Markup Language Research Articles

Conditional Flux Balance Analysis (cFBA) Toolbox for python: application to research metabolism in cyclic environments

Abstract Summary We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language (SBML) models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments. Availability Extensive documentation, installation steps, tutorials and examples are available at [https://tp-watson-python-cfba.readthedocs.io/en/]. The py_cFBA python package is available at [https://pypi.org/project/py-cfba/].

Image–Text Matching Model Based on CLIP Bimodal Encoding

Image–text matching is a fundamental task in the multimodal research field, connecting computer vision and natural language processing by aligning visual content with corresponding textual descriptions. Accurate matching is critical for applications such as image captioning and text-based image retrieval yet remains challenging due to the differences in data modalities. This paper addresses these challenges by proposing a robust image–text matching model inspired by Contrastive Language–Image Pre-training (CLIP). Our approach employs the Vision Transformer (ViT) model as the image encoder and Bidirectional Encoder Representations from Transformers (Bert) as the text encoder, integrating these into a shared vector space to measure semantic similarity. We enhance the model’s training efficiency using the LiT-tuning paradigm to optimize learning through a cosine decay strategy for dynamic adjustment of the learning rate. We validate our method on two benchmark datasets, WuKong and Flickr30k, demonstrating that our model achieves superior performance and significantly improves key evaluation metrics. The results underscore the model’s effectiveness in achieving accurate and robust image–text alignment.

File Compression System Using Huffman Coding

Abstract—File compression, which minimizes file sizes without significantly compromising the quality of information contained in the file, is among the most critical aspects of data management since it facilitates fast flow and storage of data. Storage manage- ment has become more challenging due to the exponential growth of digital data; hence, efficient file compression has been an issue of great importance to help in decreasing storage expenses and increasing data transfer rates. This paper presents a web interface for the file compression system that is based on lossless data compression known as Huffman coding to reduce the size of the files without changing their content. A standard internet portal allows the users to upload their files to the system and get back resized files within the shortest time possible. The technique employed in the compression is astute in that it uses Huffman codes which give characters codes depending with their frequencies in the file. It then follows that since most of the file will have short codes; the file will take up less space. To safeguard the compressed information, the system generates a data structure called a Huffman tree in which no code is a sub- string of any other. This application does seize to be usable from a few selected devices such as desktops and laptop computers but can also be accessed from projectors and cell phones because of the effective design. The file compression system is also versatile and accommodates various file categories such text, image and binary among others providing users with convenience in a number of sectors. Index Terms—Data Compression, Hoffman Coding, HTML( Hypertext Markup Language), CSS ( Cascading Styling Sheet ), JavaScript.

Detection and treatment of string events in the limit

A string event is a pattern that occurs in a stream of characters. The need to detect and handle string events in infinite texts emerges in many scenarios, including online treatment of logs, web crawling, and syntax highlighting. This paper describes a technique to specify and treat string events. Users determine patterns of interest via a markup language. From such examples, tokens are generalized via a semi-lattice of regular expressions. Such tokens are combined into a context-free language that recognizes patterns in the text stream. These techniques are implemented in a text processing system called Lushu, which runs on the Java Virtual Machine (JVM). Lushu intercepts strings emitted by the JVM. Once patterns are detected, it invokes a user-specified action handler. As a proof of concept, this paper shows that Lushu outperforms state-of-the-art parsers and parser generators, such as Comby, BeautifulSoup4 and ZheFuscator, in terms of memory consumption and running time.

Cross-coupled prompt learning for few-shot image recognition

Prompt learning based on large models shows great potential to reduce training time and resource costs, which has been progressively applied to visual tasks such as image recognition. Nevertheless, the existing prompt learning schemes suffer from either inadequate prompt information from a single modality or insufficient prompt interaction between multiple modalities, resulting in low efficiency and performance. To address these limitations, we propose a Cross-Coupled Prompt Learning (CCPL) architecture, which is designed with two novel components (i.e., Cross-Coupled Prompt Generator (CCPG) module and Cross-Modal Fusion (CMF) module) to achieve efficient interaction between visual and textual prompts. Specifically, the CCPG module incorporates a cross-attention mechanism to automatically generate visual and textual prompts, each of which will be adaptively updated using the self-attention mechanism in their respective image and text encoders. Furthermore, the CMF module implements a deep fusion to reinforce the cross-modal feature interaction from the output layer with the Image–Text Matching (ITM) loss function. We conduct extensive experiments on 8 image datasets. The experimental results verify that our proposed CCPL outperforms the SOTA methods on few-shot image recognition tasks. The source code of this project is released at: https://github.com/elegantTechie/CCPL.

TeachText: CrossModal text-video retrieval through generalized distillation

In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerful video encoders. By contrast, despite the natural symmetry, the design of effective algorithms for exploiting large-scale language pretraining remains under-explored. In this work, we investigate the design of such algorithms and propose a novel generalized distillation method, TeachText, which leverages complementary cues from multiple text encoders to provide an enhanced supervisory signal to the retrieval model. TeachText yields significant gains on a number of video retrieval benchmarks without incurring additional computational overhead during inference and was used to produce the winning entry in the Condensed Movie Challenge at ICCV 2021. We show how TeachText can be extended to include multiple video modalities, reducing computational cost at inference without compromising performance. Finally, we demonstrate the application of our method to the task of removing noisy descriptions from the training partitions of retrieval datasets to improve performance. Code and data can be found at https://www.robots.ox.ac.uk/~vgg/research/teachtext/.

E-Brachy: New dosimetry package for electronic brachytherapy sources.

Large reported variability in the material composition and geometrical components of the Xoft electronic high-dose-rate brachytherapy Causes inter-source discrepancy in the source output. This variability is due to the manual manufacturing and assembly of thesources. This study aimed to develop a dosimetry software tool called E-Brachy to characterize the Xoft source and quantify the discrepancies in its photon spectrum and dosimetricproperties. E-Brachy is based on the Geant4 Monte Carlo toolkit and consists of two parts. In part one, the geometry and material composition for the source received in the computer-aided design format from the vendor were converted to the geometry description markup language format using the GUIMesh Python tool and integrated into the E-Brachy software. There was a large variation in material composition and thickness for some of the tube components. The simulation started from electrons and resulted in x-ray generations in the anode region. Multithreading, a track length estimation, and the uniform bremsstrahlung splitting variance reduction techniques were used to decrease the simulation time and increase the x-ray production. The photon energy, position, and momentum were saved into a phase space file as the photon exited the source, but before interacting with the external environment. The obtained x-ray energy spectrum was compared with measurements from the National Institute of Standards and Technology (NIST). In part two, by sampling from the generated photons, the dose rates and dosimetric parameters according to the TG-43 protocol were calculated for model S7500 and compared to the ones previously calculated for model S700 source, which were deemed identical by themanufacturer. The material composition that resulted in the most similar spectrum as the measured NIST spectrum with Pearson's correlation coefficient of 0.99 and a calculated Euclidean difference of keV was chosen for further dosimetric analysis of the model S7500 source. Characteristic peaks showed the presence of tungsten, yttrium, and silver in the source components. Differences in dose rates between the two source models surpassed 20% for polar angles , reaching a peak at cm and . The differences in the radial dose function values were within 5%. The relative difference in percentage between the anisotropy function values of the two models was closer to 0 for smaller values, but at higher polar angles, they increased to 300%. A software package called E-Brachy was successfully developed for the characterization and dosimetry of Xoft electronic brachytherapy sources. E-Brachy can be combined with spectral measurements to investigate the inter- and intra-source variability. The software package was tested by comparing the simulated spectra from the S7500 Xoft source model with NIST measurements and its TG-43 parameters with the S700 model. The TG-43 parameters between the two sources significantly exceed the recommendations ofTG-56.

CLIP-DFGS: A Hard Sample Mining Method for CLIP in Generalizable Person Re-Identification

Recent advancements in pre-trained vision-language models like CLIP have shown promise in person re-identification (ReID) applications. However, their performance in generalizable person re-identification tasks remains suboptimal. The large-scale and diverse image-text pairs used in CLIP's pre-training may lead to a lack or insufficiency of certain fine-grained features. In light of these challenges, we propose a hard sample mining method called DFGS (Depth-First Graph Sampler), based on depth-first search, designed to offer sufficiently challenging samples to enhance CLIP's ability to extract fine-grained features. DFGS can be applied to both the image encoder and the text encoder in CLIP. By leveraging the powerful cross-modal learning capabilities of CLIP, we aim to apply our DFGS method to extract challenging samples and form mini-batches with high discriminative difficulty, providing the image model with more efficient and challenging samples that are difficult to distinguish, thereby enhancing the model's ability to differentiate between individuals. Our results demonstrate significant improvements over other methods, confirming the effectiveness of DFGS in providing challenging samples that enhance CLIP's performance in generalizable person re-identification.

A Data Model for Unrecognised Historical Neighbourhood (Case study: Telukbetung, Indonesia)

Abstract. Unrecognised Historical places, which are locations with history but lacking official recognition, particularly prevalent in Southeast Asia, often face a dearth of accessible historical information. To tackle this challenge, there is an urgent need to commence the collection and documentation of historical data concerning Unrecognised Historical places. This initiative aims to prevent the loss of history, given that such sites are frequently subjected to rapid development and commercialization. To date, City Geography Markup Language (CityGML), an Open Geospatial Consortium (OGC) standard 3D data format, has been developed for presenting city models along with semantic data and is often used in the heritage domain for preservation. It aids in digital infrastructure development and the conservation of urban elements. Furthermore, it provides a mechanism to extend the standard data model to meet the requirements of various purposes. While several CityGML extensions tailored to the documentation of Cultural Heritage already exist, there is currently no extension specifically designed to address the requirements of Unrecognised Historical places. The aim of this paper is to identify a suitable approach for developing a new standardised data model that integrates geometric and semantic information, which is demonstrated for the modelling of Telukbetung, an old city in Bandar Lampung through time.

New Media Advertising Information Search Method Based on XML Technology

The sheer volume and complexity of new media advertising information pose significant challenges for users in searching and filtering advertisements. To enhance the match between search content and keywords, this paper proposes a multifunctional search method for new media advertising information based on eXtensible Markup Language (XML) technology. The collected user attribute data are organized into a set, and a membership function for user attributes is established. An information certainty index and a query certainty index are constructed to obtain the integrated attributes of new media advertisements and classify their attributes. The average value of the constant threshold element is calculated, and an attraction model of new media advertisements for random Internet users is constructed. The similarity between text information and query methods is determined, along with the calculation of browsing duration and frequency in any given area. According to the search model for new media advertising information, the sorted new media advertising information documents are merged to obtain search results. Experimental results demonstrate that the introduction of interactive elements enhances attraction, and dynamic new media advertisements exhibit stronger attraction compared to static advertisements. The proposed method shows significant advantages in search efficiency, achieving over 90% search efficiency, with the match between search content and keywords consistently remaining above 98%, the user satisfaction is higher, the system response time is shorter, the results can be obtained faster, and the search efficiency is improved.

Improving Text Classification in Agricultural Expert Systems with a Bidirectional Encoder Recurrent Convolutional Neural Network

With the rapid development of internet and AI technologies, Agricultural Expert Systems (AESs) have become crucial for delivering technical support and decision-making in agricultural management. However, traditional natural language processing methods often struggle with specialized terminology and context, and they lack the adaptability to handle complex text classifications. The diversity and evolving nature of agricultural texts make deep semantic understanding and integration of contextual knowledge especially challenging. To tackle these challenges, this paper introduces a Bidirectional Encoder Recurrent Convolutional Neural Network (AES-BERCNN) tailored for short-text classification in agricultural expert systems. We designed an Agricultural Text Encoder (ATE) with a six-layer transformer architecture to capture both preceding and following word information. A recursive convolutional neural network based on Gated Recurrent Units (GRUs) was also developed to merge contextual information and learn complex semantic features, which are then combined with the ATE output and refined through max-pooling to form the final feature representation. The AES-BERCNN model was tested on a self-constructed agricultural dataset, achieving an accuracy of 99.63% in text classification. Its generalization ability was further verified on the Tsinghua News dataset. Compared to other models such as TextCNN, DPCNN, BiLSTM, and BERT-based models, the AES-BERCNN shows clear advantages in agricultural text classification. This work provides precise and timely technical support for intelligent agricultural expert systems.

Semantic-aware frame-event fusion based pattern recognition via large vision–language models

Pattern recognition through the fusion of RGB frames and Event streams has emerged as a novel research area in recent years. Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition. However, we posit that these methods may suffer from two key issues: (1). They attempt to directly learn a mapping from the input vision modality to the semantic labels. This approach often leads to sub-optimal results due to the disparity between the input and semantic labels; (2). They utilize small-scale backbone networks for the extraction of RGB and Event input features, thus these models fail to harness the recent performance advancements of large-scale visual-language models. In this study, we introduce a novel pattern recognition framework that consolidates the semantic labels, RGB frames, and event streams, leveraging pre-trained large-scale vision–language models. Specifically, given the input RGB frames, event streams, and all the predefined semantic labels, we employ a pre-trained large-scale vision model (CLIP vision encoder) to extract the RGB and event features. To handle the semantic labels, we initially convert them into language descriptions through prompt engineering and polish using ChatGPT, and then obtain the semantic features using the pre-trained large-scale language model (CLIP text encoder). Subsequently, we integrate the RGB/Event features and semantic features using multimodal Transformer networks. The resulting frame and event tokens are further amplified using self-attention layers. Concurrently, we propose to enhance the interactions between text tokens and RGB/Event tokens via cross-attention. Finally, we consolidate all three modalities using self-attention and feed-forward layers for recognition. Comprehensive experiments on the HARDVS and PokerEvent datasets fully substantiate the efficacy of our proposed SAFE model. The source code has been released at https://github.com/Event-AHU/SAFE_LargeVLM.

Text-aware small sample remote sensing image classification based on contrastive learning

In view of the fact that existing methods mainly use a single modality of remote sensing images to solve the problem of low similarity of the same category, a remote sensing image classification method based on multimodal learning is proposed. Firstly, the spatial features of the image are corrected and the image encoder is pre-trained using contrastive learning to generate image features, and the text encoder is used to generate text features. Secondly, a feature decoder is introduced to obtain text-aware visual features, and a new attention mechanism method is proposed in the feature fusion stage. Then, a new image encoder is designed to improve the classification accuracy. Finally, the similarity between the support set and the query set is calculated to further predict the category. Experiments are conducted on the NWPU-RESISC45, AID, and UC Merced datasets. Its 5-way 5-shot accuracy reaches 86.46%、85.89%and 80.32%outperforms the existing small-sample remote sensing image classification methods, respectively.

Mutually-Guided Hierarchical Multi-Modal Feature Learning for Referring Image Segmentation

Referring image segmentation aims to locate and segment the target region based on a given textual expression query. The primary challenge is to understand semantics from visual and textual modalities and achieve alignment and matching. Prior works have attempted to address this challenge by leveraging separately pretrained unimodal models to extract global visual and textual features, and perform straightforwardly fusion to establish cross-modal semantic associations. However, these methods often concentrate solely on the global semantics, disregarding the hierarchical semantics of expression and image, and struggling with complex and open real scenarios, thus failing to capture critical cross-modal information. To address these limitations, this paper introduces an innovative mutually-guided hierarchical multi-modal feature learning scheme. By leveraging the guidance of global visual features, the model mines hierarchical text features from different stages of the text encoder. Simultaneously, the guidance of global textual features is leveraged to aggregate multi-scale visual features. This mutually guided hierarchical feature learning effectively addresses the semantically inaccurate cause by free-form text and naturally occurring scale variations. Furthermore, a Segment Detail Refinement (SDR) module is designed to enhance the model’s spatial detail awareness through attention mapping of low-level visual features and cross-modal features. To evaluate the effectiveness of the proposed approach, extensive experiments are conducted on three widely used referring image object segmentation datasets. The results demonstrate the superiority of the presented method in accurately locating and segmenting objects in images.

Curating models from BioModels: Developing a workflow for creating OMEX files.

The reproducibility of computational biology models can be greatly facilitated by widely adopted standards and public repositories. We examined 50 models from the BioModels Database and attempted to validate the original curation and correct some of them if necessary. For each model, we reproduced these published results using Tellurium. Once reproduced we manually created a new set of files, with the model information stored by the Systems Biology Markup Language (SBML), and simulation instructions stored by the Simulation Experiment Description Markup Language (SED-ML), and everything included in an Open Modeling EXchange (OMEX) file, which could be used with a variety of simulators to reproduce the same results. On the one hand, the reproducibility procedure of 50 models developed a manual workflow that we would use to build an automatic platform to help users more easily curate and verify models in the future. On the other hand, these exercises allowed us to find the limitations and possible enhancement of the current curation and tooling to verify and curate models.

Post-quantum XML and SAML Single Sign-On

Extensible Markup Language (XML) is one of the most popular serialization languages. Since many security protocols are built using XML, it also provides cryptographic functionality. A central framework in this area is the Security Assertion Markup Language (SAML). This standard is one of the most widely used options for implementing Single Sign-On (SSO), which allows users to authenticate to different service providers using the credentials from a single identity provider. Like all other security protocols currently in use, the security and privacy of XML-based frameworks such as SAML is threatened by the development of increasingly powerful quantum computers. In fact, future attackers with access to scalable quantum computers will be able to break the currently used cryptographic building blocks and thus undermine the security of the SAML SSO to illegally access sensitive private information. Post-quantum cryptography algorithms have been developed to protect against such quantum attackers. While many security protocols have been migrated into the quantum age by using post-quantum cryptography, no such solutions for XML and the security protocols based on it have been developed, let alone tested. We make the following contributions to fill this gap. We have designed post-quantum solutions for the cryptographic building blocks in XML and integrated them into the SAML SSO protocol. We implemented our solutions in the OpenSAML, Apache Santuario, and BouncyCastle libraries and extensively tested their performance for various post-quantum instantiations. As a result, we have created a comprehensive and solid foundation for post-quantum XML and post-quantum SAML SSO migration.

Cross-modal recipe retrieval based on unified text encoder with fine-grained contrastive learning

Cross-modal recipe retrieval is vital for transforming visual food cues into actionable cooking guidance, making culinary creativity more accessible. Existing methods separately encode the recipe Title, Ingredient, and Instruction using different text encoders, then aggregate them to obtain recipe feature, and finally match it with encoded image feature in a joint embedding space. These methods perform well but require significant computational cost. In addition, they only consider matching the entire recipe and the image but ignore the fine-grained correspondence between recipe components and the image, resulting in insufficient cross-modal interaction. To this end, we propose Unified Text Encoder with Fine-grained Contrastive Learning (UTE-FCL) to achieve a simple but efficient model. Specifically, in each recipe, UTE-FCL first concatenates each of the Ingredient and Instruction texts composed of multiple sentences as a single text. Then, it connects these two concatenated texts with the original single-phrase Title to obtain the concatenated recipe. Finally, it encodes these three concatenated texts and the original Title by a Transformer-based Unified Text Encoder (UTE). This proposed structure greatly reduces the memory usage and improves the feature encoding efficiency. Further, we propose fine-grained contrastive learning objectives to capture the correspondence between recipe components and the image at Title, Ingredient, and Instruction levels by measuring the mutual information. Extensive experiments demonstrate the effectiveness of UTE-FCL compared to existing methods.

OphGLM: An ophthalmology large language-and-vision assistant

Vision computer-aided diagnostic methods have been used in early ophthalmic disease screening and diagnosis. However, the limited output formats of these methods lead to poor human–computer interaction and low clinical applicability value. Thus, ophthalmic visual question answering is worth studying. Unfortunately, no practical solutions exist before Large Language Models(LLMs). In this paper, we investigate the ophthalmic visual diagnostic interaction problem. We construct an ophthalmology large language-and-vision assistant, OphGLM, consisting of an image encoder, a text encoder, a fusion module, and an LLM module. We establish a new Chinese ophthalmic fine-tuning dataset, FundusTuning-CN, including the fundus instruction and conversation sets. Based on FundusTuning-CN, we establish a novel LLM-tuning strategy to introduce visual model understanding and ophthalmic knowledge into LLMs at a low cost and high efficiency. Leveraging the pre-training of the image encoder, OphGLM demonstrates strong visual understanding and surpasses open-source visual language models in common fundus disease classification tasks. The FundusTuning-CN enables OphGLM to surpass open-source medical LLMs in both ophthalmic knowledge and interactive capabilities. Our proposed OphGLM has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be publicly available at https://github.com/ML-AILab/OphGLM.

E-Learning Web Elements for Brickwork Course in theConstruction Technology forVocational Training

E-learning web is a learning material delivered in electronic media through HTML (Hyper Text Markup Language), programming for the Construction Technology Program. This study was conducted to design and develop reference material asan e-learning web for the brickwork course based on modifying the ADDIE model. The objective of this study is to identify the aspects of web element requirements, the appropriateness of design, format, and content in the e-learning web of the brickwork course and the usability of the e-learning web. This study is a development and survey involving three types of respondents: three creative multimedia experts from Universiti Tun Hussein Onn Malaysia,lecturers, and students in construction technology programs from vocational colleges.The questionnaires and feedback forms were used using dichotomous and Likert scales. Theresults of the study indicated that all lecturers and students agree with the appropriateness of the design, format, content, and usability of the e-learning web. Overall, the development of the e-learning web of the brickwork course for the construction technology program is expected to help improve student understanding and be beneficial as an input for web developers, especially for developing educational materials for Web 4.0.

Hydro-Climatic Factors and Health Problems in the Town of Ouéllé (Central-Eastern Côte d'Ivoire)

This article deals with hydro-climatic factors and their health impacts in the town of Ouéllé in a context of climate change. The aim of this study is to show the impact of hydro-climatic determinants on the spatial distribution of pathologies in the town of Ouéllé. The methodological approach used was based on data collection techniques ranging from documentary research to field surveys. Interviews were conducted with experts, while questionnaires were administered to 100 people living in the urban area of Ouéllé. In addition, photographs of the spatial units were taken using a Redmi 9A smartphone, followed by the collection of geospatial data using the UTM Geo map mobile application for data collected in KML (Keyhole Markup Language) format on the Android operating system. We also used statistical data analysis techniques to process the data. The results showed that the main hydro-climatic factors in Ouéllé are rainfall, temperature and wind. The diseases linked to these hydro-climatic factors are malaria, which is the main health risk, accounting for 63% of consultations in 2023, followed by typhoid fever (8%) and acute respiratory infections (ARI) (3%).