9,402 research outputs found
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Inferring Person-to-person Proximity Using WiFi Signals
Today's societies are enveloped in an ever-growing telecommunication
infrastructure. This infrastructure offers important opportunities for sensing
and recording a multitude of human behaviors. Human mobility patterns are a
prominent example of such a behavior which has been studied based on cell phone
towers, Bluetooth beacons, and WiFi networks as proxies for location. However,
while mobility is an important aspect of human behavior, understanding complex
social systems requires studying not only the movement of individuals, but also
their interactions. Sensing social interactions on a large scale is a technical
challenge and many commonly used approaches---including RFID badges or
Bluetooth scanning---offer only limited scalability. Here we show that it is
possible, in a scalable and robust way, to accurately infer person-to-person
physical proximity from the lists of WiFi access points measured by smartphones
carried by the two individuals. Based on a longitudinal dataset of
approximately 800 participants with ground-truth interactions collected over a
year, we show that our model performs better than the current state-of-the-art.
Our results demonstrate the value of WiFi signals in social sensing as well as
potential threats to privacy that they imply
Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook
A crucial task in the analysis of on-line social-networking systems is to
identify important people --- those linked by strong social ties --- within an
individual's network neighborhood. Here we investigate this question for a
particular category of strong ties, those involving spouses or romantic
partners. We organize our analysis around a basic question: given all the
connections among a person's friends, can you recognize his or her romantic
partner from the network structure alone? Using data from a large sample of
Facebook users, we find that this task can be accomplished with high accuracy,
but doing so requires the development of a new measure of tie strength that we
term `dispersion' --- the extent to which two people's mutual friends are not
themselves well-connected. The results offer methods for identifying types of
structurally significant people in on-line applications, and suggest a
potential expansion of existing theories of tie strength.Comment: Proc. 17th ACM Conference on Computer Supported Cooperative Work and
Social Computing (CSCW), 201
Machine learning-based motion type classification from 5G data
Abstract. To improve the quality of their services and products, nowadays every industry is using artificial intelligence and machine learning. Machine learning is a powerful tool that can be applied in many applications including wireless communications. One way to improve the reliability of wireless connections is to classify motion type of the user and hook it with beamforming and beam steering. With the user equipment’s motion type classification ability, the base station can allocate proper beamforming to the given class of users. With this motivation, the studies of ML algorithms for motion classification is conducted in this thesis. In this work, the supervised learning technique is used to predict and classify motion types using the 5G data. In this work, we used the 5G data collected in 4 different scenarios or classes which are (i) Walking (ii) Standing (ii) Driving and (iv) Drone. The data is then operated on for cleaning and feature engineering and then is fed into different classification algorithms including Logistic Regression Cross Validation (LRCv), Support Vector Classifier (SVC), k-nearest neighbors (KNN), Linear Discriminant Analysis (LDA), AdaBoost, and Extra Tree Classifier. Upon analyzing the evaluation metrics for these algorithms, we found that with the accuracy of ~99% and log-loss of 0.044, Extra Tree Classifier performed better than others. With such promising results, the output of classification process can be used in another pipeline for resource optimization or hooked with hardware for beamforming and beam steering. It can also be used as an input to a digital twin of radio to change its variables dynamically which will be reflected in the physical copy of that radio
- …