Reviewerly/exHarmony huggingface dataset:
Semi-supervised Domain Adaptation for Automatic Quality Control of FLAIR MRIs in a Clinical Data Warehouse

Domain adaptation is a very useful approach to exploit the potential of clinical data warehouses, which gather a vast amount of medical imaging encompassing various modalities, sequences, manufacturers and machines. In this study, we propose a semi-supervised domain adaptation (SSDA) framework for automatically detecting poor quality FLAIR MRIs within a clinical data warehouse. Leveraging a limited number of labelled FLAIR and a large number of labelled T1-weighted MRIs, we introduce a novel architecture based on the well known Domain Adversarial Neural Network (DANN) that incorporates a specific classifier for the target domain. Our method effectively addresses the covariate shift and class distribution shift between T1-weighted and FLAIR MRIs, surpassing existing SSDA approaches by more than 10% points.
Realistic Data Enrichment for Robust Image Segmentation in Histopathology

Poor performance of quantitative analysis in histopathological Whole Slide Images (WSI) has been a significant obstacle in clinical practice. Annotating large-scale WSIs manually is a demanding and time-consuming task, unlikely to yield the expected results when used for fully supervised learning systems. Rarely observed disease patterns and large differences in object scales are difficult to model through conventional patient intake. Prior methods either fall back to direct disease classification, which only requires learning a few factors per image, or report on average image segmentation performance, which is highly biased towards majority observations. Geometric image augmentation is commonly used to improve robustness for average case predictions and to enrich limited datasets. So far no method provided sampling of a realistic posterior distribution to improve stability, e.g. for the segmentation of imbalanced objects within images. Therefore, we propose a new approach, based on diffusion models, which can enrich an imbalanced dataset with plausible examples from underrepresented groups by conditioning on segmentation maps. Our method can simply expand limited clinical datasets making them suitable to train machine learning pipelines, and provides an interpretable and human-controllable way of generating histopathology images that are indistinguishable from real ones to human experts. We validate our findings on two datasets, one from the public domain and one from a Kidney Transplant study. $$^{1}$$ (The source code and trained models will be publicly available at the time of the conference, on huggingface and github. )
PLST: A Pseudo-labels with a Smooth Transition Strategy for Medical Site Adaptation

This study addresses the challenge of medical image segmentation when transferring a pre-trained model from one medical site to another without access to pre-existing labels. The method involves utilizing a self-training approach by generating pseudo-labels of the target domain data. To do so, a strategy that is based on a smooth transition between domains is implemented where we initially feed easy examples to the network and gradually increase the difficulty of the examples. To identify the level of difficulty, we use a binary classifier trained to distinguish between the two domains by considering that target images easier if they are classified as source examples. We demonstrate the improved performance of our method on a range of medical MRI image segmentation tasks. When integrating our approach as a post-processing step in several standard Unsupervised Domain Adaptation (UDA) algorithms, we consistently observed significant improvements in the segmentation results on test images from the target site.
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification

The domain shift between training and testing data presents a significant challenge for training generalizable deep learning models. As a consequence, the performance of models trained with the independent and identically distributed (i.i.d) assumption deteriorates when deployed in the real world. This problem is exacerbated in the medical imaging context due to variations in data acquisition across clinical centers, medical apparatus, and patients. Domain generalization (DG) aims to address this problem by learning a model that generalizes well to any unseen target domain. Many domain generalization techniques were unsuccessful in learning domain-invariant representations due to the large domain shift. Furthermore, multiple tasks in medical imaging are not yet extensively studied in existing literature when it comes to DG point of view. In this paper, we introduce a DG method that re-establishes the model objective function as a maximization of mutual information with a large pretrained model to the medical imaging field. We re-visit the problem of DG in Diabetic Retinopathy (DR) classification to establish a clear benchmark with a correct model selection strategy and to achieve robust domain-invariant representation for an improved generalization. Moreover, we conduct extensive experiments on public datasets to show that our proposed method consistently outperforms the previous state-of-the-art by a margin of 5.25% in average accuracy and a lower standard deviation. Source code available at https://github.com/BioMedIA-MBZUAI/DGM-DR .
CAF-RCNN: multimodal 3D object detection with cross-attention

ABSTRACTLiDAR and camera are pivotal sensors of 3D (three-dimensional) object detection. As a result of their different characteristics, increasingly multimodal-based object detection methods have been proposed. Now, popular methods are to hardly associate camera features with LiDAR features, but the features are frequently enhanced and aggregated, so there is a major challenge in how to align two features effectively. Therefore, we propose CAF-RCNN. On the basis of PointRCNN, using Feature Pyramid Network (FPN) to extract advanced semantic features at different scales, then fusing these features with the LiDAR features of the Set Abstraction (SA) module output in PointRCNN and subsequent steps. Regarding the features fusion module, we design a module based on the cross-attention mechanism, CAFM (Cross-Attention Fusion Module). It combines two channel attention streams in a cross-over fashion to utilize rich details about significant objects in the Image Stream and Geometric Stream. We did a lot of experiments on the KITTI dataset, and the result shows that our method is 6.43% higher than PointRCNN in 3D accuracy.KEYWORDS: 3D object detectionmultimodal fusioncross-attention mechanismfeature pyramid network Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available on request from the corresponding author, Deer Liu, upon reasonable request.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grant 42271434 and Natural Science Foundation of Jiangxi Province under Grant 20202BAB202025.
Technical leverage analysis in the Python ecosystem

Abstract Context: Technical leverage is the ratio between dependencies (other people’s code) and own codes of a software package. It has been shown to be useful to characterize the Java ecosystem and there are also studies on the NPM ecosystem available. Objective: By using this metric we aim to analyze the Python ecosystem, how it evolves, and how secure it is, as a developer would perceive it when deciding to adopt or update (or not) a library. Method: We collect a dataset of the top 600 Python packages (corresponding to 21,205 versions) and used a number of innovative approaches for its analysis including the use of a two-part statistical model to deal with excess zeros, a mathematical closed formulation to estimate vulnerabilities that we confirm with bootstrapping on the actual dataset. Results: Small Python package versions have a median technical leverage of 6.9x their own code, while bigger package versions rely on dependencies code a tenth of their own (median leverage of 0.1). In terms of evolution, Python packages tend to have stable technical leverage through their evolution (once highly leveraged, always leveraged). On security, the chance of getting a safe package version when choosing a package is actually better than previous research has shown based on the ratio of safe package versions in the ecosystem. Coclusions: Python packages ship a lot of other people’s code and tend to keep doing so. However, developers will have a good chance to choose a safe package version.
Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection

Abstract Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods. However, the AE-based methods could be limited as the feature learned from normal sounds can also fit with anomalous sounds, reducing the ability of the model in detecting anomalies from sound. The self-supervised methods are not always stable and perform differently, even for machines of the same type. In addition, the anomalous sound may be short-lived, making it even harder to distinguish from normal sound. This paper proposes an ID-constrained Transformer-based autoencoder (IDC-TransAE) architecture with weighted anomaly score computation for unsupervised ASD. Machine ID is employed to constrain the latent space of the Transformer-based autoencoder (TransAE) by introducing a simple ID classifier to learn the difference in the distribution for the same machine type and enhance the ability of the model in distinguishing anomalous sound. Moreover, weighted anomaly score computation is introduced to highlight the anomaly scores of anomalous events that only appear for a short time. Experiments performed on DCASE 2020 Challenge Task2 development dataset demonstrate the effectiveness and superiority of our proposed method.
Research on the Application of Computer Big Data Technology in English Online Translation

Aiming at the disadvantages of the traditional rule-based machine translation model that the English translation results are not accurate enough and it is difficult to accurately describe the relationship between words, the English machine translation model based on the semantic network is designed and improved. The algorithm analyses the English grammatical rules, then performs Gaussian marginalization on the semantics to obtain the rectangular window function, obtains the window feature vector, projects the semantic information entropy data, and adds the semantic correlation factors to the information entropy and information gain of the text., So as to obtain the semantic nonlinear spectral characteristics. Experimental results show that the designed English machine translation model has high translation accuracy and stability.
Online convex optimization with switching cost and delayed gradients

We consider the online convex optimization (OCO) problem with quadratic and linear switching cost in the limited information setting, where an online algorithm can choose its action using only gradient information about the previous objective function. For L-smooth and μ-strongly convex objective functions, we propose an online multiple gradient descent (OMGD) algorithm and show that its competitive ratio for the OCO problem with quadratic switching cost is at most 4(L+5)+16(L+5)μ. The competitive ratio upper bound for OMGD is also shown to be order-wise tight in terms of L,μ. In addition, we show that the competitive ratio of any online algorithm is max{Ω(L),Ω(Lμ)} in the limited information setting when the switching cost is quadratic. We also show that the OMGD algorithm achieves the optimal (order-wise) dynamic regret in the limited information setting. For the linear switching cost, the competitive ratio upper bound of the OMGD algorithm is shown to depend on both the path length and the squared path length of the problem instance, in addition to L,μ, and is shown to be order-wise, the best competitive ratio any online algorithm can achieve. Consequently, we conclude that the optimal competitive ratio for the quadratic and linear switching costs are fundamentally different in the limited information setting.
Research on College English Teaching Quality Evaluation Model Based on the GA Optimized BP Neural Network

With the continuous advancement of college English teaching reform, teaching quality is one of the most crucial parts of reform. The traditional teaching quality evaluation system has simple indexes, limited subjects, imperfect evaluation mechanism and delayed feedback, which cannot keep up with the process of college English teaching reform. With the extensive application of artificial intelligence, this paper applies GA-BP neural network to college English teaching quality evaluation. This paper discusses the application of artificial neural network technology in college English teaching quality evaluation, establishes an intelligent model of college English teaching quality evaluation based on GA-BP neural network, and realizes the accurate prediction between teaching factors and teaching quality through experiments, which helps analyze and extract effective factors affecting teaching quality and to improve the validity and accuracy of college English teaching quality evaluation.
The New Future for Special Needs Education with the used of Mobile Application with Multisensory.

Literacy is one of the most important skills to acquire knowledge. However, to gain literacy skills, the students must learn from the bottom, which is learning the alphabet. Learning the alphabet is the first step to reading, however, due to their “special ability”, learning the alphabets are not a simple task for them. Most of the kids that have been diagnosed as dyslexic will be having a difficulty in literacy. The objective of this paper is to compare the effectiveness of the use of mobile applications, multisensory applications, solely used flashcards, and conventional teaching in learning and teaching alphabets to special needs students. This study is done by interviewing 50 special needs education teachers from elementary schools all over Malaysia. The analysis of data using an inductive approach of thematic analysis. Hence, hopefully, through this study, other researchers and educators will be able to have new insight into the topic that has been discussed.
The Research on Virtual Simulation Training Teaching Mode in the Perspective of Embodied Cognition

Virtual simulation training can break through the space-time limit of traditional training and help solve the "three highs” and “three difficulties" of traditional training, which are high investment, high difficulty, high risk, difficult to implement, difficult to observe and difficult to reproduce. In order to give full play to the teaching benefits of virtual simulation training, in addition to relying on the support of VR technology, it is also necessary to integrate teaching concepts, theories and methods to continuously and effectively improve teaching. In order to improve the teaching effect of virtual simulation training and promote students' in-depth learning, it is necessary to integrate practical technology, teaching theories and methods. Firstly, this paper deeply analyzed the connotation of virtual simulation training teaching from the perspective of embodied cognition, and put forward five characteristics. Then, it constructed the virtual simulation training teaching mode, divided its process into four stages: situational perception, embodied experience, reflective observation and meaning generation. Finally, based on the self-designed and developed "Intensive Care Unit virtual simulation training system", the teaching practice was carried out, and the results showed that the model is feasible and effective. We hoped this paper can provide theoretical reference and practical experience for vocational colleges to carry out virtual simulation training teaching reform.
A Probe into College English Teaching Model under Converged Mobile Learning

This paper radically analyzes the college English teaching model under converged mobile learning. Firstly, it introduces the connotation and value of converged mobile learning, and affirms the role of converged mobile learning in scene creation and teaching quality improvement. Subsequently, it analyzes the existing state of the development of converged mobile learning in college English teaching, and on this basis, it proposes the college English teaching model under converged mobile learning, which includes the mobile teaching model based on scene creation, the task teaching model based on autonomous exploration, feedback teaching model based on classroom evaluation and the group teaching model based on cooperative learning.
Construction and validation of a junior high school biology laboratory class teaching model based on desktop VR

In the context of continuous integration of information technology and subject education, more and more educators consider combining VR technology with actual education teaching, and desktop VR technology provides fresh vitality for education teaching as an emerging technology. In junior high school biology experimental teaching, there are problems such as some experimental materials are not easy to obtain, resulting in less-than-ideal experimental effects. Therefore, how to apply desktop VR in junior high school biology laboratory teaching and whether integrating desktop VR into junior high school biology laboratory teaching can help students better master biology knowledge? In this study, the tower of Dell's experience theory is used as the theoretical basis for model construction. Based on the ARCS learning motivation model and the features of desktop VR, a model of junior high school biology laboratory class teaching based on desktop VR is explored. In this study, taking a middle school in China as an example, classes with similar grades were selected as the experimental class and the control class, each with 50 students, and the lesson of middle school biology "Observe the results of flowers" was used as an example for teaching practice. The control class was taught using traditional teaching methods, and the experimental class was taught using the teaching model of middle school biology laboratory class based on desktop VR, and a paper-and-pencil quiz, the Amabile et al. The experimental data were collected in the form of paper-and-pencil tests, learning motivation scales, etc. The statistical analysis of the data led to the conclusion that the model can promote students' learning effectiveness and have different degrees of improvement on students' learning motivation, thus verifying the effectiveness of the desktop VR-based junior high school biology laboratory class teaching model.
The Big Task Design Based Curriculum Design and Implementation of Secondary School Information Technology Teaching Theory

Abstract: This course is a compulsory basic education course for students majoring in computer science and technology (normal class), It is very important to cultivate middle school information technology teachers with information teaching ability. In view of the main pain point problems in the teaching process, such as students 'insufficient cognition of teachers' role, difficulty in active construction and poor information-based teaching ability. In the teaching process, we create a two-role classroom with students as the main body, on the basis of integrating the new content, the course content is the "total-score-total" large unit reorganization, and the teaching process is gradually promoted through the big task design. In the process of teaching implementation, the intelligent classroom teaching mode integrating online and offline teaching is adopted to give full play to the main position of students and achieve the goal of education from the perspective of information technology.
A Digital Learning Model for Teacher Education: Implementation Experience.

Recently, the comprehensive utilization of digital technologies in higher education has revealed a number of problem areas that must be addressed in the near future. These issues include the provision of high-quality educational material that meets the criteria for updating the content of higher education, the adoption of digital formats for evaluating students' achievements, psychological support for students in digital learning, the instruction of individuals with special educational needs, and the provision of student technical equipment in digital-learning conditions. To resolve these issues, three strategies must be employed: technical, systemic, and health-saving. Through the employment of digital technology in education, these techniques will enable the most effective structuring of the educational process. The study's goal is to create a complete model for the application of instructional, corrective, health-improving, and health-saving actions with students in the context of digital learning at a university. Instructors, teachers, and students pursuing pedagogical studies may find the research material valuable.
Aspect-based Sentiment Analysis in Document - FOMC Meeting Minutes on Economic Projection

The Federal Open Market Committee (FOMC) within the Federal Reserve System is responsible for managing inﬂation, maximizing employment, and stabilizing interest rates. Meeting minutes play an important role for market movements because they provide the bird’s eye view of how this economic complexity is constantly re-weighed. Therefore, there has been growing interest in analyzing and extracting sentiments on various aspects from large ﬁnancial texts for economic projection. However, Aspect-based Sentiment Analysis (ABSA) is not widely used on ﬁnancial data due to the lack of large labeled dataset. In this paper, I propose a model to train ABSA on ﬁnancial documents under weak supervision and analyze its predictive power on various macroeconomic indicators.
Workspace VR: A Social and Collaborative Telework Virtual Reality Application

COVID-19 lockdowns accelerated user adoption of remote collaboration technologies such as Zoom and Microsoft Teams. Despite the challenges of working remotely, many knowledge workers still desire the flexibility of hybrid work and its personal benefits allowing for more productive individual work. However, return-to-office (RTO) mandates suggest an inclination that in-office work allows for more productive teamwork. To resolve these conflicting desires, Workspace VR was created. Unlike other social telework virtual reality (VR) applications, Workspace VR enables uncoordinated, social, collaborative, and productive individual work and teamwork via virtual avatars, workspaces, and integration with user computers. Designed for Meta Quest devices, users can feel like they are working together with others without constraints in Workspace VR.
Realizing Values in Hybrid Environments: A SIGCHI Perspective✱

In this panel, we follow up on conversations that have been happening around hybrid environments, conducted in open sessions by the ACM Special Interest Group on Computer-Human Interaction (SIGCHI) Executive Committee (EC), at SIGs at CHI 2022 and CHI 2023 and on twitter, facebook, medium and similar media. The COVID-19 pandemic led to a shift to virtual conferences. As we go back to in-person events, it is important to reflect on what we have learned about these configurations, the types of events we desire, and how hybrid intersects with SIGCHI values such as increased accessibility, sustainability and inclusion. With this panel, we expect to engage the CSCW community in the discussion of what lies beyond the current in-person format, the possibilities created by the hybrid and what other innovations might further these values.