15,447 research outputs found
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Symbolic Computing with Incremental Mindmaps to Manage and Mine Data Streams - Some Applications
In our understanding, a mind-map is an adaptive engine that basically works
incrementally on the fundament of existing transactional streams. Generally,
mind-maps consist of symbolic cells that are connected with each other and that
become either stronger or weaker depending on the transactional stream. Based
on the underlying biologic principle, these symbolic cells and their
connections as well may adaptively survive or die, forming different cell
agglomerates of arbitrary size. In this work, we intend to prove mind-maps'
eligibility following diverse application scenarios, for example being an
underlying management system to represent normal and abnormal traffic behaviour
in computer networks, supporting the detection of the user behaviour within
search engines, or being a hidden communication layer for natural language
interaction.Comment: 4 pages; 4 figure
Distributed stream reasoning
Stream Reasoning is the combination of reasoning techniques with data streams. In this paper, we present our approach to enable rule-based reasoning on semantic data streams in a distributed manne
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Internet of things
Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing todayās pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efļ¬cient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identiļ¬ed synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth
Transportation in Social Media: an automatic classifier for travel-related tweets
In the last years researchers in the field of intelligent transportation
systems have made several efforts to extract valuable information from social
media streams. However, collecting domain-specific data from any social media
is a challenging task demanding appropriate and robust classification methods.
In this work we focus on exploring geo-located tweets in order to create a
travel-related tweet classifier using a combination of bag-of-words and word
embeddings. The resulting classification makes possible the identification of
interesting spatio-temporal relations in S\~ao Paulo and Rio de Janeiro
Managing the Provenance of Crowdsourced Disruption Reports
A paid open access option is available for this journal. Authors own final version only can be archived Publisher's version/PDF cannot be used On author's website immediately On any open access repository after 12 months from publication Published source must be acknowledged Must link to publisher version Set phrase to accompany link to published version (see policy) Articles in some journals can be made Open Access on payment of additional chargePublisher PD
Reactive Rules for Emergency Management
The goal of the following survey on Event-Condition-Action (ECA) Rules is to come to a common understanding and intuition on this topic within EMILI. Thus it does not give an academic overview on Event-Condition-Action Rules which would be valuable for computer scientists only. Instead the survey tries to introduce Event-Condition-Action Rules and their use for emergency management based on real-life examples from the use-cases identified in Deliverable 3.1. In this way we hope to address both, computer scientists and security experts, by showing how the Event-Condition-Action Rule technology can help to solve security issues in emergency management. The survey incorporates information from other work packages, particularly from Deliverable D3.1 and its Annexes, D4.1, D2.1 and D6.2 wherever possible
A large multilingual and multi-domain dataset for recommender systems
This paper presents a multi-domain interests dataset to train and test Recommender Systems, and the methodology to create the dataset
from Twitter messages in English and Italian. The English dataset includes an average of 90 preferences per user on music, books,
movies, celebrities, sport, politics and much more, for about half million users. Preferences are either extracted from messages of
users who use Spotify, Goodreads and other similar content sharing platforms, or induced from their ātopicalā friends, i.e., followees
representing an interest rather than a social relation between peers. In addition, preferred items are matched with Wikipedia articles
describing them. This unique feature of our dataset provides a mean to derive a semantic categorization of the preferred items, exploiting
available semantic resources linked to Wikipedia such as the Wikipedia Category Graph, DBpedia, BabelNet and others
- ā¦