Visual recording of everyday human activities and behaviour over the long term is now feasible and with the widespread use of wearable devices embedded with cameras this offers the potential to gain real insights into...
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Parallel Test Paper Generation (k-TPG) is a biobjective distributed resource allocation problem, which aims to generate multiple similarly optimal test papers automatically according to multiple userspecified criteria...
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ISBN:
(纸本)9781577357384
Parallel Test Paper Generation (k-TPG) is a biobjective distributed resource allocation problem, which aims to generate multiple similarly optimal test papers automatically according to multiple userspecified criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in maximizing the collective objective functions. In this paper, we propose a Collective Biobjective Optimization (CBO) algorithm for solving k-TPG. CBO is a multi-step greedy-based approximation algorithm, which exploits the submodular property for biobjective optimization of k-TPG. Experiment results have shown that CBO has drastically outperformed the current techniques in terms of paper quality and runtime efficiency.
Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or design...
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Variable or value elimination in a constraint satisfaction problem (CSP) can be used in preprocessing or during search to reduce search space size. A variable elimination rule (value elimination rule) allows the polyn...
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Gene expression data are widely used in classification tasks for medical diagnosis. data scaling is recommended and helpful for learning the classification models. In this study, we propose a data scaling algorithm to...
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Air pollution attracts extensive attention globally due to its critical impacts on human life. Monitoring systems providing real-time micro-level pollution information have been developed to provide authorities with d...
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Air pollution attracts extensive attention globally due to its critical impacts on human life. Monitoring systems providing real-time micro-level pollution information have been developed to provide authorities with data to mitigate these impacts. However, current systems are usually application-specific with fixed hardware and software configurations. They are inconvenient in maintenance, infeasible in reconfiguration, and unexpandable in sensing capabilities. This paper proposes a novel Modular Sensor System (MSS), which aims at tackling these issues by adopting the proposed Universal Sensor Interface (USI) and modular design in a sensor node. A compact MSS senor node with expandable plug-and-play sensor modules and multiple Wireless Sensor Networks (WSNs) compatibility is implemented and evaluated. Results indicate that MSS sensor node can be deployed in different scenarios while dynamically adapting to reconfigurations and monitoring air pollution at low concentration levels with high energy efficiency. We anticipate that MSS is able to relax the efforts on system maintenance, adaptation, and evolution in real-life large-scale deployment situation.
With this poster, we will present the Linked SPARQL Query (LSQ) dataset, which describes SPARQL queries taken from the logs of public endpoints. We introduce the initial four query logs that we have taken and the extr...
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With this poster, we will present the Linked SPARQL Query (LSQ) dataset, which describes SPARQL queries taken from the logs of public endpoints. We introduce the initial four query logs that we have taken and the extraction process applied: the types of meta-data captured, how the data are modelled, what vocabularies we use, etc. The LSQ dataset currently contains 73 million triples describing 5.7 million query executions and is publicly available as Linked data and through a SPARQL endpoint. We believe that by providing insights on how SPARQL is used in practice, the LSQ dataset could benefit areas of SPARQL research, including caching, benchmarking, usability, optimisations, etc.
Gene expression data are widely used in classification tasks for medical diagnosis. data scaling is recommended and helpful for learning the classification models. In this study, we propose a data scaling algorithm to...
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ISBN:
(纸本)9781467379847
Gene expression data are widely used in classification tasks for medical diagnosis. data scaling is recommended and helpful for learning the classification models. In this study, we propose a data scaling algorithm to transform the data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative density function of the data. The proposed algorithm is robust to outliers, and experimental results show that models learned using data scaled by the proposed algorithm generally outperform the ones using min-max mapping and z-score which are currently the most commonly used data scaling algorithms.
The Alternating Direction Method of Multipliers (ADMM) has been studied for years. Traditional ADMM algorithms need to compute, at each iteration, an (empirical) expected loss function on all training examples, result...
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ISBN:
(纸本)9781510810587
The Alternating Direction Method of Multipliers (ADMM) has been studied for years. Traditional ADMM algorithms need to compute, at each iteration, an (empirical) expected loss function on all training examples, resulting in a computational complexity proportional to the number of training examples. To reduce the complexity, stochastic ADMM algorithms were proposed to replace the expected loss function with a random loss function associated with one uniformly drawn example plus a Bregman divergence ter-m. The Bregman divergence, however, is derived from a simple 2nd-order proximal function, i.e., the half squared norm, which could be a subopti-mal choice. In this paper, we present a new family of stochastic ADMM algorithms with optimal 2nd-order proximal functions, which produce a new family of adaptive stochastic ADMM methods. We theoretically prove that the regret bounds are as good as the bounds which could be achieved by the best proximal function that can be chosen in hindsight. Encouraging empirical results on a variety of real-world datasets confirm the effectiveness and efficiency of the proposed algorithms.
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