13,672 research outputs found
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
Unsupervised routine discovery in egocentric photo-streams
The routine of a person is defined by the occurrence of activities throughout
different days, and can directly affect the person's health. In this work, we
address the recognition of routine related days. To do so, we rely on
egocentric images, which are recorded by a wearable camera and allow to monitor
the life of the user from a first-person view perspective. We propose an
unsupervised model that identifies routine related days, following an outlier
detection approach. We test the proposed framework over a total of 72 days in
the form of photo-streams covering around 2 weeks of the life of 5 different
camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted
F-Score for all the users. Thus, we show that our framework is able to
recognise routine related days and opens the door to the understanding of the
behaviour of people
Outlier Detection Techniques For Wireless Sensor Networks: A Survey
In the field of wireless sensor networks, measurements that
significantly deviate from the normal pattern of sensed data are
considered as outliers. The potential sources of outliers include
noise and errors, events, and malicious attacks on the network.
Traditional outlier detection techniques are not directly
applicable to wireless sensor networks due to the multivariate
nature of sensor data and specific requirements and limitations of
the wireless sensor networks. This survey provides a comprehensive
overview of existing outlier detection techniques specifically
developed for the wireless sensor networks. Additionally, it
presents a technique-based taxonomy and a decision tree to be used
as a guideline to select a technique suitable for the application
at hand based on characteristics such as data type, outlier type,
outlier degree
Growing Story Forest Online from Massive Breaking News
We describe our experience of implementing a news content organization system
at Tencent that discovers events from vast streams of breaking news and evolves
news story structures in an online fashion. Our real-world system has distinct
requirements in contrast to previous studies on topic detection and tracking
(TDT) and event timeline or graph generation, in that we 1) need to accurately
and quickly extract distinguishable events from massive streams of long text
documents that cover diverse topics and contain highly redundant information,
and 2) must develop the structures of event stories in an online manner,
without repeatedly restructuring previously formed stories, in order to
guarantee a consistent user viewing experience. In solving these challenges, we
propose Story Forest, a set of online schemes that automatically clusters
streaming documents into events, while connecting related events in growing
trees to tell evolving stories. We conducted extensive evaluation based on 60
GB of real-world Chinese news data, although our ideas are not
language-dependent and can easily be extended to other languages, through
detailed pilot user experience studies. The results demonstrate the superior
capability of Story Forest to accurately identify events and organize news text
into a logical structure that is appealing to human readers, compared to
multiple existing algorithm frameworks.Comment: Accepted by CIKM 2017, 9 page
Memory Based Online Learning of Deep Representations from Video Streams
We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution based on Reverse Nearest Neighbour and a feature forgetting strategy
that detect redundant features and discard them appropriately while time
progresses. It is shown that the proposed learning procedure is asymptotically
stable and can be effectively used in relevant applications like multiple face
identification and tracking from unconstrained video streams. Experimental
results show that the proposed method achieves comparable results in the task
of multiple face tracking and better performance in face identification with
offline approaches exploiting future information. Code will be publicly
available.Comment: arXiv admin note: text overlap with arXiv:1708.0361
Cyber-Deception and Attribution in Capture-the-Flag Exercises
Attributing the culprit of a cyber-attack is widely considered one of the
major technical and policy challenges of cyber-security. The lack of ground
truth for an individual responsible for a given attack has limited previous
studies. Here, we overcome this limitation by leveraging DEFCON
capture-the-flag (CTF) exercise data where the actual ground-truth is known. In
this work, we use various classification techniques to identify the culprit in
a cyberattack and find that deceptive activities account for the majority of
misclassified samples. We also explore several heuristics to alleviate some of
the misclassification caused by deception.Comment: 4 pages Short name accepted to FOSINT-SI 201
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