600 research outputs found

    Drone Pilot Identification by Classifying Radio-Control Signals

    No full text
    Analysis of interactions with remotely controlled devices has been used to detect the onset of hijacking attacks, as well as for forensics analysis, e.g., to identify the human controller. Its effectiveness is known to depend on the remote device type as well as on the properties of the remote control signal. This paper shows that the radio control signal sent to an unmanned aerial vehicle (UAV) using a typical transmitter can be captured and analyzed to identify the controlling pilot using machine learning techniques. Twenty trained pilots have been asked to fly a high-end research drone through three different trajectories. Control data have been collected and used to train multiple classifiers. Best performance has been achieved by a random forest classifier that achieved accuracy around 90% using simple time-domain features. Extensive tests have shown that the classification accuracy depends on the flight trajectory and that the pitch, roll, yaw, and thrust control signals show different levels of significance for pilot identification. This result paves the way to a number of security and forensics applications, including continuous identification of UAV pilots to mitigate the risk of hijacking

    A Pragmatic Analysis of Refusal Strategies in Management Communication

    No full text
    This study examines the refusal strategies employed in superior-subordinate communication during departmental meetings. It aims to reveal these strategies\u27 function, specifically exploring why individuals use particular refusal techniques in their interactions. Data is collected through observation of participants\u27 turn-taking patterns in departmental meetings. The findings indicate that both heads of departments and department members utilized direct and indirect refusal strategies. Participants employed two direct refusal strategies, as defined in the Beebe et al. (1990, pp. 55-73) framework, and five indirect refusal strategies: explanation, statement of alternatives, attempts to dissuade, acceptance functioning as a refusal, and silence. The most frequently used direct strategy is the non-performative statement, while the most common indirect strategies are explanation and statements of alternatives. Social power dynamics are evident in the heads of departments\u27 speech, as they seek to control dissenting opinions. Department members, however, exercise social power by forming coalitions to support specific viewpoints discussed in the meetings. Future research could investigate the politeness strategies used by superiors and subordinates in these meetings and their impact on influencing heads of departments

    Unpacking Cultural and Linguistic Refusal Strategies in Jordanian EFL Discourse: A Conceptual Paper

    No full text
    Refusal strategies are ubiquitous linguistic tools employed in everyday communication. Understanding the application of these strategies is crucial for interpreting the reactions of those receiving the refusals. This study investigates the diverse refusal strategies Jordanian students utilize to explore the cultural and linguistic factors influencing their choices. Data will be collected through interviews and observations and analyzed using Beebe et al.’s (1990) framework. The anticipated results include the identification of the significant influence of linguistic and cultural factors on the forms of refusal strategies employed by the students. The study also expects to identify direct and indirect refusal strategies. Ultimately, this research will provide a foundation for future investigations into the syntactic and morphological processes underlying Jordanian students\u27 construction of refusal strategies in their interactions. A deeper understanding of these processes will contribute to a more nuanced comprehension of pragmatic communication within this cultural context

    Data used in "Biologically informed deep neural network for prostate cancer discovery" publication

    No full text
    <p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p><p>fix gene names </p&gt

    Data used in "Biologically informed deep neural network for prostate cancer discovery" publication

    No full text
    <p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p><p>fix gene names </p&gt

    Teaching Translation Technology in Jordan Employability and Labor Market

    No full text
    Translator training programs worldwide offer translation technology courses to meet the market\u27s growing needs, and translation programs in the Arab world are no exception. Developing ‘technological competence’ should be reflected in the curricula of translator training programs (See, Kenny 2020, Pym and Torres-Simón 2020, O’Brien 2019). However, the focus on technological competence varies across translator training programs around the world. This study aims to pinpoint the importance of technology in MA programs in Jordan. Furthermore, it determines whether technology-connected courses are acknowledged in the program descriptions and objectives. To conduct this study, all the Universities with MA in translation and/or translation and English programs in Jordan were recognized. In addition, a corpus of all MA programs with translation technology courses, course descriptions, and program objectives was also gathered. As a final point, a comprehensive explanation and examination were carried out to recognize technology in MA programs in Jordan. The outcomes of this study indicate that ‘technological competence’ is widely disregarded in the huge mainstream of MA translation programs curricula in Jordan

    Role of Gibberellic acid in mitigating the adverse effect of sodium chloride on some grow parameters of fenugreek plant Trigonellafoenum-graecum L. by using Hydroponic Technique

    No full text
    An experiment was conducted by using the nutrient solution unit in the green house of the Biology Department, College of Education Ibn Al- Haitham/ Baghdad University during the growing season of 2008-2009 by using fenugreek plant under effect of three concentrations 0,50,100 mM. Lˉ¹ of sodium chloride and four concentrations 0,25,50,100ppm of giberellic acid of studied some growth parameters of plant diameter of root, leaf chlorophyll content, number of flower and pud's,sodium and chloride concentrations in shoot The experiment was accomplished as a completely randomized design(CRD) by using three replicates including 36 plastic pots in nutrient solution unit, the results showed the increase in sodium chloride concentration from 0_100Mm.L ̄1 in nutrient solution negative effects in mentioned parameters growth above. Results also that giberellic acid showed role in decreasing the harmful effects of sodium chloride in studied parameters
    corecore