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Scholarly search systems greatly aid the deep understanding of scholarly data and facilitate the research activities of scholars for scientific studies. Though a number of such systems have been developed, most of them either support rankings of limited search of entities or provide only basic ranking metrics. These existing systems also mainly adopt RDBMSs as their storage such that the linked feature of scholarly data is not fully exploited. In this study, we design and develop a novel scholarly search system Athena. (1) It supports four types of scholarly entity searches: articles, authors, venues and affiliations, and is equipped with five ranking metrics, including three traditional metrics and two comprehensive importance ranking metrics. (2) It also provides profiling of scholarly entities. (3) It further utilizes a graph storage to directly leverage the linked feature for speeding up the processing of complex queries. We demonstrate the advantages of Athena at scholarly search, profiling, graph storage and ranking quality.

Cluster administrators are facing great pressures to improve cluster utilization through workload co-location. Guaranteeing performance of long-running applications (LRAs), however, is far from settled as unpredictable interference across applications is catastrophic to QoS [2]. Current solutions such as [1] usually employ sandboxed and offline profiling for different workload combinations and leverage them to predict incoming interference. However, the time complexity restricts the applicability to complex co-locations. Hence, this issue entails a new framework to harness runtime performance and mitigate the time cost with machine intelligence: i) It is desirable to explore a quantitative relationship between allocated resource and consequent workload performance, not relying on analyzing interference derived from different workload combinations. The majority of works, however, depend on offline profiling and training which may lead to model aging problem. Moreover, multi-resource dimensions (e.g., LLC contention) that are not completely included by existing works but have impact on performance interference need to be considered [3]. ii) Workload co-location also necessitates fine-grained isolation and access control mechanism. Once performance degradation is detected, dynamic resource adjustment will be enforced and application will be assigned an access to specific slices of each resources. Inferring a "just enough" amount of resource adjustment ensures the application performance can be secured whilst improving cluster utilization. We present Perphon, a runtime agent on a per node basis, that decouples ML-based performance prediction and resource inference from centralized scheduler. Figure 1 outlines the proposed architecture. We initially exploit sensitivity of applications to multi-resources to establish performance prediction. To achieve this, Metric Monitor aggregates application fingerprint and system-level performance metrics including CPU, memory, Last Level Cache (LLC), memory bandwidth (MBW) and number of running threads, etc. They are enabled by Intel-RDT and precisely obtained from resource group manager. Perphon employs an Online Gradient Boost Regression Tree (OGBRT) approach to resolve model aging problem. Res-Perf Model warms up via offline learning that merely relies on a small volume of profiling in the early stage, but evolves with arrival of workloads. Consequently, parameters will be automatically updated and synchronized among agents. Anomaly Detector can timely pinpoint a performance degradation via LSTM time-series analysis and determine when and which application need to be re-allocated resources. Once abnormal performance counter or load is detected, Resource Inferer conducts a gradient ascend based inference to work out a proper slice of resources, towards dynamically recovering targeted performance. Upon receiving an updated re-allocation, Access Controller re-assigns a specific portion of the node resources to the affected application. Eventually, Isolation Executor enforces resource manipulation and ensures performance isolation across applications. Specifically, we use cgroup cpuset and memory subsystem to control usage of CPU and memory while leveraging Intel-RDT technology to underpin the manipulation of LLC and MBW. For fine-granularity management, we create different groups for LRA and batch jobs when the agent starts. Our prototype integration with Node Manager of Apache YARN shows that throughput of Kafka data-streaming application in Perphon is 2.0x and 1.82x times that of isolation execution schemes in native YARN and pure cgroup cpu subsystem.

Path planning is an optimization problem that is crucial for robot or UCAV. Among the optimization approaches, we focus in this paper on a new hybrid modified Rapidly Exploring Random Tree algorithm (RRTs) and Differential Evolution (DE), for solving the optimization path planning problem to generate a fast and optimal 3D collision-free path under complex environment. We demonstrate the proposed algorithm performance through comparative analysis with Improved Bat algorithm (IBA). The results demonstrated the robustness and effectiveness of the proposed algorithm for generating an optimal free collision path in a short time, which is suitable for the UCAV applications.

With the rapid proliferation of camera-equipped smart devices (e.g., smartphones, pads, tablets), visible light communication (VLC) over screen-camera links emerges as a novel form of near-field communication. Such communication via smart devices is highly competitive for its user-friendliness, security, and infrastructure-less (i.e., no dependency on WiFi or cellular infrastructure). However, existing approaches mostly focus on improving the transmission speed and ignore the transmission reliability. Considering the interplay between the transmission speed and reliability towards effective end-to-end communication, in this paper, we aim to boost the throughput over screen-camera links by enhancing the transmission reliability. To this end, we propose RDCode, a robust dynamic barcode which enables a novel packet-frame-block structure. Based on the layered structure, we design different error correction schemes at three levels: intra-blocks, inter-blocks and inter-frames, in order to verify and recover the lost blocks and frames. Finally, we implement RDCode and experimentally show that RDCode reaches a high level of transmission reliability (e.g., reducing the error rate to 10%) and yields a at least doubled transmission rate, compared with the existing state-of-the-art approach COBRA.

Visible light communication (VLC) over screen-camera links emerges as a novel form of near-field communication, and it offers a user-friendly, infrastructure-less and secure communication, which is highly competitive for one-time file transfer [1 - 4]. However, the limitations of smart devices and the uncertainty of user behaviors seriously impair the transmission reliability and hinder its applicability. Worse still, existing approaches [1, 2, 4]mostly focus on improving the transmission speed and ignore the transmission reliability. Hence, RDCode is proposed to boost the throughput over screen-camera links, by making use of a novel barcode design and several effective techniques to enhance the transmission reliability. In this demo, we show that our RDCode prototype system addresses many practical challenges. A short video on our prototype system is accessible from http://mashuai.buaa.edu.cn/demo/RDCode.mp4.

Recent years we have witnessed the rapid advent of mobile cloud computing, in which remote software is delivered as a service and accessed by mobile device users over the Internet. However, most existing remote display technologies for high motion application (e.g, movie) have defects in latency and bandwidth. In this paper, we designed an adaptive multimedia streaming enabled remote interactivity system, Muse, to utilize remote resources with reduced display update traffic and response latency. A window-aware updating mechanism is designed as an adaptation scheme, which allows users to focus on the current application in use and also enable them to switch between applications on the fly. Besides, a windowed display encoder using H.264 video codec is integrated into the remote frame buffer protocol to achieve high performance in compression to address the high latency limitation of mobile Internet. Experimental results show that the windowed display Muse mechanism can successfully reduce network traffic, loading time and response latency of remote display and interaction. Our system can achieve in average 22fps of 1024*768 desktop multimedia playbacks with good video quality under 1 Mbit/s of bandwidth limitation.