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

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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.

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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### Browsar Data- och informationsteknik (CSE) // Computer Science and Engineering (CSE) efter Program "Complex adaptive systems (MPCAS), MSc"

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- PostA probabilistic model for genetic regulation of metabolic networks(2013) Kallus, Jonatan; Wilsson, Joel; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)Recent advancements in gene expression pro ling and measurement of metabolic reaction rates have led to increased interest in predicting metabolic reaction rates. In this thesis we present a principled approach for using gene expression pro les to improve predictions of metabolic reaction rates. A probabilistic graphical model is presented, which addresses inherent weaknesses in the current state of the art method for data-driven reconstruction of regulatory-metabolic networks. Our model combines methods from systems biology and machine learning, and is shown to outperform the current state of the art on synthetic data. Results on real data from S. cerevisiae and M. tuberculosis are also presented.
- PostAccelerating Proximity Queries Accelerating Proximity Queries for Non-convex Geometries in a Robot Cell Context(2018) Thorén, Joakim; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)Sampling-based motion-planners, for example rapidly exploring dense tree (RRT) based planners, depend on fast proximity queries. Regrettably, bounding volume tests are significant bottlenecks of proximity queries. Sampling-based motion-planners are therefore accelerated by reducing the number of bounding volume tests. To this end, a novel algorithm called Forest Proximity Query (FPQ) is developed. Contrary to previous research, FPQ traverses several pairs of BVHs simultaneously, effectively exploiting an actuality that only a single minimal separation distance — out of several possible separation distances — is required during sampling-based motion-planning. An implementation of FPQ show that FPQ performs up to 67% fewer BV tests in comparison to the well-known Proximity Query Package, increasing proximity querying performance by up to 46%. In conclusion, FPQ is successful in its attempt at improving performance of sampling-based motion-planners.
- PostAdaptive Bounding Volume Hierarchies For Deformable Surface Models(2011) Bitar, Fadi; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)This master’s thesis explores a new mechanism for maintaining Bounding-Volume Hierarchies (BVH) of deformable surface models. Typical algorithms found in the literature are based on refitting only a portion of the BVH, leaving sometimes a large portion of the Bounding Volumes (BVs) inaccurately representing the parts of the object they should enclose. The algorithm proposed in this thesis allows the BVH’s quality to degrade as the model it represents deforms, while guaranteeing that every point in the model is contained within the BVH at all times, and thus maintaining the accuracy of any collision detection or distance measurement queries performed on the model. Through a tunable asynchronous refitting of the individual bounding volumes, the algorithm offers a computationally efficient, low memory cost solution to the accurate simulation of deformable surface models in real environments. The decision criteria for the refitting of the BVs along with the parameters of these criteria are optimized through a Genetic Algorithm search. The resulting algorithm is shown to outperform the most commonly referred to BVH-based algorithm referred to in the literature.
- PostAn Incompressible Navier-Stokes Equations Solver on the GPU Using CUDA(2013) Karlsson, Niklas; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)Graphics Processing Units (GPUs) have emerged as highly capable computational accelerators for scientific and engineering applications. Many reports claim orders of magnitude of speedup compared to traditional Central Processing Units (CPUs), and the interest for GPU computation is high in the computational world. In this thesis, the capability of using GPUs to accelerate the full computational chain of a 3D incompressible Navier-Stokes solver, including solvers and preconditioners for sparse linear systems as well as assembly routines for a finite volume discretization, has been evaluated. The CG, GMRES and BiCGStab iterative solvers have been implemented on the CUDA GPGPU platform and evaluated together with the Jacobi, and Least Square Polynomial preconditioners. A double precision Navier-Stokes solver has been implemented using CUDA, adopting a collocated cartesian grid, SIMPLEC pressure-velocity coupling scheme, and implicit time discretization. The CUDA GPU implementations of the iterative solvers and preconditioners and the Navier-Stokes solver were validated and evaluated against serial and parallel CPU implementations. For the iterative solvers, speedups of between six and thirteen were achieved against the MKL CPU library, and the implemented methods beats existing open source GPU implementations of equivalent methods. For the full Navier-Stokes solver, speedups of up to a factor twelve were achieved compared to an equivalent commercial CPU code when equivalent iterative solvers were used. A speedup of a factor two was achieved when a commercial Algebraic MultiGrid method was used to solve the pressure Poisson equation in the commercial CPU implementation. The bottleneck of the resulting implementation was found to be the solution of the pressure Poisson equation. It accounted for a significant part of the total execution time for large problems. The implemented assembly routines on the GPU were highly efficient. The combined execution time for these routines were negligible compared to the total execution time. The GPU has been assessed as a highly capable accelerator for the implemented methods. About an order of magnitude of speedups have been achieved for algorithms which can efficiently be implemented on the GPU.
- PostAnimat Navigation Using Landmarks Navigation of Simulated Animals Inspired by Bees(2018) Carlsson, Mathias; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)Machine Learning techniques have made large advancements in previously challenging problems. A common problem with these methods is that they are very specialised on their specific field problem and it requires a lot of work to apply the methods on a new problem. A solution to this general issue is Artificial General Intelligence( AGI). This is an AI that is able to identify any problem and find a solution. Animals often needs to navigate complex environments to survive. This master thesis tries to implement a homing navigation model, used by bees to find food by remembering places relative positions to landmarks, in the general animat model. The model consists of using sensors that detect landmarks relative position to the animat. The model shows a small improvement when compared against Q-learning. The result suggests that the animat model can produce more sophisticated methods of navigation but further research needs to be conducted to explore its limits.
- PostAnomaly Detection in PowerCells Auxiliary Power Unit(2015) Hjortberg, Hampus; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)In the paper of Hayton et.al [1], One-class Support Vector Machine is used for health monitoring of a jet engine in order to discover when and if an abnormal event has occured. Hayton et.al used the amplitude of the vibration data from the engine shaft as the feature data to the One-class Support Vector Machine algorithm. This approach works well when the sensor data is known to be periodic, with a certain frequency; however it can not be used if the sensor data has an irregular shape. In this paper we will extend the concept of Hayton et.al [1] and use the Discrete Wavelet Transform coefficients as input data to the OCSVM, rather than the Fourier Transform. This way we are able to classify more arbitrary sensor data found in PowerCells Auxilliary Power Unit (APU). We will also introduce a novel approach of how to select the hyperparameter s for the Radial Basis Function Kernel, in order to avoid both overfitting and underfitting.
- PostCooperative Inverse Reinforcement Learning - Cooperation and learning in an asymmetric information setting with a suboptimal teacher(2018) Ek, Johan; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)There exists many different scenarios where an artificial intelligence (AI) may have to learn from a human. One such scenario is when they both have to cooperate but only the human knows what the goal is. This is the study of cooperative inverse reinforcement learning (CIRL). The purpose of this report is to analyze CIRL when the human is not behaving fully optimally and may make mistakes. The effect of different behaviours by the human is investigated and two frameworks are developed, one for when there is a finite set of possible goals and one for the general case where the set of possible goals is infinite. Two benchmark problems are designed to compare the learning performance. The experiments show that the AI learns, but also that the humans behaviour has a large affect on learning. Also highlighted by the experiments, is the difficulty of differentiating between the actual goal and other possible goals that are similar in some aspects.
- PostData integration using machine learning: Automation of data mapping using machine learning techniques(2016) Birgersson, Marcus; Hansson, Gustav; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)Data integration involves the process of mapping the flow of data between systems. This is a task usually performed manually and much time can be saved if some parts of this can be automated. In this report three models based on statistics from earlier mapped systems is presented. The purpose of these models is to aid an expert in the mapping process by supplying a first guess on how to map two systems. The models are limited to mappings between two XML-formats, where the path to a node carrying data usually is descriptive of its data content. The developed models are the following: 1. A shortest distance model based on the concept that two nodes that have been associated with a third node but not each other most likely have something to do with each other. 2. A network flow model, which connects words with similar semantic meaning to be able to associate the words in two connected XML paths with each other. 3. A data value model which connects data values to nodes based on similarities between the value and earlier seen data. The results of the models agrees with expectations. The shortest distance model can only make suggestions based on XML-structures that are present in the training set supplied for the project. The network flow model has the advantage that it only needs to recognize parts of a path to map two nodes to each other, and even completely unfamiliar systems can be mapped if there are similarities between the two systems. Overall, the data value model performs the worst, but can make correct mappings in some cases when neither of the others can.
- PostDiscovering Novel Chemical Reactions(2021) Rydholm, Emma; Svensson, Emma; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Damaschke, Peter; Haghir Chehreghani, MortezaAccurately predicting chemical reactions can facilitate the search for optimal synthe sis routes in a chemical reaction network and as a consequence expedite the lengthy drug discovery process. As an effort in this direction, this work aims to explore AstraZeneca’s chemical knowledge graph by two complementary analyses. In a first part, graph theory related statistics is employed as a means to gain insights about the chemical reaction graph at AstraZeneca. Significant differences are observed be tween this internal reaction graph and the one based on the public dataset of United States patents as well as other reaction graphs discussed in literature. Secondly, a link prediction model is applied to and evaluated on AstraZeneca’s chemical reaction graph, in order to suggest new potential chemical reactions. In order to successfully accomplish this task, an existing link prediction model is adapted and trained. The test results are then compared to heuristic baselines, showing that the proposed implementation substantially exceeds what can be achieved with heuristic methods. One of the contribution from this research is a comparison between different ways to sample the ground truth class of non-existing links for training and evaluation. The choice of method for this task is shown to have an impact on the final predictions. Finally, a set of promising, predicted reactions are suggested and is currently under further investigation at AstraZeneca.
- PostDistributed machine learning framework for shortest path problems with stochastic weights(2023) Aspegrén, Gabriel; Nilsson Dahlberg, Olle; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Åkerblom, NiklasRange anxiety is one of the most common reasons why customers hesitate to buy an electric car. At the same time, the European Union strives toward a fully electric car fleet. Previous work has shown promising results in designing a self-learning shortest path algorithm for finding the most energy efficient path for an electric vehicle through a road network, using combinatorial multi-armed bandit methods. However, it is desirable to scale the methods for larger networks, for example countries and continents. In this project, we design a distributed framework for shortest path computations on a road network, over a computer cluster, using combinatorial multi-armed bandit methods and machine learning. The system is distributed with Apache Spark and GraphX’s version of the Pregel algorithm. An experimental study is performed to investigate the impact of partition strategy, number of partitions, network size and latency between computer nodes on the total run-time. The results show that partitioning strategy has an significant impact on the run-time and that larger networks benefit more from being partitioned.
- PostDynamic State Representation for Homeostatic Agents(2018) Mäkeläinen, Fredrik; Torén, Hampus; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)In a reinforcement learning setting an agent learns how to behave in an environment through interactions. For complex environments, the explorable state space can easily become unmanageable, and efficient approximations are needed. The Generic Animat model (GA model), heavily influenced by biology, takes an approach utilising a dynamic graph to represent the state. This thesis is part of the Generic Animat research project at Chalmers that develops the GA model. In this thesis, we identify and implement potential improvements to the GA model and make comparisons to standard Q-learning and deep Q-learning. With the improved GA model we show that in a state space larger than 232, we see substantial performance gains compared to the original model.
- PostEstimating Causal Effects with Interpretable Decision Trees(2023) Audinet De Pieuchon, Nicolas; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Damaschke, Peter; Johansson, FredrikIn this work we explore three methods for estimating treatment effects from observational data using interpretable decision trees: the outcome variance tree, the propensity tree and the linear dependence tree. Each tree attempts to split the covariate space into balanced partitions from which treatment effects can be inferred. The outcome variance tree focuses on reducing the variance in the outcome variable, and makes use of a sensitivity analysis based on the residual standard deviation in the outcome. The propensity tree attempts to build a tree that approximates a separate estimate of the propensity score whilst remaining interpretable. The linear dependence tree measures the linear dependence in the partitions and attempts to minimize it directly. The three methods are compared, along with other benchmark methods, on two data sets: a synthetic data set generated from a simple model and the more realistic semi-synthetic IHDP data set. Performance is evaluated by comparing interval widths and coverage for confidence and sensitivity intervals. A functionally-grounded evaluation of interpretability is given with tree size as proxies. The results show that the outcome variance tree and the linear dependence tree perform better than the benchmarks in terms of sensitivity intervals but worse in terms of confidence intervals. The propensity tree however did not perform as well as expected and requires more work to better understand its behavior.
- PostGraph Classification with Differential Privacy(2015) Frost, Otto; Thufvesson Retzner, Carl; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)With increasing usage of online services such as email, social networks and online shopping more data than ever before is being gathered. Many types of data for which structure, flow and relationships are important may naturally be represented by graphs. Services may want to use machine learning algorithms to gain insight about their customers or users, and to successfully use the concepts of machine learning they must take the structure and properties of graph data into account. Seemingly innocuous data could potentially be used to infer sensitive information about individuals and should be kept private, the algorithms and techniques used must therefore take privacy into consideration. In this thesis we consider the combination of machine learning and privacy by bringing together the concepts of support vector machines and differential privacy. We examine the classification of graphs by means of kernel methods and present a framework for constructing private representations of two well known graph kernels, the random walk kernel and graphlet kernels. Furthermore, to allow for classification of large graphs we present a novel sampling scheme for approximation of subgraph counts. We evaluate both sampling and classification using four real world datasets consisting of social, road and protein networks.
- PostImproved Pattern Generation for Bloom Filters with Bit Patterns Optimizing bit patterns for use in blocked Bloom filters(2018) Hedström, Björn; Josefsson, Ivar; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)Set-membership is a commonly occurring problem in many areas of computing, from networking to database applications. A popular data structure for this problem is the Bloom filter: a small, hash-based probabilistic structure which guarantees no false negatives, but can result in false positives. Recently they have been used as an important tool in bioinformatics where the data sets are huge, and as a consequence the filters also need to be large. Blocked Bloom filters with bit patterns have been suggested as an alternative to cope with the deteriorated cache- and hash-behaviour in these cases. It was recently discovered that optimal pattern design for use in these structures is linked to two-stage group testing. There has also been some recent partial results that indicate a certain structure of optimal patterns. This thesis concerns itself with investigating these structural properties to find a better pattern design for use in Blocked Bloom filters with bit patterns. Our main result is a new, deterministic, algorithm for pattern generation used in these structures based on the Chinese Remainder Theorem. The results indicate that this construction improves the false positive rate for all our testing scenarios. As a side-result we also propose a modification to a known combinatorial design used in group testing which significantly reduces the needed number of tests for high number of defectives.
- PostLatent Vector Synthesis(2023) Högberg, David; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Björk, Staffan; Tatar, KıvançGenerative deep learning models for sound synthesis applications have gathered interest recently that are able to generate novel sound material based on the characteristics of a given audio dataset. A subcategory of these models are variational autoencoders, which build generative latent spaces of audio where sounds are organised based on similarity. Although expressive uses of these models abound, questions around their practical applicability and aesthetic implications as part of an artistic process remain underexplored. This thesis investigates the technological and aesthetic affordances of latent audio spaces in the context of creative sound design and exploration. To this end, a sound synthesis tool in the form of a latent vector synthesizer is conceptualised and developed from a first-person research through design perspective. The prototype addresses issues around real-time playability of current machine learning models for sound generation by training a variational autoencoder on short samples of audio signals. The generated waveforms are incorporated as part of a wavetable- and vector synthesis engine that enables timbral interpolations and explorations of sonic textures. Positioned at the intersection of sonic art and audio technology the design implementation uncovers some latent potentials and affordances of new technologies for musical tasks.
- PostLearning to Play Games from Multiple Imperfect Teachers(2014) Karlsson, John; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)This project evaluates the modularity of a recent Bayesian Inverse Reinforcement Learning approach [1] by inferring the sub-goals correlated with winning board games from observations of a set of agents. A feature based architecture is proposed together with a method for generating the reward function space, making inference tractable in large state spaces and allowing for the combination with models that approximate stateaction values. Further, a policy prior is suggested that allows for least squares policy evaluation using sample trajectories. The model is evaluated on randomly generated environments and on Tic-tac-toe, showing that a combination of the intentions inferred from all agents can generate strategies that outperform the corresponding strategies from each individual agent.
- PostMachine Intelligence in Automated Performance Test Analysis(2018) Sigurdsson, Robin S.; Wallengren, Elona; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)Software testing is a large part of development and especially important for projects that practice Continuous Integration. In order to reduce the burden of testing and make the process more efficient, as much as possible is automated. In this thesis, a design science approach is used to investigate how machine intelligence can be used to improve the automation of the analysis of non-functional testing. A prototype is created in order to demonstrate the ability of machine intelligence methods to provide useful information about the relationships between different test cases and their histories. This prototype was found to be fairly accurate in its predictions of test results, could identify most related degradations across test cases, and was positively received by the testers. Based on the results of this thesis, machine intelligence was found to have great potential in dealing with the large amount of data created during testing.
- PostMachine Learning Based Error Prediction for Spray Painting Applications(2016) Lange, Paul; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)Physics-based simulation tools for spray painting exist but are not fast enough to be useful for automatic optimization of the spray painting process. The results of spray painting depend primarily on the process parameters, the path of the paint applicator and the geometry of the target object. These factors affect the air flow and the electrostatic field and are hard to incorporate in approximate simulation tools. This thesis proposes a novel approach based on combining fast, approximate simulations with machine learning based error correction. The proposed approach is to create a height profile of the target geometry from the local coordinate system of the paint applicator. This height field captures most parameters that affect the simulation error and can be used as an input for machine learning regression algorithms. These algorithms are then trained to estimate the painting error. The training is performed with a set of samples from common painting scenarios that are generated beforehand. Creating a training set for dynamic simulations is time-consuming. Static simulations can sufficiently approximate dynamic simulations and are therefore used for training. This drastically improves the time to create training sets and reduces training time for the machine learning models. Linear regression, tree-based regression models and support vector regression are compared on benchmarking problems and especially tree-based regression methods show promising prediction accuracy and are able to reduce the projection error more than 40% on real world benchmarks. Tree-based models are also the fastest algorithms among the compared regression models. Finally, a way to integrate the proposed method into the simulation framework is presented. The results are investigated for different artificial and real world painting scenarios.
- PostManifold Traversal for Reversing the Sentiment of Text(2017) Larsson, Maria; Nilsson, Amanda; Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers); Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)Natural language processing (NLP) is a heavily researched field within machine learning, connecting linguistics to computer science and artificial intelligence. One particular problem in NLP is sentiment classification, e.g determining if a sentence holds a positive or negative opinion. There exist many established methods for solving the sentiment classification problem but none for modifying a negatively classified input so that it receives a positive classification. In this paper we propose a method for reversing the sentiment of sentences through manifold traversal. The method utilizes a convolutional neural network (CNN) and pre-trained word vectors for encoding sentences in a continuous space. The sentence representations are traversed through optimization of a test statistic as to resemble the representations of sentences with the opposite sentiment. Finally a recurrent neural network (RNN) is used for decoding the vector representation and generating new sentences. The encoder in our model achieves 80% accuracy on the sentiment classification task and produces sentence representations in 300 dimensions. Visualizations of these representations, using PCA, shows clustering with respect to both sentiment and different topics, indicating that the representations hold information about both sentiment and textual content. Decoding the traversed feature vectors using our RNN language model produces, in most cases, understandable sentences where the sentiment has changed compared to the original sentence.
- PostMethods for detecting echo chambers in social media networks(2023) Bonafilia, Brian; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Johansson, Moa; Bruinsma, SebastianusThis thesis presents an approach to using Natural Language Processing to detect echo chambers in social media networks and to find identifying terms for those echo chambers. A dataset consisting of posts and user information from the micro-blogging service Twitter related to Sweden’s application to join the North Atlantic Treaty Organization was collected for the year leading up to the Swedish national election of 2022. Tight-knit communities of users on the platform were extracted using the Infomap and Leiden Algorithms based on user connections and interactions. From each community found using these methods, the corpus composed of the text postings of the users in that community was used to train a Word2Vec model to recover vector word embeddings for key words related to the subject of the discussion. Semantic change was quantified by assessing the differences in cosine similarity between pairs of words over time and between communities. Changes in the use of terms related to the subject over time were observed, but patterns representing possible echo chambers arose only with the aid of manual annotation of user positions on the issue. Conclusions could not be drawn about how successful the method is from the results alone, as evidence suggests that the issue was insufficiently polarized to generate strong echo chambers.