| 1. |
- Mitra, Karan, et al.
(författare)
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ALPINE : A Bayesian System For Cloud Performance Diagnosis And Prediction
- 2017
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Ingår i: 2017 IEEE International Conference on Services Computing (SCC). - Piscataway, NJ : IEEE. - 9781538620052 ; , s. 281-288
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Konferensbidrag (refereegranskat)abstract
- Cloud performance diagnosis and prediction is a challenging problem due to the stochastic nature of the cloud systems. Cloud performance is affected by a large set of factors such as virtual machine types, regions, workloads, wide area network delay and bandwidth. Therefore, necessitating the determination of complex relationships between these factors. The current research in this area does not address the challenge of modeling the uncertain and complex relationships between these factors. Further, the challenge of cloud performance prediction under uncertainty has not garnered sufficient attention. This paper proposes, develops and validates ALPINE, a Bayesian system for cloud performance diagnosis and prediction. ALPINE incorporates Bayesian networks to model uncertain and complex relationships between several factors mentioned above. It handles missing, scarce and sparse data to diagnose and predict stochastic cloud performance efficiently. We validate our proposed system using extensive real data and show that it predicts cloud performance with high accuracy of 91.93%.
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| 2. |
- Nugent, Christopher, et al.
(författare)
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Improving the Quality of User Generated Data Sets for Activity Recognition
- 2016
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Ingår i: Ubiquitous Computing and Ambient Intelligence, UCAMI 2016, PT II. - Amsterdam : Springer Publishing Company. - 9783319487991 - 9783319487984 ; , s. 104-110
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Konferensbidrag (refereegranskat)abstract
- It is fully appreciated that progress in the development of data driven approaches to activity recognition are being hampered due to the lack of large scale, high quality, annotated data sets. In an effort to address this the Open Data Initiative (ODI) was conceived as a potential solution for the creation of shared resources for the collection and sharing of open data sets. As part of this process, an analysis was undertaken of datasets collected using a smart environment simulation tool. A noticeable difference was found in the first 1-2 cycles of users generating data. Further analysis demonstrated the effects that this had on the development of activity recognition models with a decrease of performance for both support vector machine and decision tree based classifiers. The outcome of the study has led to the production of a strategy to ensure an initial training phase is considered prior to full scale collection of the data.
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| 3. |
- Balouji, Ebrahim, 1985, et al.
(författare)
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A LSTM-based Deep Learning Method with Application to Voltage Dip Classification
- 2018
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Ingår i: 2018 18TH INTERNATIONAL CONFERENCE ON HARMONICS AND QUALITY OF POWER (ICHQP). - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2164-0610. - 9781538605172 - 9781538605172 ; 2018-May
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Konferensbidrag (refereegranskat)abstract
- In this paper, a deep learning (DL)-based method for automatic feature extraction and classification of voltage dips is proposed. The method consists of a dedicated architecture of Long Short-Term Memory (LSTM), which is a special type of Recurrent Neural Networks (RNNs). A total of 5982 three-phase one-cycle voltage dip RMS sequences, measured from several countries, has been used in our experiments. Our results have shown that the proposedmethod is able to classify the voltage dips from learned features in LSTM, with 93.40% classification accuracy on the test data set. The developed architecture is shown to be novel for feature learning and classification of voltage dips. Different from the conventional machine learning methods, the proposed method is able to learn dip features without requiring transition-event segmentation, selecting thresholds, and using expert rules or human expert knowledge, when a large amount of measurement data is available. This opens a new possibility of exploiting deep learning technology for power quality data analytics and classification.
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| 4. |
- Pilipiec, Patrick, et al.
(författare)
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Surveillance of communicable diseases using social media: A systematic review
- 2023
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 18:2
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Forskningsöversikt (refereegranskat)abstract
- BackgroundCommunicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media.ObjectiveTo conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases.MethodologyBroad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.ResultsTwenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation.ConclusionText mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.
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| 5. |
- Greef, T.J., et al.
(författare)
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Connected media and presence
- 2013
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Ingår i: SAM 2013. - New York, NY, USA : ACM Press. - 9781450323949 ; , s. 43-48
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Konferensbidrag (refereegranskat)abstract
- Effective design of shared mediated spaces, information and connectedness requires theory and practice from a range of disciplines such as found in European projects like Together Anywhere, Together Anytime (TA2) and the EIT ICT Labs Mediating Presence activity. Building on this work we continue to investigate the changes in the European digital media industry such as changed traditional distribution of media content and the progressive integration of (social) communication means in information distribution and shared mediated spaces. Our past research has given valuable insights in how to design and evaluate systems and services that provide a high quality of experience, in how trust is established in mediated environments, and how the formation of tacit communication between participants in new distributed and connected media is negotiated. In the new Seventh Framework Program project COnnected Media and Presence from European Institute of Technology (COMPEIT) we aim to enhance the quality of experience in face-to-face and broadcast communication further in three domains: 1) Spatial connectedness, 2) Social connectedness and 3) Information connectedness, by developing three key services: Shared Experience with Tangible Interaction (SETI); Broadcast Presence Studio (BPS) and Mixed-Reality Interaction (MRI). The quality of experience of these services will be enhanced in terms of for example: spatial connectedness, by providing shared spaces and supporting spatial features such as eye contact; social connectedness, by using natural means for interaction suiting different settings and activities; and information connectedness, by providing better means to share, manipulate and use information suiting different task or activities. In this paper we will discuss the background of this work and give an overview of our planned future work in COMPEIT.
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| 6. |
- Kabir, Sami, PhD Student, et al.
(författare)
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An Integrated Approach of Belief Rule Base and Convolutional Neural Network to Monitor Air Quality in Shanghai
- 2022
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 206
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Tidskriftsartikel (refereegranskat)abstract
- Accurate monitoring of air quality can reduce its adverse impact on earth. Ground-level sensors can provide fine particulate matter (PM2.5) concentrations and ground images. But, such sensors have limited spatial coverage and require deployment cost. PM2.5 can be estimated from satellite-retrieved Aerosol Optical Depth (AOD) too. However, AOD is subject to uncertainties associated with its retrieval algorithms and constrain the spatial resolution of estimated PM2.5. AOD is not retrievable under cloudy weather as well. In contrast, satellite images provide continuous spatial coverage with no separate deployment cost. Accuracy of monitoring from such satellite images is hindered due to uncertainties of sensor data of relevant enviromental parameters, such as, relative humidity, temperature, wind speed and wind direction . Belief Rule Based Expert System (BRBES) is an efficient algorithm to address these uncertainties. Convolutional Neural Network (CNN) is suitable for image analytics. Hence, we propose a novel model by integrating CNN with BRBES to monitor air quality from satellite images with improved accuracy. We customized CNN and optimized BRBES to increase monitoring accuracy further. An obscure image has been differentiated between polluted air and cloud in our model. Valid environmental data (temperature, wind speed and wind direction) have been adopted to further strengthen the monitoring performance of our proposed model. Three-year observation data (satellite images and environmental parameters) from 2014 to 2016 of Shanghai have been employed to analyze and design our proposed model. The results conclude that the accuracy of our model to monitor PM2.5 of Shanghai is higher than only CNN and other conventional Machine Learning methods. Real-time validation of our model on near real-time satellite images of April-2021 of Shanghai shows average difference between our calculated PM2.5 concentrations and the actual one within ±5.51.
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| 7. |
- Synnes, Kåre, 1969-, et al.
(författare)
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CyberParks Songs and Stories : Enriching Public Spaces with Localized Culture Heritage Material such as Digitized Songs and Stories
- 2019
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Ingår i: CyberParks - The Interface Between People, Places and Technology. - Cham : Springer. - 9783030134167 ; , s. 224-237
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Bokkapitel (refereegranskat)abstract
- This chapter offers theoretical considerations and reflections on technological solutions that contribute to digitally supported documentation, access and reuse of localised heritage content in public spaces. It addresses immaterial cultural heritage, including informal stories that could emerge and be communicated by drawing hyperlinks between digitised assets, such as songs, images, drawings, texts and more, and not yet documented metadata, as well as augmenting interaction opportunities with interactive elements that relate to multiple media stored in databases and archives across Europe. The aim is to enable cultural heritage to be experienced in novel ways, supported by the proliferation of smartphones and ubiquitous Internet access together with new technical means for user profiling, personalisation, localisation, contextawareness and gamification. The chapter considers cyberparks as digitally enhanced public spaces for accessing and analyzing European cultural heritage and for enriching the interpretation of the past, along with theoretical ramifications and technological limitations. It identifies the capacities of a proposed digital environment together with design guidelines for interaction with cultural heritage assets in public spaces. The chapter concludes with describing a taxonomy of digital content that can be used in order to enhance association and occupation conditions of public spaces, and with discussing technological challenges associated with enriching public spaces with localized cultural heritage material.
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| 8. |
- Tadaros, Marduch, et al.
(författare)
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A Hybrid Clustered Ant Colony Optimization Approach for the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem with Service Times
- 2024
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Ingår i: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; :190
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, the Hierarchical Multi Switch Multi Echelon Vehicle Routing Problem with Service Times (HMSME-VRP-ST) is presented. This novel problem considers distribution applications in which goods are delivered either directly from a central depot or through intermediate facilities called switch points. Commodities are loaded into interchangeable containers called swap bodies at the central depot, and can be transferred from one vehicle to another at the switch points. The goal is to minimize fixed and variable costs of the vehicle fleet and swap-bodies while serving a predetermined set of customers. The routes are constrained by time and swap bodies by loading capacity. A mathematical model of the HMSME-VRP-ST is presented, and a Hybrid Clustered Ant Colony Optimization (HCACO) algorithm is proposed to address the complexity of the problem. The approach utilizes the ant-based clustering algorithm, combining the density and connectivity properties of clustering for creating promising neighborhoods with the solution construction methodology and pheromone-based memory of the Ant Colony Optimization framework. Additionally, two local search schemes based on Variable Neighborhood Descent are incorporated to further improve the generated solutions. The behavior of each HCACO variant is analyzed, and their results are compared to a Greedy Randomized Adaptive Search Procedure metaheuristic in 36 newly generated benchmarks comprising of clustered, uniformly random, and mixed clustered-random instances.
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| 9. |
- Bagheri, Azam, et al.
(författare)
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A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances
- 2022
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Ingår i: Energies. - : MDPI. - 1996-1073. ; 15:4
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data.
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| 10. |
- Adewumi, Tosin, 1978-, et al.
(författare)
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Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
- 2022
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Ingår i: Proceedings of the 13th Language Resources and Evaluation Conference. - : European Language Resources Association (ELRA). ; , s. 689-696
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Konferensbidrag (refereegranskat)abstract
- We present a fairly large, Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work. To the best of the authors’ knowledge, this is the first idioms corpus with classes of idioms beyond the literal and the general idioms classification. Inparticular, the following classes are labelled in the dataset: metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal. We obtain an overall inter-annotator agreement (IAA) score, between two independent annotators, of 88.89%. Many past efforts have been limited in the corpus size and classes of samples but this dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). The corpus may also be extended by researchers to meet specific needs. The corpus has part of speech (PoS) tagging from the NLTK library. Classification experiments performed on the corpus to obtain a baseline and comparison among three common models, including the state-of-the-art (SoTA) BERT model, give good results. We also make publicly available the corpus and the relevant codes for working with it for NLP tasks.
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