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Subject = big data;
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Displaying Results 1 - 25 of 74 on page 1 of 3
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‘Hacking multitude’ and Big Data: Some insights from the Turkish ‘digital coup’
(2015)
Cardullo, Paolo
‘Hacking multitude’ and Big Data: Some insights from the Turkish ‘digital coup’
(2015)
Cardullo, Paolo
Abstract:
The paper presents my first findings and reflections on how ordinary people may opportunistically and unpredictably respond to Internet censorship and tracking. I try to capture this process with the concept of ‘hacking multitude'. Working on a case study of the Turkish government's block of the social media platform Twitter (March 2014), I argue that during systemic data choke-points, a multitude of users might acquire a certain degree of reflexivity over ubiquitous software of advanced techno-capitalism. Resisting naïve parallels between urban streets and virtual global streets, the article draws on Fuller's ‘media ecologies' to make sense of complex and dynamic interactions between processes and materialities, strategies of communication and mundane practices. Such a dense space is mostly invisible to network and traffic analysis, although it comes alive under the magnifying lens of digital ethnography. As the Turkish government tried to stop protesters on bot...
http://mural.maynoothuniversity.ie/9335/
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A Big Data Approach for 3D Building Extraction from Aerial Laser Scanning
(2016)
Aljumaily, Harith; Laefer, Debra F.; Cuadra, Dolores
A Big Data Approach for 3D Building Extraction from Aerial Laser Scanning
(2016)
Aljumaily, Harith; Laefer, Debra F.; Cuadra, Dolores
Abstract:
This paper proposes a Big Data approach to automatically identify and extract buildings from a digital surface model created from aerial laser scanning data. The approach consists of two steps. The first step is a MapReduce process where neighboring points in a digital surface model are mapped into cubes. The second step uses a non-MapReduce algorithm first to remove trees and other obstructions and then to extract adjacent cubes. According to this approach, all adjacent cubes belong to the same object and an object is a set of adjacent cubes that belong to one or more adjacent buildings. Finally, an evaluation study is presented for a section of Dublin, Ireland to demonstrate the applicability of the approach resulting in a 92% quality level for the extraction of 106 buildings over 1 km2 including buildings that had more than 10 adjacent components of different heights and complicated roof geometries. The proposed approach is notable not only for its Big Data context but its usage ...
http://hdl.handle.net/10197/7450
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A cloud reservation system for big data applications
(2017)
Marinescu, Dan C.; Paya, Ashkan; Morrison, John P.
A cloud reservation system for big data applications
(2017)
Marinescu, Dan C.; Paya, Ashkan; Morrison, John P.
Abstract:
Emerging Big Data applications increasingly require resources beyond those available from a single server and may be expressed as a complex workflow of many components and dependency relationships-each component potentially requiring its own specific, and perhaps specialized, resources for its execution. Efficiently supporting this type of Big Data application is a challenging resource management problem for existing cloud environments. In response, we propose a two-stage protocol for solving this resource management problem. We exploit spatial locality in the first stage by dynamically forming rack-level coalitions of servers to execute a workflow component. These coalitions only exist for the duration of the execution of their assigned component and are subsequently disbanded, allowing their resources to take part in future coalitions. The second stage creates a package of these coalitions, designed to support all the components in the complete workflow. To minimize the communicat...
http://hdl.handle.net/10468/8414
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A real-time visual dashboard for Wikidata edits
(2020)
Graux, Damien; Orlandi, Fabrizio; Lynch, Brian; Mahon, Isobel; Mullen, Odhran; Mahon, A...
A real-time visual dashboard for Wikidata edits
(2020)
Graux, Damien; Orlandi, Fabrizio; Lynch, Brian; Mahon, Isobel; Mullen, Odhran; Mahon, Alex; Molnar, Flora; Mantiquilla, Lexes
Abstract:
During the last decades, the Web has seen the development of openly editable datasets on which users can suggest modifications at any moment. Recently, Wikidata as been the first large-scale Mediawiki-based dataset structured according to the Semantic Web standards. In this article, we propose the first version of a visual dashboard to allow real-time visualisation of Wikidata changes.
http://hdl.handle.net/2262/94268
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Algorithmic governance: Developing a research agenda through the power of collective intelligence
(2017)
Danaher, John; Hogan, Michael J.; Noone, Chris; Kennedy, Rónán; Behan, Anthony; De Paor...
Algorithmic governance: Developing a research agenda through the power of collective intelligence
(2017)
Danaher, John; Hogan, Michael J.; Noone, Chris; Kennedy, Rónán; Behan, Anthony; De Paor, Aisling; Felzmann, Heike; Haklay, Muki; Khoo, Su-Ming; Morison, John; Murphy, Maria Helen; O'Brolchain, Niall; Schafer, Burkhard; Kalpana, Shankar
Abstract:
We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider (a) barriers to legitimate and effective algorithmic governance and (b) the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplina...
http://hdl.handle.net/10379/7071
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Algorithmic governance: developing a research agenda through the power of collective intelligence
(2018)
Danaher, John; Hogan, Michael J; Noone, Chris; Kennedy, Rónán; Behan, Anthony; De Paor,...
Algorithmic governance: developing a research agenda through the power of collective intelligence
(2018)
Danaher, John; Hogan, Michael J; Noone, Chris; Kennedy, Rónán; Behan, Anthony; De Paor, Aisling; Felzmann, Heike; Haklay, Muki; Khoo, Su-Ming; Morison, John; Murphy, Maria Helen; O'Brolchain, Niall; Schafer, Burkhard; Shankar, Kalpana
Abstract:
We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider (a) barriers to legitimate and effective algorithmic governance and (b) the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplina...
http://hdl.handle.net/10379/11052
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Algorithmic governance: Developing a research agenda through the power of collective intelligence
(2018)
Danaher, John; Hogan, Michael J.; Noone, Chris; Shankar, Kalpana; et al.
Algorithmic governance: Developing a research agenda through the power of collective intelligence
(2018)
Danaher, John; Hogan, Michael J.; Noone, Chris; Shankar, Kalpana; et al.
Abstract:
We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider (a) barriers to legitimate and effective algorithmic governance and (b) the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplina...
http://hdl.handle.net/10197/9193
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BDTest, a System to Test Big Data Frameworks
(2017)
Langeois, Alexandre; Cunha de Almeida, Eduardo; Ventresque, Anthony
BDTest, a System to Test Big Data Frameworks
(2017)
Langeois, Alexandre; Cunha de Almeida, Eduardo; Ventresque, Anthony
Abstract:
10th IEEE International Conference on Software Testing, Verification and Validation Workshops (ICST), Tokyo, Japan, 13-17 March 2017
Testing Big Data Processing systems is a challenging task as these systems are usually distributed on various virtual machines (potentially hosted by remote servers). In this poster we present a platform for testing non-functional properties of Big Data framework and a first implementation with Hadoop, a well known big data management and processing platform.
Science Foundation Ireland
Lero
http://hdl.handle.net/10197/8418
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Big Data
(2017)
Kitchin, Rob
Big Data
(2017)
Kitchin, Rob
http://mural.maynoothuniversity.ie/12779/
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Big Data – Hype or Revolution?
(2016)
Kitchin, Rob
Big Data – Hype or Revolution?
(2016)
Kitchin, Rob
Abstract:
Abstract included in text.
http://mural.maynoothuniversity.ie/12775/
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Big data and human geography: Opportunities, challenges and risks
(2013)
Kitchin, Rob
Big data and human geography: Opportunities, challenges and risks
(2013)
Kitchin, Rob
Abstract:
We are entering an era of big data – data sets that are characterised by high volume, velocity, variety, exhaustivity, resolution and indexicality, relationality and flexibility. Much of these data are spatially and temporally referenced and offer many possibilities for enhancing geographical understanding, including for post-positivist scholars. Big data also, however, poses a number of challenges and risks to geographic scho- larship and raises a number of taxing epistemological, methodological and ethical questions. Geographers need to grasp the opportunities whilst at the same time tackling the challenges, ameliorating the risks and thinking critically about big data as well as conducting big data studies. Failing to do so could be quite costly as the discipline gets left behind as others leverage insights from the growing data deluge.
http://mural.maynoothuniversity.ie/5366/
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Big data and official statistics: Opportunities, challenges and risks. Programmable City Working Paper 9
(2015)
Kitchin, Rob
Big data and official statistics: Opportunities, challenges and risks. Programmable City Working Paper 9
(2015)
Kitchin, Rob
Abstract:
The development of big data is set to be a significant disruptive innovation in the production of official statistics offering a range of opportunities, challenges and risks to the work of national statistical institutions (NSIs). This paper provides a synoptic overview of these issues in detail, mapping out the various pros and cons of big data for producing official statistics, examining the work to date by NSIs in formulating a strategic and operational response to big data, and plotting some suggestions with respect to on-going change management needed to address the use of big data for official statistics.
http://mural.maynoothuniversity.ie/7231/
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Big Data Governance needs more collective responsibility: The role of harm mitigation in the governance of data use in medicine and beyond
(2019)
McMahon, Aisling; Buyx, Alena; Prainsack, Barbara
Big Data Governance needs more collective responsibility: The role of harm mitigation in the governance of data use in medicine and beyond
(2019)
McMahon, Aisling; Buyx, Alena; Prainsack, Barbara
Abstract:
Harms arising from digital data use in the big data context are often systemic and cannot always be captured by linear cause and effect. Individual data subjects and third parties can bear the main downstream costs arising from increasingly complex forms of data uses—without being able to trace the exact data flows. Because current regulatory frameworks do not adequately address this situation, we propose a move towards harm mitigation tools to complement existing legal remedies. In this article, we make a normative and practical case for why individuals should be offered support in such contexts and how harm mitigation tools can achieve this. We put forward the idea of ‘Harm Mitigation Bodies’ (HMBs), which people could turn to when they feel they were harmed by data use but do not qualify for legal remedies, or where existing legal remedies do not address their specific circumstances. HMBs would help to obtain a better understanding of the nature, severity, and frequency of harms ...
http://mural.maynoothuniversity.ie/11637/
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Big Data, new epistemologies and paradigm shifts
(2014)
Kitchin, Rob
Big Data, new epistemologies and paradigm shifts
(2014)
Kitchin, Rob
Abstract:
This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemol- ogies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering para- digm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense of culture, history, economy and society. It is argued that: (1) Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and (2) there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research...
http://mural.maynoothuniversity.ie/5364/
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Big Data: A framework for research
(2014)
Nagle, Tadhg; Sammon, David
Big Data: A framework for research
(2014)
Nagle, Tadhg; Sammon, David
Abstract:
Big Data is not the first and most definitely not the last new term that the IT industry is going to coin in order to drive interest and investment in new technology. Moreover, with these new terms, an opportunity is afforded for the research community to objectively understand the impact (or lack thereof) on organizations and decision makers. This paper provides a high-level framework to guide researchers in the area of Big Data through a conceptualization of the Information Supply Chain. The Information Supply Chain can be used as a scoping device for researchers in positioning their work but also as a tool to enable stronger objectivity and prevent an automatic resistance or acceptance of the new term/trend.
http://hdl.handle.net/10468/5117
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Big data: lessons for employers and employees
(2020)
Jeske, Debora; Calvard, Thomas
Big data: lessons for employers and employees
(2020)
Jeske, Debora; Calvard, Thomas
Abstract:
Purpose: The focus of the current article is to critically reflect on the pros and cons of using employee information in big data projects. Approach: The authors reviewed papers in the area of big data that have immediate repercussions for the experiences of employees and employers. Findings: The review of papers to date suggests that big data lessons based on employee data are still a relatively unknown area of employment literature. Particular attention is paid to discussion of employee rights, ethics, expectations, and the implications employer conduct has on employment relationships and prospective benefits of big data analytics at work for work. Originality/value: This viewpoint article highlights the need for more discussion between employees and employers about the collection, use, storage and ownership of data in the workplace. A number of recommendations are put forward to support future data collection efforts in organisations.
http://hdl.handle.net/10468/9574
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Big Data: Rewards and Risks for the Social Sciences
(2013)
Shankar, Kalpana; Wallis, Jillian
Big Data: Rewards and Risks for the Social Sciences
(2013)
Shankar, Kalpana; Wallis, Jillian
Abstract:
Big Data: Rewards and Risks for the Social Sciences workshop, Oxford Internet Institute, University of Oxford, 21 - 22 March 2013
Both applicants have been extensively involved in science data (Big Data, Small Data, and the transitions among them) and have conducted ethnographic and qualitative studies of data creation and use, but have recently shifted their interests and work to social science data. Although they have not formally worked together, they have worked on the same large science data project (Center for Embedded Networked Sensing at University of California, Los Angeles). More recent interactions and conversations have brought them together to share interests and concerns. To perhaps begin collaboration, they are interested in jointly applying for this workshop. In this paper, we briefly discuss three issues that are of interest to us in the realm of big data and the social sciences.
Irish Research Council
Author has checked copyright
Workshop details a...
http://hdl.handle.net/10197/4352
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BigDataNetSim: A Simulator for Data and Process Placement in Large Big Data Platforms
(2019)
Batista de Almeida, Leandro; Cunha de Almeida, Eduardo; Murphy, John; De Grande, Robson...
BigDataNetSim: A Simulator for Data and Process Placement in Large Big Data Platforms
(2019)
Batista de Almeida, Leandro; Cunha de Almeida, Eduardo; Murphy, John; De Grande, Robson E.; Ventresque, Anthony
Abstract:
The 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)
Big Data platforms are convoluted distributed systems which commonly comprise skill- and labour-intensive solution development to treat inherent Big Data application challenges. Several tools have been proposed to help developers and engineers to overcome the involved complexities in coordinating the execution of plenty processes/threads on multiple machines. However, no work so far has been able to combine both an accurate representation of Big Data jobs and realistic modeling of the behaviour of Big Data platforms at scale, including networking elements and data and job placement. In this paper, we propose BigDataNetSim, the first simulator which models accurately all the main components of the data movements in Big Data platforms (e.g., HDFS, YARN/MapReduce, network topologies, switching/routing protocols) in a large scale system. BigDataNetSim can serve as a valuable t...
http://hdl.handle.net/10197/10594
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Business modelling for smart continual commissioning in ESCO set-ups
(2017)
Hryshchenko, Andriy; Menzel, Karsten
Business modelling for smart continual commissioning in ESCO set-ups
(2017)
Hryshchenko, Andriy; Menzel, Karsten
Abstract:
The availability of sensors, smart meters, and so called ‘intelligent devices’ (IoT) enables owners and tenants to better understand and flexibly adjust the status of buildings and their systems according to their needs. However, it also requires a more intense and detailed knowledge about how to exploit, analyse and manage ‘big data’ compiled from these devices. Building operators, facility managers and energy suppliers are expected to collaborate and to share this data aiming to deliver more holistic, comprehensive services to clients (i.e. owners and tenants of buildings). This paper discusses how so called ESCO-business models (energy service companies) and CC-business models (continuous commissioning) can be integrated through sharing of big data and collaboration of major stakeholders involved in building operation, energy supply and engineering consultancy. It explains how building owners will benefit from the availability of such comprehensive, collaborative services.
http://hdl.handle.net/10468/5839
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Content-aware compression for big textual data analysis
(2016)
Dong, Dapeng
Content-aware compression for big textual data analysis
(2016)
Dong, Dapeng
Abstract:
A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software ...
http://hdl.handle.net/10468/2697
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Content-aware Partial Compression for Textual Big Data Analysis in Hadoop
(2018)
Dong, Dapeng
Content-aware Partial Compression for Textual Big Data Analysis in Hadoop
(2018)
Dong, Dapeng
Abstract:
A substantial amount of information in companies and on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. Compression as an effective means to reduce data size has been employed by many emerging data analytic platforms, whom the main purpose of data compression is to save storage space and reduce data transmission cost over the network. Since general purpose compression methods endeavour to achieve higher compression ratios by leveraging data transformation techniques and contextual data, this context-dependency forces the access to the compressed data to be sequential. Processing such compressed data in parallel, such as desirable in a distributed environment, is extremely challenging. This work proposes techniques for more efficient textual big data analy...
http://mural.maynoothuniversity.ie/13168/
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Content-aware partial compression for textual big data analysis in Hadoop
(2017)
Dong, Dapeng; Herbert, John
Content-aware partial compression for textual big data analysis in Hadoop
(2017)
Dong, Dapeng; Herbert, John
Abstract:
A substantial amount of information in companies and on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. Compression as an effective means to reduce data size has been employed by many emerging data analytic platforms, whom the main purpose of data compression is to save storage space and reduce data transmission cost over the network. Since general purpose compression methods endeavour to achieve higher compression ratios by leveraging data transformation techniques and contextual data, this context-dependency forces the access to the compressed data to be sequential. Processing such compressed data in parallel, such as desirable in a distributed environment, is extremely challenging. This work proposes techniques for more efficient textual big data analy...
http://hdl.handle.net/10468/5452
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Data analytics research in public universities
(2014)
Smeaton, Alan F.
Data analytics research in public universities
(2014)
Smeaton, Alan F.
Abstract:
Research into big data in publicly-funded Universities and research centres has major disadvantages compared to the private sector, and not just in the obvious areas of funding and access to data. In this abstract we highlight some of these differences around the area of ethics and privacy, and two specific examples of our work are used to illustrate this.
http://doras.dcu.ie/20257/
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Data-driven, networked urbanism. Programmable City Working Paper 14
(2015)
Kitchin, Rob
Data-driven, networked urbanism. Programmable City Working Paper 14
(2015)
Kitchin, Rob
Abstract:
For as long as data have been generated about cities various kinds of data-informed urbanism have been occurring. In this paper, I argue that a new era is presently unfolding wherein data-informed urbanism is increasingly being complemented and replaced by data-driven, networked urbanism. Cities are becoming ever more instrumented and networked, their systems interlinked and integrated, and vast troves of big urban data are being generated and used to manage and control urban life in real-time. Data-driven, networked urbanism, I contend, is the key mode of production for what have widely been termed smart cities. In this paper I provide a critical overview of data-driven, networked urbanism and smart cities focusing in particular on the relationship between data and the city (rather than network infrastructure or computational or urban issues), and critically examine a number of urban data issues including: the politics of urban data; data ownership, data control, data coverage and ...
http://mural.maynoothuniversity.ie/7235/
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Deep learning for consumer devices and services: Pushing the limits for machine learning, artificial intelligence, and computer vision
(2017)
Lemley, Joseph; Bazrafkan, Shabab; Corcoran, Peter
Deep learning for consumer devices and services: Pushing the limits for machine learning, artificial intelligence, and computer vision
(2017)
Lemley, Joseph; Bazrafkan, Shabab; Corcoran, Peter
Abstract:
In the last few years, we have witnessed an exponential growth in research activity into the advanced training of convolutional neural networks (CNNs), a field that has become known as deep learning. This has been triggered by a combination of the availability of significantly larger data sets, thanks in part to a corresponding growth in big data, and the arrival of new graphics-processing-unit (GPU)-based hardware that enables these large data sets to be processed in reasonable timescales. Suddenly, a wide variety of long-standing problems in machine learning, artificial intelligence, and computer vision have seen significant improvements, often sufficient to break through long-standing performance barriers. Across multiple fields, these achievements have inspired the development of improved tools and methodologies leading to even broader applicability of deep learning. The new generation of smart assistants, such as Alexa, Hello Google, and others, have their roots and learning al...
http://hdl.handle.net/10379/6699
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