RUBRIC: Student Performance will be evaluated based on the following criteria: Assessment Rubric Volume Precision Expression 5 – Mastery of dynamic volume – crescendos build as song culminates. 5 – Virtuoso performance, marked 5 – Total emotional commitment by high degree of accuracy throughout – conveys the song (Staccato of rapid clapping) through expressions, energy, body language and affect. 4 – Uses changes in volume to appropriately mark transitions, crescendo to end. 3 - Marks transitions in song by change of volume, with some inaccuracies 2 - Some evidence of volume change, though not controlled/appropriate to song 1 – No control of volume – same throughout or inappropriate volume 4 – All elements of song recognized and competently performed 3 – Most elements of the song present, with some inaccuracies 2 – Some elements of song recognized, with periods of indistinguishable clapping 1 – Indistinguishable Clapping – song not recognized 4 – Enthusiastically performs the song through clapping and some body language/affect 3 – Enthusiastic clapping, though not necessarily connected to message of song 2 – Some evidence of emotional connection with song – inconsistent throughout song. 1 – Performed with no emotion – shy or embarrassed affect observed
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Expected Counts in Two-Way Tables Finding the expected counts is not that difficult, as the following example illustrates. The overall proportion of French wine bought during the study was 99/243 = 0.407. So the expected counts of French wine bought under each treatment are: 99 99 experiment is that there’s no 99 The null: hypothesis in theFrench wine and music No music ×84 =34.22 music : ×75 =30.56 Italian music : ×84 =34.22 243 243 243 difference in the distribution of wine purchases in the store when no music, French accordion music, or Italian string music is played. To find the proportion expected counts, wewine startbought by assuming thatstudy H0 is was true.31/243 We can=see The overall of Italian during the 0.128. So two-way the expected wine bought under each treatment from the table counts that 99ofofItalian the 243 bottles of wine bought during theare: study were 31 French wines. French music : 31 ×75 =9.57 Italian music : 31 ×84 =10.72 No music : ×84 =10.72 243 243 243 If the specific type of music that’s playing has no effect on wine purchases, the proportion of French The overall proportion of Other wine bought during the study was 113/243 = wine sold under each music 0.465. So the expected countscondition of Other wine bought under each treatment are: should be113 99/243 = 0.407. 113 113 No music : 243 ×84 =39.06 French music : 243 ×75 =34.88 Italian music : 243 ×84 =39.06 12
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Expected Counts in Two-Way Tables 9 Finding the expected counts is not that difficult, as the following example illustrates. The overall proportion of French wine bought during the study was 99/243 = 0.407. So the expected counts of French wine bought under each treatment are: 99 99 experiment is that there’s no 99 The null: hypothesis in theFrench wine and music No music ×84 =34.22 music : ×75 =30.56 Italian music : ×84 =34.22 243 243 243 difference in the distribution of wine purchases in the store when no music, French accordion music, or Italian string music is played. To find the proportion expected counts, wewine startbought by assuming thatstudy H0 is was true.31/243 We can=see The overall of Italian during the 0.128. So two-way the expected wine bought under each treatment from the table counts that 99ofofItalian the 243 bottles of wine bought during theare: study were 31 French wines. 31 31 No music : 243 ×84 =10.72 French music : ×75 =9.57 Italian music : ×75 =34.88 Italian music : 243 243 ×84 =10.72 If the specific type of music that’s playing has no effect on wine purchases, the proportion of French The overall proportion of Other wine bought during the study was 113/243 = wine sold under each music 0.465. So the expected countscondition of Other wine bought under each treatment are: should be113 99/243 = 0.407. 113 113 No music : 243 ×84 =39.06 French music : 243 243 ×84 =39.06
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Expected Counts in Two-Way Tables 12 Finding the expected counts is not that difficult, as the following example illustrates. The overall proportion of French wine bought during the study was 99/243 = 0.407. So the expected counts of French wine bought under each treatment are: 99 99 experiment is that there’s no 99 The null: hypothesis in theFrench wine and music No music ×84 =34.22 music : ×75 =30.56 Italian music : ×84 =34.22 243 243 243 difference in the distribution of wine purchases in the store when no music, French accordion music, or Italian string music is played. To find the proportion expected counts, wewine startbought by assuming thatstudy H0 is was true.31/243 We can=see The overall of Italian during the 0.128. So two-way the expected wine bought under each treatment from the table counts that 99ofofItalian the 243 bottles of wine bought during theare: study were 31 French wines. 31 31 No music : 243 ×84 =10.72 French music : ×75 =9.57 Italian music : ×75 =34.88 Italian music : 243 243 ×84 =10.72 If the specific type of music that’s playing has no effect on wine purchases, the proportion of French The overall proportion of Other wine bought during the study was 113/243 = wine sold under each music 0.465. So the expected countscondition of Other wine bought under each treatment are: should be113 99/243 = 0.407. 113 113 No music : 243 ×84 =39.06 French music : 243 243 ×84 =39.06
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Department of Electrical and Computer Engineering Publications Book 1. H. Zhang, S. Khairy, L. X. Cai, and Z. Han, “Resource Allocation in Unlicensed Long Term Evolution HetNets," contracting with Springer Science + Business Media, LLC. Book Chapter 2. H. Zhang and Z. Han, “Distributed Resource Allocation for Network Virtualization,” Book chapter in Handbook of Cognitive Radio, Springer. Journal & Magazine 3. 4. 5. 6. 7. 8. 9. H. Zhang, Y. Zhang, Y. Gu, D. Niyato, and Z. Han, “A Hierarchical Game Framework for Resource Management in Fog Computing,” in IEEE Communication Magazine, vol. 55, no. 8, Aug. 2017. H. Zhang, Y. Xiao, S. Bu, D. Niyato, R. Yu, and Z. Han, “Computing Resource Allocation in Three-Tier IoT Fog Networks: a Joint Optimization Approach Combining Stackelberg Game and Matching,” in IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1204-1215, Oct. 2017. H. Zhang, Y. Xiao, L. X. Cai, D. Niyato, L. Song, and Z. Han, "A Multi-Leader Multi-Follower Stackelberg Game for Resource Management in LTE Unlicensed," in IEEE Transactions on Wireless Communications, vol. 16, no. 1, pp. 348-361, Jan. 2017. H. Zhang, D. Niyato, L. Song, T. Jiang, and Z. Han, “Zero-determinant Strategy for Resource Sharing in Wireless Cooperation,” IEEE Transactions on Wireless Communications, vol. 15, no. 3, pp. 21792192, Mar. 2016. H. Zhang, W. Ding, F. Yang, J. Song, and Z. Han, "Resource Allocation in Heterogeneous Network with Visible Light Communication and D2D: A Hierarchical Game Approach," submitted to Transactions on Communication. H. Zhang, B. Di, Z. Chang, X. Liu, L. Song, and Z. Han, "Equilibrium Problems with Equilibrium Constraints Analysis for Power Control and User Scheduling in NOMA Networks," submitted to Transactions on Wireless Communication. Z. Chang, L. Lei, H. Zhang, T. Ristaniemi, S. Chatzinotas, B. Ottersten, Z. Han, "Secure and Energy-Efficient Resource Allocation for Multiple-Antenna NOMA with Wireless Power Transfer," to be submitted to Transactions on Green Communications and Networking. Conference 10. H. Zhang, X. Tang, R. A. Banez, P. Ren, L. Song, and Z. Han, "An EPEC Analysis for Power Allocation in LTE-V Networks,"accepted for IEEE GLOBECOM 2017. 11. N. Raveendran, H. Zhang, Z. Zheng, L. Song, Z. Han, "Large-Scale Fog Computing Optimization using Equilibrium Problem with Equilibrium Constraints"accepted for IEEE GLOBECOM 2017. 12. X. Chen, H. Zhang, and Z. Han, "Delay-Tolerant Resource Scheduling in Large-Scale Virtualized Radio Access Networks," IEEE International Conference on Communications (ICC), Paris, France, May 2017. 13. H. Zhang, W. Ding, J. Song, and Z. Han, "A Hierarchical Game Approach for Visible Light Communication and Multi-Hop D2D Heterogeneous Network", 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, Dec. 2016. 14. H. Zhang, X. Chen, and Z. Han, "A Zero-Determinant Approach for Power Control of Multiple Wireless Operators in LTE Unlicensed", 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, Dec. 2016, 15. H. Zhang, Y. Xiao, S. Bu, D. Niyato, R. Yu, and Z. Han, “Fog Computing in Multi-Tier Data Center Networks: A Hierarchical Game Approach,” IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, May 2016. 16. H. Zhang, Y. Xiao, L. X. Cai, L. Song, N. Dusit, and Z. Han, "A Hierarchical Game Approach for Multi-Operator Spectrum Sharing in LTE Unlicensed," IEEE Global Communications Conference, San Diego, CA, Dec. 2015. 17. H. Zhang, D. Niyato, L. Song, T. Jiang, and Z. Han, "Equilibrium Analysis for Zero-Determinant Strategy in Resource Management of Wireless Network," IEEE Wireless Communications and Networking Conference, New Orleans, LA, Mar. 2015. 18. H. Zhang, D. Niyato, L. Song, T. Jiang, and Z. Han, "Zero-Determinant Strategy in Cheating Management of Wireless Cooperation," IEEE Global Communications Conference, Austin, TX, Dec. 2014. 19. H. Zhang, M. Bennis, Luiz A. DaSilva, and Z. Han, "Multi-leader Multi-follower Stackelberg Game among Wi-Fi, Small Cell and Macrocell Networks," IEEE Global Communications Conference, Austin, TX, Dec. 2014. 20. H. Zhang, F. Li, N. Dusit, L. Song, T. Jiang, and Z. Han, "Zero-determinant Strategy in Power Control of Small Cell Network", invited, IEEE International Conference on Communication Systems (ICCS), Macau, China, Nov. 2014. 51
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Blueprint analogy iPod blueprint/factory state: current song volume battery life behavior: power on/off change station/song change volume choose random song create s iPod #1 state: song = "1,000,000 Miles" volume = 17 battery life = 2.5 hrs behavior: power on/off change station/song change volume choose random iPod #2 iPod #3 state: song = "Letting You" volume = 9 battery life = 3.41 hrs state: song = "Discipline" volume = 24 battery life = 1.8 hrs behavior: power on/off change station/song change volume choose random song behavior: power on/off change station/song change volume choose random song 38
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Motivation - Scenario 5 Has: RingTone-A, C Wants: Song-B Has: RingTone-A Length= 10sec Type= monophonic Style=dance D1 RingTone-C Length= 5sec Type= polyphonic Style=dance Wants: Song• • • • • • • • • • D1 * Performer= singer-A D2 D2 Has: Has: Song-A Wants: RingTone-B Song-A Title=title-A Performer= singer-A Style=pop Wants: RingTone- * Type= polyphonic Length > 8sec D1 is interested in acquiring Song-B which is not available in the environment. D2 is searching for RingTone-B which is also unavailable. D1 and D2 could keep looking for a perfect match or could look for similar services. D1 identifies that the most important feature of the song was a performer, Singer-A. D2 determines that is interested in polyphonic ring tones that are longer then 8 sec. D1 discovers that D2 has a Song-A performed by Singer-A. D1 composes a proposal to exchange RingTone-A or RingTone-C for the Song-A. D2 determines that nether RingTone-A or RingTone-C match all of the attributes D2 decides to farther relax its requirements and ignore the length of the ring. D2 accepts the proposal from D1. D1 and D2 exchange Song-A and RingTone-C. 62 / 54
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MUSIC Music must be appropriate!! 3 minutes max Cannot use a song that has been used within the last 3 years (if a senior can remember it) • If you’re not sure if a song has been used in the past, get in touch with Sam Cillo No more than 2 songs by the same artist Song thread over the summer • Just because you write that you want a song doesn’t mean it’s yours • If 2 people audition with the same song, we will choose one or ask you to change your song
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MUSIC Music must be appropriate!! 3 minutes max Cannot use a song that has been used within the last 3 years (if a senior can remember it) • If you’re not sure if a song has been used in the past, get in touch with Alyssa Pallotti No more than 2 songs by the same artist Song thread over the summer • Just because you write that you want a song doesn’t mean it’s yours • If 2 people audition with the same song, we will choose one or ask you to change your song
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