17,763 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Textual properties and task based evaluation : investigating the role of surface properties, structure and content
This paper investigates the relationship between the results of an extrinsic, task-based evaluation of an NLG system and various metrics measuring both surface and deep semantic textual properties, including relevance. The latter rely heavily on domain knowledge. We show that they correlate systematically with some measures of performance. The core argument of this paper is that more domain knowledge-based metrics shed more light on the relationship between deep semantic properties of a text and task performance.peer-reviewe
Using transfer-resource graph for software-based verification of system-on-chip
Copyright © 2006 IEEEThe verification of system-on-chip is challenging due to its high level of integration. Multiple components in a system can behave concurrently and compete for resources. Hence, for simulation-based verification, we need a methodology that allows one to automatically generate test cases for testing concurrent and resource-competing behaviors.We introduce the use of a transferresource graph (TRG) as the model for test generation. From a high abstraction level, TRG is able to model the parallelism between heterogeneous interaction forms in a system. We show how TRG is used in generating test cases of resource competitions and how these test cases are structured in event-driven test programs. For coverage, TRG can be converted to a Petri net, allowing one to measure the completeness of concurrency in simulation.Xiaoxi Xu and Cheng-Chew Li
Explaining Vision and Language through Graphs of Events in Space and Time
Artificial Intelligence makes great advances today and starts to bridge the
gap between vision and language. However, we are still far from understanding,
explaining and controlling explicitly the visual content from a linguistic
perspective, because we still lack a common explainable representation between
the two domains. In this work we come to address this limitation and propose
the Graph of Events in Space and Time (GEST), by which we can represent, create
and explain, both visual and linguistic stories. We provide a theoretical
justification of our model and an experimental validation, which proves that
GEST can bring a solid complementary value along powerful deep learning models.
In particular, GEST can help improve at the content-level the generation of
videos from text, by being easily incorporated into our novel video generation
engine. Additionally, by using efficient graph matching techniques, the GEST
graphs can also improve the comparisons between texts at the semantic level.Comment: Accepted at IEEE International Conference on Computer Vision (ICCV)
2023 Workshops: 5th Workshop On Closing The Loop Between Vision And Languag
MINING AND VERIFICATION OF TEMPORAL EVENTS WITH APPLICATIONS IN COMPUTER MICRO-ARCHITECTURE RESEARCH
Computer simulation programs are essential tools for scientists and engineers to understand a particular system of interest. As expected, the complexity of the software increases with the depth of the model used. In addition to the exigent demands of software engineering, verification of simulation programs is especially challenging because the models represented are complex and ridden with unknowns that will be discovered by developers in an iterative process. To manage such complexity, advanced verification techniques for continually matching the intended model to the implemented model are necessary. Therefore, the main goal of this research work is to design a useful verification and validation framework that is able to identify model representation errors and is applicable to generic simulators.
The framework that was developed and implemented consists of two parts. The first part is First-Order Logic Constraint Specification Language (FOLCSL) that enables users to specify the invariants of a model under consideration. From the first-order logic specification, the FOLCSL translator automatically synthesizes a verification program that reads the event trace generated by a simulator and signals whether all invariants are respected. The second part consists of mining the temporal flow of events using a newly developed representation called State Flow Temporal Analysis Graph (SFTAG). While the first part seeks an assurance of implementation correctness by checking that the model invariants hold, the second part derives an extended model of the implementation and hence enables a deeper understanding of what was implemented. The main application studied in this work is the validation of the timing behavior of micro-architecture simulators. The study includes SFTAGs generated for a wide set of benchmark programs and their analysis using several artificial intelligence algorithms. This work improves the computer architecture research and verification processes as shown by the case studies and experiments that have been conducted
Temporal Networks
A great variety of systems in nature, society and technology -- from the web
of sexual contacts to the Internet, from the nervous system to power grids --
can be modeled as graphs of vertices coupled by edges. The network structure,
describing how the graph is wired, helps us understand, predict and optimize
the behavior of dynamical systems. In many cases, however, the edges are not
continuously active. As an example, in networks of communication via email,
text messages, or phone calls, edges represent sequences of instantaneous or
practically instantaneous contacts. In some cases, edges are active for
non-negligible periods of time: e.g., the proximity patterns of inpatients at
hospitals can be represented by a graph where an edge between two individuals
is on throughout the time they are at the same ward. Like network topology, the
temporal structure of edge activations can affect dynamics of systems
interacting through the network, from disease contagion on the network of
patients to information diffusion over an e-mail network. In this review, we
present the emergent field of temporal networks, and discuss methods for
analyzing topological and temporal structure and models for elucidating their
relation to the behavior of dynamical systems. In the light of traditional
network theory, one can see this framework as moving the information of when
things happen from the dynamical system on the network, to the network itself.
Since fundamental properties, such as the transitivity of edges, do not
necessarily hold in temporal networks, many of these methods need to be quite
different from those for static networks
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