Children learn their first language simply by listening to the linguistic utterances provided by their caregivers and other speakers around them. In order to extract meaning and grammatical rules from these utterances, children must track regularities in the input, which are omnipresent in language. The ability to discover and adapt to these statistical regularities in the input is termed statistical learning and has been suggested to be one of the key mechanisms underlying language acquisition. In this thesis, I investigated a special case of statistical learning, non-adjacent dependency (NAD) learning. NADs are grammatical dependencies between distant elements in an utterance, such as is and -ing in the sentence Mary is walking. I examined which factors play a role in the development of NAD learning by illuminating this process from different stand points: the first study compares NAD learning in the linguistic and the non-linguistic domain during the earliest stages of development, at 4 months of age. This study suggests that at this age, NAD learning seems to be domain-specific to language. The second study puts a spotlight on the development of NAD learning in the linguistic domain and proposes that there may be a sensitive period for linguistic NAD learning during early childhood. Finally, the third study shows that children can not only recall newly learned NADs in a test immediately following familiarization, but also recall them after a retention period, which is critical to show more long-term learning. Overall, the findings in this thesis further illuminate how NADs, as a spotlight into language acquisition, are learned, stored in memory, and recalled.

Two-dimensional scan design is an effective BIST architecture that uses multiple scan chains in parallel to test the Circuit Under Test (CUT). Linear Finite State Machines (LFSMs) are often used as on-board Pseudo Random Pattern Generators (PRPGs) in two-dimensional scan designs. However, linear dependencies present in the LFSM generated test-bit sequences adversely affect the resultant fault coverage in two-dimensional scan designs. In this work, we present methods that improve the resultant fault coverage in two-dimensional scan designs through the minimization of linear dependencies. Currently, metric of channel separation and matrix-based metric are used in order to estimate linear dependencies in a CUT. When the underlying sub-circuit (cone) structure of a CUT is available, the matrix-based metric can be used more effectively. Fisrt, we present two methods that use matrix-based metric and minimize the overall linear dependencies in a CUT through explicitly minimizing linear dependencies in the highest number of underlying cones of the CUT. The first method minimizes linear dependencies in a CUT through the selection of an appropriate LFSM structure. On the other hand, the second method synthesizes a phase shifter for a specified LFSM structure such that the overall linear dependencies in a CUT are minimized. However, the underlying structure of a CUT is not always available and in such cases the metric of channel separation can be used more effectively. The metric of channel separation is an empirical measure of linear dependencies and an ad-hoc large channel separation is imposed between the successive scan chains of a two-dimensional scan design in order to minimize the linear dependencies. Present techniques use LFSMs with additional phase shifters (LFSM/PS) as PRPGs in order to obtain desired levels of channel separation. We demonstrate that Generalized LFSRs (GLFSRs) are a better choice as PRPGs compared to LFSM/PS and obtain desired levels of channel separations at a lower hardware cost than the LFSM/PS. Experimental results corroborate the effectiveness of the proposed methods through increased levels of the resultant fault coverage in two-dimensional scan designs.

Genom arkeologins historia har skepp studerats som symboler för makt, status och krig. Även om skeppen studerats väldigt mycket inom dessa aspekter har relationerna och nätverken runt skeppens produktion och underhåll fallit i skymundan. Målet med uppsatsen är därför att studera dessa relationer ur ett holistiskt perspektiv, och att försöka tolka hur dessa relationer i sin tur påverkat skeppets roll i gravsammanhang genom att använda Osebergsskeppet som grund för studien. Tidsramen som satts är yngre järnålder, detta för att kunna tolka skeppens symboliska värde effektivt. För att göra det immateriella greppbart används entanglement-teori, och kvantitativa uträkningar för naturliga- och mänskliga resurser används som hjälpmedel för detta. Studien visar att allt rörande skeppet är intrasslade med varandra, och att en kollektiv agens av samhället var nödvändig för produktion och senare underhåll av skeppen. Skeppens symboliska värde är också format av dessa relationer, och det är möjligt att det är dessa relationer som lett till skeppens roll i gravseden under perioden. / For much of the history of archaeology ships have been studied as symbols for power, wealth, and warfare. Even though viking-age ships have been studied immensely in these regards the relationships and the network of organization around the construction and maintenance have not received as much attention from archaeologists. The aim of this essay is therefore to study these relationships in a holistic view, and to try to interpret how these relationships affected the ships’ place in the funerary sphere using the Oseberg ship as the basis for this study. The timeframe for the study is set to late iron-age since the study of symbolism needs a longer timeframe to be interpreted effectively. To get the intangible to become tangible the entanglement theory is applied, and quantitative estimations on natural- and human resources are used as support to do so. The study shows that everything surrounding the ship is entangled, and that a collective agency of the communities was necessary for the production and maintenance of the ships. The symbolic meaning of the ship is also formed by these relationships, and that it might be these relationships that gave the ships a place in the funerary sphere during the period.

Software systems are becoming increasingly complex, with developers frequentlyutilizing numerous dependencies. In this landscape, accurate tracking and understanding of dependencies within JavaScript and TypeScript codebases are vital formaintaining software security and quality. However, there exists a gap in how existing vulnerability scanning tools, such as Dependabot, convey information aboutthe usage of these dependencies. This study addresses the problem of providing amore comprehensive dependency usage overview, a topic critical to aiding developers in securing their software systems. To bridge this gap, a custom pipeline wasimplemented to supplement Dependabot, extracting the dependencies identified asvulnerable and providing specific information about their usage within a repository.The results highlight the pros and cons of this approach, showing an improvement inthe understanding of dependency usage. The effort opens a pathway towards moresecure software systems.

No description available.

In traditional software development, bug detection or testing comes as an afterthought. However, bugs are difficult to detect in the later stages of software development that result in long debugging time. Usually, bugs are left out because of higher concentration on development effort, leaving lesser time for testing. Test-Driven Development (TDD) is a software development process that can reduce the debugging time by catching most of the bugs during development. The development is carried on in small and repeated steps based on test cases. However, TDD is designed to work for object-oriented languages. This thesis investigates the suitability of TDD for non-Object Oriented (OO) languages such as C. TDD can be used with C language with the help of stubbing concept. This thesis also evolves the concepts of stubs and mocks in TDD to be used with C to solve dependency related problems. Finally this thesis analyses some frameworks for TDD in C and provides the trade-offs between them.

I varje projekt finns det brister i kommunikation som kan leda till att kostnad, tid och kvalitét påverkas. Varje projekt är en tillfällig organisation med olika företag och olika personer med varierande erfarenheter inom projektering. Detta medför att varje projekt är i sig unikt och hur alla ska kommunicera med varandra kan vara stora frågetecken under projekteringens förlopp. Denna rapport tittar närmare på vilka brister och svårigheter som kan uppkomma i projekteringskedet.

No description available.

Reducing interference noise in a noisy speech recording has been a challenging task for many years yet has a variety of applications, for example, in handsfree mobile communications, in speech recognition, and in hearing aids. Traditional single-channel noise reduction schemes, such as Wiener filtering, do not work satisfactorily in the presence of non-stationary background noise. Alternatively, supervised approaches, where the noise type is known in advance, lead to higher-quality enhanced speech signals. This dissertation proposes supervised and unsupervised single-channel noise reduction algorithms. We consider two classes of methods for this purpose: approaches based on nonnegative matrix factorization (NMF) and methods based on hidden Markov models (HMM).  The contributions of this dissertation can be divided into three main (overlapping) parts. First, we propose NMF-based enhancement approaches that use temporal dependencies of the speech signals. In a standard NMF, the important temporal correlations between consecutive short-time frames are ignored. We propose both continuous and discrete state-space nonnegative dynamical models. These approaches are used to describe the dynamics of the NMF coefficients or activations. We derive optimal minimum mean squared error (MMSE) or linear MMSE estimates of the speech signal using the probabilistic formulations of NMF. Our experiments show that using temporal dynamics in the NMF-based denoising systems improves the performance greatly. Additionally, this dissertation proposes an approach to learn the noise basis matrix online from the noisy observations. This relaxes the assumption of an a-priori specified noise type and enables us to use the NMF-based denoising method in an unsupervised manner. Our experiments show that the proposed approach with online noise basis learning considerably outperforms state-of-the-art methods in different noise conditions.  Second, this thesis proposes two methods for NMF-based separation of sources with similar dictionaries. We suggest a nonnegative HMM (NHMM) for babble noise that is derived from a speech HMM. In this approach, speech and babble signals share the same basis vectors, whereas the activation of the basis vectors are different for the two signals over time. We derive an MMSE estimator for the clean speech signal using the proposed NHMM. The objective evaluations and performed subjective listening test show that the proposed babble model and the final noise reduction algorithm outperform the conventional methods noticeably. Moreover, the dissertation proposes another solution to separate a desired source from a mixture with arbitrarily low artifacts.  Third, an HMM-based algorithm to enhance the speech spectra using super-Gaussian priors is proposed. Our experiments show that speech discrete Fourier transform (DFT) coefficients have super-Gaussian rather than Gaussian distributions even if we limit the speech data to come from a specific phoneme. We derive a new MMSE estimator for the speech spectra that uses super-Gaussian priors. The results of our evaluations using the developed noise reduction algorithm support the super-Gaussianity hypothesis. / <p>QC 20130916</p>

We study the approximate answers to OLAP queries on data warehouses. We consider the relative answers to OLAP queries on a schema, as distributions with the L1 distance and approximate the answers without storing the entire data warehouse. We first introduce three specific methods: the uniform sampling, the measure-based sampling and the statistical model. We introduce also an edit distance between data warehouses with edit operations adapted for data warehouses. Then, in the OLAP data exchange, we study how to sample each source and combine the samples to approximate any OLAP query. We next consider a streaming context, where a data warehouse is built by streams of different sources. We show a lower bound on the size of the memory necessary to approximate queries. In this case, we approximate OLAP queries with a finite memory. We describe also a method to discover the statistical dependencies, a new notion we introduce. We are looking for them based on the decision tree. We apply the method to two data warehouses. The first one simulates the data of sensors, which provide weather parameters over time and location from different sources. The second one is the collection of RSS from the web sites on Internet.