Learning to Generate Posters of Scientific Papers

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 201

Energy Efficiency in the ICT - Profiling Power Consumption in Desktop Computer Systems

Energy awareness in the ICT has become an important issue. Focusing on software, recent work suggested the existence of a relationship between power consumption, software configuration and usage patterns in computer systems. The aim of this work was collecting and analysing power consumption data of general-purpose computer systems, simulating common usage scenarios, in order to extract a power consumption profile for each scenario. We selected two desktop systems of different generations as test machines. Meanwhile, we developed 11 usage scenarios, and conducted several test runs of them, collecting power consumption data by means of a power meter. Our analysis resulted in an estimation of a power consumption value for each scenario and software application used, obtaining that each single scenario introduced an overhead from 2 to 11 Watts, which corresponds to a percentage increase that can reach up to 20% on recent and more powerful systems. We determined that software and its usage patterns impact consistently on the power consumption of computer systems. Further work will be devoted to evaluate how power consumption is affected by the usage of specific system resource

Semantic multimedia remote display for mobile thin clients

Current remote display technologies for mobile thin clients convert practically all types of graphical content into sequences of images rendered by the client. Consequently, important information concerning the content semantics is lost. The present paper goes beyond this bottleneck by developing a semantic multimedia remote display. The principle consists of representing the graphical content as a real-time interactive multimedia scene graph. The underlying architecture features novel components for scene-graph creation and management, as well as for user interactivity handling. The experimental setup considers the Linux X windows system and BiFS/LASeR multimedia scene technologies on the server and client sides, respectively. The implemented solution was benchmarked against currently deployed solutions (VNC and Microsoft-RDP), by considering text editing and WWW browsing applications. The quantitative assessments demonstrate: (1) visual quality expressed by seven objective metrics, e.g., PSNR values between 30 and 42 dB or SSIM values larger than 0.9999; (2) downlink bandwidth gain factors ranging from 2 to 60; (3) real-time user event management expressed by network round-trip time reduction by factors of 4-6 and by uplink bandwidth gain factors from 3 to 10; (4) feasible CPU activity, larger than in the RDP case but reduced by a factor of 1.5 with respect to the VNC-HEXTILE

Handover management in mobile WiMAX using adaptive cross-layer technique

The protocol type and the base station (BS) technology are the main communication media between the Vehicle to Infrastructure (V2I) communication in vehicular networks. During high speed vehicle movement, the best communication would be with a seamless handover (HO) delay in terms of lower packet loss and throughput. Many studies have focused on how to reduce the HO delay during lower speeds of the vehicle with data link (L2) and network (L3) layers protocol. However, this research studied the Transport Layer (L4) protocol mobile Stream Control Transmission Protocol (mSCTP) used as an optimal protocol in collaboration with the Location Manager (LM) and Domain Name Server (DNS). In addition, the BS technology that performs smooth HO employing an adaptive algorithm in L2 to perform the HO according to current vehicle speed was also included in the research. The methods derived from the combination of L4 and the BS technology methods produced an Adaptive Cross-Layer (ACL) design which is a mobility oriented handover management scheme that adapts the HO procedure among the protocol layers. The optimization has a better performance during HO as it is reduces scanning delay and diversity level as well as support transparent mobility among layers in terms of low packet loss and higher throughput. All of these metrics are capable of offering maximum flexibility and efficiency while allowing applications to refine the behaviour of the HO procedure. Besides that, evaluations were performed in various scenarios including different vehicle speeds and background traffic. The performance evaluation of the proposed ACL had approximately 30% improvement making it better than the other handover solutions

BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking

Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data)

Given Enough Eyeballs, all Bugs are Shallow - A Literature Review for the Use of Crowdsourcing in Software Testing

Over the last years, the use of crowdsourcing has gained a lot of attention in the domain of software engineering. One key aspect of software development is the testing of software. Literature suggests that crowdsourced software testing (CST) is a reliable and feasible tool for manifold kinds of testing. Research in CST made great strides; however, it is mostly unstructured and not linked to traditional software testing practice and terminology. By conducting a literature review of traditional and crowdsourced software testing literature, this paper delivers two major contributions. First, it synthesizes the fields of crowdsourcing research and traditional software testing. Second, the paper gives a comprehensive overview over findings in CST-research and provides a classification into different software testing types