As the de facto Internet inter-domain routing protocol, BGP protocol has a number of vulnerabilities and weakness. Monitoring BGP is an effective way to improve the security of inter-domain routing. This paper present...
详细信息
Improving the channel utilization is a significant issue to enhance the performance in WLAN. This paper presents, Pillow Talks MAC (PT-MAC), a novel spectrum sharing mechanism to create an extra channel (pt-channel) f...
详细信息
Nowadays GPS embedded in mobile device such as smartphones can easily identify people's physical locations. However, in daily life people are more concerned about semantic locations (such as dormitories, laborator...
详细信息
ISBN:
(纸本)9781467372121
Nowadays GPS embedded in mobile device such as smartphones can easily identify people's physical locations. However, in daily life people are more concerned about semantic locations (such as dormitories, laboratories, shopping malls, etc.). Usually GPS positioning uses continuous sampling method, which results in a lot of semantically independent sample points. We call these points outliers. How to remove outliers from GPS data and thereby cluster meaningful semantic places is a research challenge in current field of pervasive computing. Aiming at the characteristics of this problem, we first propose a novel approach to add semantic annotations to newly discovered places every day. We use an unsupervised method to discover semantic places, which ensures accuracy of the results and reduces the amount of calculation. Secondly, we discuss the concept of outliers in GPS data collected in daily life, and then eliminate outliers using a density-based method. Moreover, we perform experiments to validate its effectiveness. Thirdly by taking advantage of rule-based inference and reverse geocoding we proposed an approach to calculate the probable semantic labels, which can help user annotate places and reduce the burden on users. Finally, we develop a local System Annotating Semantic Label of Location(SASLL) and by carrying out experiments we demonstrate the validity of our research.
Sparse coding has shown its great potential in learning image feature representation. Recent developed methods such as group sparse coding prefer discovering the group relationships among examples and have achieved th...
详细信息
Sparse coding has shown its great potential in learning image feature representation. Recent developed methods such as group sparse coding prefer discovering the group relationships among examples and have achieved the state-of-the-art results in image classification. However, they suffer from poor robustness shortcomings in practice. This paper proposes a robust weighted supervised sparse coding method(RWSSC) to address this ***, RWSSC distinguishes different classes' contributions to the sparse coding by a novel weighting strategy meanwhile removes the out liers by imposing1 l-regularization over the noisy entries. Benefitting from these strategies, RWSSC can effectively boost performance of sparse coding in image ***, we developed the block coordinate descent algorithm to optimize it, and proved its *** results of image classification on two popular datasets show that RWSSC outperforms the representative sparse coding methods in quantities.
Stragglers can temporize jobs and reduce cluster efficiency *** researches have been contributed to the solution,such as Blacklist[8],speculative execution[1,6],Dolly[8].In this paper,we put forward a new approach for...
详细信息
Stragglers can temporize jobs and reduce cluster efficiency *** researches have been contributed to the solution,such as Blacklist[8],speculative execution[1,6],Dolly[8].In this paper,we put forward a new approach for mitigating stragglers in Map Reduce,name *** starts task clones only for high-risk delaying *** experiments have been carried and results show that it can decrease the job delaying risk with fewer resources *** small jobs,Hummer also improves job completion time by 48% and 10% compared to LATE and Dolly.
The coupling of microwaves into apertures plays an important part in many electromagnetic physics and engineering fields. When the width of apertures is very small, Finite Difference Time Domain (FDTD) simulation of t...
详细信息
Multi-NoC (multiple network-on-chip) has demonstrated its advantages in power gating for reducing leakage power. This work presents Chameleon, a novel heterogeneous Multi-NoC design. Chameleon employs a fine-grained p...
详细信息
Multi-NoC (multiple network-on-chip) has demonstrated its advantages in power gating for reducing leakage power. This work presents Chameleon, a novel heterogeneous Multi-NoC design. Chameleon employs a fine-grained power gating algorithm which exploits power saving opportunities at different levels of granularity simultaneously. Integrated with a performance-aware traffic allocation policy, Chameleon is able to achieve both high power efficiency and good performance at varying network utilization. Our experimental results show that Chameleon delivers an average of 3.39% higher performance than Catnap, the best in the literature. More importantly, Chameleon consumes an average of 17.16% less power than Catnap.
The contribution of parasitic bipolar amplification to SETs is experimentally verified using two P-hit target chains in the normal layout and in the special layout. For PMOSs in the normal layout, the single-event cha...
详细信息
The contribution of parasitic bipolar amplification to SETs is experimentally verified using two P-hit target chains in the normal layout and in the special layout. For PMOSs in the normal layout, the single-event charge collection is composed of diffusion, drift, and the parasitic bipolar effect, while for PMOSs in the special layout, the parasitic bipolar junction transistor cannot turn on. Heavy ion experimental results show that PMOSs without parasitic bipolar amplification have a 21.4% decrease in the average SET pulse width and roughly a 40.2% reduction in the SET cross-section.
Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this...
详细信息
ISBN:
(纸本)9781467365994
Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this paper, we propose to leverage approximation techniques to data clustering to obtain the trade-off between clustering efficiency and result quality, along with online accuracy estimation. The proposed method is based on the bootstrap trials. We implemented this method as an Intelligent Bootstrap Library (IBL) on Spark to support efficient data clustering. Intensive evaluations show that IBL can provide a 2x speed-up over the state of art solution with the same error bound.
In data center networks, resource allocation based on workload is an effective way to allocate the infrastructure resources to diverse cloud applications and satisfy the quality of service for the users, which refers ...
详细信息
In data center networks, resource allocation based on workload is an effective way to allocate the infrastructure resources to diverse cloud applications and satisfy the quality of service for the users, which refers to mapping a large number of workloads provided by cloud users/tenants to substrate network provided by cloud providers. Although the existing heuristic approaches are able to find a feasible solution, the quality of the solution is not guaranteed. Concerning this issue, based on the minimum mapping cost, this paper solves the resource allocation problem by modeling it as a distributed constraint optimization problem. Then an efficient approach is proposed to solve the resource allocation problem, aiming to find a feasible solution and ensuring the optimality of the solution. Finally, theoretical analysis and extensive experiments have demonstrated the effectiveness and efficiency of our proposed approach.
暂无评论