

Quantum computers open up new fields of application for hard-to-compute problems due to potential super-polynomial speedup. The design of quantum algorithms is complex and thus error-prone, which makes them a prime target for formal methods. We present our tool QIn to translate quantum circuits into a classical host language (Java). As a result, we can use any tool developed for the host language – and thereby leverage the full power of available formal methods for that language to reason about quantum circuits and create a verification/validation toolchain. Furthermore, QIn enables us to reason about hybrid programs, consisting of classical code and quantum circuits. This is crucial as, for the time being, only single subroutines of a program will be implemented on quantum computers. To show the possibilities of our approach, we present an example of a toolchain based on QIn. This toolchain relies on a software bounded-model checker and can prove the correctness of hybrid programs combining the host language Java with quantum circuits. We use the Java Modeling Language (JML) as the specification language and show the feasibility of our approach on several examples including a bounded version of Shor’s algorithm.
Personalized Re-ranking for Recommendation with Mask Pretraining

Abstract Re-ranking is to refine the candidate ranking list of recommended items, such that the re-ranked list attracts users to purchase or click more items than the candidate one without re-ranking. Items in the candidate list are often ranked by their relevance to users’ interests. It is thus important to exploit the mutual influence between items in the re-ranking process. Existing re-ranking models focus on only the pairwise influence between two items, and have limited capability to exploit the local mutual influence in a group of items. Users often show successive interests on a group of relevant items, e.g., mobile phone, phone covers, wireless headset , namely scene. We propose a novel re-ranking model that jointly exploits the local mutual influence in scenes and the global mutual influence between different scenes. Scene representations are learned by GNN and multi-head attention, where GNN aims to learn local mutual influence while multi-head attention is to learn global mutual influence. To study the interaction between users and scenes, matrix factorization on users is utilized to obtain the user preference, which can be further applied to scenes to compute the scene scores. The final re-ranking list is generated by sorting the predicted scores of all scenes. To further mine user history information and item related user information, we also develop the extension pretraining module which relies on mask mechanism to support users and items high-quality embedding generation. We conduct a comprehensive evaluation on several real-world datasets. The experimental results demonstrate that our model substantially outperforms existing approaches.