PPT-Spectrogram of “Being in a crowd is tiring.”

Time Frequency Speech synthesis has been the most important tool used to answer these kinds of questions Method Spend huge amounts of time studying spectrograms and coming up with hypotheses. Workshop. . Prof. . Tova. Milo. Slava. . Novgorodov. Tel Aviv University 2014/2015. Projects. The team size is around 4 students and should be decided by the next week. The projects should be done during the semester. Grammar. Smart Choice. Level 3. Grammar. Adjectives with –. ed. / -. ing. -. ed. = feelings or emotions. I am tired. / I feel tired.. I am relaxed. / I feel relaxed.. I am bored. / I feel bored.. Ivanecká. Simona. Fill. . the. . gaps. . with. . the. . adjectives. in . brackets. ... 1. . . He's. . such. a . monotonous. speaker. I . was. ________________ . stiff. . (. bored. / . boring. Major Crowd Disturbances. This is a joint work with Piper . Jackson, PhD, . Andrew . Reid, PhD Student, . Vijay . Mago. , PhD . and . Vahid. . , PhD. Valerie . Spicer, PhD . and Hilary Kim . Morden. Jesin. . Zakaria. Department of Computer Science and Engineering. University of California Riverside. 124. Time (second). 125. 40. kHz. 100. l. aboratory. mice. Mouse Vocalizations. Figure 1: . top. Algorithms, Machines & People. Michael Franklin. EECS, Computer Science. UC Berkeley. Emory U. December 7, 2011. Defining the Big Data Problem. 2. Size. +. Complexity. =. Answers that don’t meet. N   is for Never - tiring Meaning: Part of being human is that we get tired. We need sleep. Not only do we run out of physical stamina, we get weary mentally, emotionally and spiritually of the Tian. . Tian. 1. . Jun. . Zhu. 1. . . Fen. . Xia. 2. . Xin. . Zhuang. 2. . Tong. . Zhang. 2. Tsinghua. . University. 1. . Baidu. . Inc.. 2. 1. Outline. Motivation. Characteristic Analysis. Time . Frequency. Speech synthesis . has been the most important tool used to answer these kinds of questions.. Method:. Spend huge amounts of time studying spectrograms and coming up with hypotheses.. Jesin. . Zakaria. Department of Computer Science and Engineering. University of California Riverside. 124. Time (second). 125. 40. kHz. 100. l. aboratory. mice. Mouse Vocalizations. Figure 1: . top. Qiuxi Zhu. Background. Pollution is a severe problem in many densely populated urban areas. Pollution monitoring provides guidelines for residents and feedback to decision makers.. Internet of Things (IoT) helps build fine-grained (high-resolution) pollution maps for cities. This include deployed systems and mobile devices owned by crowd.. . quiet. . . difficult. . . boring. . . funny. . interesting. . attractive cheap . tiring. healthy. . scary. . dangerous. . great. . expensive. . . . disgusting. . tasty. Authors: . Manoop. . Talasila. , Reza Curtmola, and Cristian . Borcea. Presenter: . Hillol. . Debnath. Department . of Computer Science. New Jersey Institute of . Technology. Crowd. Sensing. Background – Crowd Sensing. 10/1/2013. 2. Background. - . Crowd (Data) sourcing. 2. Crowd Mining. Crowdsourcing. Challenges (or, shameless self-advertisement . ). What questions to ask? . [SIGMOD13, VLDB13. ] .