Reviewerly/exHarmony huggingface dataset:
Unlearnable Clusters: Towards Label-Agnostic Unlearnable Examples

There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimization framework with a surrogate model to remove (minimize) errors from the original samples, and then applied to protect the data against unknown target models. However, existing UE generation methods all rely on an ideal assumption called label-consistency, where the hackers and protectors are assumed to hold the same label for a given sample. In this work, we propose and promote a more practical label-agnostic setting, where the hackers may exploit the protected data quite differently from the protectors. E.g., amclass unlearnable dataset held by the protector may be exploited by the hacker as a n-class dataset. Existing UE generation methods are rendered ineffective in this challenging setting. To tackle this challenge, we present a novel technique called Unlearnable Clusters (UCs) to generate label-agnostic unlearnable examples with cluster-wise perturbations. Furthermore, we propose to leverage Vision-and-Language Pre-trained Models (VLPMs) like CLIP as the surrogate model to improve the transferability of the crafted UCs to diverse domains. We empirically verify the effectiveness of our proposed approach under a variety of settings with different datasets, target models, and even commercial platforms Microsoft Azure and Baidu PaddlePaddle. Code is available at https://github.com/jiamingzhang94/Unlearnable-Clusters.
Research on wind tunnel test data prediction and fault diagnosis technology based on machine learning

Wind tunnel test is very important for aircraft design and new aircraft flight verification. With the continuous development of science and technology, intelligent technology has been successfully applied to many fields. It has become the general trend of future aircraft development to combine intelligence, informatization and aerodynamic research, and improve the level of wind tunnel test research through scientific and technological innovation. In this paper, an accurate prediction method of wind tunnel test data based on predictive learning machine is proposed to realize the rapid construction of conventional static test prediction model and the rapid prediction of model aerodynamic coefficient. At the same time, because the fault data formed by different wind tunnels, test equipment and models are roughly the same, but the difference between the data changes greatly, it is difficult to accurately locate and track the cause of the fault. Moreover, the fault data of wind tunnel test belongs to the characteristics of small sample data, and it is impossible to establish a typical problem of relatively large fault data set. A data expansion and fault diagnosis analysis method based on unstructured data is proposed to realize the intelligent troubleshooting and guidance analysis of wind tunnel test data. Improve the efficiency of wind tunnel test data analysis, reduce the cost of model design wind tunnel test, and initially achieve the purpose of improving the efficiency of wind tunnel test and model development.
Historical-Domain Pre-trained Language Model for Historical Extractive Text Summarization

In recent years, pre-trained language models (PLMs) have shown remarkable advancements in the extractive summarization task across diverse domains.However, there remains a lack of research specifically in the historical domain.In this paper, we propose a novel method for extractive historical single-document summarization that leverages the potential of a domain-aware historical bidirectional language model, pre-trained on a large-scale historical corpus.Subsequently, we fine-tune the language model specifically for the task of extractive historical single-document summarization.One major challenge for this task is the lack of annotated datasets for historical summarization.To address this issue, we construct a dataset by collecting archived historical documents from the Centre Virtuel de la Connaissance sur l'Europe (CVCE) group at the University of Luxembourg.Furthermore, to better learn the structural features of the input documents, we use a sentence position embedding mechanism that enables the model to learn the position information of sentences.The overall experimental results on our historical dataset collected from the CVCE group show that our method outperforms recent state-of-the-art methods in terms of ROUGE-1, ROUGE-2, and ROUGE-L F1 scores.To the best of our knowledge, this is the first work on extractive historical text summarization.
A Multi-Viewpoint Approach For Semantic Multimedia Documents Adaptation

Multimedia documents have to be played on multiple device types.Hence, a user not disabled or handicapped requires document adaptation according to execution contexts.Currently, advanced technological hardware can offer mobile devices, which fit in the hand with the capacity to consult documents at anytime and anywhere.Multiple user context constraints as well as mobile device capabilities may involve the adaptation of multimedia content.In this article, the authors propose a new Multi-viewpoints ontological-based method for adapting multimedia documents; each viewpoint of de multimedia document could correspond to a physical handicap and therefore triggers an adaptation action using multiviewpoint ontological reasoning.Our proposed ontology has the great advantage to offer to users a flexible infrastructure to easily govern the response time and the quality assembly of their applications at runtime.
Blending the Physical and the Virtual: Multi-Modal Human-Computer Interaction for Music

We are at the dawn of a new ear in human-computer interaction that is finally starting to escape the limited possibilities of the screen and the keyboard.In this talk I will broadly outline a vision of how the physical and virtual worlds can blend through expressive musical interaction.Embodiment (and consequently having a body) is fundamental in realizing the vision of creating artificial musicians capable of improvising with human musicians.At the heart of this quest is the challenge of achieving expressivity and musicianship.I will describe several projects that me and my students have worked on that can be viewed as steps toward realizing this vision.These projects include: the sound plane, a new 3D highly responsive touch surface for playing music, the MechDrummer a state-of-the art percussion robot that is capable of human-like control of timing and dynamics, and MR:MIN a mixed reality environment for teaching how to play the Theremin.
Dynamic Inference with Grounding Based Vision and Language Models

Transformers have been recently utilized for vision and language tasks successfully. For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks. On the other hand, there exists a large amount of computational redundancy in these large models which skips their run-time efficiency. To address this problem, we propose dynamic inference for grounding based vision and language models conditioned on the input image-text pair. We first design an approach to dynamically skip multihead self-attention and feed forward network layers across two backbones and multimodal network. Additionally, we propose dynamic token pruning and fusion for two backbones. In particular, we remove redundant tokens at different levels of the backbones and fuse the image tokens with the language tokens in an adaptive manner. To learn policies for dynamic inference, we train agents using reinforcement learning. In this direction, we replace the CNN backbone in a recent grounding-based vision and language model, MDETR, with a vision transformer and call it ViTMDETR. Then, we apply our dynamic inference method to ViTMDETR, called D-ViTDMETR, and perform experiments on image-language tasks. Our results show that we can improve the run-time efficiency of the state-of-the-art models MDETR and GLIP by up to ~ 50% on Referring Expression Comprehension and Segmentation, and VQA with only maximum ~ 0.3% accuracy drop.
Adversarially Robust Neural Architecture Search for Graph Neural Networks

Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk-sensitive domains. Existing defensive methods neither guarantee performance facing new data/tasks or adversarial attacks nor provide insights to understand GNN robustness from an architectural perspective. Neural Architecture Search (NAS) has the potential to solve this problem by automating GNN architecture designs. Nevertheless, current graph NAS approaches lack robust design and are vulnerable to adversarial attacks. To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA). Specifically, we design a robust search space for the message-passing mechanism by adding graph structure mask operations into the search space, which comprises various defensive operation candidates and allows us to search for defensive GNNs. Furthermore, we define a robustness metric to guide the search procedure, which helps to filter robust architectures. In this way, G-RNA helps understand GNN robustness from an architectural perspective and effectively searches for optimal adversarial robust GNNs. Extensive experimental results on benchmark datasets show that G-RNA significantly outperforms manually designed robust GNNs and vanilla graph NAS baselines by 12.1% to 23.4% under adversarial attacks.
Meta-Personalizing Vision-Language Models to Find Named Instances in Video

Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as “My dog Biscuit” appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and Deep-Fashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset.
A hybrid-qudit representation of digital RGB images

Abstract Quantum image processing is an emerging topic in the field of quantum information and technology. In this paper, we propose a new quantum image representation of RGB images with deterministic image retrieval, which is an improvement over all the similar existing representations in terms of using minimum resource. We use two entangled quantum registers constituting of total 7 qutrits to encode the color channels and their intensities. Additionally, we generalize the existing encoding methods by using both qubits and qutrits to encode the pixel positions of a rectangular image. This hybrid-qudit approach aligns well with the current progress of NISQ devices in incorporating higher dimensional quantum systems than qubits. We then describe the image encoding method using higher-order qubit-qutrit gates, and demonstrate the decomposition of these gates in terms of simpler elementary gates. We use the Google Cirq’s quantum simulator to verify the image preparation in both the ideal noise-free scenario and in presence of realistic noise modelling. We show that the complexity of the image encoding process is linear in the number of pixels. Lastly, we discuss the image compression and some basic RGB image processing protocols using our representation.
A Fast Evolutionary Knowledge Transfer Search for Multiscale Deep Neural Architecture

The emergence of neural architecture search (NAS) algorithms has removed the constraints on manually designed neural network architectures, so that neural network development no longer requires extensive professional knowledge, trial and error. However, the extremely high computational cost limits the development of NAS algorithms. In this article, in order to reduce computational costs and to improve the efficiency and effectiveness of evolutionary NAS (ENAS) is investigated. In this article, we present a fast ENAS framework for multiscale convolutional networks based on evolutionary knowledge transfer search (EKTS). This framework is novel, in that it combines global optimization methods with local optimization methods for search, and searches a multiscale network architecture. In this article, evolutionary computation is used as a global optimization algorithm with high robustness and wide applicability for searching neural architectures. At the same time, for fast search, we combine knowledge transfer and local fast learning to improve the search speed. In addition, we explore a multiscale gray-box structure. This gray box structure combines the Bandelet transform with convolution to improve network approximation, learning, and generalization. Finally, we compare the architectures with more than 40 different neural architectures, and the results confirmed its effectiveness.
Retraction Notice: Visual Saliency Analysis of Artistic Photography based on Single-Angle Image Reconstruction Algorithm with Attention Model

Aiming at the limitations of existing visual saliency models that do not have universal applicability, this paper analyzes the visual saliency Is model based on single-angle image reconstruction. In order to solve the problem that the saliency of the IS model is not ideal when describing large targets, this model is used the method is simple, the amount of calculation is small, and the feasibility is good. The similar area is assigned a similar saliency value as a constraint. The engineering algorithm needs to downsample the original image. The saliency map is only the original image. The algorithm has high computational efficiency and the output saliency map It is the full-resolution algorithm that avoids image segmentation, so the effect is very good for images that are difficult to automatically segment.
A Community-Driven Strategy for Addressing Fossil Taxonomy Challenges

Taxonomy has long been a cornerstone issue in the paleontological collections community, especially as digitization has enabled us to share data at a global scale. Communities of practice such as the Paleo Data Working Group (PDWG, Krimmel et al. 2021) have made concerted efforts to understand and evaluate challenges with the taxonomic information published via fossil occurrence records (Little et al. 2021, Little et al. 2022). Since 2020, PDWG has been leading discussions to define the nuances of challenges associated with fossil taxonomy, to look at the landscape of cyberinfrastructure tools available to tackle these challenges, and to develop community guidelines. This work has led to a deeper understanding of the full range of challenges surrounding the use of fossil taxonomy, from local practices and systems to representation at the data aggregator level. Here we share our initial strategy for tackling challenges with fossil data, particularly taxonomy, centered on developing a reusable framework for assessing and addressing issues so that data can be made more discoverable and usable for transformative and translational research. An essential element of this approach is the coordination of efforts across stakeholder communities. Through these collaborations, we ultimately seek to improve systems for handling and curating taxonomic data, increase the quality and discoverability of taxonomic data presented by aggregators, and better integrate data provided by fossil occurrence records into the research lifecycle. The required coordination will be driven through the development of a partner network from across the global bio- and geo- science data ecosystem. This network is composed of representatives with a variety of perspectives and roles, including participation from Catalogue of Life, Global Biodiversity Information Facility, and Biodiversity Information Standards (TDWG), as well as paleontology collections professionals (led by PDWG), informaticians, researchers, and other end-users. Integrating the existing PDWG community of practice into this larger network allows us to learn from work already underway in the landscape. In addition, recognizing that the paleo community is not alone in these challenges, we believe that a successful strategy for this network is to create processes and envision solutions that can be adapted by other disciplines with similar needs. Our strategy underscores the need for the combined efforts of both community engagement and evaluation of the existing technical landscape. Through this work we aim to establish an enhanced network of FAIR (Findable, Accessible, Interoperable, Reusable; Wilkinson et al. 2016) and research-ready fossil data, improving the support for and quality of fossil taxonomy data along the way.
Social competences in pre-service education: what do future secondary teachers think?

ABSTRACTCoexistence, participation and collaborative culture are part of the social competence of teachers and are values that cannot be renounced in the educational contexts of today and tomorrow. Teacher education must therefore become an ideal space for developing these competences in future teachers. This study aims to investigate the importance that future secondary teachers give to social competences and analyse whether there are significant differences between students and graduates in initial teacher education. The methodology used is based on a quantitative analysis, in particular a survey study, with a sample of 233 participants. Main findings indicate that pre-service teachers give high importance to social competences. There are significant differences between the two groups: graduates value these competences more than students (in particular, a collaborative attitude, teamwork and the centre’s relationship with its community). These data show the importance of the master’s degree and the need to continue developing these competences both in this brief period and along the career continuum.KEYWORDS: Social competencesteacher educationstudent teachersecondary educationteacher frameworks Disclosure statementNo potential conflict of interest was reported by the author(s).
A deep learning framework to scale linear facial measurements to actual size using horizontal visible iris diameter: a study on an Iranian population

Abstract Digital images allow for the objective evaluation of facial appearance and abnormalities as well as treatment outcomes and stability. With the advancement of technology, manual clinical measurements can be replaced with fully automatic photographic assessments. However, obtaining millimetric measurements on photographs does not provide clinicians with their actual value due to different image magnification ratios. A deep learning tool was developed to estimate linear measurements on images with unknown magnification using the iris diameter. A framework was designed to segment the eyes’ iris and calculate the horizontal visible iris diameter (HVID) in pixels. A constant value of 12.2 mm was assigned as the HVID value in all the photographs. A vertical and a horizontal distance were measured in pixels on photographs of 94 subjects and were estimated in millimeters by calculating the magnification ratio using HVID. Manual measurement of the distances was conducted on the subjects and the actual and estimated amounts were compared using Bland–Altman analysis. The obtained error was calculated as mean absolute percentage error (MAPE) of 2.9% and 4.3% in horizontal and vertical measurements. Our study shows that due to the consistent size and narrow range of HVID values, the iris diameter can be used as a reliable scale to calibrate the magnification of the images to obtain precise measurements in further research.
TumorDet: A Breast Tumor Detection Model Based on Transfer Learning and ShuffleNet

background: Breast tumor is among the most malignant tumors and early detection can improve patient’s survival rate. Currently, mammography is the most reliable method for diagnosing breast tumor because of high image resolution. Because of the rapid development of medical and artificial intelligence techniques, computer-aided diagnosis technology can greatly improve the detection accuracy of breast tumors and medical imaging has begun to use deep-learning-based approaches. In this study, the TumorDet model is proposed to detect the benign and malignant lesions of breast tumor, which has positive significance for assisting doctors in diagnosis. objective: We use the proposed TumorDet to analyze and predict breast tumors on the real MRI dataset. method: (1) We introduce an adaptive gamma correction (AGC) method to balance brightness equalization and increase the contrast of mammography images; (2) we use the ShuffleNet model to exchange information between different feature layers and extract the hidden high-level features of medical images; and (3) we use the transfer learning method to fine-tune the ShuffleNet model and obtain the optimal parameters. result: The proposed TumorDet model has shown that accuracy, sensitivity, and specificity reach 90.43%, 89.37%, and 87.81%, respectively. TumorDet performs well in the breast tumor detection task. In addition, we use the proposed TumorDet to conduct experiments on other tasks, such as forest fires, and the robustness of TumorDet is proved by experimental results. conclusion: TumorDet employs the ShuffleNet model to exchange information between different feature layers without increasing the number of network parameters and applies transfer learning methods to further extract the basic features of medical images by fine-tuning. The model is beneficial for the localization and classification of breast tumors and also performs well in forest fire detection.
Rapid Prototyping of Automotive Magnetic Positioning Systems

Magnetic position and orientation systems offer the advantages of contactless sensing, high precision, robustness and low cost.They are heavily employed in automotive and industrial applications.The standard development process of such system starts with a simulation that is followed by validation with experimental data.This work presents a platform to which different sensors and magnets can be attached, with complete six degrees of freedom for relative movement.It is thus quite flexible in terms of experiments it can perform.A system calibration scheme based on dipole approximation is presented, yielding sensor tolerances and allowing to compute the field of a corresponding magnet.