Markup Language Research Articles

Bayesian modelling of time series data (BayModTS) - a FAIR workflow to process sparse and highly variable data.

Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilise such information while consistently handling uncertainties. We present BayModTS (Bayesian Modelling of Time Series data), a new FAIR (Findable, Accessible, Interoperable and Reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterised models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection. The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (Systems Biology Markup Language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub 10.15490/fairdomhub.1.study.1070.1.

Modal-aware prompt tuning with deep adaptive feature enhancement

Prompt learning has recently emerged as a promising method for fine-tuning vision-language models. By introducing prompts in the text encoder or image encoder, the pre-trained model can quickly adapt to downstream tasks without updating the pre-trained weights. However, prior multi-modal prompt tuning works do not consider the difference in feature distributions between text and images, and adopt the same prompts for both encoders, thus achieving sub-optimal performance in the downstream few-shot learning. In this paper, we propose Modal-Aware Prompt (MAP) to alleviate this issue. Specifically, considering the stability of text features, we design text-specific prompts, which can acquire text class-related information from a general template (i.e., “a photo of a <category>”) by unidirectional attention-based interaction. Additionally, considering the diversity of image features, we design visual-specific prompts to acquire image class-related information and adjust the image features by bidirectional attention-based interaction. To learn hierarchical prompt representations and reinforce the prompt features, we further propose a Deep Adaptive Feature Enhancement (DAFE) module to adaptively utilize the prompt output of the former layer, which can combine instance-level and task-level information simultaneously. Combining the above two designs, our method MAP-DAFE obtains state-of-the-art results on 11 image recognition datasets and has the fastest convergence rate. This proves our MAP-DAFE is effective and efficient.

RoUIE: A Method for Constructing Knowledge Graph of Power Equipment Based on Improved Universal Information Extraction

The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from multiple sources related to power equipment is a valuable approach to enhance the intelligence level of asset management. Through the collection of pertinent authentic datasets, we have established a dataset for the state evaluation of power equipment, encompassing 35 types of relationships. To better suit the characteristics of concentrated relationship representations and varying lengths in textual descriptions, we propose a generative model called RoUIE, which is a method for constructing a knowledge graph of power equipment based on improved Universal Information Extraction (UIE). This model first utilizes a pre-trained language model based on rotational position encoding as the text encoder in the fine-tuning stage. Subsequently, we innovatively leverage the Distribution Focal Loss (DFL) to replace Binary Cross-Entropy Loss (BCE) as the loss function, further enhancing the model’s extraction performance. The experimental results demonstrate that compared to the UIE model and mainstream joint extraction benchmark models, RoUIE exhibits superior performance on the dataset we constructed. On a general Chinese dataset, the proposed model also outperforms baseline models, showcasing the model’s universal applicability.

HEALPAL: YOUR PERSONAL HEALTH COMAPANION

In an era where rapid technological advancements intersect with healthcare needs, the development of an intelligent healthcare chatbot has emerged as a promising solution. This study presents a comprehensive exploration into the conceptualization, design, and implementation of an AI-powered chatbot tailored to provide accurate and personalized healthcare information. Integrating Natural Language Processing (NLP) algorithms and Artificial Intelligence Markup Language (AIML), the chatbot aims to bridge the gap between individuals and reliable medical knowledge. Keywords—Chatbot, NLP, Markup Language

COLLEGE ENQUIRY CHAT-BOT SYSTEM

Nowadays, many people are using smartphone with many new applications i.e. technology is growing day by day. Today Artificial Intelligence is playing a major role in a variety of fields ranging from industries in product manufacturing, to customer care in public relations. As there are many online Artificial Intelligence (AI) systems or chat bots which are in existence that help people solve their problems. So, we are going to implement a virtual assistant based on AI that can solve any college related query. This will work as a College Oriented Intelligence machine. This virtual machine will respond the queries of students on college related issues. A chat bot has information stored in its database to identify the sentences and making a decision itself as response to answer a given question. The college enquiry chat bot will be built using algorithm that analyses queries and understand user’s message Chatbots are conversational systems that can do chat interactions with human automatically. It is developed to be virtual assistant, making entertainment for people, helping for answering the questions, getting driving directions, serving as human partner in smart homes etc. Most of the chatbots utilize the algorithms of artificial intelligence (AI) in order to get the required responses. In this paper, we provide the design of a University Chatbot that provides an efficient and accurate answer for any user questions about university information. This is the first University Chatbot for inquiring about school information in Myanmar Language based on Artificial Intelligence Markup Language and uses Pandora bots as the interpreter. This paper introduces approach made for improving the efficiency of the chatbot or artificial conversational entity used in various commercial and banking sector. Humanizing is to improving the response generation ability of the chatbot. Key Words: Artificial Intelligence, Database. Intelligence Machine.

Forging identity in the cloud – dissecting Silver SAML

An attack method used to forge identities by faking Security Assertion Markup Language (SAML) has been with us for years. But now threat actors have raised their game, targeting Microsoft's Entra ID service, which is extensively used by major cloud services.

X 2-VLM: All-in-One Pre-Trained Model for Vision-Language Tasks.

Vision language pre-training aims to learn alignments between vision and language from a large amount of data. Most existing methods only learn image-text alignments. Some others utilize pre-trained object detectors to leverage vision language alignments at the object level. In this paper, we propose to learn multi-grained vision language alignments by a unified pre-training framework that learns multi-grained aligning and multi-grained localization simultaneously. Based on it, we present X 2-VLM, an all-in-one model with a flexible modular architecture, in which we further unify image-text pre-training and video-text pre-training in one model. X 2-VLM is able to learn unlimited visual concepts associated with diverse text descriptions. Experiment results show that X 2-VLM performs the best on base and large scale for both image-text and video-text tasks, making a good trade-off between performance and model scale. Moreover, we show that the modular design of X 2-VLM results in high transferability for it to be utilized in any language or domain. For example, by simply replacing the text encoder with XLM-R, X 2-VLM outperforms state-of-the-art multilingual multi-modal pre-trained models without any multilingual pre-training.

A Knowledge Graph Completion Algorithm Based on the Fusion of Neighborhood Features and vBiLSTM Encoding for Network Security

Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security information in the field of network security knowledge graphs. The limited information provided by traditional text encoding models leads to insufficient reasoning ability, greatly restricting the development of this field. Starting from text encoding, this paper first addresses the issue of the inadequate capabilities of traditional models using a deep learning model for assistance. It designs a vBiLSTM model based on a word2vec and BiLSTM combination to process network security texts. By utilizing word vector models to retain semantic information in entities and extract key features to input processed data into BiLSTM networks for extracting higher-level features that better capture and express their deeper meanings, this design significantly enhances understanding and expression capabilities toward complex semantics in long sentences before inputting final feature vectors into the KGC-N model. The KGC-N model uses feature vectors combined with graph structure information to fuse forward and reverse domain features and then utilizes a Transformer decoder to decode predictions and complete missing information within the network security knowledge map. Compared with other models using evaluation metrics such as MR, MRR demonstrates that employing our proposed method effectively improves performance on completion tasks and increases comprehension abilities toward complex relations, thereby enhancing accuracy and efficiency when completing knowledge graphs.

Waves of Food- Food Ordering App: A Descriptive Study

A fantastic new app called "Waves of Food" is revolutionising the way we have our favourite dishes delivered. It makes ordering meals a joy and is quite simple to use. With only a few touches, you may explore a wide variety of culinary options, and the app makes recommendations based on your preferences. You can monitor your order in real time, allowing you to know the exact location of your food at all times. Additionally, the app offers secured payment alternatives so you don't have to worry about losing your money. It also guarantees that your food will taste well and arrive on schedule. Waves of Food is the food delivery industry's super hero, improving and streamlining everything for everyone. "Food ordering App " introduces a game-changing mobile application in the food delivery landscape. This innovative platform redefines convenience and satisfaction, offering users a seamless experience from start to finish. With intuitive navigation and personalized recommendations, users can easily explore a diverse range of cuisines. Real-time order tracking ensures transparency and peace of mind, while secure payment options guarantee financial security. The app optimizes delivery routes for efficiency and freshness, promising timely arrivals of delicious meals. Waves of Food sets a new standard for food delivery, making every dining experience a breeze. The food delivery industry has witnessed a significant transformation in recent years, driven by advancements in mobile technology and changing consumer preferences. This abstract presents a comprehensive overview of a cutting-edge food delivery application designed to revolutionize the way consumers order and receive meals. Keywords—Hyper test markup langage(HTML), Cascading Style Sheets(CSS), Application Programming Interface(API), Hypertext Transfer Protocol(HTTP), Extensible Markup Language(XML).

EmoAsst: emotion recognition assistant via text-guided transfer learning on pre-trained visual and acoustic models

Children diagnosed with Autism Spectrum Disorder (ASD) often struggle to grasp social conventions and promptly recognize others' emotions. Recent advancements in the application of deep learning (DL) to emotion recognition are solidifying the role of AI-powered assistive technology in supporting autistic children. However, the cost of collecting and annotating large-scale high-quality human emotion data and the phenomenon of unbalanced performance on different modalities of data challenge DL-based emotion recognition. In response to these challenges, this paper explores transfer learning, wherein large pre-trained models like Contrastive Language-Image Pre-training (CLIP) and wav2vec 2.0 are fine-tuned to improve audio- and video-based emotion recognition with text- based guidance. In this work, we propose the EmoAsst framework, which includes a visual fusion module and emotion prompt fine-tuning for CLIP, in addition to leveraging CLIP's text encoder and supervised contrastive learning for audio-based emotion recognition on the wav2vec 2.0 model. In addition, a joint few-shot emotion classifier enhances the accuracy and offers great adaptability for real-world applications. The evaluation results on the MELD dataset highlight the outstanding performance of our methods, surpassing the majority of existing video and audio-based approaches. Notably, our research demonstrates the promising potential of the proposed text-based guidance techniques for improving video and audio-based Emotion Recognition and Classification (ERC).

PhishingRTDS: A real-time detection system for phishing attacks using a Deep Learning model

In recent years, phishing attacks have become more intelligent and more challenging to detect using typical phishing methods. Moreover, attackers have leveraged some web development techniques to increase the website's legitimacy in victims' eyes, such as using JFrame to design a window that looks like a browser inside the webpage. In this paper, we design a system that detects three types of phishing attacks: Tiny Uniform Resource Locators (TinyURLs), Browsers in the Browser (BiTB), and regular phishing attacks. In this system, we aim to protect victims from mistakenly downloading malicious software into their systems. We split our system into three parts: Deep Learning model (DL), browser extension, and docker container. First, we design a DL model using bidirectional long short-term memory (BiLSTM) and an attention mechanism to classify the URL as phishing or benign. Our model shows 99% in its precision, recall, and F1 score. Second, we design a browser extension to extract the original URL from the suspect webpage and then send it to the docker container. Then, the docker container opens the webpage and extracts all URLs from its HyperText Markup Language (HTML) and JavaScript. Then, each URL passes to a DL model for classification, resulting in a list of labels for each webpage. Therefore, we use three decision strategies: Single Phishing Strategy (SPhS), Mean Sum Strategy (MSS), and Weighted Average Strategy (WeAS) to decide whether the webpage is phishing or benign. Our findings indicate that the best results among the three strategies were WeAS.

Empowering Innovation in Banking Insurance: Expert System for Information Management

Background Nowadays, financial institutions face and solve challenges to optimise Information Management (IM), so the use of new technologies such as Expert Systems (ES) is indispensable. Therefore, the objective of this research is to implement an ES to improve IM in insurance companies. Method In terms of approach, the agile methodology SCRUM was chosen, which consists of five phases: initiation, planning and estimation, implementation, review and retrospective, launch, and thus decision making and optimisation of the IM process. In addition, the following technologies were chosen: ASP.NET as programming language, HTML as markup language, SQL Server as database management and CSS for design and visual styling. Results The results also showed a significant increase of 35% in user service, accompanied by a significant improvement of 44% in report delivery. Finally, a significant improvement of 24% was observed following the implementation of the expert system. This streamlines processes, reduces waiting times, improves the user experience in real time and optimises the management of large volumes of data in the insurance company. Conclusions The system demonstrated that this tool improves decision making, reduces errors in the issuing area and provides a user-friendly interface for information management.

Dynamic switching of psychological test questions and design of scalable database: A study of web page data visualization and front-end interaction

As the need for efficient and user-friendly web applications in the field of psychological assessment continues to grow, traditional web development approaches are challenged to provide a dynamic and adaptive user experience. This study focuses on addressing these challenges through a comprehensive study that provides insight into the current state of psychological assessment web development, highlighting the shortcomings of traditional approaches, including the static limitations of Hyper Text Markup Language (HTML) and limited client-side scripting. The result is a user experience that lacks the responsiveness and flexibility that modern users expect. To tackle these problems, the intended solution suggested in this work combines a strong and scalable database design with a dynamic switching mechanism for psychological exam questions. Features-rich, aesthetically pleasing, and functioning client-server Web applications are created using technologies such as Servlets and Java Server Pages (JSP). The research contains practical case studies that demonstrate how Java-based user interface design can be successfully applied in real-world scenarios. The advantages of modularity, code reusability, and maintainability are demonstrated in these case studies. The study examines how selected technologies affect the performance, security, and accessibility of web UI, while also addressing load times, data integrity, and user inclusion issues. To summarize, this investigation underscores the crucial role of dynamic and scalable databases and Java-based technologies in enhancing the design of psychological assessment Web UIs.Developers can create web applications that exceed user expectations and provide superior user experience in the evolving digital landscape by using the proposed frameworks comprehensive toolkit.

Generating images using generative adversarial networks based on text descriptions

Modern developments in the fields of natural language processing (NLP) and computer vision (CV) emphasize the increasing importance of generating images from text descriptions. The presented article analyzes and compares two key methods in this area: generative adversarial network with conditional latent semantic analysis (GAN-CLS) and ultra-long transformer network (XLNet). The main components of GAN-CLS, including the generator, discriminator, and text encoder, are discussed in the context of their functional tasks—generating images from text inputs, assessing the realism of generated images, and converting text descriptions into latent spaces, respectively. A detailed comparative analysis of the performance of GAN-CLS and XLNet, the latter of which is widely used in the organic light-emitting diode (OEL) field, is carried out. The purpose of the study is to determine the effectiveness of each method in different scenarios and then provide valuable recommendations for selecting the best method for generating images from text descriptions, taking into account specific tasks and resources. Ultimately, our paper aims to be a valuable research resource by providing scientific guidance for NLP and CV experts.

Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval

Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited by the difficulty of gigapixels, the variable size of WSIs, and the dependence on manual annotations. In this work, we propose a novel histopathology language-image representation learning framework for fine-grained digital pathology cross-modal retrieval, which utilizes paired diagnosis reports to learn fine-grained semantics from the WSI. An anchor-based WSI encoder is built to extract hierarchical region features and a prompt-based text encoder is introduced to learn fine-grained semantics from the diagnosis reports. The proposed framework is trained with a multivariate cross-modal loss function to learn semantic information from the diagnosis report at both the instance level and region level. After training, it can perform four types of retrieval tasks based on the multi-modal database to support diagnostic requirements. We conducted experiments on an in-house dataset and a public dataset to evaluate the proposed method. Extensive experiments have demonstrated the effectiveness of the proposed method and its advantages to the present histopathology retrieval methods. The code is available at https://github.com/hudingyi/FGCR.

Development and implementation of real-time staff performance monitoring and evaluation system based on Global Positioning System (GPS)

This work presents the development and implementation of a Real-Time Staff Performance Monitoring and Evaluation System (RT-SPMES) using Global Positioning System (GPS) technology. In line with the dynamism of the workplace, due to rapid changes in technology, existing staff performance appraisal systems have fallen short of expectations due to their manual nature. Hence, the need arises for a more robust approach to monitoring and evaluating employee work performance. To achieve this, this work carried out an exhaustive review of existing performance evaluation methods within the academic and a conceptual architecture was developed. A real-time monitoring and evaluation system was developed, using PHP programming language, Hyper Text Markup Language and Cascading Styles Sheet. The system is hosted on Azure Cloud infrastructure. Through sufficient testing of the developed system developed in this work, it was asserted effectively monitors employee performance in real-time, paving the way for an objective appraisal process devoid of bias and manipulation.

BioKC: a collaborative platform for curation and annotation of molecular interactions.

Curation of biomedical knowledge into systems biology diagrammatic or computational models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever-increasing growth of domain literature. New findings demonstrating elaborate relationships between multiple molecules, pathways and cells have to be represented in a format suitable for systems biology applications. Importantly, curation should capture the complexity of molecular interactions in such a format together with annotations of the involved elements and support stable identifiers and versioning. This challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, community-based curation, an important source of curated knowledge, requires support in role management, reviewing features and versioning. Here, we present Biological Knowledge Curation (BioKC), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML). BioKC offers a graphical user interface for curation of complex molecular interactions and their annotation with stable identifiers and supporting sentences. With the support of collaborative curation and review, it allows to construct building blocks for systems biology diagrams and computational models. These building blocks can be published under stable identifiers and versioned and used as annotations, supporting knowledge building for modelling activities.

DGL: Dynamic Global-Local Prompt Tuning for Text-Video Retrieval

Text-video retrieval is a critical multi-modal task to find the most relevant video for a text query. Although pretrained models like CLIP have demonstrated impressive potential in this area, the rising cost of fully finetuning these models due to increasing model size continues to pose a problem. To address this challenge, prompt tuning has emerged as an alternative. However, existing works still face two problems when adapting pretrained image-text models to downstream video-text tasks: (1) The visual encoder could only encode frame-level features and failed to extract global-level general video information. (2) Equipping the visual and text encoder with separated prompts failed to mitigate the visual-text modality gap. To this end, we propose DGL, a cross-modal Dynamic prompt tuning method with Global-Local video attention. In contrast to previous prompt tuning methods, we employ the shared latent space to generate local-level text and frame prompts that encourage inter-modal interaction. Furthermore, we propose modeling video in a global-local attention mechanism to capture global video information from the perspective of prompt tuning. Extensive experiments reveal that when only 0.67% parameters are tuned, our cross-modal prompt tuning strategy DGL outperforms or is comparable to fully finetuning methods on MSR-VTT, VATEX, LSMDC, and ActivityNet datasets. Code will be available at https://github.com/knightyxp/DGL.

SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model

Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains. However, it remains a challenging task due to the inherent spherical distortion and geometry characteristics, resulting in low-quality content generation. In this paper, we introduce a novel framework of SphereDiffusion to address these unique challenges, for better generating high-quality and precisely controllable spherical panoramic images. For the spherical distortion characteristic, we embed the semantics of the distorted object with text encoding, then explicitly construct the relationship with text-object correspondence to better use the pre-trained knowledge of the planar images. Meanwhile, we employ a deformable technique to mitigate the semantic deviation in latent space caused by spherical distortion. For the spherical geometry characteristic, in virtue of spherical rotation invariance, we improve the data diversity and optimization objectives in the training process, enabling the model to better learn the spherical geometry characteristic. Furthermore, we enhance the denoising process of the diffusion model, enabling it to effectively use the learned geometric characteristic to ensure the boundary continuity of the generated images. With these specific techniques, experiments on Structured3D dataset show that SphereDiffusion significantly improves the quality of controllable spherical image generation and relatively reduces around 35% FID on average.

Novel Class Discovery in Chest X-rays via Paired Images and Text

Novel class discover(NCD) aims to identify new classes undefined during model training phase with the help of knowledge of known classes. Many methods have been proposed and notably boosted performance of NCD in natural images. However, there has been no work done in discovering new classes based on medical images and disease categories, which is crucial for understanding and diagnosing specific diseases. Moreover, most of the existing methods only utilize information from image modality and use labels as the only supervisory information. In this paper, we propose a multi-modal novel class discovery method based on paired images and text, inspired by the low classification accuracy of chest X-ray images and the relatively higher accuracy of the paired text. Specifically, we first pretrain the image encoder and text encoder with multi-modal contrastive learning on the entire dataset and then we generate pseudo-labels separately on the image branch and text branch. We utilize intra-modal consistency to assess the quality of pseudo-labels and adjust the weights of the pseudo-labels from both branches to generate the ultimate pseudo-labels for training. Experiments on eight subset splits of MIMIC-CXR-JPG dataset show that our method improves the clustering performance of unlabeled classes by about 10% on average compared to state-of-the-art methods. Code is available at: https://github.com/zzzzzzzzjy/MMNCD-main.