405,076 research outputs found
Process-oriented Iterative Multiple Alignment for Medical Process Mining
Adapted from biological sequence alignment, trace alignment is a process
mining technique used to visualize and analyze workflow data. Any analysis done
with this method, however, is affected by the alignment quality. The best
existing trace alignment techniques use progressive guide-trees to
heuristically approximate the optimal alignment in O(N2L2) time. These
algorithms are heavily dependent on the selected guide-tree metric, often
return sum-of-pairs-score-reducing errors that interfere with interpretation,
and are computationally intensive for large datasets. To alleviate these
issues, we propose process-oriented iterative multiple alignment (PIMA), which
contains specialized optimizations to better handle workflow data. We
demonstrate that PIMA is a flexible framework capable of achieving better
sum-of-pairs score than existing trace alignment algorithms in only O(NL2)
time. We applied PIMA to analyzing medical workflow data, showing how iterative
alignment can better represent the data and facilitate the extraction of
insights from data visualization.Comment: accepted at ICDMW 201
Consensus-based approach to peer-to-peer electricity markets with product differentiation
With the sustained deployment of distributed generation capacities and the
more proactive role of consumers, power systems and their operation are
drifting away from a conventional top-down hierarchical structure. Electricity
market structures, however, have not yet embraced that evolution. Respecting
the high-dimensional, distributed and dynamic nature of modern power systems
would translate to designing peer-to-peer markets or, at least, to using such
an underlying decentralized structure to enable a bottom-up approach to future
electricity markets. A peer-to-peer market structure based on a Multi-Bilateral
Economic Dispatch (MBED) formulation is introduced, allowing for
multi-bilateral trading with product differentiation, for instance based on
consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is
described to solve the MBED in fully decentralized manner. A set of realistic
case studies and their analysis allow us showing that such peer-to-peer market
structures can effectively yield market outcomes that are different from
centralized market structures and optimal in terms of respecting consumers
preferences while maximizing social welfare. Additionally, the RCI solving
approach allows for a fully decentralized market clearing which converges with
a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System
Evidence Propagation and Consensus Formation in Noisy Environments
We study the effectiveness of consensus formation in multi-agent systems
where there is both belief updating based on direct evidence and also belief
combination between agents. In particular, we consider the scenario in which a
population of agents collaborate on the best-of-n problem where the aim is to
reach a consensus about which is the best (alternatively, true) state from
amongst a set of states, each with a different quality value (or level of
evidence). Agents' beliefs are represented within Dempster-Shafer theory by
mass functions and we investigate the macro-level properties of four well-known
belief combination operators for this multi-agent consensus formation problem:
Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging
operator. The convergence properties of the operators are considered and
simulation experiments are conducted for different evidence rates and noise
levels. Results show that a combination of updating on direct evidence and
belief combination between agents results in better consensus to the best state
than does evidence updating alone. We also find that in this framework the
operators are robust to noise. Broadly, Yager's rule is shown to be the better
operator under various parameter values, i.e. convergence to the best state,
robustness to noise, and scalability.Comment: 13th international conference on Scalable Uncertainty Managemen
Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Growing progress in sensor technology has constantly expanded the number and
range of low-cost, small, and portable sensors on the market, increasing the
number and type of physical phenomena that can be measured with wirelessly
connected sensors. Large-scale deployments of wireless sensor networks (WSN)
involving hundreds or thousands of devices and limited budgets often constrain
the choice of sensing hardware, which generally has reduced accuracy,
precision, and reliability. Therefore, it is challenging to achieve good data
quality and maintain error-free measurements during the whole system lifetime.
Self-calibration or recalibration in ad hoc sensor networks to preserve data
quality is essential, yet challenging, for several reasons, such as the
existence of random noise and the absence of suitable general models.
Calibration performed in the field, without accurate and controlled
instrumentation, is said to be in an uncontrolled environment. This paper
provides current and fundamental self-calibration approaches and models for
wireless sensor networks in uncontrolled environments
Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory
In Internet of Vehicles (IoV), data sharing among vehicles is essential to
improve driving safety and enhance vehicular services. To ensure data sharing
security and traceability, highefficiency Delegated Proof-of-Stake consensus
scheme as a hard security solution is utilized to establish blockchain-enabled
IoV (BIoV). However, as miners are selected from miner candidates by
stake-based voting, it is difficult to defend against voting collusion between
the candidates and compromised high-stake vehicles, which introduces serious
security challenges to the BIoV. To address such challenges, we propose a soft
security enhancement solution including two stages: (i) miner selection and
(ii) block verification. In the first stage, a reputation-based voting scheme
for the blockchain is proposed to ensure secure miner selection. This scheme
evaluates candidates' reputation by using both historical interactions and
recommended opinions from other vehicles. The candidates with high reputation
are selected to be active miners and standby miners. In the second stage, to
prevent internal collusion among the active miners, a newly generated block is
further verified and audited by the standby miners. To incentivize the standby
miners to participate in block verification, we formulate interactions between
the active miners and the standby miners by using contract theory, which takes
block verification security and delay into consideration. Numerical results
based on a real-world dataset indicate that our schemes are secure and
efficient for data sharing in BIoV.Comment: 12 pages, submitted for possible journal publicatio
Evaluation of e-learning web sites using fuzzy axiomatic design based approach
High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations for future research are offered
Blockchain For Food: Making Sense of Technology and the Impact on Biofortified Seeds
The global food system is under pressure and is in the early stages of a major transition towards more transparency, circularity, and personalisation. In the coming decades, there is an increasing need for more food production with fewer resources. Thus, increasing crop yields and nutritional value per crop is arguably an important factor in this global food transition.
Biofortification can play an important role in feeding the world. Biofortified seeds create produce with increased nutritional values, mainly minerals and vitamins, while using the same or less resources as non-biofortified variants. However, a farmer cannot distinguish a biofortified seed from a regular seed. Due to the invisible nature of the enhanced seeds, counterfeit products are common, limiting wide-scale adoption of biofortified crops. Fraudulent seeds pose a major obstacle in the adoption of biofortified crops.
A system that could guarantee the origin of the biofortified seeds is therefore required to ensure widespread adoption. This trust-ensuring immutable proof for the biofortified seeds, can be provided via blockchain technology
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