899 resultados para Information Filtering, Pattern Mining, Relevance Feature Discovery, Text Mining
Resumo:
Mobile messaging is an integral and vital part of the mobile industry and contributes significantly to worldwide total mobile service revenues. In today’s competitive world, differentiation is a significant factor in the success of the business communication. SMS (Short Message Service) provides a powerful vehicle for service differentiation. What is missing, however, is the availability of personalized SMS messages. In particular, the exploitation of user profile information allows a selection and content delivery that meets preferences and interests for the individual. Personalization of mobile messages is important in today’s service-oriented society, and has proven to be crucial for the acceptance of services provided by the mobile telecommunication networks. In this paper we focus on user profile description and the mechanism for delivering the relevant information to the mobile user in accordance with his/her profile.
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Mobile advertising is a rapidly growing sector providing brands and marketing agencies the opportunity to connect with consumers beyond traditional and digital media and instead communicate directly on their mobile phones. Mobile advertising will be intrinsically linked with mobile search, which has transported from the internet to the mobile and is identified as an area of potential growth. The result of mobile searching show that as a general rule such search result exceed 160 characters; the dialog is required to deliver the relevant portion of a response to the mobile user. In this paper we focus initially on mobile search and mobile advert creation, and later the mechanism of interaction between the user’s request, the result of searching, advertising and dialog.
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Mobile phones have the potential of fostering political mobilisation. There is a significant political power in mobile technology. Like the Internet, mobile phones facilitate communication and rapid access to information. Compared to the Internet, however, mobile phone diffusion has reached a larger proportion of the population in most countries, and thus the impact of this new medium is conceivably greater. There are now more mobile phones in the UK than there are people (averaging at 121 mobile phones for every 100 people). In this paper, the attempt to use modern mobile technology to handle the General Election, is discussed. The pre-election advertising, election day issues, including the election news and results as they come in, and answering questions via text message regarding the results of current and/or previous general elections are considered.
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Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the number of occurrences of the sequences (supports of sequences), but also concerned about importance of sequences (weights). When generating subsequence candidates we use both the support and the weight of the candidates while maintaining the downward closure property of these patterns which allows to accelerate the process of candidate generation.
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Florida International University has partnered with the City of Coral Gables to create a geospatially-enabled Virtual Historic City. With this tool, users may navigate to any point in the city at a selected time period (e.g. the Biltmore Hotel from the 1920s to 1940s) and experience the city as it was, through a wide variety of cultural artifacts and textual materials. A virtual walking tour of Coral Gables was also created, which includes an audio narration and 3D simulations providing the façades of historic buildings and landscape. Students, educators, historians, public policy administrators, architects, sociologists, environmental analysts, urban planners, and the general public can explore over 8,000 historical documents, photographs, oral histories, and maps that have been spatially registered to their relevant locations and time.
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A presentation of maps illustrating the percentage of land surface remaining at varying sea level rise from 1ft - 12ft in Miami-Dade County.
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The DTRF2014 is a realization of the the fundamental Earth-fixed coordinate system, the International Terrestrial Reference System (ITRS). It has been computed by the Deutsches Geodätisches Forschungsinstitut der Technischen Universität München (DGFI-TUM). The DTRF2014 consists of station positions and velocities of 1712 globally distributed geodetic observing stations of the observation techniques VLBI, SLR, GNSS and DORIS. Additionally, for the first time, non-tidal atmospheric and hydrological loading is considered in the solution. The DTRF2014 was released in August 2016 and incorporates observation data of the four techniques up 2014. The observation data were processed and submitted by the corresponding technique services: IGS (International GNSS Service, http://igscb.jpl.nasa.gov) IVS (International VLBI Service, http://ivscc.gsfc.nasa.gov) ILRS (International Laser Ranging Service, http://ilrs.gsfc.nasa.gov) IDS (International DORIS Service, http://ids-doris.org). The DTRF2014 is an independent ITRS realization. It is computed on the basis of the same input data as the realizations JTRF2014 (JPL, Pasadena) and ITRF2014 (IGN, Paris). The three realizations of the ITRS differ conceptually. While DTRF2014 and ITRF2014 are based on station positions at a reference epoch and velocities, the JTRF2014 is based on time series of station positions. DTRF2014 and ITRF2014 result from different combination strategies: The ITRF2014 is based on the combination of solutions, the DTRF2014 is computed by the combination of normal equations. The DTRF2014 comprises 3D coordinates and coordinate changes of 1347 GNSS-, 113 VLBI-, 99 SLR- and 153 DORIS-stations. The reference epoch is 1.1.2005, 0h UTC. The Earth Orientation Parameters (EOP) - that means the coordinates of the terrestrial and the celestial pole, UT1-UTC and the Length of Day (LOD) - were simultaneously estimated with the station coordinates. The EOP time series cover the period from 1979.7 to 2015.0. The station names are the official IERS identifiers: CDP numbers or 4-character IDs and DOMES numbers (http://itrf.ensg.ign.fr/doc_ITRF/iers_sta_list.txt). The DTRF2014 solution is available in one comprehensive SINEX file and four technique-specific SINEX files, see below. A detailed description of the solution is given on the website of DGFI-TUM (http://www.dgfi.tum.de/en/science-data-products/dtrf2014/). More information can be made available by request.
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3D Reconstruction is the process used to obtain a detailed graphical model in three dimensions that represents some real objectified scene. This process uses sequences of images taken from the scene, so it can automatically extract the information about the depth of feature points. These points are then highlighted using some computational technique on the images that compose the used dataset. Using SURF feature points this work propose a model for obtaining depth information of feature points detected by the system. At the ending, the proposed system extract three important information from the images dataset: the 3D position for feature points; relative rotation and translation matrices between images; the realtion between the baseline for adjacent images and the 3D point accuracy error found.
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LAPMv2 is a research software solution specifically developed to allow marine scientists to produce geo-referenced visual maps of the seafloor, known as mosaics, from a set of underwater images and navigation data. LAPMv2 has a graphical user interface that guides the user through the different steps of the mosaicking workflow. LAPMv2 runs on 64-bit Windows, MacOS X and Linux operating systems. There are two versions for each operating system: (1) the WEB-installers (lightweight but require an internet connection during the installation) and (2) the MCR installers (large files but can be installed on computer without internet-connection). The user manual explains how to install and start the program on the different operating systems. Go to http://www.lapm.eu.com for further information about the latest versions of LAPMv2.
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Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.
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Interstitial waters recovered from Ocean Drilling Program, Leg 161, site 976 in the western Mediterranean Sea are used in conjunction with a numerical model to constrain the delta18O of seawater in the basin since the Last Glacial Maximum, including Sapropel Event 1. To resolve the oxygen isotopic composition of the deep Mediterranean, we use a model that couples fluid diffusion with advective transport, thus producing a profile of seawater delta18O variability that is unaffected by glacial-interglacial variations in marine temperature. Comparing our reconstructed seawater delta18O to recent determinations of 1.0 per mil for the mean ocean change in glacial-interglacial delta18O due to the expansion of global ice volume, we calculate an additional 0.2 per mil increase in Mediterranean delta18O caused by local evaporative enrichment. This estimate of delta18O change, due to salinity variability, is smaller than previous studies have proposed and demonstrates that Mediterranean records of foraminiferal calcite delta18O from the last glacial period include a strong temperature component. Paleotemperatures determined in combination with a stacked record of foraminiferal calcite depict almost 9°C of regional cooling for the Last Glacial Maximum. Model results suggest a decrease of ~1.1 per mil in seawater delta18O relative to the modern value caused by increased freshwater input and reduced salinity accompanying the formation of the most recent sapropel. The results additionally indicate the existence of isotopically light water circulating down to bottom water depths, at least in the western Mediterranean, supporting the existence of an 'anti-estuarine' thermohaline circulation pattern during Sapropel Event 1.
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Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.
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The primary dataset represents the vouchered botanical collections of James Graham and Jose Schunke Vigo in the Department of Ucayali, Peru, from October 1997 until the present. Over 3500 separate collections are included to date, many of which have associated images. We present here primary collections data. Other data on natural history collections for the Department of Ucayali have been compiled, but are not presented at this time. We hope to make this data available as part of a Ucayali Collections dataset, as time and resources permit. It is hoped that this data will will contribute to the exchange of scientific information, and will enhance our knowledge of, and appreciation for, the complex of species, habitats, communities, ecosystems and ecoregions of Amazonia.
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The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.