Reviewerly/exHarmony huggingface dataset:
Data-Free Class-Incremental Hand Gesture Recognition

This paper investigates data-free class-incremental learning (DFCIL) for hand gesture recognition from 3D skeleton sequences. In this class-incremental learning (CIL) setting, while incrementally registering the new classes, we do not have access to the training samples (i.e. data-free) of the already known classes due to privacy. Existing DFCIL methods primarily focus on various forms of knowledge distillation for model inversion to mitigate catastrophic forgetting. Unlike SOTA methods, we delve deeper into the choice of the best samples for inversion. Inspired by the well-grounded theory of max-margin classification, we find that the best samples tend to lie close to the approximate decision boundary within a reasonable margin. To this end, we propose BOAT-MI – a simple and effective boundary-aware prototypical sampling mechanism for model inversion for DFCIL. Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D – which we extract from a publicly available RGBD dataset. Both our codebase and the EgoGesture3D skeleton dataset are publicly available: https://github.com/humansensinglab/dfcil-hgr.
An Embarrassingly Simple Backdoor Attack on Self-supervised Learning

As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels. In addition to eliminating the need for labeled data, research has found that SSL improves the adversarial robustness over supervised learning since lacking labels makes it more challenging for adversaries to manipulate model predictions. However, the extent to which this robustness superiority generalizes to other types of attacks remains an open question.We explore this question in the context of backdoor attacks. Specifically, we design and evaluate Ctrl, an embarrassingly simple yet highly effective self-supervised backdoor attack. By only polluting a tiny fraction of training data (≤ 1%) with indistinguishable poisoning samples, Ctrl causes any trigger-embedded input to be misclassified to the adversary's designated class with a high probability (≥ 99%) at inference time. Our findings suggest that SSL and supervised learning are comparably vulnerable to backdoor attacks. More importantly, through the lens of Ctrl, we study the inherent vulnerability of SSL to backdoor attacks. With both empirical and analytical evidence, we reveal that the representation invariance property of SSL, which benefits adversarial robustness, may also be the very reason making SSL highly susceptible to backdoor attacks. Our findings also imply that the existing defenses against supervised backdoor attacks are not easily retrofitted to the unique vulnerability of SSL. Code is available at: https://github.com/meet-cjli/CTRL
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods

The field of explainable artificial intelligence (XAI) aims to uncover the inner workings of complex deep neural models. While being crucial for safety-critical domains, XAI inherently lacks ground-truth explanations, making its automatic evaluation an unsolved problem. We address this challenge by proposing a novel synthetic vision dataset, named FunnyBirds, and accompanying automatic evaluation protocols. Our dataset allows performing semantically meaningful image interventions, e.g., removing individual object parts, which has three important implications. First, it enables analyzing explanations on a part level, which is closer to human comprehension than existing methods that evaluate on a pixel level. Second, by comparing the model output for inputs with removed parts, we can estimate ground-truth part importances that should be reflected in the explanations. Third, by mapping individual explanations into a common space of part importances, we can analyze a variety of different explanation types in a single common framework. Using our tools, we report results for 24 different combinations of neural models and XAI methods, demonstrating the strengths and weaknesses of the assessed methods in a fully automatic and systematic manner.
Unsupervised Open-Vocabulary Object Localization in Videos

In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization. We propose a method that first localizes objects in videos via a slot attention approach and then assigns text to the obtained slots. The latter is achieved by an unsupervised way to read localized semantic information from the pre-trained CLIP model. The resulting video object localization is entirely unsupervised apart from the implicit annotation contained in CLIP, and it is effectively the first unsupervised approach that yields good results on regular video benchmarks.
Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations

In recent years, discriminative self-supervised methods have made significant strides in advancing various visual tasks. The central idea of learning a data encoder that is robust to data distortions/augmentations is straightforward yet highly effective. Although many studies have demonstrated the empirical success of various learning methods, the resulting learned representations can exhibit instability and hinder downstream performance. In this study, we analyze discriminative self-supervised methods from a causal perspective to explain these unstable behaviors and propose solutions to overcome them. Our approach draws inspiration from prior works that empirically demonstrate the ability of discriminative self-supervised methods to demix ground truth causal sources to some extent. Unlike previous work on causality-empowered representation learning, we do not apply our solutions during the training process but rather during the inference process to improve time efficiency. Through experiments on both controlled image datasets and realistic image datasets, we show that our proposed solutions, which involve tempering a linear transformation with controlled synthetic data, are effective in addressing these issues.
Advancements in Quantum Machine Learning and Quantum Deep Learning: A Comprehensive Review of Algorithms, Challenges, and Future Directions

At the nexus of quantum computing and machine learning, quantum machine learning (QML) and quantum deep learning (QDL) have emerged as promising fields. By enhancing computational capabilities and addressing challenging data analysis problems, these approaches have the potential to revolutionise a number of domains by leveraging the special properties of quantum systems, such as superposition and entanglement. In this article, we give a thorough analysis of QML and QDL algorithms, discussing their underlying ideas, benefits over conventional models, and potential uses. We talk about the difficulties these algorithms face, such as noisy quantum hardware, constrained qubit resources, training and optimisation issues, input representation and data encoding problems, and limited interpretability. Additionally, we look at current developments and lines of inquiry that aim to address these issues, including hybrid classical-quantum methods, error-mitigating techniques, better data representation techniques, quantum transfer learning, and resource optimisation.
Enhancing Person Re-identification Across Modalities with Local and Global Feature Correlation

We investigate the challenging problem of visible infrared person re-identification (VI-ReID). VI-ReID addresses pedestrian retrieval by matching images across different modalities. Given a infrared(visible) image of a person, the VI-ReID module finds the corresponding images from the visible(infrared) gallery set. This task is particularly difficult due to significant differences between visible and infrared images, such as color and texture. For VI-ReID, learning discriminative features from cross-modal images is crucial. In this paper, we propose a novel approach for modeling local and global structural relationships and capturing both modality-specific and modality-shareable information. Specifically, we introduce the Global Feature Attention Module (GFAM) to learn the feature correlation between two modalities and the Local Feature Alignment Module (LFAM) to align intra-modality feature representations. Our end-to-end unsupervised learning architecture also employs the InfoNCE loss to maximize the mutual information between positive samples while minimizing the mutual information between negative samples. Experimental results conducted on two benchmark datasets demonstrate that our proposed method outperforms state-of-the-art methods for both all-search and indoor-search situations, achieving gains of 2.05% Rank-1 and 1.12% mAP, respectively.
Navigating to Objects Specified by Images

Images are a convenient way to specify which particular object instance an embodied agent should navigate to. Solving this task requires semantic visual reasoning and exploration of unknown environments. We present a system that can perform this task in both simulation and the real world. Our modular method solves sub-tasks of exploration, goal instance re-identification, goal localization, and local navigation. We re-identify the goal instance in egocentric vision using feature-matching and localize the goal instance by projecting matched features to a map. Each sub-task is solved using off-the-shelf components requiring zero fine-tuning. On the HM3D InstanceImageNav benchmark, this system outperforms a baseline end-to-end RL policy 7x and a state-of-the-art ImageNav model 2.3x (56% vs . 25% success). We deploy this system to a mobile robot platform and demonstrate effective real-world performance, achieving an 88% success rate across a home and an office environment.
A new method for radar emitter individual identification based on VMD and multi-image feature combination

In order to solve the difficulty of extracting and selecting individual features manually in radar emitter individual identification, and further improve the accuracy of individual identification. A radar emitter individual identification method based on variational mode decomposition and multiple image features combination is proposed in this paper. Firstly, the radar signal is decomposed by variational mode decomposition. Then, the feature extraction in transform domain is performed on the obtained modal components, and they are reconstructed into VMD-Hilbert spectrum and VMD-envelope spectrum. Finally, the image is sent to the SEResNet50-based feature fusion and recognition network to realize radar emitter individual identification. Through the verification of the measured data, the recognition accuracy of six types of individuals can reach more than 90% when the signal-to-noise ratio is 20db, which has a relatively ideal recognition result and certain engineering application value.
RealVSR: a real-world video super-resolution method based on CascadeGAN

Video Super Resolution methods demonstrate superior performance on synthetic dataset.However, the fixed mapping from HR frames to construct the LR frames for training also constrains the generalization ability of models to real scenarios since pretrained models on bicubic samples cannot transfer to real-world applications. Based on this issue, we propose a novel framework RealVSR by a CascadeGAN which can greatly improve the perceptual quality of SR videos. RealVSR learns super-resolution transformation by two GANs: one to estimate the accurate degradation kernel of specific video and the other to generate a super-resolution result, and they are optimized in a cascade manner. RealVSR is trained using only the test video by a CascadeGAN and incorporates degradation kernel estimation, noise collection and super-resolution in a unified framework. Extensive experiments on synthetic noisy dataset and real-world videos demonstrate the superiority of our method.In addition, RealVSR is the first real-world unsupervised video super-resolution method which learns from only a single video and produce visual favorable corresponding super-resolution result.
Open Set Video HOI detection from Action-centric Chain-of-Look Prompting

Human-Object Interaction (HOI) detection is essential for understanding and modeling real-world events. Existing works on HOI detection mainly focus on static images and a closed setting, where all HOI classes are provided in the training set. In comparison, detecting HOIs in videos in open set scenarios is more challenging. First, under open set circumstances, HOI detectors are expected to hold strong generalizability to recognize unseen HOIs not included in the training data. Second, accurately capturing temporal contextual information from videos is difficult, but it is crucial for detecting temporal-related actions such as open, close, pull, push. To this end, we propose ACoLP, a model of Action-centric Chain-of-Look Prompting for open set video HOI detection. ACoLP regards actions as the carrier of semantics in videos, which captures the essential semantic information across frames. To make the model generalizable on unseen classes, inspired by the chain-of-thought prompting in natural language processing, we introduce the chain-of-look prompting scheme that decomposes prompt generation from large-scale vision-language model into a series of intermediate visual reasoning steps. Consequently, our model captures complex visual reasoning processes underlying the HOI events in videos, providing essential guidance for detecting unseen classes. Extensive experiments on two video HOI datasets, VidHOI and CAD120, demonstrate that ACoLP achieves competitive performance compared with the state-of-the-art methods in the conventional closed setting, and outperforms existing methods by a large margin in the open set setting. Our code is avaliable at https://github.com/southnx/ACoLP.
Research on the development issues and countermeasures of the China higher education foundation

Strong financial support is an essential prerequisite for ensuring the quality of education. As the scale of higher education institutions continues to expand, the demand for educational funding keeps increasing. Social donations, as a significant source of funds, highlight the importance of educational foundations. This paper reviews the development history, policy support, and primary fundraising targets of China's higher education foundations. Based on the existing issues in the development of higher education foundations in China, it proposes specific recommendations in four areas: strengthening the construction of a professional talent pool, enhancing the depth and breadth of information disclosure, increasing transparency in the use of donation funds, improving the quality of talent development, and paying continuous attention to graduate employment and career development. It also suggests engaging in digital philanthropy and utilizing social media platforms to enhance two-way interaction, thereby promoting higher education foundations to better fulfill their role and support the sustainable development of China's higher education.
Reflections on the development of education in Argentina and the current education system

The role of education is crucial to the advancement of societies, as it is a key means of nurturing future leaders and innovators and reflects a country's culture and values as well as its social system. As a large country located in South America, Argentina's education system has undergone many rounds of reform and evolution, resulting in a unique and distinctive trajectory and model. This study will analyze in depth the growth of education in Argentina, including the structure of education from the colonial period to the modern day, and will discuss the problems encountered and potential opportunities for development.
Optimizing Reading Ability Through the CIRC Model Assisted by Story Card Media in the Context of Multicultural Education in Elementary Schools

This research aims to analyze how the CIRC model assisted by story card media can support multicultural education in elementary schools. The research used is classroom action research (CAR), with research procedures including planning, action, observation and reflection. The subjects used were 24 first grade students consisting of 14 boys and 10 girls. The achievement results at the initial observation showed that, of the 24 students, only 6 (27%) students were in the capable reading category. In the first cycle, as many as 6 (33.3%) students were able to read, 18 students (66.7%) were in the unable to read category. The number and percentage increased in the second cycle to 24 (100%) students who were able to read. Based on the results of the research and discussion above, it can be concluded that through the Cooperative Integrated Reading and Composition (CIRC) model it can improve reading skills for first grade students at SD Inpres Tenubot, Belu Regency. The use of story card media in the CIRC model adds an inclusive and interactive dimension to the learning process, allowing students to explore various themes and stories that reflect cultural diversity.
Online/on-offline blended learning experiences of students in the Department of Healthcare Administration at Cyber University

Objectives: This study explored the learning experiences associated with online/on-offline blended learning methods. It aimed to identify strategies to enhance learning effectiveness to improve adaptation and utilization in the healthcare field. Methods: This qualitative study conducted in-depth interviews with seven students who expressed their willingness to participate in interviews among the graduating students majoring in healthcare administration at the H cyber university. The phenomenological analysis method proposed by Giorgi was applied. Results: In the on-offline blended learning format, students felt a sense of belonging, enhanced focus in learning, feedback, and increased interaction among learners. Above all, it demonstrated that learners’ enthusiasm and engagement led to a clear and satisfying learning experience. Conclusion: Therefore, an educational program should be designed to incorporate the advantages of blended online and offline methods for healthcare-related adult learners. This method enhances learners’ competence through quality learning experiences in the healthcare field and can improve the effectiveness of major studies.
Investigation of the current situation of Chongqing Shapingba District School Cup Basketball League

With the vigorous development of sports, basketball as one of the three balls is also more and more loved by people, the influence of basketball leagues such as the NBA, CBA and so on to promote the continuous improvement and development of Chinese middle school basketball leagues, and the atmosphere of middle school campus basketball also highlights its important role in the impact of campus culture under this trend, forming a unique campus cultural phenomenon and environment. In this paper, 6 head coaches, 36 small coaches, and relevant responsible persons of various schools participating in the league are surveyed through questionnaire survey method and interview method. Investigate the development status and management mechanism of the Shapingba District Campus Cup Basketball League, analyze the operation system of the Shapingba District Campus Cup Basketball League and the situation of the players and coaches of the basketball teams of each school, propose solutions to the problems that arise in the development of the league, make suggestions for the development of the Shapingba District Campus Cup Basketball League, guide and improve the training of the basketball teams of each school, improve the professional level of the Shapingba District Campus Cup Basketball League, and promote the progress and development of basketball culture in Shapingba District.
Development and Usability Testing of an Exercise-Based Primary Care Fall Prevention Clinical Decision Support Tool.

For older patients, falls are the leading cause offatal and nonfatal injuries. Guidelines recommend that at-risk older adults are referred to appropriate fall-prevention exercise programs, but many do not receive support for fall-risk management in the primary care setting. Advances in health information technology may be able to address this gap. This article describes the development and usability testing of a clinical decision support (CDS) tool for fall prevention exercise. Using rapid qualitative analysis and human-centered design, our team developed and tested the usability of our CDS prototype with primary care team members. Across 31 Health-Information Technology Usability Evaluation Scale surveys, our CDS prototype received a median score of 5.0, mean (SD) of 4.5 (0.8), and a range of 4.1-4.9. This study highlights the features and usability offall prevention CDS for helping primary care providers deliver patient-centeredfall prevention care.
Auditing Learned Associations in Deep Learning Approaches to Extract Race and Ethnicity from Clinical Text.

Complete and accurate race and ethnicity (RE) patient information is important for many areas of biomedical informatics research, such as defining and characterizing cohorts, performing quality assessments, and identifying health inequities. Patient-level RE data is often inaccurate or missing in structured sources, but can be supplemented through clinical notes and natural language processing (NLP). While NLP has made many improvements in recent years with large language models, bias remains an often-unaddressed concern, with research showing that harmful and negative language is more often used for certain racial/ethnic groups than others. We present an approach to audit the learned associations of models trained to identify RE information in clinical text by measuring the concordance between model-derived salient features and manually identified RE-related spans of text. We show that while models perform well on the surface, there exist concerning learned associations and potential for future harms from RE-identification models if left unaddressed.
I can’t believe there’s no images! : Learning Visual Tasks Using Only Language Supervision

Many high-level skills that are required for computer vision tasks, such as parsing questions, comparing and contrasting semantics, and writing descriptions, are also required in other domains such as natural language processing. In this paper, we ask whether it is possible to learn those skills from text data and then transfer them to vision tasks without ever training on visual training data. Key to our approach is exploiting the joint embedding space of contrastively trained vision and language encoders. In practice, there can be systematic differences between embedding spaces for different modalities in contrastive models, and we analyze how these differences affect our approach and study strategies to mitigate this concern. We produce models using only text training data on four representative tasks: image captioning, visual entailment, visual question answering and visual news captioning, and evaluate them on standard benchmarks using images. We find these models perform close to models trained on images, while surpassing prior work for captioning and visual entailment in this text-only setting by over 9 points, and outperforming all prior work on visual news by over 30 points. We also showcase a variety of stylistic image captioning models that are trained using no image data and no human-curated language data, but instead using readily-available text data from books, the web, or language models.