3,763 research outputs found
Neural Network Matrix Factorization
Data often comes in the form of an array or matrix. Matrix factorization
techniques attempt to recover missing or corrupted entries by assuming that the
matrix can be written as the product of two low-rank matrices. In other words,
matrix factorization approximates the entries of the matrix by a simple, fixed
function---namely, the inner product---acting on the latent feature vectors for
the corresponding row and column. Here we consider replacing the inner product
by an arbitrary function that we learn from the data at the same time as we
learn the latent feature vectors. In particular, we replace the inner product
by a multi-layer feed-forward neural network, and learn by alternating between
optimizing the network for fixed latent features, and optimizing the latent
features for a fixed network. The resulting approach---which we call neural
network matrix factorization or NNMF, for short---dominates standard low-rank
techniques on a suite of benchmark but is dominated by some recent proposals
that take advantage of the graph features. Given the vast range of
architectures, activation functions, regularizers, and optimization techniques
that could be used within the NNMF framework, it seems likely the true
potential of the approach has yet to be reached.Comment: Minor modifications to notation. Added additional experiments and
discussion. 7 pages, 2 table
A Survey on Artificial Intelligence and Data Mining for MOOCs
Massive Open Online Courses (MOOCs) have gained tremendous popularity in the
last few years. Thanks to MOOCs, millions of learners from all over the world
have taken thousands of high-quality courses for free. Putting together an
excellent MOOC ecosystem is a multidisciplinary endeavour that requires
contributions from many different fields. Artificial intelligence (AI) and data
mining (DM) are two such fields that have played a significant role in making
MOOCs what they are today. By exploiting the vast amount of data generated by
learners engaging in MOOCs, DM improves our understanding of the MOOC ecosystem
and enables MOOC practitioners to deliver better courses. Similarly, AI,
supported by DM, can greatly improve student experience and learning outcomes.
In this survey paper, we first review the state-of-the-art artificial
intelligence and data mining research applied to MOOCs, emphasising the use of
AI and DM tools and techniques to improve student engagement, learning
outcomes, and our understanding of the MOOC ecosystem. We then offer an
overview of key trends and important research to carry out in the fields of AI
and DM so that MOOCs can reach their full potential.Comment: Working Pape
Next Steps for Human-Centered Generative AI: A Technical Perspective
Through iterative, cross-disciplinary discussions, we define and propose
next-steps for Human-centered Generative AI (HGAI) from a technical
perspective. We contribute a roadmap that lays out future directions of
Generative AI spanning three levels: Aligning with human values; Accommodating
humans' expression of intents; and Augmenting humans' abilities in a
collaborative workflow. This roadmap intends to draw interdisciplinary research
teams to a comprehensive list of emergent ideas in HGAI, identifying their
interested topics while maintaining a coherent big picture of the future work
landscape
Cogniculture: Towards a Better Human-Machine Co-evolution
Research in Artificial Intelligence is breaking technology barriers every
day. New algorithms and high performance computing are making things possible
which we could only have imagined earlier. Though the enhancements in AI are
making life easier for human beings day by day, there is constant fear that AI
based systems will pose a threat to humanity. People in AI community have
diverse set of opinions regarding the pros and cons of AI mimicking human
behavior. Instead of worrying about AI advancements, we propose a novel idea of
cognitive agents, including both human and machines, living together in a
complex adaptive ecosystem, collaborating on human computation for producing
essential social goods while promoting sustenance, survival and evolution of
the agents' life cycle. We highlight several research challenges and technology
barriers in achieving this goal. We propose a governance mechanism around this
ecosystem to ensure ethical behaviors of all cognitive agents. Along with a
novel set of use-cases of Cogniculture, we discuss the road map ahead for this
journey
Sensemaking on the Pragmatic Web: A Hypermedia Discourse Perspective
The complexity of the dilemmas we face on an organizational, societal and global scale forces us into sensemaking activity. We need tools for expressing and contesting perspectives flexible enough for real time use in meetings, structured enough to help manage longer term memory, and powerful enough to filter the complexity of extended deliberation and debate on an organizational or global scale. This has been the motivation for a programme of basic and applied action research into Hypermedia Discourse, which draws on research in hypertext, information visualization, argumentation, modelling, and meeting facilitation. This paper proposes that this strand of work shares a key principle behind the Pragmatic Web concept, namely, the need to take seriously diverse perspectives and the processes of meaning negotiation. Moreover, it is argued that the hypermedia discourse tools described instantiate this principle in practical tools which permit end-user control over modelling approaches in the absence of consensus
Towards In-Transit Analytics for Industry 4.0
Industry 4.0, or Digital Manufacturing, is a vision of inter-connected
services to facilitate innovation in the manufacturing sector. A fundamental
requirement of innovation is the ability to be able to visualise manufacturing
data, in order to discover new insight for increased competitive advantage.
This article describes the enabling technologies that facilitate In-Transit
Analytics, which is a necessary precursor for Industrial Internet of Things
(IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things
(iThings-2017), Exeter, UK, 201
The Future of Spreadsheets in the Big Data Era
The humble spreadsheet is the most widely used data storage, manipulation and
modelling tool. Its ubiquity over the past 30 years has seen its successful
application in every area of life. Surprisingly the spreadsheet has remained
fundamentally unchanged over the past three decades. As spreadsheet technology
enters its 4th decade a number of drivers of change are beginning to impact
upon the spreadsheet. The rise of Big Data, increased end-user computing and
mobile computing will undoubtedly increasingly shape the evolution and use of
spreadsheet technology.
To explore the future of spreadsheet technology a workshop was convened with
the aim of "bringing together academia and industry to examine the future
direction of spreadsheet technology and the consequences for users". This paper
records the views of the participants on the reasons for the success of the
spreadsheet, the trends driving change and the likely directions of change for
the spreadsheet. We then set out key directions for further research in the
evolution and use of spreadsheets. Finally we look at the implications of these
trends for the end users who after all are the reason for the remarkable
success of the spreadsheet.Comment: 13 Pages, 1 Tabl
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Distributed High Accuracy Peer-to-Peer Localization in Mobile Multipath Environments
In this paper we consider the problem of high accuracy localization of mobile
nodes in a multipath-rich environment where sub-meter accuracies are required.
We employ a peer to peer framework where the vehicles/nodes can get pairwise
multipath-degraded ranging estimates in local neighborhoods together with a
fixed number of anchor nodes. The challenge is to overcome the
multipath-barrier with redundancy in order to provide the desired accuracies
especially under severe multipath conditions when the fraction of received
signals corrupted by multipath is dominating. We invoke a message passing
analytical framework based on particle filtering and reveal its high accuracy
localization promise through simulations.Comment: 5 pages, 5 figures, Accepted at IEEE Globecom 2010, Miami, F
BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats
In community-based software development, developers frequently rely on
live-chatting to discuss emergent bugs/errors they encounter in daily
development tasks. However, it remains a challenging task to accurately record
such knowledge due to the noisy nature of interleaved dialogs in live chat
data. In this paper, we first formulate the task of identifying and
synthesizing bug reports from community live chats, and propose a novel
approach, named BugListener, to address the challenges. Specifically,
BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive
chat logs by using a Feed-Forward neural network; 2) Identify the bug-report
dialogs from separated dialogs by modeling the original dialog to the
graph-structured dialog and leveraging the graph neural network to learn the
contextual information; 3) Synthesize the bug reports by utilizing the TextCNN
model and Transfer Learning network to classify the sentences into three
groups: observed behaviors (OB), expected behaviors (EB), and steps to
reproduce the bug (SR). BugListener is evaluated on six open source projects.
The results show that: for bug report identification, BugListener achieves the
average F1 of 74.21%, improving the best baseline by 10.37%; and for bug report
synthesis task, BugListener could classify the OB, EB, and SR sentences with
the F1 of 67.37%, 87.14%, and 65.03%, improving the best baselines by 7.21%,
7.38%, 5.30%, respectively. A human evaluation also confirms the effectiveness
of BugListener in generating relevant and accurate bug reports. These
demonstrate the significant potential of applying BugListener in
community-based software development, for promoting bug discovery and quality
improvement
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