Mathematical information retrieval remains fragmented as readers commonly view math-heavy PDFs in standard viewers while relying on separate specialized formula search tools. This separation restricts exploration of documents where meaning arises from the combination of symbolic notation and natural language. MathMex-PDF is an interactive PDF reader that unifies visual mathematics extraction and multimodal retrieval within a single workflow. The system processes raw PDF pages to detect and recognize mathematical expressions, grounds those expressions in the document layout, and provides unified search over both text and formulas. Users may submit natural-language, LaTeX, or hybrid queries and perform query-by-example interactions such as click-to-search Reverse Formula Lookup directly from rendered pages. Retrieval uses a late-fusion pipeline that combines structure-aware formula matching with dense text embeddings and applies reciprocal rank fusion to adopt modality-specific rankings. Lightweight client-side rendering is paired with cached server-side processing to support responsive interaction on standard hardware. The demo provides interactive formula grounding, hybrid math–text querying, visual navigation of mathematical content, and spoken-math annotations to improve accessibility.

Interactive segmentation methods have been investigated to address the potential need for additional refinement in automatic segmentation via human-in-the-loop techniques. For accurate segmentation of 3D images, we propose Slice-and-Conquer, a novel planar-to-3D pipeline formulating volumetric mask construction into two stages: 1) 2D interactive segmentation and 2) guided 3D segmentation. Specifically, the first stage enables users to focus on a single 2D slice and provides the corresponding 2D prediction results as strong shape priors. Taking the planar guidance, an accurate 3D mask can be constructed with minimal interactions. To support a flexible iterative refinement, our system recommends a next slice to annotate at the end of the second stage. Since volumetric segmentation can be completed by consecutively annotating a few recommended 2D slices, our method significantly reduces the cognitive burden of exploring volumetric space for users. Through extensive experiments on various datasets of 3D biomedical images, we demonstrate the effectiveness of the proposed pipeline.

Bootstrapping has been a primary tool for ensemble and uncertainty quantification in machine learning and statistics. However, due to its nature of multiple training and resampling, bootstrapping deep neural networks is computationally burdensome; hence it has difficulties in practical application to the uncertainty estimation and related tasks. To overcome this computational bottleneck, we propose a novel approach called Neural Bootstrapper (NeuBoots), which learns to generate bootstrapped neural networks through single model training. NeuBoots injects the bootstrap weights into the high-level feature layers of the backbone network and outputs the bootstrapped predictions of the target, without additional parameters and the repetitive computations from scratch. We apply NeuBoots to various machine learning tasks related to uncertainty quantification, including prediction calibrations in image classification and semantic segmentation, active learning, and detection of out-of-distribution samples. Our empirical results show that NeuBoots outperforms other bagging based methods under a much lower computational cost without losing the validity of bootstrapping.

Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial resolution due to limited access to the three-dimensional optical transfer function. This missing cone problem has been addressed through regularization algorithms that use a priori information, such as non-negativity and sample smoothness. However, the iterative nature of these algorithms and their parameter dependency make real-time visualization impossible. In this article, we propose and experimentally demonstrate a deep neural network, which we term DeepRegularizer, that rapidly improves the resolution of a three-dimensional refractive index map. Trained with pairs of datasets (a raw refractive index tomogram and a resolution-enhanced refractive index tomogram via the iterative total variation algorithm), the three-dimensional U-net-based convolutional neural network learns a transformation between the two tomogram domains. The feasibility and generalizability of our network are demonstrated using bacterial cells and a human leukaemic cell line, and by validating the model across different samples. DeepRegularizer offers more than an order of magnitude faster regularization performance compared to the conventional iterative method. We envision that the proposed data-driven approach can bypass the high time complexity of various image reconstructions in other imaging modalities.

Traceability between software requirements and UML design helps requirement engineers and software developers understand the project goal and software architecture. However, the traceability mapping between the two elements is not well-defined and as a result there is no consistent way to make the traceability between two. A practical guideline is proposed for the traceability between software requirement and UML design. The guideline includes meta-model and process step. The process step defines detailed processes and most importantly, the Requirement-type and its mapping to UML diagrams. Requirements can be categorized based on their aspects and this categorization can be made as Requirement-type. This pre-defined Requirement-type is added to each requirement and this Requirement-type enforces to use certain types of UML diagrams as solutions. This mapping between each Requirement-type and UML diagram is the key of proposed guideline. With the help of the Requirement-type and mapping, traceability link can be made easily and checked automatically. The guideline was applied to the Missile Project and Helicopter Project of ADD (Agency of Defense Development) in South Korea.

Automatic human action recognition is a core functionality of systems for video surveillance and human-object interaction. However, the diverse nature of human actions and the noisy nature of most video content make it difficult to achieve effective human action recognition. To overcome the aforementioned problems, Sparse Representation (SR) has recently attracted substantial research attention. However, although SR-based approaches have proven to be reasonably effective, the computational complexity of the testing stage prohibits their usage by applications requiring support for real-time operation and a vast number of human action classes. In this paper, we propose a novel method for human action recognition, leveraging coarse-to-fine sparse representations that have been obtained through dictionary sub-sampling. Comparative experimental results obtained for the UCF50 dataset demonstrate that the proposed method is able to achieve efficient human action recognition, at no substantial loss in recognition accuracy.

Automatic human action recognition is a core functionality of systems for video surveillance and human-object interaction. Conventional vision-based systems for human action recognition require the use of segmentation in order to achieve an acceptable level of recognition effectiveness. However, generic techniques for automatic segmentation are currently not available yet. Therefore, in this paper, we propose a novel sparse representation-based method for human action recognition, taking advantage of the observation that, although the location and size of the action region in a test video clip is unknown, the construction of a dictionary can leverage information about the location and size of action regions in training video clips. That way, we are able to segment, implicitly, action and context information in a test video clip, thus improving the effectiveness of classification. That way, we are also able to develop a context-adaptive classification strategy. As shown by comparative experimental results obtained for the UCF Sports Action data set, the proposed method facilitates effective human action recognition, even when testing does not rely on explicit segmentation.

Techniques for video fingerprinting are helpful in managing vast libraries of video clips. Recent advances have shown that video tomography and Bag-of-Visual-Words (BoVW) can be successfully used for the purpose of video fingerprinting. In this paper, we introduce a novel video signature (i.e., a novel video fingerprint) that takes advantage of both video tomography and BoVW. Specifically, the proposed video signature is created by first extracting inclined tomography images from the video content, and by subsequently applying the BoVW approach to the inclined tomography images obtained. The key to our approach is that we make the angle of inclination of the tomography images dependent on the amount of motion in the video content. That way, the proposed video signature is able to capture both spatial and temporal information. Experimental results obtained for the publicly available TREVID-2009 video set indicate that video copy detection by means of the proposed video signature is robust against spatial and temporal transformations.

Motivated by the observation that content transformations tend to preserve the semantic information conveyed by video clips, this paper introduces a novel technique for near-duplicate video clip (NDVC) detection, leveraging model-free semantic concept detection and adaptive semantic distance measurement. In particular, model-free semantic concept detection is realized by taking advantage of the collective knowledge in an image folksonomy (which is an unstructured collection of user-contributed images and tags), facilitating the use of an unrestricted concept vocabulary. Adaptive semantic distance measurement is realized by means of the signature quadratic form distance (SQFD), making it possible to flexibly measure the similarity between video shots that contain a varying number of semantic concepts, and where these semantic concepts may also differ in terms of relevance and nature. Experimental results obtained for the MIRFLICKR-25000 image set (used as a source of collective knowledge) and the TRECVID 2009 video set (used to create query and reference video clips) demonstrate that model-free semantic concept detection and SQFD can be successfully used for the purpose of identifying NDVCs.

Given the observation that content transformations tend to preserve semantic information, we demonstrated in previous research that model-free semantic concept detection can be successfully leveraged for identifying NDVCs. In this paper, we seek a better understanding of the usefulness of model-free semantic concept detection for both the task of annotation and NDVC detection. In particular, through extensive experiments, we demonstrate that the problem of detecting semantic concepts for the goal of identifying NDVCs is more relaxed than the problem of detecting semantic concepts for annotation purposes: whereas incorrectly detected semantic concepts negatively affect the effectiveness of annotation, they do not negatively affect the effectiveness of NDVC detection, as long as the same incorrect semantic concepts are detected for both the reference and near-duplicate video clips. This observation has practical implications for the design of a video management system that makes use of model-free semantic concept detection for both the purpose of annotation and NDVC detection.

Being able to detect near-duplicate video clips (NDVCs) is a prerequisite for a plethora of multimedia applications. Given the observation that content transformations tend to preserve semantic information, techniques for NDVC detection may benefit from the use of a semantic approach. This paper discusses how an image folksonomy (i.e., community-contributed images and metadata) and the Signature Quadratic Form Distance (SQFD) can be leveraged for the purpose of identifying NDVCs. Experimental results obtained for the MIRFLICKR-25000 image set and the TRECVID 2009 video set indicate that an image folksonomy and SQFD can be successfully used for detecting NDVCs. In addition, our findings show that model-free NDVC detection (i.e., NDVC detection using an image folksonomy) has a higher semantic coverage than model-based NDVC detection (i.e., NDVC detection using the VIREO-374 semantic concept models).

An increasing number of duplicates and near-duplicates can be found on websites for video sharing. These duplicates and near-duplicates often infringe copyright or clutter search results. Consequently, a high need exists for techniques that allow identifying duplicates and near-duplicates. In this paper, we propose a semantic-based approach towards the task of identifying near-duplicates. Our approach makes use of semantic video signatures that are constructed by detecting semantic concepts along the temporal axis of video sequences. Specifically, we make use of an image folksonomy (i.e., a set of user-contributed images annotated with user-supplied tags) to detect semantic concepts in video sequences, making it possible to exploit an unrestricted concept vocabulary. Comparative experiments using the MUSCLE-VCD-2007 dataset and folksonomy images retrieved from Flickr show that our approach is successful in identifying near-duplicates.

An increasing number of near-duplicate video clips (NDVCs) can be found on websites for video sharing. These NDVCs often infringe copyright or clutter search results. Consequently, a high need exists for techniques that allow identifying NDVCs. NDVC detection techniques represent a video clip with a unique set of features. Conventional video signatures typically make use of low-level visual features (e.g., color histograms, local interest points). However, low-level visual features are sensitive to transformations of the video content. In this paper, given the observation that transformations preserve the semantic information in the video content, we study the use of semantic features for the purpose of identifying NDVCs. Experimental results obtained for the MUSCLE-VCD-2007 dataset indicate that semantic features have a high level of robustness against transformations and different keyframe selection strategies. In addition, when relying on the temporal variation of semantic features, semantic video signatures are characterized by a high degree of uniqueness, even when a vocabulary with a low number of semantic concepts is in use (for a query video clip that is sufficiently long).

The increasing popularity of user-generated content (UGC) requires effective annotation techniques in order to facilitate precise content search and retrieval. In this paper, we propose a new approach for the semantic annotation of personal video content, taking advantage of user-contributed tags available in an image folksonomy. Video shots and folksonomy images are first represented by a semantic vector. Next, the semantic vectors are used to measure the semantic similarity between each video shot and the folksonomy images. Tags assigned to semantically similar folksonomy images are then used to annotate the video shots. To verify the effectiveness of the proposed annotation method, experiments were performed with video sequences retrieved from YouTube and images downloaded from Flickr. Our experimental results demonstrate that the proposed method is able to successfully annotate personal video content with user-contributed tags retrieved from an image folksonomy. In addition, the size of our tag vocabulary is significantly higher than the size of the tag vocabulary used by conventional annotation methods.

Methods for video copy detection are typically based on the use of low-level visual features. However, low-level features may vary significantly for near-duplicates, which are video sequences that have been the subject of spatial or temporal modifications. As such, the use of low-level visual features may be inadequate for detecting near-duplicates. In this paper, we present a new video copy detection method that aims to identify near-duplicates for a given query video sequence. More specifically, the proposed method is based on identifying semantic concepts along the temporal axis of a particular video sequence, resulting in the construction of a so-called semantic video signature. The semantic video signature is then used for the purpose of similarity measurement. The main advantage of the proposed method lies in the fact that the presence of semantic concepts is highly robust to spatial and temporal video transformations. Our experimental results show that the use of a semantic video signature allows for the efficient and effective detection of near-duplicates.