| 61. |
- de Dios, Eddie, et al.
(författare)
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Introduction to Deep Learning in Clinical Neuroscience
- 2022
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Ingår i: Acta Neurochirurgica, Supplement. - Cham : Springer International Publishing. - 2197-8395 .- 0065-1419. ; 134, s. 79-89
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the brain. We provide examples of DL in analyzing MRI data and discuss potential applications and methodological caveats. Important aspects are data pre-processing, volumetric segmentation, and specific task-performing DL methods, such as CNNs and AEs. Additionally, GAN-expansion and domain mapping are useful DL techniques for generating artificial data and combining several smaller datasets. We present results of DL-based segmentation and accuracy in predicting glioma subtypes based on MRI features. Dice scores range from 0.77 to 0.89. In mixed glioma cohorts, IDH mutation can be predicted with a sensitivity of 0.98 and specificity of 0.97. Results in test cohorts have shown improvements of 5–7% in accuracy, following GAN-expansion of data and domain mapping of smaller datasets. The provided DL examples are promising, although not yet in clinical practice. DL has demonstrated usefulness in data augmentation and for overcoming data variability. DL methods should be further studied, developed, and validated for broader clinical use. Ultimately, DL models can serve as effective decision support systems, and are especially well-suited for time-consuming, detail-focused, and data-ample tasks.
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| 62. |
- Dobslaw, Felix, 1983, et al.
(författare)
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Boundary Value Exploration for Software Analysis
- 2020
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Ingår i: Proceedings - 2020 IEEE 13th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2020. - : IEEE. ; , s. 346-353
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Konferensbidrag (refereegranskat)abstract
- For software to be reliable and resilient, it is widely accepted that tests must be created and maintained alongside the software itself. One safeguard from vulnerabilities and failures in code is to ensure correct behavior on the boundaries between subdomains of the input space. So-called boundary value analysis (BVA) and boundary value testing (BVT) techniques aim to exercise those boundaries and increase test effectiveness. However, the concepts of BVA and BVT themselves are not generally well defined, and it is not clear how to identify relevant sub-domains, and thus the boundaries delineating them, given a specification. This has limited adoption and hindered automation. We clarify BVA and BVT and introduce Boundary Value Exploration (BVE) to describe techniques that support them by helping to detect and identify boundary inputs. Additionally, we propose two concrete BVE techniques based on information-theoretic distance functions: (i) an algorithm for boundary detection and (ii) the usage of software visualization to explore the behavior of the software under test and identify its boundary behavior. As an initial evaluation, we apply these techniques on a much used and well-tested date handling library. Our results reveal questionable behavior at boundaries highlighted by our techniques. In conclusion, we argue that the boundary value exploration that our techniques enable is a step towards automated boundary value analysis and testing, which can foster their wider use and improve test effectiveness and efficiency.
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| 63. |
- Dolonius, Dan, 1985
(författare)
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Sparse Voxel DAGs for Shadows and for Geometry with Colors
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Triangles are probably the most common format for shapes in computer graphics. Nevertheless, when high detail is desired, Sparse Voxel Octrees (SVO) and Sparse Voxel Directed Acyclic Graphs (DAG) can be considerably more memory efficient. One of the first practical use cases for DAGs was to use the structure to represent precomputed shadows. However, previous methods were very time consuming in building the DAG and did not support any other attributes than discretized geometry. Furthermore, when used for scene object representation, the DAGs lacked proper support for properties such as object colors. The focus on this thesis is to speed up the build times of the DAG and to allow other, important, attributes such as colors to be encoded. This thesis is a collection of three papers where we in Paper I solve the problem with slow construction times while also further compressing the DAG, allowing much faster feedback to an artist making changes to a scene and also opening up the possibility to recompute the DAG in run time for slowly moving shadows. If a unique color per voxel is desired, which uncompressed would require 3 bytes per voxel, we realize that the benefit from compressing the geometry (down to or even below one bit per voxel) is rendered practically useless. We thus need to find a way to compress the colors as well. In Paper IIA, we solve this issue by mapping the voxel colors to a texture, allowing for the use of conventional compression algorithms, as well as a novel format designed for real-time performance. In Paper IIB, we further significantly improve the compression.
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| 64. |
- Ebadi Tavallaei, Hamid, 1987, et al.
(författare)
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Sampling and Partitioning for Differential Privacy
- 2016
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Ingår i: Privacy Security & Trust Conference 2016. - 9781509043798 ; , s. 664-673
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Konferensbidrag (refereegranskat)abstract
- Differential privacy enjoys increasing popularity thanks to both a precise semantics for privacy and effective enforcement mechanisms. Many tools have been proposed to spread its use and ease the task of the concerned data scientist. The most promising among them completely discharge the user of the privacy concerns by transparently taking care of the privacy budget. However, their implementation proves to be delicate, and introduce flaws by falsifying some of the theoretical assumptions made to guarantee differential privacy. Moreover, such tools rely on assumptions leading to over-approximations which artificially reduce utility. In this paper we focus on a key mechanism that tools do not support well: sampling. We demonstrate an attack on PINQ (McSherry, SIGMOD 2009), one of these tools, relying on the difference between its internal mechanics and the formal theory for the sampling operation, and study a range of sampling methods and show how they can be correctly implemented in a system for differential privacy.
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| 65. |
- Elowsson, Anders
(författare)
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Deep Layered Learning in MIR
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Deep learning has boosted the performance of many music information retrieval (MIR) systems in recent years. Yet, the complex hierarchical arrangement of music makes end-to-end learning hard for some MIR tasks – a very deep and structurally flexible processing chain is necessary to extract high-level features from a spectrogram representation. Mid-level representations such as tones, pitched onsets, chords, and beats are fundamental building blocks of music. This paper discusses how these can be used as intermediate representations in MIR to facilitate deep processing that generalizes well: each music concept is predicted individually in learning modules that are connected through latent representations in a directed acyclic graph. It is suggested that this strategy for inference, defined as deep layered learning (DLL), can help generalization by (1) – enforcing the validity of intermediate representations during processing, and by (2) – letting the inferred representations establish disentangled structures that support high-level invariant processing. A background to DLL and modular music processing is provided, and relevant concepts such as pruning, skip connections, and layered performance supervision are reviewed.
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| 66. |
- Elowsson, Anders
(författare)
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Modeling Music : Studies of Music Transcription, Music Perception and Music Production
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This dissertation presents ten studies focusing on three important subfields of music information retrieval (MIR): music transcription (Part A), music perception (Part B), and music production (Part C).In Part A, systems capable of transcribing rhythm and polyphonic pitch are described. The first two publications present methods for tempo estimation and beat tracking. A method is developed for computing the most salient periodicity (the “cepstroid”), and the computed cepstroid is used to guide the machine learning processing. The polyphonic pitch tracking system uses novel pitch-invariant and tone-shift-invariant processing techniques. Furthermore, the neural flux is introduced – a latent feature for onset and offset detection. The transcription systems use a layered learning technique with separate intermediate networks of varying depth. Important music concepts are used as intermediate targets to create a processing chain with high generalization. State-of-the-art performance is reported for all tasks.Part B is devoted to perceptual features of music, which can be used as intermediate targets or as parameters for exploring fundamental music perception mechanisms. Systems are proposed that can predict the perceived speed and performed dynamics of an audio file with high accuracy, using the average ratings from around 20 listeners as ground truths. In Part C, aspects related to music production are explored. The first paper analyzes long-term average spectrum (LTAS) in popular music. A compact equation is derived to describe the mean LTAS of a large dataset, and the variation is visualized. Further analysis shows that the level of the percussion is an important factor for LTAS. The second paper examines songwriting and composition through the development of an algorithmic composer of popular music. Various factors relevant for writing good compositions are encoded, and a listening test employed that shows the validity of the proposed methods.The dissertation is concluded by Part D - Looking Back and Ahead, which acts as a discussion and provides a road-map for future work. The first paper discusses the deep layered learning (DLL) technique, outlining concepts and pointing out a direction for future MIR implementations. It is suggested that DLL can help generalization by enforcing the validity of intermediate representations, and by letting the inferred representations establish disentangled structures supporting high-level invariant processing. The second paper proposes an architecture for tempo-invariant processing of rhythm with convolutional neural networks. Log-frequency representations of rhythm-related activations are suggested at the main stage of processing. Methods relying on magnitude, relative phase, and raw phase information are described for a wide variety of rhythm processing tasks.
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| 67. |
- Fredriksson, Teodor, 1992, et al.
(författare)
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Machine Learning Algorithms for Labeling: Where and How They are Used?
- 2022
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Ingår i: SysCon 2022 - 16th Annual IEEE International Systems Conference, Proceedings.
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Konferensbidrag (refereegranskat)abstract
- With the increased availability of new and better computer processing units (CPUs) as well as graphical processing units (GPUs), the interest in statistical learning and deep learning algorithms for classification tasks has grown exponentially. These classification algorithms often require the presence of fully labeled instances during the training period for maximum classification accuracy. However, in industrial applications, data is commonly not fully labeled, which both reduces the prediction accuracy of the learning algorithms as well as increases the project cost to label the missing instances. The purpose of this paper is to survey the current state-of-the-art literature on machine learning algorithms that are used for assisted or automatic labeling and to understand where these are used. We performed a systematic mapping study and identified 52 primary studies relevant to our research. This paper provides three main contributions. First, we identify the existing machine learning algorithms for labeling and we present a taxonomy of these algorithms. Second, we identify the datasets that are used to evaluate the algorithms and we provide a mapping of the datasets based on the type of data and the application area. Third, we provide a process to support people in industry to optimally label their dataset. The results presented in this paper can be used by both researchers and practitioners aiming to improve the missing labels with the aid of machine algorithms or to select appropriate datasets to compare new state-of-the art algorithms in their respective application area.
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| 68. |
- Furia, Carlo A, 1979, et al.
(författare)
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Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality
- 2022
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Ingår i: ACM Transactions on Software Engineering and Methodology. - : Association for Computing Machinery (ACM). - 1049-331X .- 1557-7392. ; 31:3
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Tidskriftsartikel (refereegranskat)abstract
- Statistical analysis is the tool of choice to turn data into information and then information into empirical knowledge. However, the process that goes from data to knowledge is long, uncertain, and riddled with pitfalls. To be valid, it should be supported by detailed, rigorous guidelines that help ferret out issues with the data or model and lead to qualified results that strike a reasonable balance between generality and practical relevance. Such guidelines are being developed by statisticians to support the latest techniques for Bayesian data analysis. In this article, we frame these guidelines in a way that is apt to empirical research in software engineering.To demonstrate the guidelines in practice, we apply them to reanalyze a GitHub dataset about code quality in different programming languages. The dataset's original analysis [Ray et al. 55] and a critical reanalysis [Berger et al. 6] have attracted considerable attention-in no small part because they target a topic (the impact of different programming languages) on which strong opinions abound. The goals of our reanalysis are largely orthogonal to this previous work, as we are concerned with demonstrating, on data in an interesting domain, how to build a principled Bayesian data analysis and to showcase its benefits. In the process, we will also shed light on some critical aspects of the analyzed data and of the relationship between programming languages and code quality-such as the impact of project-specific characteristics other than the used programming language.The high-level conclusions of our exercise will be that Bayesian statistical techniques can be applied to analyze software engineering data in a way that is principled, flexible, and leads to convincing results that inform the state-of-The-Art while highlighting the boundaries of its validity. The guidelines can support building solid statistical analyses and connecting their results. Thus, they can help buttress continued progress in empirical software engineering research.
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| 69. |
- Furia, Carlo A, 1979, et al.
(författare)
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Bayesian Data Analysis in Empirical Software Engineering Research
- 2021
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Ingår i: IEEE Transactions on Software Engineering. - 0098-5589 .- 1939-3520. ; 47:9, s. 1786-1810
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Tidskriftsartikel (refereegranskat)abstract
- IEEE Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical reasons, frequentist statistics have traditionally dominated empirical data analysis, and certainly remain prevalent in empirical software engineering. This situation is unfortunate because frequentist statistics suffer from a number of shortcomings---such as lack of flexibility and results that are unintuitive and hard to interpret---that curtail their effectiveness when dealing with the heterogeneous data that is increasingly available for empirical analysis of software engineering practice. In this paper, we pinpoint these shortcomings, and present Bayesian data analysis techniques that provide tangible benefits---as they can provide clearer results that are simultaneously robust and nuanced. After a short, high-level introduction to the basic tools of Bayesian statistics, we present the reanalysis of two empirical studies on the effectiveness of automatically generated tests and the performance of programming languages, respectively. By contrasting the original frequentist analyses with our new Bayesian analyses, we demonstrate the concrete advantages of the latter. To conclude we advocate a more prominent role for Bayesian statistical techniques in empirical software engineering research and practice.
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| 70. |
- Gerdes, Alex, 1978, et al.
(författare)
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Understanding formal specifications through good examples
- 2018
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Ingår i: Erlang 2018 - Proceedings of the 17th ACM SIGPLAN International Workshop on Erlang, co-located with ICFP 2018. - New York, NY, USA : ACM. ; , s. 13-24
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Konferensbidrag (refereegranskat)abstract
- Formal specifications of software applications are hard to understand, even for domain experts. Because a formal specification is abstract, reading it does not immediately convey the expected behaviour of the software. Carefully chosen examples of the software’s behaviour, on the other hand, are concrete and easy to understand—but poorly-chosen examples are more confusing than helpful. In order to understand formal specifications, software developers need good examples. We have created a method that automatically derives a suite of good examples from a formal specification. Each example is judged by our method to illustrate one feature of the specification. The generated examples give users a good understanding of the behaviour of the software. We evaluated our method by measuring how well students understood an API when given different sets of examples; the students given our examples showed significantly better understanding.
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