6,461 research outputs found
Surveying human habit modeling and mining techniques in smart spaces
A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field
Look, Listen and Learn
We consider the question: what can be learnt by looking at and listening to a
large number of unlabelled videos? There is a valuable, but so far untapped,
source of information contained in the video itself -- the correspondence
between the visual and the audio streams, and we introduce a novel
"Audio-Visual Correspondence" learning task that makes use of this. Training
visual and audio networks from scratch, without any additional supervision
other than the raw unconstrained videos themselves, is shown to successfully
solve this task, and, more interestingly, result in good visual and audio
representations. These features set the new state-of-the-art on two sound
classification benchmarks, and perform on par with the state-of-the-art
self-supervised approaches on ImageNet classification. We also demonstrate that
the network is able to localize objects in both modalities, as well as perform
fine-grained recognition tasks.Comment: Appears in: IEEE International Conference on Computer Vision (ICCV)
201
Semantic Sentiment Analysis of Twitter Data
Internet and the proliferation of smart mobile devices have changed the way
information is created, shared, and spreads, e.g., microblogs such as Twitter,
weblogs such as LiveJournal, social networks such as Facebook, and instant
messengers such as Skype and WhatsApp are now commonly used to share thoughts
and opinions about anything in the surrounding world. This has resulted in the
proliferation of social media content, thus creating new opportunities to study
public opinion at a scale that was never possible before. Naturally, this
abundance of data has quickly attracted business and research interest from
various fields including marketing, political science, and social studies,
among many others, which are interested in questions like these: Do people like
the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about
the Brexit? Answering these questions requires studying the sentiment of
opinions people express in social media, which has given rise to the fast
growth of the field of sentiment analysis in social media, with Twitter being
especially popular for research due to its scale, representativeness, variety
of topics discussed, as well as ease of public access to its messages. Here we
present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the
Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition.
201
Combination of Facebook Prophet and Attention-Based LSTM with Multi- Source data for Indian Stock Market Prediction
The stock market prediction has been the subject of interest to various researchers and analysts due to its highly unpredictable nature and serves as a perfect example for time series forecasting. Over the years deep learning models such as Long-Term Short-Term Memory and statistical models such as Autoregressive Integrated Moving Average have shown promising results in predicting future stock prices. But the results from these models cannot be generalized as they fail to incorporate the dynamics of the market and influence of several external factors such as political, social, investor\u27s emotion, etc on stock markets. Recently Facebook’s creation of the Prophet model solely for time series forecasting has been successful in fitting the trends and seasonality of the data accurately compared to vanilla models.
This research proposes a unique combination of the newly developed Facebook Prophet model and Attention-Based Long-Term Short-Term Memory model to predict the adjacent closing price of NIFTY 50 stocks to fit both the seasonality and non-linearity component of stock price data. Further to encompass both market and investor sentiments influencing stock prediction, data from five sources are collected from 01/01/2015 to 31/12/2019 namely historic stock price, technical indicators, news articles scraped from multiple news sources, and tweets collected from a verified Twitter account. To extract sentiments from unlabelled news and tweet data this research takes upon an unsupervised approach by implementing a pre-trained Bidirectional Encoder Representations from Transformers base uncased model. The proposed model is trained and validated on eight combinations of the dataset created by merging data from multiple sources and compared with the performance of the baseline Facebook Prophet model trained and tested with data from a single source i.e., historic stock prices. The proposed model resulted in the least Mean Absolute Percentage Error ranging from 3.3 to 7.7 for all the combinations of the data in comparison to the baseline model which achieved the highest Mean Absolute Percentage Error of 11.67
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