Theses and Projects: Difference between revisions

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* (P) Student project

=== * '''New!''' Task Offloading and Resource Allocation Optimization===

With the continuous advancement of the next-generation wireless communication technologies and the population of mobile devices, a variety of Internet of Things (IoT) applications are emerging and seeking efficient task execution paradigms. This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency. We expect you have a background in edge computing, optimization algorithms, and programming skills.

Please contact Dongkuo Wu [dongkuo.wu@cs.uni-goettingen.de]

=== * '''New!''' Efficient Live Volumetric Video Streaming System===

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Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]

=== * '''New!''' ~~Blockchain-based Spectrum and Computation Resources Sharing in Mobile Networks~~===

=== * '''New!''' Using LLM for Sign Language Translation (B/M/P)===

~~The sixth~~-~~generation~~ (6G) system ~~is widely envisioned as a global network consisting~~ of ~~pervasive devices that interact with each other. Besides exchanging information~~, ~~these peer entities also share heterogeneous and distributed network resources. Blockchain is a promising technology to secure resource sharing in a peer-to-peer way~~. We ~~need~~ students ~~for this topic. We expect you have a background in computer network~~ and ~~programming skills in Python~~.

Sign language is the primary means of communication for the deaf and hard-of-hearing community, yet most people do not understand it. This topic explores the integration of Large Language Models (LLMs) with computer vision to build an advanced sign language translation system - with special focus on overcoming the critical challenge of understanding long, continuous sign language videos. We welcome students passionate about Natural Language Processing (NLP) and Computer Vision (CV) to explore the cutting edge of sign language translation technology.

Please contact ~~Jin Xie~~ [~~jin~~.~~xie~~@~~stud~~.uni-goettingen.de]

Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]

=== * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)===

Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure~~. We need students for this topic~~. We expect you have a background in knowledge graph and programming skills in Python.

Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure. We expect you have a background in knowledge graph and programming skills in Python.

Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]

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Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]

=== * '''New!''' Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===

=== * '''New!''' Context Specific Self-supervised Pre-Training for Remote Sensing Applications (Semantic Segmentation, Change Detection, Socio-Economic Indicator Estimation, ...) (B/M/P) ===

Satellite images in combination with Machine/Deep Learning models have shown to be an effective tool for analysis and monitoring tasks regarding disasters, deforestation, climate change, socio-economic estimation and others. The training of these models usually rely on labelled ground-truth data, which is labour intensive and therefore often scarcely available. To overcome this limitation, models are often trained in self-supervised approaches with unlabelled data, such as Contrastive Learning or Masked Autoencoders. However, these approaches are completely independent and not related to the intended downstream task. In this project/thesis the relationship between the pre-training task and the model performance on the downstream task will be explored and self-supervised training approaches tailored for a selected remote sensing downstream task (semantic segmentation of trees, tree crown share per pixel estimation, change detection of disasters, socio-economic estimation, ...) will be developed.

Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]

=== [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===

A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was (un)successful.

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Please contact Jing Li [jing.li@cs.uni-goettingen.de]

=== * '''New!''' Rumor control and detection method for social networks based on GCN===

In social networks, rumors spread quickly and have a wide impact. Through GCN, the complex relationships between nodes in the network can be effectively captured, and the detection and propagation paths of rumors can be modeled and controlled. The core of this method is to improve the ability to identify and control the spread of rumors by jointly learning user behavior, information content, and network topology by building an information propagation graph. We need students in this topic. We expect you have a background in rumor detection and programming skills in Python.

Please contact Fei Gao [fei.gao@cs.uni-goettingen.de]

=== [Occupied] Image-to-Image Translation of Different Nightlight Image Types (B/M/P) ===

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Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P)

=== * Privacy-preserved Video Analytics===

=== * '''[Closed]''' Privacy-preserved Video Analytics===

This project/thesis topic focuses on the protection of privacy in video analytics.

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[1] Pecam: privacy-enhanced video streaming and analytics via securely-reversible transformation [https://dl.acm.org/doi/abs/10.1145/3447993.3448618].

=== * AI for networking adaption ===

=== * '''[Closed]''' AI for networking adaption ===

In this project/theses topic, you will explore how to make AI meets networking requirements (e.g., fluctuating network states).

You will (1) deploy and test Genet[1]; (2)extend the Genet environment to multi-client environment (e.g., ABR); (3) deploy multi-agent algorithms on Genet and valid the performance.

@@ Line 21: / Line 21: @@
 * (P) Student project
+===  * '''New!''' Task Offloading and Resource Allocation Optimization===
+With the continuous advancement of the next-generation wireless communication technologies and the population of mobile devices, a variety of Internet of Things (IoT) applications are emerging and seeking efficient task execution paradigms. This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency. We expect you have a background in edge computing, optimization algorithms, and programming skills.
+Please contact Dongkuo Wu [dongkuo.wu@cs.uni-goettingen.de]
 ===  * '''New!''' Efficient Live Volumetric Video Streaming System===
@@ Line 34: / Line 39: @@
 Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]
-===  * '''New!''' Blockchain-based Spectrum and Computation Resources Sharing in Mobile Networks===
+===  * '''New!''' Using LLM for Sign Language Translation (B/M/P)===
-The sixth-generation (6G) system is widely envisioned as a global network consisting of pervasive devices that interact with each other. Besides exchanging information, these peer entities also share heterogeneous and distributed network resources. Blockchain is a promising technology to secure resource sharing in a peer-to-peer way. We need students for this topic. We expect you have a background in computer network and programming skills in Python.
+Sign language is the primary means of communication for the deaf and hard-of-hearing community, yet most people do not understand it. This topic explores the integration of Large Language Models (LLMs) with computer vision to build an advanced sign language translation system - with special focus on overcoming the critical challenge of understanding long, continuous sign language videos. We welcome students passionate about Natural Language Processing (NLP) and Computer Vision (CV) to explore the cutting edge of sign language translation technology.
-Please contact Jin Xie [jin.xie@stud.uni-goettingen.de]
+Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]
 ===  * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)===
-Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure.  We need students for this topic. We expect you have a background in knowledge graph and programming skills in Python.
+Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure. We expect you have a background in knowledge graph and programming skills in Python.
 Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]
@@ Line 52: / Line 57: @@
 Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]
-===  * '''New!''' Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===
+===  * '''New!''' Context Specific Self-supervised Pre-Training for Remote Sensing Applications (Semantic Segmentation, Change Detection, Socio-Economic Indicator Estimation, ...) (B/M/P) ===
+Satellite images in combination with Machine/Deep Learning models have shown to be an effective tool for analysis and monitoring tasks regarding disasters, deforestation, climate change, socio-economic estimation and others. The training of these models usually rely on labelled ground-truth data, which is labour intensive and therefore often scarcely available. To overcome this limitation, models are often trained in self-supervised approaches with unlabelled data, such as Contrastive Learning or Masked Autoencoders. However, these approaches are completely independent and not related to the intended downstream task. In this project/thesis the relationship between the pre-training task and the model performance on the downstream task will be explored and self-supervised training approaches tailored for a selected remote sensing downstream task (semantic segmentation of trees, tree crown share per pixel estimation, change detection of disasters, socio-economic estimation, ...) will be developed.
+Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]
+===  [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===
 A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was  (un)successful.
@@ Line 63: / Line 76: @@
 Please contact Jing Li [jing.li@cs.uni-goettingen.de]
+===  * '''New!''' Rumor control and detection method for social networks based on GCN===
+In social networks, rumors spread quickly and have a wide impact. Through GCN, the complex relationships between nodes in the network can be effectively captured, and the detection and propagation paths of rumors can be modeled and controlled. The core of this method is to improve the ability to identify and control the spread of rumors by jointly learning user behavior, information content, and network topology by building an information propagation graph. We need students in this topic. We expect you have a background in rumor detection and programming skills in Python.
+Please contact Fei Gao [fei.gao@cs.uni-goettingen.de]
 ===  [Occupied]  Image-to-Image Translation of Different Nightlight Image Types (B/M/P) ===
@@ Line 106: / Line 125: @@
 Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P)
-===   *  Privacy-preserved Video Analytics===
+===   * '''[Closed]''' Privacy-preserved Video Analytics===
 This project/thesis topic focuses on the protection of privacy in video analytics.
@@ Line 121: / Line 140: @@
 [1] Pecam: privacy-enhanced video streaming and analytics via securely-reversible transformation [https://dl.acm.org/doi/abs/10.1145/3447993.3448618].
-===  *  AI for networking adaption  ===
+===  * '''[Closed]''' AI for networking adaption  ===
 In this project/theses topic, you will explore how to make AI meets networking requirements (e.g., fluctuating network states).
 You will (1) deploy and test Genet[1]; (2)extend the Genet environment to multi-client environment (e.g., ABR); (3) deploy multi-agent algorithms on Genet and valid the performance.