Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

Mining dependency in distributed systems through unstructured logs analysis

Abstract: Dependencies among system components are crucial to locating root errors in a distributed system. In this paper, we propose an approach to mine intercomponent dependencies from unstructured logs. The technique requires neither additional system instrumentation nor any application specific knowledge. In the approach, we first parse each log message into its log key and parameters. Then, we find dependent log key pairs belong to different components by leveraging co-occurrence analysis and parameter correspondence. After that, we use Bayesian decision theory to estimate the dependency direction of each dependent log key pair. We further apply time delay consistency to remove false positive detections. Case studies on Hadoop show that the technique successfully identifies the dependencies among the distributed system components.

High-Q and high-sensitivity width-modulated photonic crystal single nanobeam air-mode cavity for refractive index sensing

Abstract: We propose a novel optical sensor based on a one-dimensional (1D) photonic crystal (PhC) single nanobeam air-mode cavity (SNAC). The performance of the device is investigated theoretically. By introducing a quadratically modulated width tapering structure, a waveguide-coupled 1D-PhC SNAC with a calculated high quality factor of 5.16×10(6) and an effective mode volume of V(eff)∼2.18(λ/n(si))(3) can be achieved. For the air mode mentioned above, the light field can be strongly localized inside the air region (low index) and overlaps sufficiently with the analytes. Thus, the suggested PhC SNAC can be used for high-sensitivity refractive index sensing with an estimated high sensitivity of 537.8 nm/RIU. To the best of our knowledge, this is the first PhC single nanobeam geometry that features both high Q-factors and high sensitivity, and is potentially an ideal platform for realizing ultracompact lab-on-a-chip applications with dense arrays of functionalized spots for multiplexed sensing.

An Efficient Collaboration and Incentive Mechanism for Internet of Vehicles (IoV) With Secured Information Exchange Based on Blockchains

Abstract: With the rapid development of Internet of Things (IoT), mobile crowdsensing (MCS), i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, MCS lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet of Vehicles (IoV)—a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice, some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multivehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we first propose a new model for the scenario of two vehicles collaboration, considering the situation of the emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Second, for an emergent task, a novel time-window-based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.

Multi-Dimensional QoS Prediction for Service Recommendations

Abstract: Advances in mobile Internet technology have enabled the clients of Web services to be able to keep their service sessions alive while they are on the move. Since the services consumed by a mobile client may be different over time due to client location changes, a multi-dimensional spatiotemporal model is necessary for analyzing the service consumption relations. Moreover, competitive Web service recommenders for the mobile clients must be able to predict unknown quality-of-service (QoS) values well by taking into account the target client's service requesting time and location, e.g., performing the prediction via a set of multi-dimensional QoS measures. Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e.g., time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient Web service recommendation for mobile clients via tensor decomposition and reconstruction optimization algorithms. In light of the unavailability of measured multi-dimensional QoS datasets in the public domain, this paper also presents a transformational approach to creating a credible multi-dimensional QoS dataset from a measured taxi usage dataset which contains high dimensional time and space information. Comparative experimental evaluation results show that the proposed QoS prediction approach can result in much better accuracy in recommending Web services than several other representative ones.

The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification

Abstract: Key for solving fine-grained image categorization is finding discriminate and local regions that correspond to subtle visual traits. Great strides have been made, with complex networks designed specifically to learn part-level discriminate feature representations. In this paper, we show it is possible to cultivate subtle details without the need for overly complicated network designs or training mechanisms -- a single loss is all it takes. The main trick lies with how we delve into individual feature channels early on, as opposed to the convention of starting from a consolidated feature map. The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component. The discriminality component forces all feature channels belonging to the same class to be discriminative, through a novel channel-wise attention mechanism. The diversity component additionally constraints channels so that they become mutually exclusive on spatial-wise. The end result is therefore a set of feature channels that each reflects different locally discriminative regions for a specific class. The MC-Loss can be trained end-to-end, without the need for any bounding-box/part annotations, and yields highly discriminative regions during inference. Experimental results show our MC-Loss when implemented on top of common base networks can achieve state-of-the-art performance on all four fine-grained categorization datasets (CUB-Birds, FGVC-Aircraft, Flowers-102, and Stanford-Cars). Ablative studies further demonstrate the superiority of MC-Loss when compared with other recently proposed general-purpose losses for visual classification, on two different base networks. Code available at this https URL