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NUIG at TIAD 2021: Cross-lingual word embeddings for translation inference
(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
(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?
(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
(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
(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
(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
(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
(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
(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
(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
(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 ... -
End-to-end tracing and congestion in a blockchain: A supply chain use case in hyperledger fabric
(IGI Global, 2021)Modern supply chain applications are complex systems that play an important role in many different sectors. Supply chain management systems are implemented to handle increasing complexity and flows of goods. However, most ... -
RCE-NN: a five-stage pipeline to execute neural networks (CNNs) on resource-constrained IoT edge devices
(Association for Computing Machinery (ACM), 2020-10-06)Microcontroller Units (MCUs) in edge devices are resource constrained due to their limited memory footprint, fewer computation cores, and low clock speeds. These limitations constrain one from deploying and executing machine ... -
Edge2Train: A framework to train machine learning models (SVMs) on resource-constrained IoT edge devices
(Association for Computing Machinery (ACM), 2020-10-06)In recent years, ML (Machine Learning) models that have been trained in data centers can often be deployed for use on edge devices. When the model deployed on these devices encounters unseen data patterns, it will either ... -
Enabling machine learning on the edge using SRAM conserving efficient neural networks execution approach
(National University of Ireland Galway, 2021-09-13)Edge analytics refers to the application of data analytics and Machine Learning (ML) algorithms on IoT devices. The concept of edge analytics is gaining popularity due to its ability to perform AI-based analytics at the ... -
Enhancing multiple-choice question answering with causal knowledge
(Association for Computational Linguistics, 2021-06-10)The task of causal question answering aims to reason about causes and effects over a provided real or hypothetical premise. Recent approaches have converged on using transformer-based language models to solve question ... -
Ultra-fast machine learning classifier execution on IoT devices without SRAM consumption
(Institute of Electrical and Electronics Engineers (IEEE), 2021-05-25)With the introduction of edge analytics, IoT devices are becoming smart and ready for AI applications. A few modern ML frameworks are focusing on the generation of small-size ML models (often in kBs) that can directly be ... -
Porting and execution of anomalies detection models on embedded systems in IoT: Demo abstract
(Association for Computing Machinery (ACM), 2021-05-18)In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were shipped, and over 250 billion MCUs ... -
SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: Demo abstract
(Association for Computing Machinery (ACM), 2021-05-19)With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications. However, any increase in the training data results in a linear increase in the space complexity of the trained Machine ... -
TinyML benchmark: Executing fully connected neural networks on commodity microcontrollers
(National University of Ireland Galway, 2021-06-20)Recent advancements in the field of ultra-low-power machine learning (TinyML) promises to unlock an entirely new class of edge applications. However, continued progress is restrained by the lack of benchmarking Machine ...