29,884 research outputs found
CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code Modeling
Recently deep learning based Natural Language Processing (NLP) models have
shown great potential in the modeling of source code. However, a major
limitation of these approaches is that they take source code as simple tokens
of text and ignore its contextual, syntactical and structural dependencies. In
this work, we present CodeGRU, a gated recurrent unit based source code
language model that is capable of capturing source code's contextual,
syntactical and structural dependencies. We introduce a novel approach which
can capture the source code context by leveraging the source code token types.
Further, we adopt a novel approach which can learn variable size context by
taking into account source code's syntax, and structural information. We
evaluate CodeGRU with real-world data set and it shows that CodeGRU outperforms
the state-of-the-art language models and help reduce the vocabulary size up to
24.93\%. Unlike previous works, we tested CodeGRU with an independent test set
which suggests that our methodology does not requisite the source code comes
from the same domain as training data while providing suggestions. We further
evaluate CodeGRU with two software engineering applications: source code
suggestion, and source code completion. Our experiment confirms that the source
code's contextual information can be vital and can help improve the software
language models. The extensive evaluation of CodeGRU shows that it outperforms
the state-of-the-art models. The results further suggest that the proposed
approach can help reduce the vocabulary size and is of practical use for
software developers
Mobile Multimedia Recommendation in Smart Communities: A Survey
Due to the rapid growth of internet broadband access and proliferation of
modern mobile devices, various types of multimedia (e.g. text, images, audios
and videos) have become ubiquitously available anytime. Mobile device users
usually store and use multimedia contents based on their personal interests and
preferences. Mobile device challenges such as storage limitation have however
introduced the problem of mobile multimedia overload to users. In order to
tackle this problem, researchers have developed various techniques that
recommend multimedia for mobile users. In this survey paper, we examine the
importance of mobile multimedia recommendation systems from the perspective of
three smart communities, namely, mobile social learning, mobile event guide and
context-aware services. A cautious analysis of existing research reveals that
the implementation of proactive, sensor-based and hybrid recommender systems
can improve mobile multimedia recommendations. Nevertheless, there are still
challenges and open issues such as the incorporation of context and social
properties, which need to be tackled in order to generate accurate and
trustworthy mobile multimedia recommendations
Personalized Context-Aware Point of Interest Recommendation
Personalized recommendation of Points of Interest (POIs) plays a key role in
satisfying users on Location-Based Social Networks (LBSNs). In this paper, we
propose a probabilistic model to find the mapping between user-annotated tags
and locations' taste keywords. Furthermore, we introduce a dataset on
locations' contextual appropriateness and demonstrate its usefulness in
predicting the contextual relevance of locations. We investigate four
approaches to use our proposed mapping for addressing the data sparsity
problem: one model to reduce the dimensionality of location taste keywords and
three models to predict user tags for a new location. Moreover, we present
different scores calculated from multiple LBSNs and show how we incorporate new
information from the mapping into a POI recommendation approach. Then, the
computed scores are integrated using learning to rank techniques. The
experiments on two TREC datasets show the effectiveness of our approach,
beating state-of-the-art methods.Comment: To appear at ACM Transactions on Information Systems (TOIS
Enhanced Integrated Scoring for Cleaning Dirty Texts
An increasing number of approaches for ontology engineering from text are
gearing towards the use of online sources such as company intranet and the
World Wide Web. Despite such rise, not much work can be found in aspects of
preprocessing and cleaning dirty texts from online sources. This paper presents
an enhancement of an Integrated Scoring for Spelling error correction,
Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as
part of a text preprocessing phase in an ontology engineering system. New
evaluations performed on the enhanced ISSAC using 700 chat records reveal an
improved accuracy of 98% as compared to 96.5% and 71% based on the use of only
basic ISSAC and of Aspell, respectively.Comment: More information is available at
http://explorer.csse.uwa.edu.au/reference
Character Development In Mathematics Problem-Based Learning
Concept of learning is an active process to construct meaning as the result relating new ideas on the previous understanding. Therefore, learning mathematics in the classroom is a process to build a deep understanding by the students through activities designed by teacher. Teacher-designed activities in learning are not only to developing means but also to developing student’s character that will be used for the provision of social life. Therefore education is required not only to build an understanding to students, but also must be able to perform his role and function to inculcate moral values and character. Then, the purpose of education is really a human being who has the knowledge and personality fit with the character of the Indonesian nation. Mathematics Problem based learning could be the way to reach those purposes. The results of this research are (1) problem-based learning can be used as a means to build character behavior and social skills students at the junior high school, (2) Character behavior and social skills that can be built include trustworthy, respect, individual accountability, social responsibility, concern, questioning skills, gives an idea or opinion, being a good listener and cooperation.
Key words: Mathematics problem-based learning, Nation characte
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
Recommender systems help users deal with information overload by providing
tailored item suggestions to them. The recommendation of news is often
considered to be challenging, since the relevance of an article for a user can
depend on a variety of factors, including the user's short-term reading
interests, the reader's context, or the recency or popularity of an article.
Previous work has shown that the use of Recurrent Neural Networks is promising
for the next-in-session prediction task, but has certain limitations when only
recorded item click sequences are used as input. In this work, we present a
contextual hybrid, deep learning based approach for session-based news
recommendation that is able to leverage a variety of information types. We
evaluated our approach on two public datasets, using a temporal evaluation
protocol that simulates the dynamics of a news portal in a realistic way. Our
results confirm the benefits of considering additional types of information,
including article popularity and recency, in the proposed way, resulting in
significantly higher recommendation accuracy and catalog coverage than other
session-based algorithms. Additional experiments show that the proposed
parameterizable loss function used in our method also allows us to balance two
usually conflicting quality factors, accuracy and novelty.
Keywords: Artificial Neural Networks, Context-Aware Recommender Systems,
Hybrid Recommender Systems, News Recommender Systems, Session-based
RecommendationComment: 20 pgs. Published at IEEE Access, Volume 7, 2019.
https://ieeexplore.ieee.org/document/890868
Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems
We analyze the problem of using Explore-Exploit techniques to improve
precision in multi-result ranking systems such as web search, query
autocompletion and news recommendation. Adopting an exploration policy directly
online, without understanding its impact on the production system, may have
unwanted consequences - the system may sustain large losses, create user
dissatisfaction, or collect exploration data which does not help improve
ranking quality. An offline framework is thus necessary to let us decide what
policy and how we should apply in a production environment to ensure positive
outcome. Here, we describe such an offline framework.
Using the framework, we study a popular exploration policy - Thompson
sampling. We show that there are different ways of implementing it in
multi-result ranking systems, each having different semantic interpretation and
leading to different results in terms of sustained click-through-rate (CTR)
loss and expected model improvement. In particular, we demonstrate that
Thompson sampling can act as an online learner optimizing CTR, which in some
cases can lead to an interesting outcome: lift in CTR during exploration. The
observation is important for production systems as it suggests that one can get
both valuable exploration data to improve ranking performance on the long run,
and at the same time increase CTR while exploration lasts
Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation
Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation
Venue recommendation aims to assist users by making personalised
suggestions of venues to visit, building upon data available from
location-based social networks (LBSNs) such as Foursquare. A
particular challenge for this task is context-aware venue recommendation
(CAVR), which additionally takes the surrounding context of
the user (e.g. the user’s location and the time of day) into account
in order to provide more relevant venue suggestions. To address the
challenges of CAVR, we describe two approaches that exploit word
embedding techniques to infer the vector-space representations of
venues, users’ existing preferences, and users’ contextual preferences.
Our evaluation upon the test collection of the TREC 2015
Contextual Suggestion track demonstrates that we can significantly
enhance the effectiveness of a state-of-the-art venue recommendation
approach, as well as produce context-aware recommendations
that are at least as effective as the top TREC 2015 systems
Context-aware person identification in personal photo collections
Identifying the people in photos is an important need for users of photo management systems. We present MediAssist, one such system which facilitates browsing, searching and semi-automatic annotation of personal photos, using analysis of both image content and the context in which the photo is captured. This semi-automatic annotation includes annotation of the identity of people in photos. In this paper, we focus on such person annotation, and propose person identification techniques based on a combination of context and content. We propose language modelling and nearest neighbor approaches to context-based person identification, in addition to novel face color and image color content-based features (used alongside face recognition and body patch features). We conduct a comprehensive empirical study of these techniques using the real private photo collections of a number of users, and show that combining context- and content-based analysis improves performance over content or context alone
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