Data- och informationsteknik (CSE) // Computer Science and Engineering (CSE)

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https://odr.chalmers.se/handle/20.500.12380/11

Vi utbildar för framtiden och skapar samhällsnytta genom vår forskning som levandegörs i nära samarbete med näringslivet. Vi bedriver forskning inom computer science, datateknik, software engineering och interaktionsdesign - från grundforskning till direkta tillämpningar. Institutionen har en stark internationell prägel och är delad mellan Chalmers och Göteborgs universitet.

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För forskning och forskningspublikationer, se https://research.chalmers.se/organisation/data-och-informationsteknik/

We are engaged in research and education across the full spectrum of computer science, computer engineering, software engineering, and interaction design, from foundations to applications. We educate for the future, conduct research with high international visibility, and create societal benefits through close cooperation with businesses and industry. The department is joint between Chalmers and the University of Gothenburg.

Studying at the Department of Computer Science and Engineering at Chalmers

For research and research output, please visit https://research.chalmers.se/en/organization/computer-science-and-engineering/

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An Empirical Survey of Bandits in an Industrial Recommender System Setting
(2023) Brandby, Johan; Schwarz, Tobias; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Dubhashi, Devdatt; Jorge, Emilio
In this thesis, the effects of incorporating unstructured data—images in the wild—in contextual multi-armed bandits are investigated, when used within a recommender system setting, which focuses on picture-based content suggestion. The idea is to employ image features, extracted by a pre-trained convolutional neural network, and study the resulting bandit behaviors when including respective excluding this information in the typical context creation, which normally relies on structured data sources—such as metadata. The evaluation is made both online, through A/B-testing enabled by the industrial partner YouPic AB, and offline, effectuated by a simulation pipeline that models the online counterpart. The results are compiled as a survey, covering a selection of contextual bandit algorithms, highlighting the differences brought by the unstructured data. The offline result points towards that if the contextual bandit utilizes a joint or hybrid action-value function, with respect to the parameterization, the addition of the image vectors can significantly outperform the instances without it; however, if a disjoint model is instead employed, no noticeable change is observed. In comparison, those from the online trials can be interpreted as supporting the inclusion of convolutional features, but due to meager and unbalanced sample sizes, the outcomes are deemed inconclusive. To summarize, though there is support for incorporating unstructured data, given that the action-value function is joint or hybrid, the online experiments gave too little evidence for any trustworthy findings; in other words, the question is still partially open.
Importance Sampling in Deep Learning Object Detection - An empirical investigation into accelerating deep learning object detection training by exploiting informative data points
(2023) Huang, Alexander; Kvernes, Johannes; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Angelov, Krasimir; Yu, Yinan
In recent years, the field of deep learning object detection has witnessed a notable surge in progress, largely fueled by the growth of available data. While the ever growing amount of data has enabled complex object detection models to improve generalization performance and to facilitate training phases that are less prone to over-fitting, the time to reach desired performance with this remarkable amount of data has been reported to be an emerging problem in practice. This is certainly the case when each sample consist of high resolution image data that could burden the capacity of loading data. This thesis explores the possibilities of leveraging importance sampling as a means to accelerate gradient-based training of an object detection model. This avenue of research aims at biasing the sampling of training examples during training, in the hope of exploiting informative samples and reduce the amount of computation on uninformative, noisy and out-of-distribution samples. While previous art shows compelling evidence of importance sampling in deep learning, it is consistently reported in a context of less complex tasks. In this work, we propose techniques that can be applied to single-stage anchor-free object detectors, in order to adopt importance sampling to accelerate training. Our methods do not require modifications to the objective function, and allow for a biased sampling procedure that remains consistent across runs and samples. Our results suggest that an uncertainty-based heuristic outperforms loss-based heuristics, and that object detection training can be subject to a remarkable speed-up in terms of reaching the baseline’s performance in fewer iterations, where the baseline samples the training examples uniformly without replacement. Furthermore, the empiric observations reported in this work also indicate that an increased final generalization performance can be achieved given an equal amount of training time when compared to the baseline.
Low latency video analytics system with multi-exit neural networks
(2022) HARINDRAN, NEETHU; POOJARY, BHARATH; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Tsigas, Philippas; Ali-Eldin Hassan, Ahmed
Computer vision-based control systems have become increasingly powerful and promising in tackling real-world problems. This can be accredited to the use of deep learning methods in these systems with state-of-the-art performance sometimes outperforming humans in tasks which require subjective decision making. This has resulted in increased interest in these systems from Swedish industry, including Volvo. One example system where these systems are used is the Volvo GPSS system, where semantic segmentation is used to perform real-time decisions based on pixel level classification of a monitored area. However, such systems frequently deal with a trade-off between latency and accuracy. This is primarily due to the increasing number of model layers being used to develop Deep-Neural-Network models for vision systems, resulting in equal resource utilization regardless of input complexity. In this thesis, we develop an approach that employs input adaptive multi-exit strategy to exploit latency benefits of dynamic processing based on the input complexity. The proposed approach aims to have a reduced average inference time as the simple input samples takes an early exit and only the complex samples need more computation offered by all the model layers. The open source CityScapes dataset and the Volvo dataset were used in a number of multi-exit semantic segmentation experiments with HRNet architecture chosen as the backbone. The thesis work studies three novel exit strategies, including reinforcement learning, auxiliary models, and fast Fourier transform. Out of all the methods examined, the reinforcement learningbased exit strategy displayed the best performance advantages, with accuracy on par with unbranched HRNet and a significant decrease in latency and computation.
Machine Learning for Detecting Gender Bias at Chalmers
(2023) Nilsson, Linnea; Lindau, Sarah; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Johansson, Moa; Ljunglöf, Peter
This thesis studies gender bias in course evaluations through the lens of machine learning and NLP. Different methods are used to examine and explore the data and find differences in what students write about courses depending on the gender of the examiner. The data is also examined using more traditional statistical methods to get an understanding of how the students’ impressions of the courses are related to the gender of the examiner. Other aspects related to gender and gender bias are also examined, such as how the proportion of female students relates to the gender of the examiner and whether male or female examiners give different grades to their students. Student grades and teaching language are also factors that are being examined to see whether there is any bias against female examiners or students that is easily detectable in the data. The main findings are that courses with female examiners seem to get lower overall impression scores than those with male examiners. Courses taught in Swedish also receive lower scores, compared to the English courses. No clear patterns as to what words are used when writing comments about a course with a male or female examiner were found. When trying to predict the author gender the patterns were clearer, finding that men write more words directly related to the course and women write more words related to communication.
Machine Learning for generative painting informed by visual arts
(2023) Wang, Chaoming; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Dahlstedt, Palle; Tatar, Kıvanç
Visual art practice is a complicated, varied, creative process based on the artist’s style and preferences. Although many studies have attempted to apply artificial intelligence techniques to art production and statistical analysis, there is still significant scope for exploring how to incorporate the techniques in visual arts practices into generative painting pipelines using Machine Learning. This thesis applies machine learning to analyzing painting techniques in painting practices with a research-through-design approach. The problem is mainly presented as tasks such as segmentation of artworks (in this thesis, paintings), stroke prediction, and the presentation of painting processes based on different painting techniques through different algorithmic pipelines. The results show that most segmentation models based on photo training are challenging to apply to the segmentation of artwork components directly, and relevant improvement solutions are discussed in Chapter 6. In addition, due to the diverse presentation of painting art, this paper presents different painting techniques based on the foreground and background segmentation and ’blocking-in’ techniques based on line detection. It discusses the possibility of transferring these painting processes to other painting processes.
Model-based deadlock prevention for traffic planning of autonomous vehicles
(2023) Möller, David; Ohlin, Alexander; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Damaschke, Peter; Gheorghiu, Andru
Volvo Autonomous Solutions are developing a system for planning the routes of fleets of autonomous vehicles. Autonomous control creates several problems that must be solved; among these is the possibility for the policy of said vehicles to end up in deadlock. This thesis proposes new concepts to describe the problem and methods for preventing a vehicle fleet from deadlocking. As the action that led to deadlock might not be recent, the term implicit deadlock was introduced, which is a configuration of vehicle positions from which deadlock is inevitable. The methods developed successfully prevent deadlocks at several pilot and test sites. However, results indicate that time for computing implicit deadlocks grows exponentially in the size of the site and the number of vehicles in the fleet. A neural network model was also trained using data generated from preprocessing of deadlocks to approximate the process and enable deadlock predictions not discovered before.
Predictive Maintenance in Production Robots in a Real World Industrial Setting
(2023) Wennerström, Karl; Svensson, Adam; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Sheeran, Mary; Yu, Yinan
With the exponential growth of data and advancements in AI technology, Predictive Maintenance (PdM) has emerged as a vital practice for optimizing equipment maintenance and minimizing unplanned downtime. This study was performed in collaboration with Sandvik Coromant, a company producing steel products and actively collecting data for evaluation purposes, to investigate how the collected data can be utilized for decision-making processes. Specifically, the study analyses vibrational data to address the issues related to unexpected tool malfunctions. Based on the available data, anomaly detection was identified as the most suitable approach to leverage the stored data based on its characteristics. A comparative study where various anomaly detection models were evaluated demonstrated that a reconstruction-based LSTM autoencoder yields the highest performance. The reconstruction approach exhibited its effectiveness in detecting and flagging potential abnormalities, capturing 71% of the malfunctions with an F1 score of 0.75 for the data used for the comparison. Extending the model to other tools displayed the challenges posed within time-series analysis, proving unique characteristics for each case. The findings from this study provide valuable insights into the implementation of anomaly detection techniques for leveraging collected data and enhancing decision-making processes in Sandvik Coromant and similar industrial settings.

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Browsar Data- och informationsteknik (CSE) // Computer Science and Engineering (CSE) efter Program "Data science and AI (MPDSC), MSc"

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