The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take...
详细信息
The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The "conversion" here refers to actu- ally a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product's ac- tual conversion period and the application's target conversion period has been the subtle culprit compromising many exist- ing recommendation algorithms. The challenging question: what products should be recom- mended for a given time period to maximize conversion--is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model (rTCP), which con- siders both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accom- modate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high good- ness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for ad- vertising and product recommendation.
We propose and experiment a Social Networking Service (SNS) for local communities for tsunami disaster control. It is an easy-to-use GIS-based system with powerful GIS analysis capabilities. One of the features of the...
详细信息
ISBN:
(纸本)9780780397408
We propose and experiment a Social Networking Service (SNS) for local communities for tsunami disaster control. It is an easy-to-use GIS-based system with powerful GIS analysis capabilities. One of the features of the system is resident participation. The GIS layer structure proposed in this paper nicely supports this user participation. The system architecture and the use of the system for tsunami disaster control are discussed. We report ongoing developments in Hachinohe-City, Japan.
We propose a novel hybrid phase-locked loop (PLL) architecture for overcoming the trade-off between fast locking time and low spur. To reduce the settling time and meanwhile suppress the reference spurs, we employ a...
详细信息
We propose a novel hybrid phase-locked loop (PLL) architecture for overcoming the trade-off between fast locking time and low spur. To reduce the settling time and meanwhile suppress the reference spurs, we employ a wide-band single-path PLL and a narrow-band dual-path PLL in a transient state and a steady state, respectively, by changing the loop bandwidth according to the gain of voltage controlled oscillator (VCO) and the resister of the loop filter. The hybrid PLL is implemented in a 0.18-μm complementary metal oxide semiconductor (CMOS) process with a total die area of 1.4×0.46 mm2. The measured results exhibit a reference spur level of lower than -73 dB with a reference frequency of 10 MHz and a settling time of 20 μs with 40 MHz frequency jump at 2 GHz. The total power consumption of the hybrid PLL is less than 27 mW with a supply voltage of 1.8 V.
cloudcomputing environment is becoming increasingly complex due to its large-scale information growth and increasing heterogeneity of computing resources. Hierarchical cloudcomputing dividing the system into multi-l...
详细信息
cloudcomputing environment is becoming increasingly complex due to its large-scale information growth and increasing heterogeneity of computing resources. Hierarchical cloudcomputing dividing the system into multi-levels with multiple subsystems to support the adaptability to abundant requests from users has been widely applied and brings great challenges to resource scheduling. It is critical to find an effective way to address the complex scheduling problems in hierarchical cloudcomputing, whose scenarios and optimization objectives often change with the types of subsystems. In this paper, we propose a scheduling framework to select the scheduling algorithms (SFSSA) for different scheduling scenarios considering no algorithm well suitable to all scenarios. To concretize SFSSA, we propose deep learning-based algorithms selectors (DLS) trained by labeled data and deep reinforcement learning-based algorithms selectors (DRLS) trained by feedback from dynamic scenarios to complete the algorithms selection regarding the scheduling algorithms as selectable tools. Then, we apply strategies including pre-trained model, long experience reply and joint training to improve the performance of DRLS. To enable the quantitative comparison of selectors, we introduce a weighted cost model for the trade-off between solution and complexity. Through multiple sets of experiments in hierarchical cloudcomputing with multi subsystems for five types of scheduling problems and varying weights of cost, we demonstrate DLS and DRLS outperform baseline strategies. Compared with random selector, greedy selector, round-robin selector, single best selector, virtual best selector and single fast selector, DLS reduces the cost by 47.4%, 46.1%, 33.9%, 47.9%, 19.3%, 18.8% under stable parameter ranges, and DRLS reduces the cost by 41.1%, 40.6%, 11.7%, 42.3%, 11.5%, 12.5% in dynamic scenarios respectively. In experiments, we also validate DRLS has stronger adaptability than DLS in dynamic schedulin
作者:
Liu, YunpengQu, DanSchool of Information Systems Engineering
University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Department of Artificial Intelligence Henan China Systems Engineering
University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Henan China
Limited data availability remains a significant challenge for Whisper's low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully re...
详细信息
Self-supervised models have demonstrated remarkable performance in speech processing by learning latent representations from large amounts of unlabeled data. Although these models yield promising results on low-resour...
详细信息
Contemporary distributedcomputingsystems (DCS) such as cloud Data Centers are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Interne...
详细信息
ISBN:
(数字)9781728182667
ISBN:
(纸本)9781728182674
Contemporary distributedcomputingsystems (DCS) such as cloud Data Centers are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things (IoT)-driven applications are producing a huge amount of data that requires real-time processing and fast response. Managing these resources efficiently to provide reliable services to end-users or applications is a challenging task. The existing Resource Management systems (RMS) rely on either static or heuristic solutions inadequate for such composite and dynamic systems. The advent of Artificial Intelligence (AI) due to data availability and processing capabilities manifested into possibilities of exploring data-driven solutions in RMS tasks that are adaptive, accurate, and efficient. In this regard, this paper aims to draw the motivations and necessities for data-driven solutions in resource management. It identifies the challenges associated with it and outlines the potential future research directions detailing where and how to apply the data-driven techniques in the different RMS tasks. Finally, it provides a conceptual data-driven RMS model for DCS and presents the two real-time use cases (GPU frequency scaling and data centre resource management from Google cloud and Microsoft Azure) demonstrating AI-centric approaches' feasibility.
Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an Unmanned Aerial Vehicle (UAV) swarm. However, traditional FL a...
详细信息
Machine learning time series models have been used to predict COVID-19 pandemic infections. Based on the public dataset from Johns Hopkins, we present a novel framework for forecasting COVID-19 infections. We implemen...
详细信息
暂无评论