313,603 research outputs found
The Recommendation Architecture: Lessons from Large-Scale Electronic Systems Applied to Cognition
A fundamental approach of cognitive science is to understand cognitive systems by separating them into modules. Theoretical reasons are described which force any system which learns to perform a complex combination of real time functions into a modular architecture. Constraints on the way modules divide up functionality are also described. The architecture of such systems, including biological systems, is constrained into a form called the recommendation architecture, with a primary separation between clustering and competition. Clustering is a modular hierarchy which manages the interactions between functions on the basis of detection of functionally ambiguous repetition. Change to previously detected repetitions is limited in order to maintain a meaningful, although partially ambiguous context for all modules which make use of the previously defined repetitions. Competition interprets the repetition conditions detected by clustering as a range of alternative behavioural recommendations, and uses consequence feedback to learn to select the most appropriate recommendation. The requirements imposed by functional complexity result in very specific structures and processes which resemble those of brains. The design of an implemented electronic version of the recommendation architecture is described, and it is demonstrated that the system can heuristically define its own functionality, and learn without disrupting earlier learning. The recommendation architecture is compared with a range of alternative cognitive architectural proposals, and the conclusion reached that it has substantial potential both for understanding brains and for designing systems to perform cognitive functions
Hikester - the event management application
Today social networks and services are one of the most important part of our
everyday life. Most of the daily activities, such as communicating with
friends, reading news or dating is usually done using social networks. However,
there are activities for which social networks do not yet provide adequate
support. This paper focuses on event management and introduces "Hikester". The
main objective of this service is to provide users with the possibility to
create any event they desire and to invite other users. "Hikester" supports the
creation and management of events like attendance of football matches, quest
rooms, shared train rides or visit of museums in foreign countries. Here we
discuss the project architecture as well as the detailed implementation of the
system components: the recommender system, the spam recognition service and the
parameters optimizer
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows
Human beings always engage in a vast range of activities and tasks that
demonstrate their ability to adapt to different scenarios. Any human activity
can be represented as a temporal sequence of actions performed to achieve a
certain goal. Unlike the time series datasets extracted from electronics or
machines, these action sequences are highly disparate in their nature -- the
time to finish a sequence of actions can vary between different persons.
Therefore, understanding the dynamics of these sequences is essential for many
downstream tasks such as activity length prediction, goal prediction, next
action recommendation, etc. Existing neural network-based approaches that learn
a continuous-time activity sequence (or CTAS) are limited to the presence of
only visual data or are designed specifically for a particular task, i.e.,
limited to next action or goal prediction. In this paper, we present ProActive,
a neural marked temporal point process (MTPP) framework for modeling the
continuous-time distribution of actions in an activity sequence while
simultaneously addressing three high-impact problems -- next action prediction,
sequence-goal prediction, and end-to-end sequence generation. Specifically, we
utilize a self-attention module with temporal normalizing flows to model the
influence and the inter-arrival times between actions in a sequence. In
addition, we propose a novel addition over the ProActive model that can handle
variations in the order of actions, i.e., different methods of achieving a
given goal. We demonstrate that this variant can learn the order in which the
person or actor prefers to do their actions. Extensive experiments on sequences
derived from three activity recognition datasets show the significant accuracy
boost of ProActive over the state-of-the-art in terms of action and goal
prediction, and the first-ever application of end-to-end action sequence
generation.Comment: Extended version of Gupta and Bedathur [arXiv:2206.05291] (SIGKDD
2022). Under review in a journa
Egocentric Hand Detection Via Dynamic Region Growing
Egocentric videos, which mainly record the activities carried out by the
users of the wearable cameras, have drawn much research attentions in recent
years. Due to its lengthy content, a large number of ego-related applications
have been developed to abstract the captured videos. As the users are
accustomed to interacting with the target objects using their own hands while
their hands usually appear within their visual fields during the interaction,
an egocentric hand detection step is involved in tasks like gesture
recognition, action recognition and social interaction understanding. In this
work, we propose a dynamic region growing approach for hand region detection in
egocentric videos, by jointly considering hand-related motion and egocentric
cues. We first determine seed regions that most likely belong to the hand, by
analyzing the motion patterns across successive frames. The hand regions can
then be located by extending from the seed regions, according to the scores
computed for the adjacent superpixels. These scores are derived from four
egocentric cues: contrast, location, position consistency and appearance
continuity. We discuss how to apply the proposed method in real-life scenarios,
where multiple hands irregularly appear and disappear from the videos.
Experimental results on public datasets show that the proposed method achieves
superior performance compared with the state-of-the-art methods, especially in
complicated scenarios
Security models for trusting network appliances
A significant characteristic of pervasive computing is the need for secure interactions between highly mobile entities and the services in their environment. Moreover,these decentralised systems are also characterised by partial views over the state of the global environment, implying that we cannot guarantee verification of the properties of the mobile entity entering an unfamiliar domain. Secure in this context encompasses both the need for cryptographic security and the need for trust, on the part of both parties, that the interaction is functioning as expected. In this paper we make a broad assumption that trust and cryptographic security can be considered as orthogonal concerns (i.e. cryptographic measures do not ensure transmission of correct information). We assume the existence of reliable encryption techniques and focus on the characteristics of a model that supports the management of the trust relationships between two devices during ad-hoc interactions
- …