9,311 research outputs found
Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction
The emerging field of action prediction plays a vital role in various
computer vision applications such as autonomous driving, activity analysis and
human-computer interaction. Despite significant advancements, accurately
predicting future actions remains a challenging problem due to high
dimensionality, complex dynamics and uncertainties inherent in video data.
Traditional supervised approaches require large amounts of labelled data, which
is expensive and time-consuming to obtain. This paper introduces a novel
self-supervised video strategy for enhancing action prediction inspired by DINO
(self-distillation with no labels). The Temporal-DINO approach employs two
models; a 'student' processing past frames; and a 'teacher' processing both
past and future frames, enabling a broader temporal context. During training,
the teacher guides the student to learn future context by only observing past
frames. The strategy is evaluated on ROAD dataset for the action prediction
downstream task using 3D-ResNet, Transformer, and LSTM architectures. The
experimental results showcase significant improvements in prediction
performance across these architectures, with our method achieving an average
enhancement of 9.9% Precision Points (PP), highlighting its effectiveness in
enhancing the backbones' capabilities of capturing long-term dependencies.
Furthermore, our approach demonstrates efficiency regarding the pretraining
dataset size and the number of epochs required. This method overcomes
limitations present in other approaches, including considering various backbone
architectures, addressing multiple prediction horizons, reducing reliance on
hand-crafted augmentations, and streamlining the pretraining process into a
single stage. These findings highlight the potential of our approach in diverse
video-based tasks such as activity recognition, motion planning, and scene
understanding
Cross-layer design of multi-hop wireless networks
MULTI -hop wireless networks are usually defined as a collection of nodes
equipped with radio transmitters, which not only have the capability to
communicate each other in a multi-hop fashion, but also to route each others’ data
packets. The distributed nature of such networks makes them suitable for a variety of
applications where there are no assumed reliable central entities, or controllers, and
may significantly improve the scalability issues of conventional single-hop wireless
networks.
This Ph.D. dissertation mainly investigates two aspects of the research issues
related to the efficient multi-hop wireless networks design, namely: (a) network
protocols and (b) network management, both in cross-layer design paradigms to
ensure the notion of service quality, such as quality of service (QoS) in wireless mesh
networks (WMNs) for backhaul applications and quality of information (QoI) in
wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of
this Ph.D. dissertation, different network settings are used as illustrative examples,
however the proposed algorithms, methodologies, protocols, and models are not
restricted in the considered networks, but rather have wide applicability.
First, this dissertation proposes a cross-layer design framework integrating
a distributed proportional-fair scheduler and a QoS routing algorithm, while using
WMNs as an illustrative example. The proposed approach has significant performance
gain compared with other network protocols. Second, this dissertation proposes
a generic admission control methodology for any packet network, wired and
wireless, by modeling the network as a black box, and using a generic mathematical
0. Abstract 3
function and Taylor expansion to capture the admission impact. Third, this dissertation
further enhances the previous designs by proposing a negotiation process,
to bridge the applications’ service quality demands and the resource management,
while using WSNs as an illustrative example. This approach allows the negotiation
among different service classes and WSN resource allocations to reach the optimal
operational status. Finally, the guarantees of the service quality are extended to
the environment of multiple, disconnected, mobile subnetworks, where the question
of how to maintain communications using dynamically controlled, unmanned data
ferries is investigated
Internship Programme Online System using SMS (IPOS SMS SYSTEM)
Internship Programme has been introduced as part of the curriculum for most of higher learning institutions worldwide. Its main purpose is to expose students to a real working environment and relate theoretical knowledge with applications in the industries. The purpose of this project is to develop a portal for PETRONAS Internship Programme. Internship Programme Online System Using Short Message Service Technology (IPOS SMS System) has been developed to automate the currently manual business processes. This portal is act as a one stop centre for students, trainee and administrator. By login to this website, it allows online internship application, internship bulletin, forms downloading, resume checker and result notification via short message service system. It is a web-based internship application system which allows data centralization. In order to achieve this objective, the author will do a lot of research in order to have a deep understanding about online internship application, resume checker, notification via short message service and how to design and develop a website. The methodology used for designing and developing this website is Rapid Application Development (RAD) which consists of four core phases which are planning, analysis, design and development and implementation. Apart from that, the author also included the results and findings from the survey carried out. The website interface designs are also included based on the comparison of existing website and user feedback. Last but not least the author concludes with few recommendations in developing this website
Artificial intelligence and distance learning philosophy in support of PfP mandate
Computers have long been utilised in the legal environment. The main use of computers however, has merely been to automate office tasks. More exciting is the prospect of using artificial intelligence (AI) technology to create computers that can emulate the substantive legal jobs performed by lawyers, to create computers that can autonomously reason with the law to determine legal solutions, for example: structuring and support of Partnership for Peace (PfP) mandate. Such attempts have not been successful jet. Modelling the law and emulating the processes of legal reasoning have proved to be more complex and subtle than originally envisaged.
The adoption by AI researchers specialising in law of new AI techniques, such as case based reasoning, neural networks, fuzzy logic, deontic logics and non-monotonic logics, may move closer to achieving an automation of legal reasoning. Unfortunately these approaches also suffer several drawbacks that will need to be overcome if this is to be achieved. Even if these new techniques do not achieve an automation of legal reasoning however, they will still be valuable in better automating office tasks and in providing insights about the nature of law.
An idea to apply the technology of intelligent multi-agent systems to the computer aided learning (CAL) in law, is currently being developed as a research project by the author of this article (see e.g. [Antoliš, 2002.]). Similar projects are usually based on the most modern technologies of multimedia and hypermedia, as it was implemented in this article. The theoretical foundations of the design and architecture of intelligent system for decision support process in law and distance-learning environment are, however, at their early stage of development
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