https://dasya.itu.dk/semester/fall-2021/ Recent content in Fall 2021 on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Thu, 01 Jul 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/wind_gnn/ Thu, 01 Jul 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/wind_gnn/ The integration of wind power in the energy grid is dependent on accurate production forecasts. The power output curves between neighbouring wind farms are often correlated temporally and spatially, but currently, these spatiotemporal dependencies are under-utilised in prediction models. Graph neural networks allow for modelling these dependencies. In this project the student will implement a prediction model using a newly released Python library for temporal geometric graph neural networks in order to analyse dependencies across wind farms at different locations in Denmark. https://dasya.itu.dk/for-students/proposals/archive/diversityrelevancefeedback/ Tue, 01 Jun 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/diversityrelevancefeedback/ In relevance feedback, the choice of images to present to the user is a difficult problem, as a naïve approach may present too many similar images. The challenge addressed in this project is to ensure diversity (aka “one of each”) as well as relevance. A particularly interesting project for students interested in efficient algorithms. Read more… https://dasya.itu.dk/for-students/proposals/archive/hashbasedindexing/ Tue, 01 Jun 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/hashbasedindexing/ In interactive learning systems, such as Exquisitor, the system presents potentially relevant images to users who label them as either relevant or irrelevant. Currently, Exquisitor uses a cluster-based index, which allows it to return results from a collection of 100 million images in 0.3 seconds. The goal of this project is to study the application of hash-based indexing to interactive learning and compare to the current approach. Read more… https://dasya.itu.dk/for-students/proposals/archive/videobrowsershowdown/ Tue, 01 Jun 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/videobrowsershowdown/ The goal of this project is to enhance PhotoCube as a competior for the Video Browser Showdown, an international video retrieval competition where competing systems are judged based on speed, accuracy and recall. We propose to develop new versions of the C++-based media server and JS-based media browser, to expand the data model to videos and improve the performance sufficiently to take part in the Video Browser Showdown. Read more… https://dasya.itu.dk/for-students/proposals/archive/virtualreality/ Tue, 01 Jun 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/virtualreality/ We are actively developing a new prototype for analysing large multimedia collections in virtual reality, based on the ObjectCube data model. There are many ways in which students can contribute to the project, including work on the user interface and the back-end, and later on running large-scale user experiments. Read more… https://dasya.itu.dk/for-students/proposals/archive/vc-med-nlp/ Thu, 22 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/vc-med-nlp/ Machine learning is used extensively in different applications, including medical imaging and natural language processing. As different types of data are involved, it is reasonable to assume that different methods are needed for each application. However, there are also opportunities in translating a method successful in one application, to the other application where it is not widely used. The goal of this project is to examine trends in recent conference papers for both fields, and identify relevant similarities/differences between the approaches used. https://dasya.itu.dk/for-students/proposals/archive/crowdsourcing-similarity/ Thu, 22 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/crowdsourcing-similarity/ Machine learning algorithms for skin lesion classification typically learn from images which have been labeled as malignant (for example, melanoma) or not. Such tasks can still suffer from overfitting due to limited dataset size. In other computer vision tasks, crowdsourcing labels has been effective, but the average person typically does not have the background to classify skin lesions. However, assessing the similarity of images might still be possible, even without such background. https://dasya.itu.dk/for-students/proposals/archive/ssdcharacterizationnew/ Tue, 20 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/ssdcharacterizationnew/ NVMe SSDs are not a uniform class of devices. IO software stack is not uniform either. Understanding the performance characteristics of new-generation SSDs and the impact of the IO stack on their performance is crucial while determining how to design data-intensive systems. In this project, we would like to characterize the performance of a range of NVMe SSDs (e.g., Samsung Z-SSD, Intel Optane, etc.) using different libraries for submitting IOs (e. https://dasya.itu.dk/for-students/proposals/archive/armbenchmark/ Tue, 20 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/armbenchmark/ Disaggregated storage has gained acceptance in data centers. With disaggregated storage, storage resources are decoupled from compute resources, and made available through fabric. We are particularly interested in storage resources composed of an ARM-based smartNIC, which acts as fabric target as well as storage controller for a collection of SSDs. The performance characteristics of the storage stack are well understood on x86, but not on ARMv8. The goal of this project (research + MSc) is to (i) compile and run various forms of storage stack on ARM cores, (ii) make the storage stacks available through fabric (at present, SPDK does not run on ARM) and (iii) benchmark these storage stacks to identify bottlenecks and potential for hardware acceleration. https://dasya.itu.dk/for-students/proposals/archive/workloadcharacterization/ Tue, 20 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/workloadcharacterization/ A data science infrastructure orchestrates the execution of widely used machine learning frameworks (e.g., TensorFlow , PyTorch) on a heterogeneous set of processing units (e.g., CPU, GPU, TPU, FPGA) while powering an increasingly diverse and complex range of applications (e.g., fraud detection, healthcare, virtual assistance, automatic driving). Understanding the resource consumption characteristics of different data science workloads is a necessary step before designing effective hardware resource management in any data science infrastructure. https://dasya.itu.dk/for-students/proposals/archive/learningfromwoodacoustics/ Tue, 25 Aug 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/learningfromwoodacoustics/ For this project, you would be working with a partner company who are looking to re-establish wood as a building material for sustainable architecture, and thus are using sensors for quality control - to detect damages and deterioration in buildings. Wood such as timber may be analyzed by non-intrusive acoustic impact testing and subsequent waveform analysis, and the expectation is that machine learning from such measurements would allow us to detect and classify the internal structure and quality of materials. https://dasya.itu.dk/for-students/proposals/archive/human2satellite/ Tue, 25 Aug 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/human2satellite/ Recent progress in LoRaWAN development has made a new generation of satellite communications offerings available to IoT devices. In these, the LoRaWAN gateway is satellite born, and collecting data from small inexpensive ground stations. So far, this is predominantly seen as a means of communciation for remote sensor data, e.g. in agriculture, logistics or wildlife monitoring. However, one can also imagine these communication channels to be valuable for human communications, in scenarios in which terrestrial infrastructure is not accessible, e.