Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Other Computer Engineering (33)
- Electrical and Computer Engineering (28)
- Digital Communications and Networking (26)
- Computer and Systems Architecture (17)
- Arts and Humanities (12)
-
- Music (9)
- Electrical and Electronics (8)
- Other Music (8)
- Signal Processing (7)
- Systems and Communications (7)
- Architectural Engineering (6)
- Architectural Technology (6)
- Architecture (6)
- Civil and Environmental Engineering (6)
- Computer Sciences (6)
- Construction Engineering (6)
- Construction Engineering and Management (6)
- Physical Sciences and Mathematics (6)
- Social and Behavioral Sciences (6)
- Civil Engineering (5)
- Other Civil and Environmental Engineering (5)
- Other Engineering (5)
- Education (4)
- Environmental Engineering (4)
- Robotics (4)
- Communication (3)
- Data Storage Systems (3)
- Databases and Information Systems (3)
- Keyword
-
- Data sonification (8)
- SDN (7)
- Machine Learning (6)
- Contour icons (5)
- Deep Learning (5)
-
- POLQA (5)
- Machine learning (4)
- NSIM (4)
- ViSQOL (4)
- VoIP (4)
- Auditory periphery model (3)
- Building Information Modelling (3)
- Deep learning (3)
- Dna sonification (3)
- Education (3)
- Ireland (3)
- Mobile devices (3)
- Objective Speech Quality (3)
- P.853 (3)
- Software quality (3)
- Speech (3)
- Technology (3)
- Active Learning (2)
- Artificial intelligence (2)
- Authentication (2)
- Avatar (2)
- BIM (2)
- Backdoors (2)
- Chaos (2)
- Charging Stations (2)
Articles 31 - 60 of 178
Full-Text Articles in Computer Engineering
A Tutoring Framework To Support Computer Science Programmes In Higher Education, Emer Thornbury, Frances Sheridan, Pramod Pathak, Cristina Hava Muntean, Paul Stynes
A Tutoring Framework To Support Computer Science Programmes In Higher Education, Emer Thornbury, Frances Sheridan, Pramod Pathak, Cristina Hava Muntean, Paul Stynes
Conference papers
Computing Support is the provision of academic supports such as individual tutoring and support classes to students studying computing at third level. Students can struggle with computing as it requires practice involving trial and error. This work proposes a research informed tutoring framework to support computer science students at third level. The tutoring framework combines three pillars; staff and training, pedagogies and activities. Support is put in place to help students develop technical and programming skills. Essential tutoring is provided for those who might otherwise drop out of college. The framework was applied to first and second-year undergraduate programmes and …
A Real-Time Machine Learning Framework For Smart Home-Based Yoga Teaching System, Jothika Sunney, Musfira Jilani, Pramod Pathak, Paul Stynes
A Real-Time Machine Learning Framework For Smart Home-Based Yoga Teaching System, Jothika Sunney, Musfira Jilani, Pramod Pathak, Paul Stynes
Conference papers
Practicing yoga poses in a home-based environment has increased due to Covid19. Yoga poses without a trainer can be challenging, and incorrect yoga poses can cause muscle damage. Smart home-based yoga teaching systems may aid in performing accurate yoga poses. However, the challenge with such systems is the computational time required to detect yoga poses. This research proposes a real-time machine learning framework for teaching accurate yoga poses. It combines a pose estimation model, a pose classification model, and a real-time feedback mechanism. The dataset consists of five popular yoga poses namely the downdog pose, the tree pose, the goddess …
Work In Progress: A Virtual Educational Robotics Coding Club Framework To Improve K-6 Students Emotional Engagement In Stem, Kate Carmody, Julie Booth, Jospehine Bleach, Pramod Pathak, Paul Styles
Work In Progress: A Virtual Educational Robotics Coding Club Framework To Improve K-6 Students Emotional Engagement In Stem, Kate Carmody, Julie Booth, Jospehine Bleach, Pramod Pathak, Paul Styles
Conference papers
The growing popularity and deployment of Internet of Things (IoT) devices has led to serious security concerns. The integration of a security operations center (SOC) becomes increasingly important in this situation to ensure the security of IoT devices. In this article, we will present a summary of IoT device security issues, their vulnerabilities, a review of current challenges to keep these devices secure, and discuss the role that SOC can bring in protecting IoT devices while considering the challenges encountered and the directions to consider when implementing a reliable SOC for IoT monitoring.
Robustness Of Image-Based Malware Classification Models Trained With Generative Adversarial Networks, Ciaran Reilly, Stephen O Shaughnessy, Christina Thorpe
Robustness Of Image-Based Malware Classification Models Trained With Generative Adversarial Networks, Ciaran Reilly, Stephen O Shaughnessy, Christina Thorpe
Conference papers
As malware continues to evolve, deep learning models are increasingly used for malware detection and classification, including image based classification. However, adversarial attacks can be used to perturb images so as to evade detection by these models. This study investigates the effectiveness of training deep learning models with Generative Adversarial Network-generated data to improve their robustness against such attacks. Two image conversion methods, byte plot and space-filling curves, were used to represent the malware samples, and a ResNet-50 architecture was used to train models on the image datasets. The models were then tested against a projected gradient descent attack. It …
Queer In Ai: A Case Study In Community-Led Participatory Ai, Anaelia Ovalle, Arjun Subramonian, Ashwiin Singh, Claas Voelcker, Danica Sutherland, Davide Locatelli, Eva Breznik, Felip Klubicka, Hang Yuan, Hetvi J, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Melind Agarwal, Nyx Mclean, Pan Xu, A. Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, S.T. John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew Mcnamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark
Queer In Ai: A Case Study In Community-Led Participatory Ai, Anaelia Ovalle, Arjun Subramonian, Ashwiin Singh, Claas Voelcker, Danica Sutherland, Davide Locatelli, Eva Breznik, Felip Klubicka, Hang Yuan, Hetvi J, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Melind Agarwal, Nyx Mclean, Pan Xu, A. Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, S.T. John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew Mcnamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark
Conference papers
Queerness and queer people face an uncertain future in the face of ever more widely deployed and invasive artificial intelligence (AI). These technologies have caused numerous harms to queer people, including privacy violations, censoring and downranking queer content, exposing queer people and spaces to harassment by making them hypervisible, deadnaming and outing queer people. More broadly, they have violated core tenets of queerness by classifying and controlling queer identities. In response to this, the queer community in AI has organized Queer in AI, a global, decentralized, volunteer-run grassroots organization that employs intersectional and community-led participatory design to build an inclusive …
Bilstm−Bigru: A Fusion Deep Neural Network For Predicting Air Pollutant Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan
Bilstm−Bigru: A Fusion Deep Neural Network For Predicting Air Pollutant Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan
Conference papers
Predicting air pollutant concentrations is an efficient way to prevent incidents by providing early warnings of harmful air pollutants. A precise prediction of air pollutant concentrations is an important factor in controlling and preventing air pollution. In this paper, we develop a bidirectional long-short-term memory and a bidirectional gated recurrent unit (BiLSTM−BiGRU) to predict PM 2.5 concentrations in a target city for different lead times. The BiLSTM extracts preliminary features, and the BiGRU further extracts deep features from air pollutant and meteorological data. The fully connected (FC) layer receives the output and makes an accurate prediction of the PM 2.5 …
Graph-Based Mutations For Music Generation, Maziar Kanani, Sean O'Leary, James Mcdermott
Graph-Based Mutations For Music Generation, Maziar Kanani, Sean O'Leary, James Mcdermott
Conference papers
Our study aims to compare the effects of direct mutation and graphbased mutation on representations of music domain. We focus on short tunes from the Irish folk tradition, represented as integer sequences, and use a graph-based representation based on Pathway Assembly (a directed acyclic graph) and the Sequitur algorithm. We define multiple mutation operators to work directly on the sequences or on the graphs, hypothesizing that graph-based mutations will tend to preserve the pattern used per tune, while direct mutation of sequences will tend to destroy patterns, resulting in new generated tunes that are more complex. We perform experiments on …
Combinedeepnet: A Deep Network For Multistep Prediction Of Near-Surface Pm2.5 Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan
Combinedeepnet: A Deep Network For Multistep Prediction Of Near-Surface Pm2.5 Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan
Conference papers
PM2.5 is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM2.5 ( μg/m3 ) concentration prediction may help reduce health concerns and provide early warnings. To better understand air pollution, a number of approaches have been presented for predicting PM2.5 concentrations. Previous research used deep learning models for hourly predictions of air pollutants due to their success in pattern recognition, however, these models were unsuitable for multisite, long-term predictions, particularly in regard to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep networks, including a …
Actor-Centric Spatio-Temporal Feature Extraction For Action Recognition, Anil Kunchala, Bianca Schoen-Phelan, Mélanie Bouroche
Actor-Centric Spatio-Temporal Feature Extraction For Action Recognition, Anil Kunchala, Bianca Schoen-Phelan, Mélanie Bouroche
Conference papers
Action understanding involves the recognition and detection of specific actions within videos. This crucial task in computer vision gained significant attention due to its multitude of applications across various domains. The current action detection models, inspired by 2D object detection methods, employ two-stage architectures. The first stage is to extract actor-centric video sub-clips, i.e. tubelets of individuals, and the second stage is to classify these tubelets using action recognition networks. The majority of these recognition models utilize a frame-level pre-trained 3D Convolutional Neural Networks (3D CNN) to extract spatio-temporal features of a given tubelet. This, however, results in suboptimal spatio-temporal …
Reality Analagous Synthetic Dataset Generation With Daylight Variance For Deep Learning Classification, Thomas Lee, Susan Mckeever, Jane Courtney
Reality Analagous Synthetic Dataset Generation With Daylight Variance For Deep Learning Classification, Thomas Lee, Susan Mckeever, Jane Courtney
Conference papers
For the implementation of Autonomously navigating Unmanned Air Vehicles (UAV) in the real world, it must be shown that safe navigation is possible in all real world scenarios. In the case of UAVs powered by Deep Learning algorithms, this is a difficult task to achieve, as the weak point of any trained network is the reduction in predictive capacity when presented with unfamiliar input data. It is possible to train for more use cases, however more data is required for this, requiring time and manpower to acquire. In this work, a potential solution to the manpower issues of exponentially scaling …
Towards Emulation Of Intelligent Iot Networks On Eu-Us Testbeds, Sachin Sharma, Saish Urumkar, Gianluca Fontanesi, Venkat Sai Suman Lamba Karanam, Boyang Hu, Byrav Ramamurthy, Avishek Nag
Towards Emulation Of Intelligent Iot Networks On Eu-Us Testbeds, Sachin Sharma, Saish Urumkar, Gianluca Fontanesi, Venkat Sai Suman Lamba Karanam, Boyang Hu, Byrav Ramamurthy, Avishek Nag
Conference papers
This paper introduces our project on experimental validation of intelligent Internet of Things (IoT) networks. The project is a part of the NGIAtlantic H2020 third open call to perform experiments on EU and US wireless testbeds. The project proposes five different experiments to be performed on EU/US testbeds: (1) automatic configuration/discovery of Software Defined Networking (SDN) in wireless IoT sensor networks, (2) Machine Learning (ML) assisted control and data traffic path discovery experiments, (3) GPU and Hadoop cluster assisted experiments for ML algorithms, (4) Inter-testbed experiments, and (5) Failure recovery intercity experiments. Further, initial experimentation on EU/US testbeds is explored …
Demonstrating Configuration Of Software Defined Networking In Real Wireless Testbeds, Saish Urumkar, Gianluca Fontanesi, Avishek Nag, Sachin Sharma
Demonstrating Configuration Of Software Defined Networking In Real Wireless Testbeds, Saish Urumkar, Gianluca Fontanesi, Avishek Nag, Sachin Sharma
Conference papers
Currently, several wireless testbeds are available to test networking solutions including Fed4Fire testbeds such as w-ilab. t and CityLab in the EU, and POWDER and COSMOS in the US. In this demonstration, we use the w-ilab.t testbed to set up a wireless ad-hoc Software-Defined Network (SDN). OpenFlow is used as an SDN protocol and is deployed using a grid wireless ad-hoc topology in w-ilab.t. In this paper, we demonstrate: (1) the configuration of a wireless ad-hoc network based on w-ilab.t and (2) the automatic deployment of OpenFlow in an ad-hoc wireless network where some wireless nodes are not directly connected …
Experimenting An Edge-Cloud Computing Model On The Gpulab Fed4fire Testbed, Vikas Tomer, Sachin Sharma
Experimenting An Edge-Cloud Computing Model On The Gpulab Fed4fire Testbed, Vikas Tomer, Sachin Sharma
Conference papers
There are various open testbeds available for testing algorithms and prototypes, including the Fed4Fire testbeds. This demo paper illustrates how the GPULAB Fed4Fire testbed can be used to test an edge-cloud model that employs an ensemble machine learning algorithm for detecting attacks on the Internet of Things (IoT). We compare experimentation times and other performance metrics of our model based on different characteristics of the testbed, such as GPU model, CPU speed, and memory. Our goal is to demonstrate how an edge-computing model can be run on the GPULab testbed. Results indicate that this use case can be deployed seamlessly …
Addressing The "Leaky Pipeline": A Review And Categorisation Of Actions To Recruit And Retain Women In Computing Education, Alina Berry, Susan Mckeever, Brenda Murphy, Sarah Jane Delany
Addressing The "Leaky Pipeline": A Review And Categorisation Of Actions To Recruit And Retain Women In Computing Education, Alina Berry, Susan Mckeever, Brenda Murphy, Sarah Jane Delany
Conference papers
Gender imbalance in computing education is a well-known issue around the world. For example, in the UK and Ireland, less than 20% of the student population in computer science, ICT and related disciplines are women. Similar figures are seen in the labour force in the field across the EU. The term "leaky pipeline"; is often used to describe the lack of retention of women before they progress to senior roles. Numerous initiatives have targeted the problem of the leaky pipeline in recent decades. This paper provides a comprehensive review of initiatives related to techniques used to boost recruitment and improve …
Generating Reality-Analogous Datasets For Autonomous Uav Navigation Using Digital Twin Areas, Thomas Lee, Susan Mckeever, Jane Courtney
Generating Reality-Analogous Datasets For Autonomous Uav Navigation Using Digital Twin Areas, Thomas Lee, Susan Mckeever, Jane Courtney
Conference papers
In order for autonomously navigating Unmanned Air Vehicles(UAVs) to be implemented in day-to-day life, proof of safe operation will be necessary for all realistic navigation scenarios. For Deep Learning powered navigation protocols, this requirement is challenging to fulfil as the performance of a network is impacted by how much the test case deviates from data that the network was trained on. Though networks can generalise to manage multiple scenarios in the same task, they require additional data representing those cases which can be costly to gather. In this work, a solution to this data acquisition problem is suggested by way …
Machine Learning For Accessible Web Navigation, Tlamelo W. Makati
Machine Learning For Accessible Web Navigation, Tlamelo W. Makati
Conference papers
This research looks at the application of Machine Learning to Web Accessibility. It considers how Machine Learning (ML) can be used to help make the processes of Web Navigation more accessible in line with Web Content Accessibility Guideline (WCAG) 2.4 Navigable, which demands that ways be provided "to help users navigate, find content, and determine where they are." ML techniques such as reinforcement learning have been applied to website navigation in diverse ways. These include goal-directed search to answer questions and task-oriented problems such as booking a flight. Related work includes Web Automation and Testing. These techniques typically involve a …
Formulating Automated Responses To Cognitive Distortions For Cbt Interactions, Ignacio De Toledo, Giancarlo Salton, Robert J. Ross
Formulating Automated Responses To Cognitive Distortions For Cbt Interactions, Ignacio De Toledo, Giancarlo Salton, Robert J. Ross
Conference papers
One of the key ideas of Cognitive Behavioural Therapy (CBT) is the ability to convert negative or distorted thoughts into more realistic alternatives. Although modern machine learning techniques can be successfully applied to a variety of Natural Language Processing tasks, including Cognitive Behavioural Therapy, the lack of a publicly available dataset makes supervised training difficult for tasks such as reforming distorted thoughts. In this research, we constructed a small CBT dataset via crowd-sourcing, and leveraged state of the art pre-trained architectures to transform cognitive distortions, producing text that is relevant and more positive than the original negative thoughts. In particular, …
Finding Bert’S Idiomatic Key, Vasudevan Nedumpozhimana, John Kelleher
Finding Bert’S Idiomatic Key, Vasudevan Nedumpozhimana, John Kelleher
Conference papers
Sentence embeddings encode information relating to the usage of idioms in a sentence. This paper reports a set of experiments that combine a probing methodology with input masking to analyse where in a sentence this idiomatic information is taken from, and what form it takes. Our results indicate that BERT’s idiomatic key is primarily found within an idiomatic expression, but also draws on information from the surrounding context. Also, BERT can distinguish between the disruption in a sentence caused by words missing and the incongruity caused by idiomatic usage.
Bayesian Adaptive Path Allocation Techniques For Intra-Datacenter Workloads, Ali Malik, Ruairí De Fréin, Chih-Heng Ke, Hasanen Alyasiri, Obinna Izima
Bayesian Adaptive Path Allocation Techniques For Intra-Datacenter Workloads, Ali Malik, Ruairí De Fréin, Chih-Heng Ke, Hasanen Alyasiri, Obinna Izima
Conference papers
Data center networks (DCNs) are the backbone of many cloud and Internet services. They are vulnerable to link failures, that occur on a daily basis, with a high frequency. Service disruption due to link failure may incur financial losses, compliance breaches and reputation damage. Performance metrics such as packet loss and routing flaps are negatively affected by these failure events. We propose a new Bayesian learning approach towards adaptive path allocation that aims to improve DCN performance by reducing both packet loss and routing flaps ratios. The proposed approach incorporates historical information about link failure and usage probabilities into its …
A Comparison Of Risk Factors And Risk Models For Stroke By Age Group Using Tilda Data, Elizabeth Hunter, John D. Kelleher
A Comparison Of Risk Factors And Risk Models For Stroke By Age Group Using Tilda Data, Elizabeth Hunter, John D. Kelleher
Conference papers
Models to predict stroke risk with the aim of stroke prevention often use age as a factor in the model (Choudhury et al., 2015; Conroy et al., 2003; D’Agostino etal., 2008; Wolf et al., 1991). However, stroke risk scores often underestimate risk
for specific age groups, particularly younger age groups and the contribution of different risk factors to overall stroke risk changes over time (Boehme et al., 2017; Seshadri et al., 2006). Additionally, because age is a strong predictor of stroke, age can dominate the risk score (Leening et al., 2017). Longitudinal Studies such as the Irish Longitudinal study on …
Codec-Aware Video Delivery Over Sdns, Obinna Izima, Ruairi Defrein, Ali Malik
Codec-Aware Video Delivery Over Sdns, Obinna Izima, Ruairi Defrein, Ali Malik
Conference papers
To guarantee quality of delivery for video streaming over software defined networks, efficient predictors and adaptive routing frameworks are required. We demonstrate an agent that predicts video quality of delivery metrics in a scalable way using a bespoke codec-aware learning model. We also demonstrate the integration of this agent with an adaptive framework for centrally controlled software-defined networks that re-configures network operational paths in response to the learning agent, ensuring that good quality of delivery of video is maintained during periods of congestion. The demo scenario highlights the feasibility, scalability and accuracy of the framework
Detecting Interlocutor Confusion In Situated Human-Avatar Dialogue: A Pilot Study, Na Li, John D. Kelleher, Robert J. Ross
Detecting Interlocutor Confusion In Situated Human-Avatar Dialogue: A Pilot Study, Na Li, John D. Kelleher, Robert J. Ross
Conference papers
In order to enhance levels of engagement with conversational systems, our long term research goal seeks to monitor the confusion state of a user and adapt dialogue policies in response to such user confusion states. To this end, in this paper, we present our initial research centred on a user-avatar dialogue scenario that we have developed to study the manifestation of confusion and in the long term its mitigation. We present a new definition of confusion that is particularly tailored to the requirements of intelligent conversational system development for task-oriented dialogue. We also present the details of our Wizard-of-Oz based …
Moving Targets: Addressing Concept Drift In Supervised Models For Hacker Communication Detection, Susan Mckeever, Brian Keegan, Andrei Quieroz
Moving Targets: Addressing Concept Drift In Supervised Models For Hacker Communication Detection, Susan Mckeever, Brian Keegan, Andrei Quieroz
Conference papers
Abstract—In this paper, we are investigating the presence of concept drift in machine learning models for detection of hacker communications posted in social media and hacker forums. The supervised models in this experiment are analysed in terms of performance over time by different sources of data (Surface web and Deep web). Additionally, to simulate real-world situations, these models are evaluated using time-stamped messages from our datasets, posted over time on social media platforms. We have found that models applied to hacker forums (deep web) presents an accuracy deterioration in less than a 1-year period, whereas models applied to Twitter (surface …
A Proactive-Restoration Technique For Sdns, Ali Malik, Ruairí De Fréin
A Proactive-Restoration Technique For Sdns, Ali Malik, Ruairí De Fréin
Conference papers
Failure incidents result in temporarily preventing the network from delivering services properly. Such a deterioration in services called service unavailability. The traditional fault management techniques, i.e. protection and restoration, are inevitably concerned with service unavailability due to the convergence time that is required to achieve the recovery when a failure occurs. However, with the global view feature of software-defined networking a failure prediction is becoming attainable, which in turn reduces the service interruptions that originated by failures. In this paper, we propose a proactive restoration technique that reconfigure the vulnerable routes which are likely to be affected if the …
Intelligent Sdn Traffic Classification Using Deep Learning: Deep-Sdn, Ali Malik, Ruairí De Fréin, Mohammed Al-Zeyadi, Javier Andreu-Perez
Intelligent Sdn Traffic Classification Using Deep Learning: Deep-Sdn, Ali Malik, Ruairí De Fréin, Mohammed Al-Zeyadi, Javier Andreu-Perez
Conference papers
Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control …
Exploration Of Approaches To Arabic Named Entity Recognition, Husamelddin Balla, Sarah Jane Delany
Exploration Of Approaches To Arabic Named Entity Recognition, Husamelddin Balla, Sarah Jane Delany
Conference papers
Abstract. The Named Entity Recognition (NER) task has attracted significant attention in Natural Language Processing (NLP) as it can enhance the performance of many NLP applications. In this paper, we compare English NER with Arabic NER in an experimental way to investigate the impact of using different classifiers and sets of features including language-independent and language-specific features. We explore the features and classifiers on five different datasets. We compare deep neural network architectures for NER with more traditional machine learning approaches to NER. We discover that most of the techniques and features used for English NER perform well on Arabic …
Sla-Aware Routing Strategy For Multi-Tenant Software-Defined Networks, Ali Malik, Ruairí De Fréin
Sla-Aware Routing Strategy For Multi-Tenant Software-Defined Networks, Ali Malik, Ruairí De Fréin
Conference papers
A crucial requirement for the network service provider is to satisfy the Service Level Agreements (SLA) that it has made with its customers. Coexisting network tenants may have agreed different SLAs, and thus, the service provider must be able to provide QoS differentiation in order to meet his contractual commitments. Current one-size-fits-all routing models are not appropriate for all network tenants if their individual SLA requirements are to be efficiently met. We propose a SDN-based multi-cost routing approach which allocates network resources based on a portfolio of tenant SLA, which achieves the goal of accommodating multiple tenants, given their SLAs. …
Active Learning For Auditory Hierarchy, William Coleman, Sarah Jane Delany, Charlie Cullen, Ming Yan
Active Learning For Auditory Hierarchy, William Coleman, Sarah Jane Delany, Charlie Cullen, Ming Yan
Conference papers
Much audio content today is rendered as a static stereo mix: fundamentally a fixed single entity. Object-based audio envisages the delivery of sound content using a collection of individual sound ‘objects’ controlled by accompanying metadata. This offers potential for audio to be delivered in a dynamic manner providing enhanced audio for consumers. One example of such treatment is the concept of applying varying levels of data compression to sound objects thereby reducing the volume of data to be transmitted in limited bandwidth situations. This application motivates the ability to accurately classify objects in terms of their ‘hierarchy’. That is, whether …
Brexit Election: Forecasting A Conservative Party Victory Through The Pound Using Arima And Facebook's Prophet, James Usher, Pierpaolo Dondio
Brexit Election: Forecasting A Conservative Party Victory Through The Pound Using Arima And Facebook's Prophet, James Usher, Pierpaolo Dondio
Conference papers
On the 30th October, 2019, the markets watched as British Prime Minister, Boris Johnson, took a massive political gamble to call a general election to break the Withdrawal Agreement stalemate in the House of Commons to “Get BREXIT Done”. The pound had been politically sensitive owing to BREXIT uncertainty. With the polls indicating a Conservative win on 4thDecember, 2019, the margin of victory could be observed through increases in the pound. The outcome of a Conservative party victory would benefit the pound by removing the current market turbulence. We look to provide a short-term forecast of the pound. Our approach …
Android Compcache Based On Graphics Processing Unit, Muder Almi'ani, Abdu Razaque, Saleh Atiewi, Mohammed Alweshah, Ayman Al-Dmour, Basel Magableh
Android Compcache Based On Graphics Processing Unit, Muder Almi'ani, Abdu Razaque, Saleh Atiewi, Mohammed Alweshah, Ayman Al-Dmour, Basel Magableh
Conference papers
Android systems have been successfully developed to meet the demands of users. The following four methods are used in Android systems for memory management: backing swap, CompCache, traditional Linux swap, and low memory killer. These memory management methods are fully functioning.
However, Android phones cannot swap memory into solid-state drives, thus slowing the processor and reducing storage lifetime. In addition, the compression and decompression processes consume additional energy and latency. Therefore, the CompCache requires an extension. An extended Android CompCache using a graphics processing unit to compress and decompress memory pages on demand and reduce the latency is introduced in …