Recent Submissions

  • An SRAM optimized approach for constant memory consumption and ultra-fast execution of ML classifiers on TinyML hardware 

    Sudharsan, Bharath; Yadav, Piyush; Breslin, John G.; Ali, Muhammad Intizar (Institute of Electrical and Electronics Engineers, 2021-11-15)
    With the introduction of ultra-low-power machine learning (TinyML), IoT devices are becoming smarter as they are driven by Machine Learning (ML) models. However, any increase in the training data results in a linear ...
  • CURED4NLG: A dataset for table-to-text generation 

    Pasricha, Nivranshu; Arcan, Mihael; Buitelaar, Paul (University of Galway, 2023)
    We introduce CURED4NLG, a dataset for the task of table-to-text generation focusing on the public health domain. The dataset consists of 280 pairs of tables and documents extracted from weekly epidemiological reports ...
  • Towards bootstrapping a chatbot on industrial heritage through term and relation extraction 

    Arcan, Mihael; O’Halloran, Rory; Robin, Cecile; Buitelaar, Paul (Association for Computational Linguistics (ACL), 2022-11-20)
    We describe initial work in developing a methodology for the automatic generation of a conversational agent or ‘chatbot’ through term and relation extraction from a relevant corpus of language data. We develop our ...
  • Intent classification by the use of automatically generated knowledge graphs 

    Arcan, Mihael; Manjunath, Sampritha; Robin, Cécile; Verma, Ghanshyam; Pillai, Devishree; Sarkar, Simon; Dutta, Sourav; Assem, Haytham; McCrae, John P.; Buitelaar, Paul (MDPI, 2023-05-12)
    Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers¿ goals. Although intent classification performs well in general settings, domain-specific user goals can ...
  • ULD@NUIG at SemEval-2020 Task 9: Generative morphemes with an attention model for sentiment analysis in code-mixed text 

    Goswami, Koustava; Rani, Priya; Chakravarthi, Bharathi Raja; Fransen, Theodorus; McCrae, John P. (International Committee for Computational Linguistics, 2020)
    Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over different social media sites have led to an ...
  • Unsupervised deep language and dialect identification for short texts 

    Goswami, Koustava; Sarkar, Rajdeep; Chakravarthi, Bharathi Raja; Fransen, Theodorus; McCrae, John P. (International Committee on Computational Linguistics, 2020-12)
    Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Language identification ...
  • Findings of the LoResMT 2021 shared task on COVID and sign language for low-resource languages 

    Ojha, Atul Kr.; Liu, Chao-Hong; Kann, Katharina; Ortega, John; Shatam, Sheetal; Fransen, Theodorus (Association for Machine Translation in the Americas, 2021-08)
    We present the findings of the LoResMT 2021 shared task which focuses on machine translation (MT) of COVID-19 data for both low-resource spoken and sign languages. The organization of this task was conducted as part of the ...
  • Cross-lingual sentence embedding using multi-task learning 

    Goswami, Koustava; Dutta, Sourav; Assem, Haytham; Fransen, Theodorus; McCrae, John P. (Association for Computational Linguistics, 2021-11-07)
    Multilingual sentence embeddings capture rich semantic information not only for measuring similarity between texts but also for catering to a broad range of downstream cross-lingual NLP tasks. State-of-the-art multilingual ...
  • Towards an integrative approach for making sense distinctions 

    McCrae, John P.; Fransen, Theodorus; Ahmadi, Sina; Buitelaar, Paul; Goswami, Koustava (Frontiers Media, 2022-02-07)
    Word senses are the fundamental unit of description in lexicography, yet it is rarely the case that different dictionaries reach any agreement on the number and definition of senses in a language. With the recent rise in ...
  • NUIG at TIAD 2021: Cross-lingual word embeddings for translation inference 

    Ahmadi, Sina; Ojha, Atul Kr.; Banerjee, Shubhanker; McCrae, John P. (2021-09-01)
    Inducing new translation pairs across dictionaries is an important task that facilitates processing and maintaining lexicographical data. This paper describes our submissions to the Translation Inference Across Dictionaries ...
  • Do city dashboards make sense? Conceptualising user experiences and challenges in using city dashboards. A case study 

    Vornhagen, Heike; Zarrouk, Manel; Davis, Brian; Young, Karen (Association for Computing Machinery (ACM), 2021-06-09)
    City dashboards present information about a city to a broad audience with some thought given as to how some of these audiences might understand the information. However, little research has looked at how ’citizens’ make ...
  • Understanding my city through dashboards. How hard can it be? 

    Vornhagen, Heike; Young, Karen; Zarrouk, Manel (International Federation for Information Processing (IFIP) and eJournal of eDemocracy and Open Government (JeDEM), 2019-09-02)
    This paper describes research into how current city dashboards support users' sensemaking processes. It uses criteria identified in previous research concerning visualisation and applies these to a number of city dashboards ...
  • Is downloading this App consistent with my values? Conceptualizing a value-centered privacy assistant 

    Carter, Sarah E. (Springer, Cham, 2021-08-25)
    Digital privacy notices aim to provide users with information to make informed decisions. They are, however, fraught with difficulties. Instead, I propose that data privacy decisions can be understood as an expression of ...
  • Sensemaking of complex sociotechnical systems: the case of governance dashboards 

    Vornhagen, Heike; Davis, Brian; Zarrouk, Manel (Association for Computing Machinery (ACM), 2018-05-30)
    This research project is concerned with developing a suitable visualization model to depict a complex socio-technical system such as a city. It focuses on governance dashboards as the main starting point as these aim to ...
  • Knowledge graph driven approach to represent video streams for spatiotemporal event pattern matching in complex event processing 

    Yadav, Piyush; Salwala, Dhaval; Das, Dibya Prakash; Curry, Edward (World Scientific Publishing, 2020)
    Complex Event Processing (CEP) is an event processing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, CEP is limited to process structured data stream. Video ...
  • Query-driven video event processing for the internet of multimedia things 

    Yadav, Piyush; Salwala, Dhaval; Arruda Pontes, Felipe; Dhingra, Praneet; Curry, Edward (VLDB Endowment, 2021-08)
    Advances in Deep Neural Network (DNN) techniques have revolutionized video analytics and unlocked the potential for querying and mining video event patterns. This paper details GNOSIS, an event processing platform to ...
  • Traffic prediction framework for OpenStreetMap using deep learning based complex event processing and open traffic cameras 

    Yadav, Piyush; Sarkar, Dipto; Salwala, Dhaval; Curry, Edward (Dagstuhl Research Online Publication Server (DROPS), 2020-09-25)
    Displaying near-real-time traffic information is a useful feature of digital navigation maps. However, most commercial providers rely on privacy-compromising measures such as deriving location information from cellphones ...
  • NUIG-DSI’s submission to the GEM Benchmark 2021 

    Pasricha, Nivranshu; Arcan, Mihael; Buitelaar, Paul (Association for Computational Linguistics, 2021-08-05)
    This paper describes the submission by NUIG-DSI to the GEM benchmark 2021. We participate in the modeling shared task where we submit outputs on four datasets for data-to-text generation, namely, DART, WebNLG (en), E2E and ...
  • VID-WIN: Fast video event matching with query-aware windowing at the edge for the internet of multimedia things 

    Yadav, Piyush; Salwala, Dhaval; Curry, Edward (Institute of Electrical and Electronics Engineers (IEEE), 2021-04-23)
    Efficient video processing is a critical component in many IoMT applications to detect events of interest. Presently, many window optimization techniques have been proposed in event processing with an underlying assumption ...
  • Toward distributed, global, deep learning using IoT devices 

    Sudharsan, Bharath; Patel, Pankesh; Breslin, John; Ali, Muhammad Intizar; Mitra, Karan; Dustdar, Schahram; Rana, Omer; Jayaraman, Prem Prakash; Ranjan, Rajiv (Institute of Electrical and Electronics Engineers (IEEE), 2021-07-20)
    Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed ...

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