ECMM447 Social Networks & Text AnalysisPython

Java Python Social Networks & Text Analysis

Coursework topic

Ø The project has to mainly use methods from network or text analysis

Ø Neural networks are not networks in the context of this module, so it is not enough to use a neural network for the project to be in scope (but you can use neural networks as part of the analysis, for instance after extracting features from some text, you can then use a neural network with those features as input)

Objectives

Ø Independent learning

Ø Expand on what was presented in lectures

Ø Find and read relevant papers

Ø Learn and practice how to work on a network or text analysis problem

Ø Learn and practice how to present your results scientifically in a report

Key questions

Ø What is the problem you are studying?

Ø Why is it interesting?

Ø What is your proposed solution/approach?

Ø What are your results?

Deliverables: pitch deck project presentation

Ø A pre-recorded presentation outlining your mini-project proposal

Ø No more than 5 minutes!

Ø You can use any software you like to record it (Zoom, Teams, …)

Ø To submit, upload a PDF containing a (private) link to the presentation on Teams, Youtube, OneDrive, …

Ø What is the research question and why is it interesting?

Ø How has this question been studied in the literature?

Ø How will you try to solve/study it?

Ø What data will you use?

Ø What methods will you use and how?

Ø Think about slides design: is there too much text? Are the figures clearly legible?

Ø Think about your style. are you speaking too fast or too slow?

Ø Think about the structure: does the presentation have a narrative?

Deliverables: mini-project report and code

Ø 3 page report: figures are included in the 3 page limit, references are not included so can go on a fourth page

Ø The code used for the project (Jupyter notebook/Python script. & PDF version)

Ø The data (or a link to it)

Ø ALL IN A ZIPPED ECMM447 Social Networks & Text AnalysisPython /COMPRESSED FOLDER

Ø Think about how you write: is it clear, concise and understandable? Remember that we don’t know what you are thinking so you need to write it

Ø Think about figures: do they support what you wrote? Are they legible and high resolution?

Ø Think about the structure: is it clear? Does it follow a structure? Did you provide all the relevant details?

Ø Problem statement

Ø Related work

Ø Your solution

Ø Results

Report

Problem statement

Clearly define what is the problem you were studying. Explain the problem and its importance. Include a mathematical formulation if possible. Keep in mind that the description should be readable by anyone without specialized knowledge.

Related work

Mention any relevant papers and state what problem they are solving and the general approach. This should be really short for each paper, just a couple of sentences. You do not need to explain their works in detail. Explain how your works relates to these. Is it better in some way? Does it solve a different problem from all of them? Why is your work important compared to these?

Your solution

Describe in details your idea for solving the problem. Show why it is different from previous work where possible. Explain why the problem is challenging, explain what your ideas are and justify them. Time may not permit you do everything, in which case explain what you are presenting here and what can be done later.

Results

Describe your results: algorithms, proofs, general arguments and analysis. Give plots, tables that show your results. Discuss them. You need to explain the results and their importance to us. We were not there during the work, so we cannot understand things that you do not explain. Discuss what else can be done on this topic etc.

Report structure – a suggestion

Ø Introduction: context and motivation

Ø Description of methods and data

Ø Discussion about experiments

Ø Conclusions

The code

Ø A Jupyter notebook (ideally) or a Python script         

内容概要:本文围绕六自由度机械臂的人工神经网络(ANN)设计展开,重点研究了正向与逆向运动学求解、正向动力学控制以及基于拉格朗日-欧拉法推导逆向动力学方程,并通过Matlab代码实现相关算法。文章结合理论推导与仿真实践,利用人工神经网络对复杂的非线性关系进行建模与逼近,提升机械臂运动控制的精度与效率。同时涵盖了路径规划中的RRT算法与B样条优化方法,形成从运动学到动力学再到轨迹优化的完整技术链条。; 适合人群:具备一定机器人学、自动控制理论基础,熟悉Matlab编程,从事智能控制、机器人控制、运动学六自由度机械臂ANN人工神经网络设计:正向逆向运动学求解、正向动力学控制、拉格朗日-欧拉法推导逆向动力学方程(Matlab代码实现)建模等相关方向的研究生、科研人员及工程技术人员。; 使用场景及目标:①掌握机械臂正/逆运动学的数学建模与ANN求解方法;②理解拉格朗日-欧拉法在动力学建模中的应用;③实现基于神经网络的动力学补偿与高精度轨迹跟踪控制;④结合RRT与B样条完成平滑路径规划与优化。; 阅读建议:建议读者结合Matlab代码动手实践,先从运动学建模入手,逐步深入动力学分析与神经网络训练,注重理论推导与仿真实验的结合,以充分理解机械臂控制系统的设计流程与优化策略。
ArnetMiner是一个用于提取和挖掘学术社交网络的工具和平台。它旨在通过分析学术文献中的作者、机构、论文和引用等信息,构建出一个具有丰富关系和结构的学术社交网络。这种学术社交网络可以帮助研究人员了解不同学科领域中的合作关系、学术影响力以及研究趋势等。 ArnetMiner的主要功能之一是提取学术文献中的作者和机构信息。它能够识别出文献中作者的姓名和机构,并将它们归类到相应的学术社交网络中。这对于研究人员来说非常有用,因为它们可以了解到某个作者在特定领域中的研究成果和合作伙伴。此外,ArnetMiner还可以分析引用关系,揭示不同论文之间的引用情况,帮助研究人员追踪学术研究的发展脉络。 ArnetMiner还提供了一种挖掘学术社交网络的方法。它利用机器学习和数据挖掘技术,识别出学术社交网络中的关键人物、研究领域以及合作关系等。这些信息可以帮助研究人员了解某个研究领域的核心学者,以及他们的合作伙伴和影响力。此外,ArnetMiner还可以通过分析学术文献的关键词和引文数等指标,评估学术成果的影响力和质量。 总之,ArnetMiner是一个强大的工具和平台,可用于提取和挖掘学术社交网络。它为研究人员提供了一个了解学术界合作关系、研究趋势和影响力的途径,从而促进学术研究的发展和创新。
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