Recommendation accuracy is especially important in mobile e-commerce environments due to the limited screen size of mobile devices and relatively expensive connection costs. Mobile content tends to be fashionable and ...
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ISBN:
(纸本)1601320639
Recommendation accuracy is especially important in mobile e-commerce environments due to the limited screen size of mobile devices and relatively expensive connection costs. Mobile content tends to be fashionable and are geared for young users. This paper presents a novel method of building a more accurate recommender system for mobile content in a mobile ecommerce environment. The method is based on collaborative filtering, and models content diffusion and user preference transition and incorporates them in constructing pseudo ratings from implicit feedback data. In a variety of experiments, recommender systems based on the method showed significantly better recommendation accuracy than a pure collaborative filtering-based recommender system.
One of the most known applications of Discrete Optimization is on scheduling. In contrast, one of the most known applications of Continuous Nonlinear Optimization is on the control of dynamic systems. In this paper, w...
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ISBN:
(纸本)9789955282839
One of the most known applications of Discrete Optimization is on scheduling. In contrast, one of the most known applications of Continuous Nonlinear Optimization is on the control of dynamic systems. In this paper, we combine both views, solving scheduling problems as dynamic systems, modeled as discrete-time nonlinear optimal control problems with state and control continuous variables subjected to upper and lower bounds. The proposed formulation has the following advantages over discrete (mixedinteger) models: a smaller number of variables is employed, and no 0-1 variable is needed. Therefore, the scheduling problem can be solved as a standard continuous nonlinear program. Complementarity constraints are used to represent scheduling decisions, defining a nonconvex problem, which can be solved with Global Optimization (GO) and Nonlinear programming (NLP) methods. Applications with a continuous process background are discussed, such as the ones from petroleum and water & wastewater industries, because they pose challenging issues, with a combination of nonlinear and combinatorial aspects. One example we discuss in detail is the crude oil scheduling in ports, with tanks, pipelines, jetties, and tanker vessels and blending operations. The recent literature on this problem is rich in mixedinteger linear programming (MILP) models, therefore we developed a procedure to reformulate certain mixed-integer constraints as complementarity constraints, discarding the associated binary variables. The resulting NLP model is equivalent to the original MILP, in a sense that a feasible point in the NLP is also a feasible point in the MILP. A number of numerical cases are discussed to illustrate the validity of this approach. Despite obtaining good results with the NLP approach, we acknowledge that the MILP has the desirable feature of having only global optima, whereas the NLP is non-convex. Therefore, we present an hybrid NLP-MILP scheme that uses the NLP to generate new MILP inte
A pulsed power system has been constructed to explore conditions under which accurate pulsed power measurements can be made with both a thermal sensor and a diode sensor. For the thermal sensor, pulsed power is estima...
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A pulsed power system has been constructed to explore conditions under which accurate pulsed power measurements can be made with both a thermal sensor and a diode sensor. For the thermal sensor, pulsed power is estimated from a simple calculation based on the average power of the pulsed RF signal. For the diode sensor, gating is used to enable direct measurement of the pulsed power. As expected, the results of measurements taken with varying pulse lengths and a constant period show that the dynamic range of the thermal sensor is approximately proportional to the pulse length. The results also indicate that, while a thermal sensor can provide accurate results for many situations, the diode sensor can be used to measure with higher precision than the thermal sensor for lower duty cycles. The paper demonstrates a benchmarking procedure that can be used to explore limitations and capabilities of power sensors as well as elements such as the input RF switch used for pulsed power measurements.
The WaveScalar is the first dataflow architecture that can efficiently provide the sequential memory semantics required by imperative languages. This work presents a speculative memory disambiguation mechanism for thi...
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The WaveScalar is the first dataflow architecture that can efficiently provide the sequential memory semantics required by imperative languages. This work presents a speculative memory disambiguation mechanism for this architecture, the transaction WaveCache. Our mechanism maintains the execution order of memory operations within blocks of code, called waves, but adds the ability to speculatively execute, out-of-order, operations from different waves. This mechanism is inspired by progress in supporting transactional memories. Waves are considered as atomic regions and executed as nested transactions. Wave that have finished the execution of all their memory operations are committed, as soon as the previous waves are also committed. If a hazard is detected in a speculative wave, all the following waves (children) are aborted and re-executed. We evaluated the transactional WaveCache on a set of benchmarks from Spec 2000, Mediabench and Mibench (telecomm). Speedups ranging from 1.31 to 2.24 (related to the original WaveScalar) where observed when the benchmark doesn't perform lots of emulated function calls or access memory very often. Low speedups of 1.1 to slowdowns of 0.96 were observed when the opposite happens or when the memory concurrency was high.
The third international conference on Human-Robot Interaction (HRI-2008) was held in Amsterdam, The Netherlands, March 12-15, 2008. The theme of HRI-2008, "living with robots," highlights the importance of t...
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Clustering is the process of discovering groups within multidimensional data, based on similarities, with a minimal, if any, knowledge of their structure. Distributed data clustering is a recent approach to deal with ...
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Clustering is the process of discovering groups within multidimensional data, based on similarities, with a minimal, if any, knowledge of their structure. Distributed data clustering is a recent approach to deal with geographically distributed databases, since traditional clustering methods require centering all databases in a single dataset. Moreover, current privacy requirements in distributed databases demand algorithms with the ability to process clustering securely. Among the unsupervised neural network models, the self-organizing map (SOM) plays a major role. SOM features include information compression while trying to preserve the topological and metric relationship of the primary data space. This paper presents a strategy for efficient cluster analysis in geographically distributed databases using SOM networks. Local datasets relative to database vertical partitions are applied to distinct maps in order to obtain partial views of the existing clusters. Units of each local map are chosen to represent original data and are sent to a central site, which performs a fusion of the partial results. Experimental results are presented for different datasets.
Emerging 64 bitOSpsilas supply a huge amount of memory address space that is essential for new applications using very large data. It is expected that the memory in connected nodes can be used to store swapped pages e...
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Emerging 64 bitOSpsilas supply a huge amount of memory address space that is essential for new applications using very large data. It is expected that the memory in connected nodes can be used to store swapped pages efficiently, especially in a dedicated cluster which has a high-speed network such as 10 GbE and Infiniband. In this paper, we propose the distributed large memory system (DLM), which provides very large virtual memory by using remote memory distributed over the nodes in a cluster. The performance of DLM programs using remote memory is compared to ordinary programs using local memory. The results of STREAM, NPB and Himeno benchmarks show that the DLM achieves better performance than other remote paging schemes using a block swap device to access remote memory. In addition to performance, DLM offers the advantages of easy availability and high portability, because it is a user-level software without the need for special hardware. To obtain high performance, the DLM can tune its parameters independently from kernel swap parameters. We also found that DLMpsilas independence of kernel swapping provides more stable behavior.
Ethernet-based Passive Optical Network(EPON) is considered a very promising solution for the first mile problem of the next generation *** to its particular characteristic of shared media structure,EPON suffers many s...
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Ethernet-based Passive Optical Network(EPON) is considered a very promising solution for the first mile problem of the next generation *** to its particular characteristic of shared media structure,EPON suffers many security vulnerabilities. Communication security must be guaranteed when EPON is applied in *** paper gives a general introduction to the EPON system,analyzes the potential threats and attacks pertaining to the EPON system,and presents effective countermea-sures against these threats and attacks with emphasis on the authentication protocols and key distribution.
HER-2/neu (HER2) has been shown to be a valuable biomarker for breast cancer. However, inter-observer variability has been reported in the evaluation of HER2 with immunohistochemistry. It has been suggested that autom...
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HER-2/neu (HER2) has been shown to be a valuable biomarker for breast cancer. However, inter-observer variability has been reported in the evaluation of HER2 with immunohistochemistry. It has been suggested that automated computer-based evaluation can provide a consistent and objective measure of HER2 expression. In this manuscript, we present an automated method for the quantitative assessment of HER2 using digital microscopy. The method employs imaging algorithms on whole slide images of tissue specimens for the extraction of two features describing HER2 membrane staining, namely membrane staining completeness and membrane staining intensity. A classifier was trained to merge the extracted features into an overall slide assessment score. Preliminary results showed good agreement with the provided truth. The developed automated method has the potential to be used as a computer aid for the immunohistochemical evaluation of HER2 expression with the objective of increasing observer reproducibility.
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