SOLUTION: Harvard University Legalization of Marijuana Proposal
June 2019 v1 Faculty of Science and Technology - Department of Computing & Informatics Unit Title: Computer Vision Assessment Title: Limitation and rerediscontinuance of a real-world Computer Trust accumulateion Unit Level: 7 Assessment Number: 1 of 1 Credit Value of Unit: 20 Date Issued: 20/01/2020 Marker(s): Prof. Marcin Budka Submission Due Date: 05/06/2020 Time: 12.30pm Quality Assessor: Dr Rashid Bakirov Submission Location: Video meekness (Panopto) Jupyter voicebook (Brightspace) Project balanceture (Brightspace) Peer impost (Brightspace) Feedback arrangement: Brightspace This is class assignment which carries 100% of the last ace symptom ASSESSMENT TASK This is a class assignment stay an restricted multiply. Your class should keep 4 limbs, and must be confirmed stay the ace train, Prof. Marcin Budka via e-mail by 21/02/2020. This assignment gain demand you to: 1. Define a real-world Computer Trust accumulateion your class gain apparatus. Establish believing that the accumulateion is newlight in a perception that there are no bulky openorigin resolutions to your accumulateion or that your resolution gain be sufficiently contrariant from the bulky ones (which you must own). Full use the produce in Appendix 1 at the end of this muniment. The produce deficiencys to be suggestted via Brightspace by the deadline absorbed in the subjoined minority of this muniment. Your balanceture must be beloved by the ace train anteriorly you can abide stay your assignment. 2. Acquire the grounds certain to artfulnesst a rerediscontinuance to your accumulateion (e.g. suite your neural network). Avoid using rule groundssets approve MNIST or Cifar – these keep been premeditated in large purpose already. Instead you are encouraged to follow up stay notpower newlight which gain demand grounds labelling/annotation using tools approve https://dataturks.com/ or https://www.lighttag.io/. 3. Design the evaluation protocol for your resolution: How gain you understand that it is-sues? How gain you understand how good-tempered-tempered it is? What gain you use as the baseline? How gain you assimilate multiple moulds? 4. Design the rerediscontinuance to your accumulateion. This gain be an iterative prescribe involving testing your ideas, evaluating the vulgar rerediscontinuance according to the protocol defined in the precedent purpose (including deception analysis), which should in shape inproduce your proximate steps approve hyper-parameter tuning, changing the architecture, hypothetically accumulateing/annotating additional grounds etc. Relimb to exonerate your choices at each step. The rerediscontinuance itself can be a neural network, notpower naturalized on the OpenCV techniques you keep learn encircling in the primitive multiply of this ace, or a cabal of twain of these (e.g. a video classifier naturalized in an ImageNet-pretrained neural netis-sue which is fine-tuned stay optical career grounds produced by OpenCV from the video indulge). 5. Put all the adjudication in a Jupyter voicebook stay narrative, tables, figures, etc. such that it is self-contained and reproducible (i.e. anyone can generate your fruits simply by re-running your adjudication in the notebook), covering demandments 2, 3 and 4 balance. If any additional Python packages are demandd to run the voicebook (i.e. they are not comprised in the environment we keep been using in class), full apprehend applicable “!conda/pip install” minority at the inauguration of the voicebook, stay paltry representation why these packages are deficiencyed. Twain the raw and annotated grounds should be made accessible concomitantly stay the voicebook via BU OneDrive. 6. Proceedings a defective 7-10 exact video donation of your class’s rerediscontinuance naturalized on the Jupyter notebook you keep easy as forcible balance. Each class limb should proceedings their own video and suggest it on an restricted premise. Page 1 of 6 June 2019 v1 SUBMISSION FORMAT 1. Collection ageation (Brightspace, Group) Completed produce that can be artfulnesst in Appendix 1 at the end of this muniment. These are due by 6/03/2020 and gain be beloved or peculiar stay comments on how to reconstruct stayin 2 weeks. 2. Jupyter voicebook (Brightspace, Group) As ageed in the precedent minority. 3. Peer-impost of the class limbs (Brightspace, Individual) Each limb of each class gain be asked to anonymously assess the contributions to the operation of all the other limbs in their relative class. 4. Video donation (Panopto, Individual) A defective 7-10 exact video donation of your class’s rerediscontinuance by the meekness deadline. This multiply is mandatory, and symptoms gain solely be awarded to those who suggest the video. MARKING CRITERIA The subjoined criteria gain be used to assess the assignment. Each standard gain be considered according to the Plane 7 Grade Dispose (Masters Level), artfulnesst in the 6F - Generic Impost Criteria: Procedure. 1. Limitation of the accumulateion a. Context b. Rationale c. Aims and objectives 10% 2. Grounds merit and preparation 15% a. Grounds characteristics and statistics b. Grounds visualisation c. Grounds prescribeing 3. Modelling 25% a. Choosing misapportion techniques b. Well-organised approach c. Later steps appriseed by precedent findings d. Automation (i.e. avoiding manual steps) 4. Evaluation 20% a. Comparison across baseline(s) b. Visualisations c. Deception analysis d. Discussion and good-fortune of objectives 5. Notebook 15% a. Reproducibility b. Erection and organisation c. Coding conventions and principles d. Scholarship and clarity e. Referencing 6. Video donation 15% Page 2 of 6 June 2019 v1 The subjoined minoritys reestimate what are the expectations for each artfulnesse of good-fortune: To finished a Pass: Define a moderately challenging computer trust accumulateion, solely requiring basic grounds merit and preparation (e.g. grounds downloaded from the Internet, using either bulky annotations or annotations derived from bulky). Apportion and evaluate at mediumest two contrariant computer trust approaches (e.g. two contrariant neural netis-sue architectures), assimilate and dissimilarity their accomplishment/applicpower for the collection at influence stay the succor of basic grounds visualisation techniques. The voicebook gain ardispose some reproducibility, stay concludeably disentangled erection and organisation, stay some assignencing. To finished a eminent symptom: Define a balance challenging computer trust accumulateion, requiring balance demanding grounds merit and preparation (e.g. grounds fascinated and annotated by the design team). Apportion and evaluate a dispose of computer trust approaches, including hybrids of unwritten and neural networks. Robustly assimilate and dissimilarity their accomplishment/applicpower for the accumulateion at influence stay the succor of balance slow (e.g. interactive) grounds visualisation techniques. The voicebook gain ardispose generous reproducibility, stay disentangled erection and organisation, and wasting assignencing. LEARNING OUTCOMES This assignment tests your power to demonstrate: 1. understandledge and construction of Computer Trust techniques and their applications, 2. precarious assuredness of the strengths and ageations of several techniques and the classes of accumulateions to which they may be effectively applied, 3. power to procure handy knowledge by pairing restricted techniques to office objectives, 4. adequacy in use of the introduced techniques, approaches and tools, concomitantly stay the power to correctly reintroduce and evaluate the fruits. QUESTIONS ABOUT THE BRIEF You are encouraged to ask interrogations encircling the paltry as introduce as practicable, giving you the opening to finished the best symptoms practicable stayout any stay. You are invited to ask interrogations during termtabled sessions and electronically. Signature Marker: Marcin Budka Page 3 of 6 June 2019 v1 HELP AND SUPPORT • If a share of courseis-sue is not suggestted by the demandd deadline, the subjoined gain apportion: 1. If courseis-sue is suggestted stayin 72 hours behind the deadline, the apex symptom that can be awarded is 50%. If the impost finisheds a ignoring symptom and interrogation to the balanceall accomplishment of the ace and the novice’s line for the artfulnesse, it gain be real by the Impost Board as the reimpost share. The ace gain estimate towards the reimpost amercement for the artfulnesse; This controlling gain apportion to written courseis-sue and artefacts solely; This controlling gain apportion to the primitive underassume solely (including any later underassume charmed as a primitive underassume due to peculiar circumstances). 2. If a primitive underassume courseis-sue is suggestted balance than 72 hours behind the deadline, a symptom of zero (0%) gain be awarded. 3. Failure to suggest/finished any other types of courseis-sue (which apprehends resubmission courseis-sue stayout peculiar stipulation) by the demandd deadline gain fruit in a symptom of zero (0%) life awarded. The Rule Impost Regulations can be artfulnesst on Brightspace. • If you keep any efficient peculiar stipulation which medium that you cannot disunitee an assignment meekness deadline and you eagerness to supplicate an production, you gain deficiency to finished and suggest the Exceptional Stipulation Produce for consequence to your Programme Support Officer (naturalized in C114) concomitantly stay misapportion sustaining appearance (e.g, GP voice) normally anteriorly the courseis-sue deadline. Raise purposes on the proceeding and the peculiar stipulation produce can be artfulnesst on Brightspace. Full establish believing that you learn these muniments carefully anteriorly submitting totalthing for consequence. For raise direction on peculiar stipulation full see your Programme Leader. • You must own your origin total term you assign to others’ is-sue, using the BU Harvard Referencing arrangement (Author Date Method). Failure to do so amounts to plagiarism which is across University regulations. Full assign to http://libguides.bournemouth.ac.uk/bu-referencing-harvardphraseology for the University’s train to citation in the Harvard phraseology. Also be assured of Self-plagiarism, this primarily occurs when a novice suggests a share of is-sue to purport the impost demandment for a particular ace and all or multiply of the full has been precedently suggestted by that novice for produceal impost on the same/a contrariant ace. Raise appriseation on academic offences can be artfulnesst on Brightspace and from https://www1.bournemouth.ac.uk/discover/library/using-library/howguides/how-avoid-academic-offences • Students stay Additional Learning Needs may continuity Learning Support on www.bournemouth.ac.uk/als Disclaimer: The appriseation procured in this assignment paltry is rectify at term of divulgation. In the unlikely fruit that any changes are deemed certain, they gain be transmitted disentangledly via e-mail and Brightspace and a new statement of this assignment paltry gain be circulated. Page 4 of 6 June 2019 v1 APPENDIX 1 – L7 COMPUTER VISION PROBLEM OUTLINE FORM 1. Class limbs (names and BU email addresses) 1. 2. 3. 4. Word Guide1 2. Title 20 2. Collection designation 200300 3. Aims and objectives 150200 The aim of this design is…. The subjoined measurable objectives keep been identified: 1. 2. 3. 4. Approach 300400 In prescribe to save this design, the subjoined steps are artfulnessned: 1. 2. 3. 1 The term train is fair a instigation, demolish bountiful to go balance the age stayin conclude (i.e. max 1.5x the train). Page 5 of 6 June 2019 v1 5. Timescale (Gantt chart) N/A 6. Bibliography/references N/A Page 6 of 6 L7 COMPUTER VISION PROJECT PROPOSAL 1. Class limbs (names and BU email addresses) 1. PUNEET SURYA ANEM – firstname.lastname@example.org 2. ABHINASH CHILAKALA – email@example.com 3. PAPAMMAGARI AKHIL KUMAR REDDY- firstname.lastname@example.org 4. RAJESH GOUD BALAGANI – email@example.com Word Guide 2. TITLE IMPORTANCE OF NATURAL LANGUAGE DESCRIPTION IN SEARCHING PEOPLE. 3. PROBLEM DESCRIPTION 20 Finding a peculiar using shadow-naturalized is-sue or attribute-naturalized is-sue gain assume prodigious term or sometimes end stay an indeferential fruit and keep ageations such as: In shadow-naturalized is-sue, there are few pairing techniques such as Blob exposure technique, Template pairing, etc. and each of them has its own ageations. For fruit, in Blob exposure technique, this arrangement is prodigiously relative on the shadow to be grayscale, very sensitive to uproar, balance memberation, etc. In attribute-naturalized is-sue, there are few ageations such as, for the reanimation to be 100% human message must be uproarless and vivid, misrendering of the arrangement, constant monitoring for the mediumings of the attributes, etc. Here are the accumulateions of unless articulation designation in profound a peculiar: The grounds in-reference-to to peculiar such as hairstyle, drapery and footwear must be unmoved and stored in peculiar groundsbase, then repair it to most deferential illustration, if there is no groundsset which pair to this illustration, we deficiency to accumulate contrariant groundsset illustrations from purposeed unless articulation peculiar illustrations. At introduce days, smooth in trivial town hundreds of surveillance cameras are life naturalized, which employ a lot of gigabits to ammunition and demandd a lot of term to regain it, whereas automated peculiar inquiry using textual designation purports the aim stayout the real shadow. 4. Aims and objectives The aim of this design is to “find the peculiars using Unless Articulation Description”. Few objectives of our design are as follows: The peculiar inquiry algorithm’s job is to class the illustrations in the peculiar groundsbase and then quote the most applicable illustration according to the interrogation, unintermittently the textual designation of the peculiar is absorbed. Collection of groundssets in-reference-to large-scale peculiar designation stay thorough unless articulation annotations and peculiar illustrations from contrariant types of origins, named as “CUHK Peculiar Designation Dataset (CUHK – PEDES)”. Comparison of the peculiar inquiry benchsymptom via evaluated baselines and an extensive dispose of practicable moulds beneficial. Proposal of “Revulgar Neural Netis-sue stay Gated Neural Attention mechanism 200-300 (RNN-GNA)” to demonstrate the slow accomplishment on peculiar inquiry. 5. Approach 300-400 To save this design, we artfulness the subjoined steps: Gaining the Articulation groundssets for trust such as MS-COCO, Flickr8K, Visual Genome, etc. Developing the Heartfelt Articulation prototypes for trust. Using Unless Articulation Designation for benchmarking, the peculiar inquiry, named as CUHK-PEDES. Collection of several shadows and peculiars from contrariant beneficial groundssets and giving annotations to these shadows. Studying the statistics of the Datasets and investigating the articulation descriptions, estimate of dooms and doom extension, term types. Developing the RNN-GNA mould. After the fruit, we deficiency to commence contrariant experiments on Dataset and evaluation metrics, comparing arrangements and baselines, and analyze Quantitative and indispensable fruits. 6. Timescale (Gantt chart) N/A 7. References N/A A. Karpathy and L. Fei-Fei. Heartfelt visual-semantic alignments for generating shadow descriptions. In CVPR, pages 3128–3137, 2015. A. Toshev, O. Vinyals, S. Bengio, and D. Erhan. Show and tell: A neural shadow caption generator. In CVPR, pages 3156–3164, 2015. B. Zhou, Y. Tian, S. Sukhbaatar, A. Szlam, and R. Fergus. Simple baseline for visual interrogation obedient. arXiv preprint arXiv:1512.02167, 2015. S. Reed, Z. Akata, B. Schiele, and H. Lee. Learning heartfelt representations of fine-grained visual designations. arXiv preprint arXiv:1605.05395, 2016. 2, 3, 6, 7  M. Ren, R. Kiros, and R. Zemel. Exploring moulds and grounds for shadow interrogation obedient. In NIPS, pages 2953–2961, 2015. ...
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