248 research outputs found

    Dataflow Programming and Acceleration of Computationally-Intensive Algorithms

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    The volume of unstructured textual information continues to grow due to recent technological advancements. This resulted in an exponential growth of information generated in various formats, including blogs, posts, social networking, and enterprise documents. Numerous Enterprise Architecture (EA) documents are also created daily, such as reports, contracts, agreements, frameworks, architecture requirements, designs, and operational guides. The processing and computation of this massive amount of unstructured information necessitate substantial computing capabilities and the implementation of new techniques. It is critical to manage this unstructured information through a centralized knowledge management platform. Knowledge management is the process of managing information within an organization. This involves creating, collecting, organizing, and storing information in a way that makes it easily accessible and usable. The research involved the development textual knowledge management system, and two use cases were considered for extracting textual knowledge from documents. The first case study focused on the safety-critical documents of a railway enterprise. Safety is of paramount importance in the railway industry. There are several EA documents including manuals, operational procedures, and technical guidelines that contain critical information. Digitalization of these documents is essential for analysing vast amounts of textual knowledge that exist in these documents to improve the safety and security of railway operations. A case study was conducted between the University of Huddersfield and the Railway Safety Standard Board (RSSB) to analyse EA safety documents using Natural language processing (NLP). A graphical user interface was developed that includes various document processing features such as semantic search, document mapping, text summarization, and visualization of key trends. For the second case study, open-source data was utilized, and textual knowledge was extracted. Several features were also developed, including kernel distribution, analysis offkey trends, and sentiment analysis of words (such as unique, positive, and negative) within the documents. Additionally, a heterogeneous framework was designed using CPU/GPU and FPGAs to analyse the computational performance of document mapping

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Multimodal machine learning in medical screenings

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    The healthcare industry, with its high demand and standards, has long been considered a crucial area for technology-based innovation. However, the medical field often relies on experience-based evaluation. Limited resources, overloading capacity, and a lack of accessibility can hinder timely medical care and diagnosis delivery. In light of these challenges, automated medical screening as a decision-making aid is highly recommended. With the increasing availability of data and the need to explore the complementary effect among modalities, multimodal machine learning has emerged as a potential area of technology. Its impact has been witnessed across a wide range of domains, prompting the question of how far machine learning can be leveraged to automate processes in even more complex and high-risk sectors. This paper delves into the realm of multimodal machine learning in the field of automated medical screening and evaluates the potential of this area of study in mental disorder detection, a highly important area of healthcare. First, we conduct a scoping review targeted at high-impact papers to highlight the trends and directions of multimodal machine learning in screening prevalent mental disorders such as depression, stress, and bipolar disorder. The review provides a comprehensive list of popular datasets and extensively studied modalities. The review also proposes an end-to-end pipeline for multimodal machine learning applications, covering essential steps from preprocessing, representation, and fusion, to modelling and evaluation. While cross-modality interaction has been considered a promising factor to leverage fusion among multimodalities, the number of existing multimodal fusion methods employing this mechanism is rather limited. This study investigates multimodal fusion in more detail through the proposal of Autofusion, an autoencoder-infused fusion technique that harnesses the cross-modality interaction among different modalities. The technique is evaluated on DementiaBank’s Pitt corpus to detect Alzheimer’s disease, leveraging the power of cross-modality interaction. Autofusion achieves a promising performance of 79.89% in accuracy, 83.85% in recall, 81.72% in precision, and 82.47% in F1. The technique consistently outperforms all unimodal methods by an average of 5.24% across all metrics. Our method consistently outperforms early fusion and late fusion. Especially against the late fusion hard-voting technique, our method outperforms by an average of 20% across all metrics. Further, empirical results show that the cross-modality interaction term enhances the model performance by 2-3% across metrics. This research highlights the promising impact of cross-modality interaction in multimodal machine learning and calls for further research to unlock its full potential

    Graph Neural Networks for Natural Language Processing

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    By constructing graph-structured data from the input data, Graph Neural Network (GNN) enhances the performance of numerous Natural Language Processing (NLP) tasks. In this thesis, we mainly focus on two aspects of NLP: text classification and knowledge graph completion. TextGCN shows excellent performance in text classification by leveraging the graph structure of the entire corpus without using any external resources, especially under a limited labelled data setting. Two questions are explored: (1) Under the transductive semi-supervised setting, how to utilize the documents better and learn the complex relationship between nodes. (2) How to transform TextGCN into an inductive model and also reduce the time and space complexity? In detail, firstly, a comprehensive analysis was conducted on TextGCN and its variants. Secondly, we propose ME-GCN, a novel method for text classification that utilizes multi-dimensional edge features in a graph neural network (GNN) for the first time. It uses the corpus-trained word and document-based edge features for semi-supervised classification and has been shown to be effective through experiments on benchmark datasets under the limited labelled data setting. Thirdly, InducT-GCN, an inductive framework for GCN-based text classification that does not require additional resources is introduced. The framework introduces a novel approach to make transductive GCN-based text classification models inductive, improving performance and reducing time and space complexity. Most existing work for Temporal Knowledge Graph Completion (TKGC) overlooks the significance of explicit temporal information and fails to skip irrelevant snapshots based on the entity-related relation in the query. To address this, we introduced Re-Temp (Relation-Aware Temporal Representation Learning), a model that leverages explicit temporal embedding and a skip information flow after each timestamp to eliminate unnecessary information for prediction

    INSAM Journal of Contemporary Music, Art and Technology 10 (I/2023)

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    Having in mind the foundational idea not only of our Journal but also the INSAM Institute itself, the main theme of this issue is titled “Technological Aspects of Contemporary Artistic and Scientific Research”. This theme was recognized as important, timely, and necessary by a number of authors coming from various disciplines. The (Inter)Views section brings us three diverse pieces; the issue is opened by Aida Adžović’s interview with the legendary Slovene act Laibach regarding their performance of the Wir sing das Volk project at the Sarajevo National Theater on May 9, 2023. Following this, Marija Mitrović prepared an interview with media artist Leon Eckard, concerning this artist’s views on contemporary art and the interaction between technology and human sensitivity. An essay by Alexander Liebermann on the early 20th-century composer Erwin Schulhoff, whose search for a unique personal voice could be encouraging in any given period, closes this rubric. The Main theme section contains seven scientific articles. In the first one, Filipa Magalhães, Inês Filipe, Mariana Silva and Henrique Carvalho explore the process and details of technological and artistic challenges of reviving the music theater work FE...DE...RI...CO... (1987) by Constança Capdeville. The second article, written by Milan Milojković, is dedicated to the analysis of historical composer Vojislav Vučković and his ChatGPT-generated doppelganger and opera. The fictional narrative woven around the actual historical figure served as an example of the current possibilities of AI in the domain of musicological work. In the next paper, Luís Arandas, Miguel Carvalhais and Mick Grierson expand on their work on the film Irreplaceable Biography, which was created via language-guided generative models in audiovisual production. Thomas Moore focuses on the Belgium-based Nadar Ensemble and discusses the ways in which the performers of the ensemble understand the concept of the integrated concert and distinguish themselves from it, specifying the broadening of performers’ competencies and responsibilities. In her paper, Dana Papachristou contributes to the discussion on the politics of connectivity based on the examination of three projects: the online project Xenakis Networked Performance Marathon 2022, 2023Eleusis Mystery 91_Magnetic Dance in Elefsina European Capital of Culture, and Spaces of Reflection offline PirateBox network in the 10th Berlin Biennale. The penultimate article in the section is written by Kenrick Ho and presents us with the author’s composition Flou for solo violin through the prism of the relationship between (historically present) algorithmic processes, the composer, and the performer. Finally, Rijad Kaniža adds to the critical discourse on the reshaping of the musical experience via technology and the understanding of said technology using the example of musique concrète. In the final Review section, Bakir Memišević gives an overview of the 13th International Symposium “Music in Society” that was held in Sarajevo in December 2022

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

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    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    Computational acquisition of knowledge in small-data environments: a case study in the field of energetics

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    The UK’s defence industry is accelerating its implementation of artificial intelligence, including expert systems and natural language processing (NLP) tools designed to supplement human analysis. This thesis examines the limitations of NLP tools in small-data environments (common in defence) in the defence-related energetic-materials domain. A literature review identifies the domain-specific challenges of developing an expert system (specifically an ontology). The absence of domain resources such as labelled datasets and, most significantly, the preprocessing of text resources are identified as challenges. To address the latter, a novel general-purpose preprocessing pipeline specifically tailored for the energetic-materials domain is developed. The effectiveness of the pipeline is evaluated. Examination of the interface between using NLP tools in data-limited environments to either supplement or replace human analysis completely is conducted in a study examining the subjective concept of importance. A methodology for directly comparing the ability of NLP tools and experts to identify important points in the text is presented. Results show the participants of the study exhibit little agreement, even on which points in the text are important. The NLP, expert (author of the text being examined) and participants only agree on general statements. However, as a group, the participants agreed with the expert. In data-limited environments, the extractive-summarisation tools examined cannot effectively identify the important points in a technical document akin to an expert. A methodology for the classification of journal articles by the technology readiness level (TRL) of the described technologies in a data-limited environment is proposed. Techniques to overcome challenges with using real-world data such as class imbalances are investigated. A methodology to evaluate the reliability of human annotations is presented. Analysis identifies a lack of agreement and consistency in the expert evaluation of document TRL.Open Acces

    Towards Usable API Documentation

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    The learning and usage of an API is supported by documentation. Like source code, API documentation is itself a software product. Several research results show that bad design in API documentation can make the reuse of API features difficult. Indeed, similar to code smells, poorly designed API documentation can also exhibit 'smells'. Such documentation smells can be described as bad documentation styles that do not necessarily produce incorrect documentation but make the documentation difficult to understand and use. This thesis aims to enhance API documentation usability by addressing such documentation smells in three phases. In the first phase, we developed a catalog of five API documentation smells consulting literature on API documentation issues and online developer discussion. We validated their presence in the real world by creating a benchmark of 1K official Java API documentation units and conducting a survey of 21 developers. The developers confirmed that these smells hinder their productivity and called for automatic detection and fixing. In the second phase, we developed machine-learning models to detect the smells using the 1K benchmark, however, they performed poorly when evaluated on larger and more diverse documentation sources. We explored more advanced models; employed re-training and hyperparameter tuning to further improve the performance. Our best-performing model, RoBERTa, achieved F1-scores of 0.71-0.93 in detecting different smells. In the third phase, we first focused on evaluating the feasibility and impact of fixing various smells in the eyes of practitioners. Through a second survey of 30 practitioners, we found that fixing the lazy smell was perceived as the most feasible and impactful. However, there was no universal consensus on whether and how other smells can/should be fixed. Finally, we proposed a two-stage pipeline for fixing lazy documentation, involving additional textual description and documentation-specific code example generation. Our approach utilized a large language model, GPT- 3, to generate enhanced documentation based on non-lazy examples and to produce code examples. The generated code examples were refined iteratively until they were error-free. Our technique demonstrated a high success rate with a significant number of lazy documentation instances being fixed and error-free code examples being generated

    Geographic information extraction from texts

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    A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
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