Granite: A scientific database model and implementation

The principal goal of this research was to develop a formal comprehensive model for representing highly complex scientific data. An effective model should provide a conceptually uniform way to represent data and it should serve as a framework for the implementation of an efficient and easy-to-use software environment that implements the model. The dissertation work presented here describes such a model and its contributions to the field of scientific databases. In particular, the Granite model encompasses a wide variety of datatypes used across many disciplines of science and engineering today. It is unique in that it defines dataset geometry and topology as separate conceptual components of a scientific dataset. We provide a novel classification of geometries and topologies that has important practical implications for a scientific database implementation. The Granite model also offers integrated support for multiresolution and adaptive resolution data. Many of these ideas have been addressed by others, but no one has tried to bring them all together in a single comprehensive model. The datasource portion of the Granite model offers several further contributions. In addition to providing a convenient conceptual view of rectilinear data, it also supports multisource data. Data can be taken from various sources and combined into a unified view. The rod storage model is an abstraction for file storage that has proven an effective platform upon which to develop efficient access to storage. Our spatial prefetching technique is built upon the rod storage model, and demonstrates very significant improvement in access to scientific datasets, and also allows machines to access data that is far too large to fit in main memory. These improvements bring the extremely large datasets now being generated in many scientific fields into the realm of tractability for the ordinary researcher. We validated the feasibility and viability of the model by implementing a significant portion of it in the Granite system. Extensive performance evaluations of the implementation indicate that the features of the model can be provided in a user-friendly manner with an efficiency that is competitive with more ad hoc systems and more specialized application specific solutions

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

An Improved Algorithm for Tor Circuit Scheduling

Tor is a popular anonymity-preserving network, consisting of routers run by volunteers all around the world. It protects Internet users’ privacy by relaying their network traffic through a series of routers, thus concealing the linkage between the sender and the recipient. Despite the advantage of Tor’s anonymizing capabilities, it also brings extra latency, which discourages more users from joining the network. One of the factors that causes the latency lies in Tor’s circuit scheduling algorithm, which allows busy circuits to crowd out bursty circuits. In this work, we propose and implement a more advanced scheduling algorithm which treats circuits differently, based on their recent activity. In this way, bursty circuits such as those used for web browsing can gain higher priority over busy ones such as used for bulk transfer; the performance for most activities over Tor is improved, while minimal overhead is incurred. Our algorithm has been incorporated into the latest build of Tor

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Towards Next Generation Bug Tracking Systems

Although bug tracking systems are fundamental to support virtually any software development process, they are currently suboptimal to support the needs and complexities of large communities. This dissertation first presents a study showing empirical evidence that the traditional interface used by current bug tracking systems invites much noise—unreliable, unuseful, and disorganized information—into the ecosystem. We find that noise comes from, not only low-quality contributions posted by inexperienced users or from conflicts that naturally arise in such ecosystems, but also from the difficulty of fitting the complex bug resolution process and knowledge into the linear sequence of comments that current bug tracking systems use to collect and organize information. Since productivity in bug tracking systems relies on bug reports with accessible and realible information, this leaves contributors struggling to work on and to make sense of the dumps of data submitted to bug reports and, thus, impacting productivity. Next generation bug tracking systems should be more than a tool for exchanging unstructured textual comments. They should be an ecosystem that is tailored for collaborative knowledge building, leveraging the power of the masses to collect reliable and useful information about bugs, providing mechanisms and incentives to verify the validity of such information and mechanisms to organize such information, thus, facilitating comprehension and reasoning. To bring bug tracking systems towards this vision, we present three orthogonal approaches aiming at increasing the usefulness and realiability of contributions and organizing information to improve understanding and reasoning. To improve the usefulness and realibility of contributions we propose the addition of game mechanisms to bug tracking systems, with the objective of motivating contributors to post higher-quality content. Through an empirical investigation of Stack Overflow we evaluate the effects of the mechanisms in such a collaborative software development ecosystem and map a promissing approach to use game mechanisms in bug tracking systems. To improve data organization, we propose two complementary approaches. The first is an automated approach to data organization, creating bug report summaries that make reading and working with bug reports easier, by highlighting the portions of bug reports that expert developers would focus on, if reading the bug report in a hurry. The second approach to improve data organization is a fundamental change on how data is collected and organized, eliminating comments as the main component of bug reports. Instead of comments, users contribute informational posts about bug diagnostics or solutions, allowing users to post contextual comments for each of the different diagnostic iiior solution posts. Our evaluations with real bug tracking system users find that they consider the bug report summaries to be very useful in facilitating common bug tracking system tasks, such as finding duplicate bug reports. In addition, users found that organzing content though diagnostic and solution posts to significanly facilitate reasoning about and searching for relevant information. Finally, we present future directions of work investigating how next generation bug tracking systems could combine the use of the three approaches, such that they benefit from and build upon the results of the other approaches. Next generation bug tracking systems should be more than a tool for exchanging unstructured textual comments. They should be an ecosystem that is tailored for collaborative knowledge building, leveraging the power of the masses to collect reliable and useful information about bugs, providing mechanisms and incentives to verify the validity of such information and mechanisms to organize such information, thus, facilitating comprehension and reasoning. To bring bug tracking systems towards this vision, we present three orthogonal approaches aiming at increasing the usefulness and realiability of contributions and organizing information to improve understanding and reasoning. To improve the usefulness and realibility of contributions we propose the addition of game mechanisms to bug tracking systems, with the objective of motivating contributors to post higher-quality content. Through an empirical investigation of Stack Overflow we evaluate the effects of the mechanisms in such a collaborative software development ecosystem and map a promissing approach to use game mechanisms in bug tracking systems. To improve data organization, we propose two complementary approaches. The first is an automated approach to data organization, creating bug report summaries that make reading and working with bug reports easier, by highlighting the portions of bug reports that expert developers would focus on, if reading the bug report in a hurry. The second approach to improve data organization is a fundamental change on how data is collected and organized, eliminating comments as the main component of bug reports. Instead of comments, users contribute informational posts about bug diagnostics or solutions, allowing users to post contextual comments for each of the different diagnostic iiior solution posts. Our evaluations with real bug tracking system users find that they consider the bug report summaries to be very useful in facilitating common bug tracking system tasks, such as finding duplicate bug reports. In addition, users found that organzing content though diagnostic and solution posts to significanly facilitate reasoning about and searching for relevant information. Finally, we present future directions of work investigating how next generation bug tracking systems could combine the use of the three approaches, such that they benefit from and build upon the results of the other approaches

Lauletun musiikin vaikutus AVH:n jälkeiseen kielelliseen muistiin sekä pitkäaikaiseen AVH:sta toipumiseen

The prevalence of stroke increases in the ageing population entailing an enormous economic and societal burden. This has raised the need for motivating, effective and easily applicable rehabilitation tools to enhance recovery and neuroplasticity. Music is an important source of enjoyment and well-being across life and it provides a multidomain stimulus that is both pleasant and rewarding, and engages the brain extensively. Previous evidence suggests that daily music listening can enhance cognitive recovery and mood and induce functional and structural neuroplasticity changes after stroke. Songs may also function as a verbal learning aid in healthy subjects. The aim of this thesis was to further explore the specific role of vocal (sung) music as a tool to aid verbal learning and long-term recovery after stroke. In Studies I and II, stroke patients (N = 31) performed a verbal learning task where novel narrative stories were presented in both spoken and sung formats, and underwent MRI at acute and 6-month post-stroke stages. Study I showed that stroke patients, especially those with mild aphasia, learned and recalled the stories better when they were presented in sung than spoken format at the 6-month stage. Exploring the cognitive and neural mechanisms underlying this effect, Study II further showed that non-aphasic patients exhibited more stable recall, indicated by reduced serial position effects, whereas aphasic patients showed a larger recency effect and enhanced chunking in the sung than spoken task. Diffusion tensor imaging and voxel-based morphometry results indicated that these effects were coupled with greater volume of the left arcuate fasciculus in non-aphasics, and with greater volume of the right inferior fronto-occipital fasciculus and grey matter in a bilateral network of temporal, frontal, and parietal regions in aphasics. In Study III, data was pooled from two randomized controlled trials where stroke patients (N = 83) received an intervention involving daily listening to self-selected vocal music, instrumental music, or audiobooks during the first three months after stroke. The recovery was assessed with neuropsychological tests and a mood questionnaire at acute, 3-month and 6-month stages, and structural MRI and functional MRI (fMRI) at acute and 6-month stages. Compared to audiobooks, listening to music enhanced the recovery of language skills and verbal memory and reduced negative mood. Vocal music had the strongest rehabilitative effect on both language and verbal memory, and the positive effects of music listening on language recovery were seen especially in patients with aphasia. Results from voxel-based morphometry and resting-state, and task-based fMRI analyses showed that vocal music listening selectively increased grey matter volume in left temporal areas and functional connectivity in the default mode network from acute to 6-month stage. The findings of the present thesis provide further evidence that listening to vocal music is a useful tool to support cognitive and emotional recovery after stroke and to enhance early language recovery in aphasia. The rehabilitative effects are driven by both structural and functional plasticity changes in temporoparietal networks, which are crucial for emotional processing, language and memory.Väestön ikääntyessä yhä useampi sairastuu aivoverenkiertohäiriöön (AVH), minkä aiheuttama yksilöllinen ja yhteiskunnallinen haitta on valtava. Tästä johtuen tarvitaan motivoivia, tehokkaita ja helposti saatavilla olevia työkaluja tehostamaan kuntoutusta ja edesauttamaan aivojen muovautuvuutta toipumisvaiheessa. Musiikki on tärkeä nautinnon ja hyvinvoinnin lähde ja monipuolinen virike, joka miellyttää, palkitsee ja aktivoi aivoja laajalti. Aiemmissa tutkimuksissa on havaittu, että päivittäinen musiikin kuuntelu AVH:n jälkeisten kuukausien aikana tehostaa kognitiivisten toimintojen ja mielialan kuntoutumista ja saa aikaan toiminnallista ja rakenteellista muovautuvuutta otsa- ja ohimolohkoalueilla sekä limbisillä alueilla, ja että laulut toimivat kielellisen oppimisen tukena terveillä henkilöillä. Tässä väitöskirjassa tarkastellaan erityisesti lauletun musiikin vaikutusta kielelliseen oppimiseen sekä pitkäkestoiseen toipumiseen AVH:n jälkeen. Tutkimuksissa I ja II AVH-potilaille (N = 31) tehtiin kielellinen oppimistehtävä, jossa heille esitettiin uusia tarinoita sekä laulettuna että puhuttuna, ja aivojen rakenteellinen magneettikuvaus (MRI) akuuttivaiheessa ja 6 kk sairastumisen jälkeen. Tutkimus I osoitti, että erityisesti ne potilaat, joilla oli lievä afasia, oppivat ja muistivat toipumisvaiheessa 6 kk sairastumisen jälkeen laulettuna esitetyn tarinan paremmin verrattuna puhuttuna esitettyyn. Tutkimus II selvitti tämän taustalla olevia kognitiivisia ja neuraalisia mekanismeja ja osoitti, että ei-afaattiset potilaat muistivat lauletun tarinan puhuttua tasaisemmin, mikä näkyi pienentyneenä sarjapositiovaikutuksena, kun taas afasiapotilailla lauletun tarinan muistamisessa ilmeni suurempi äskeisyysvaikutus ja tehokkaampi mieltämisyksiköiden muodostaminen (engl. chunking). Diffuusiotensorikuvantamisella ja vokselipohjaisella morfometrialla (VBM) saadut tulokset osoittivat, että nämä efektit olivat yhteydessä vasemman arcuate fasciculus (AF) –radaston tilavuuteen ei-afasiapotilailla ja oikean inferior fronto-occipital fasciculus (IFOF) –radaston sekä bilateraalisten ohimo-, otsa- ja päälakilohkoalueiden tilavuuteen afasiapotilailla. Tutkimuksessa III yhdistettiin kahden satunnaistetun kontrolloidun tutkimuksen AVH-potilaiden aineistot (N = 83) ja tutkittiin, miten kahden kuukauden ajan tapahtuva päivittäinen laulumusiikin, instrumentaalimusiikin tai äänikirjojen kuuntelu vaikuttaa toipumiseen. Toipumista arvioitiin neuropsykologisella tutkimuksella, mielialakyselyllä ja aivojen rakenteellisella ja toiminnallisella MRI (fMRI) -tutkimuksella akuuttivaiheesta aina 6 kk:n vaiheeseen. Äänikirjoihin verrattuna musiikin kuuntelu edisti puhetoimintojen ja kielellisen muistin kuntoutumista sekä vähensi negatiivista mielialaa. Laulumusiikilla oli voimakkain vaikutus sekä puheen että muistin kuntoutumiseen etenkin afasiapotilailla. VBM- ja fMRI-tulokset osoittivat, että laulumusiikin kuuntelu lisäsi harmaan aineen tilavuutta vasemmalla ohimolohkolla ja toiminnallista konnektiivisuutta oletustilaverkostossa 6 kk aikana. Tämän väitöskirjan tulokset tuovat lisää näyttöä päivittäisen musiikin kuuntelun positiivisesta vaikutuksesta ja tukevat sen käyttöä toimivana, helppona ja edullisena työkaluna, joka edistää AVH:n jälkeistä kognitiivista ja emotionaalista toipumista. Tämä juontuu rakenteellisista ja toiminnallisista muutoksista ohimo- ja päälakilohkoalueiden verkostoissa, mitkä ovat ratkaisevia tunteiden käsittelyn, kielen sekä muistin kannalta. Tutkimus tuo uutta tietoa etenkin laulumusiikin kuuntelun vaikutuksesta kuntoutumiseen sekä laulujen käytöstä oppimisen ja muistin tukena, erityisesti afasiasta toipumisessa