44 research outputs found
Intelligence Science I: Second IFIP TC 12 International Conference, ICIS 2017, Shanghai, China, October 25â28, 2017, Proceedings
International audienceBook Front Matter of AICT 510 (contains Keynote andInvited Presentations
Support matrix machine: A review
Support vector machine (SVM) is one of the most studied paradigms in the
realm of machine learning for classification and regression problems. It relies
on vectorized input data. However, a significant portion of the real-world data
exists in matrix format, which is given as input to SVM by reshaping the
matrices into vectors. The process of reshaping disrupts the spatial
correlations inherent in the matrix data. Also, converting matrices into
vectors results in input data with a high dimensionality, which introduces
significant computational complexity. To overcome these issues in classifying
matrix input data, support matrix machine (SMM) is proposed. It represents one
of the emerging methodologies tailored for handling matrix input data. The SMM
method preserves the structural information of the matrix data by using the
spectral elastic net property which is a combination of the nuclear norm and
Frobenius norm. This article provides the first in-depth analysis of the
development of the SMM model, which can be used as a thorough summary by both
novices and experts. We discuss numerous SMM variants, such as robust, sparse,
class imbalance, and multi-class classification models. We also analyze the
applications of the SMM model and conclude the article by outlining potential
future research avenues and possibilities that may motivate academics to
advance the SMM algorithm
Exploiting general-purpose background knowledge for automated schema matching
The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process.
In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources.
A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems.
One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented.
In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications
Educational Technology and Related Education Conferences for June to December 2015
The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next
Business Intelligence and Analytics in Small and Medium-Sized Enterprises
This thesis presents a study of Business Intelligence and Analytics (BI&A) adoption in small and medium-sized enterprises (SMEs). Although the importance of BI&A is widely accepted, empirical research shows SMEs still lag in BI&A proliferation. Thus, it is crucial to understand the phenomenon of BI&A adoption in SMEs.
This thesis will investigate and explore BI&A adoption in SMEs, addressing the main research question: How can we understand the phenomenon of BI&A adoption in SMEs? The adoption term in this thesis refers to all the IS adoption stages, including investment, implementation, utilization, and value creation. This research uses a combination of a literature review, a qualitive exploratory approach, and a ranking-type Delphi study with a grounded Delphi approach. The empirical part includes interviews with 38 experts and Delphi surveys with 39 experts from various Norwegian industries.
The research strategy investigates the factors influencing BI&A adoption in SMEs. The study examined the investment, implementation, utilization, and value creation of BI&A technologies in SMEs. A thematic analysis was adopted to collate the qualitative expert interview data and search for potential themes. The Delphi survey findings were further examined using the grounded Delphi method. To better understand the studyâs findings, three theoretical perspectives were applied: resource-based view theory, dynamic capabilities, and IS value process models.
The thesisâ research findings are presented in five articles published in international conference proceedings and journals. This thesis summary will coherently integrate and discuss these results.publishedVersio
Advanced Computational Methods for Oncological Image Analysis
[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with cliniciansâ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operationsâsuch as segmentation, co-registration, classification, and dimensionality reductionâand multi-omics data integration.
Security and trust in cloud computing and IoT through applying obfuscation, diversification, and trusted computing technologies
Cloud computing and Internet of Things (IoT) are very widely spread and commonly used technologies nowadays. The advanced services offered by cloud computing have made it a highly demanded technology.
Enterprises and businesses are more and more relying on the cloud to deliver services to their customers. The prevalent use of cloud means that more data is stored outside the organizationâs premises, which raises concerns about the security and privacy of the stored and processed data. This highlights the significance of effective security practices to secure the cloud infrastructure.
The number of IoT devices is growing rapidly and the technology is being employed in a wide range of sectors including smart healthcare, industry automation, and smart environments. These devices collect and exchange a great deal of information, some of which may contain critical and personal data of the users of the device. Hence, it is highly significant to protect the collected and shared data over the network; notwithstanding, the studies signify that attacks on these devices are increasing, while a high percentage of IoT devices lack proper security measures to protect the devices, the data, and the privacy of the users.
In this dissertation, we study the security of cloud computing and IoT and propose software-based security approaches supported by the hardware-based technologies to provide robust measures for enhancing the security of these environments. To achieve this goal, we use obfuscation and diversification as the potential software security techniques. Code obfuscation protects the software from malicious reverse engineering and diversification mitigates the risk of large-scale exploits. We study trusted computing and Trusted Execution Environments (TEE) as the hardware-based security solutions. Trusted Platform Module (TPM) provides security and trust through a hardware root of trust, and assures the integrity of a platform. We also study Intel SGX which is a TEE solution that guarantees the integrity and confidentiality of the code and data loaded onto its protected container, enclave.
More precisely, through obfuscation and diversification of the operating systems and APIs of the IoT devices, we secure them at the application level, and by obfuscation and diversification of the communication protocols, we protect the communication of data between them at the network level. For securing the cloud computing, we employ obfuscation and diversification techniques for securing the cloud computing software at the client-side. For an enhanced level of security, we employ hardware-based security solutions, TPM and SGX. These solutions, in addition to security, ensure layered trust in various layers from hardware to the application.
As the result of this PhD research, this dissertation addresses a number of security risks targeting IoT and cloud computing through the delivered publications and presents a brief outlook on the future research directions.Pilvilaskenta ja esineiden internet ovat nykyÀÀn hyvin tavallisia ja laajasti sovellettuja tekniikkoja. Pilvilaskennan pitkÀlle kehittyneet palvelut ovat tehneet siitÀ hyvin kysytyn teknologian. Yritykset enenevÀssÀ mÀÀrin nojaavat pilviteknologiaan toteuttaessaan palveluita asiakkailleen. Vallitsevassa pilviteknologian soveltamistilanteessa yritykset ulkoistavat tietojensa kÀsittelyÀ yrityksen ulkopuolelle, minkÀ voidaan nÀhdÀ nostavan esiin huolia taltioitavan ja kÀsiteltÀvÀn tiedon turvallisuudesta ja yksityisyydestÀ. TÀmÀ korostaa tehokkaiden turvallisuusratkaisujen merkitystÀ osana pilvi-infrastruktuurin turvaamista.
Esineiden internet -laitteiden lukumÀÀrÀ on nopeasti kasvanut. Teknologiana sitÀ sovelletaan laajasti monilla sektoreilla, kuten ÀlykkÀÀssÀ terveydenhuollossa, teollisuusautomaatiossa ja Àlytiloissa. Sellaiset laitteet kerÀÀvÀt ja vÀlittÀvÀt suuria mÀÀriÀ informaatiota, joka voi sisÀltÀÀ laitteiden kÀyttÀjien kannalta kriittistÀ ja yksityistÀ tietoa. TÀstÀ syystÀ johtuen on erittÀin merkityksellistÀ suojata verkon yli kerÀttÀvÀÀ ja jaettavaa tietoa. Monet tutkimukset osoittavat esineiden internet -laitteisiin kohdistuvien tietoturvahyökkÀysten mÀÀrÀn olevan nousussa, ja samaan aikaan suuri osuus nÀistÀ laitteista ei omaa kunnollisia teknisiÀ ominaisuuksia itse laitteiden tai niiden kÀyttÀjien yksityisen tiedon suojaamiseksi.
TÀssÀ vÀitöskirjassa tutkitaan pilvilaskennan sekÀ esineiden internetin tietoturvaa ja esitetÀÀn ohjelmistopohjaisia tietoturvalÀhestymistapoja turvautumalla osittain laitteistopohjaisiin teknologioihin. Esitetyt lÀhestymistavat tarjoavat vankkoja keinoja tietoturvallisuuden kohentamiseksi nÀissÀ konteksteissa. TÀmÀn saavuttamiseksi työssÀ sovelletaan obfuskaatiota ja diversifiointia potentiaalisiana ohjelmistopohjaisina tietoturvatekniikkoina. Suoritettavan koodin obfuskointi suojaa pahantahtoiselta ohjelmiston takaisinmallinnukselta ja diversifiointi torjuu tietoturva-aukkojen laaja-alaisen hyödyntÀmisen riskiÀ. VÀitöskirjatyössÀ tutkitaan luotettua laskentaa ja luotettavan laskennan suoritusalustoja laitteistopohjaisina tietoturvaratkaisuina. TPM (Trusted Platform Module) tarjoaa turvallisuutta ja luottamuksellisuutta rakentuen laitteistopohjaiseen luottamukseen. PyrkimyksenÀ on taata suoritusalustan eheys. TyössÀ tutkitaan myös Intel SGX:ÀÀ yhtenÀ luotettavan suorituksen suoritusalustana, joka takaa suoritettavan koodin ja datan eheyden sekÀ luottamuksellisuuden pohjautuen suojatun sÀiliön, saarekkeen, tekniseen toteutukseen.
Tarkemmin ilmaistuna työssÀ turvataan kÀyttöjÀrjestelmÀ- ja sovellusrajapintatasojen obfuskaation ja diversifioinnin kautta esineiden internet -laitteiden ohjelmistokerrosta. Soveltamalla samoja tekniikoita protokollakerrokseen, työssÀ suojataan laitteiden vÀlistÀ tiedonvaihtoa verkkotasolla. Pilvilaskennan turvaamiseksi työssÀ sovelletaan obfuskaatio ja diversifiointitekniikoita asiakaspuolen ohjelmistoratkaisuihin. Vankemman tietoturvallisuuden saavuttamiseksi työssÀ hyödynnetÀÀn laitteistopohjaisia TPM- ja SGX-ratkaisuja. Tietoturvallisuuden lisÀksi nÀmÀ ratkaisut tarjoavat monikerroksisen luottamuksen rakentuen laitteistotasolta ohjelmistokerrokseen asti.
TÀmÀn vÀitöskirjatutkimustyön tuloksena, osajulkaisuiden kautta, vastataan moniin esineiden internet -laitteisiin ja pilvilaskentaan kohdistuviin tietoturvauhkiin. TyössÀ esitetÀÀn myös nÀkemyksiÀ jatkotutkimusaiheista
AusgewĂ€hlte Chancen und Herausforderungen der digitalen Transformation fĂŒr die Produktentwicklung und Unternehmensorganisation im Finanzdienstleistungssektor
Vor dem Hintergrund der digitalen Transformation sind Finanzdienstleistungsunternehmen auf unterschiedlichen Ebenen zahlreichen Chancen sowie Herausforderungen ausgesetzt. WĂ€hrend der Einsatz neuer Technologien die Optimierung bestehender GeschĂ€ftsprozesse sowie das Angebot digitalisierter Finanzdienstleistungen ermöglicht, geht dies zugleich mit verĂ€nderten Arbeitsbedingungen innerhalb der Unternehmensorganisation einher. DarĂŒber hinaus sind Finanzdienstleister dazu angehalten die sich Ă€ndernden Kundenerwartungen bei den bisherigen GeschĂ€ftsaktivitĂ€ten sowie bei der Produktentwicklung zu berĂŒcksichtigen.
Das Ziel der vorliegenden kumulativen Dissertation ist es, bestehende Forschungsdesiderate hinsichtlich der Auswirkungen der digitalen Transformation auf den Finanzdienstleistungssektor, differenziert nach der Kunden- und Produktperspektive sowie der internen Unternehmensperspektive, vertiefend zu analysieren. Das Technology-Organization-Environment (TOE)-Framework von DePietro et al. (1990) wird dabei als theoretischer Rahmen zur Einordnung und Strukturierung der Forschungsmodule verwendet.
Die Ergebnisse der acht Module zeigen, dass die KundenbedĂŒrfnisse und âerwartungen im Finanzdienstleistungssektor verstĂ€rkt von der digitalen Transformation beeinflusst werden. Dies zeigt sich in der BeratungstĂ€tigkeit bspw. durch das Angebot neuer KundenkanĂ€le sowie der aus dem steigenden Wettbewerbsdruck resultierenden erhöhten Preistransparenz. Im Rahmen der Produktentwicklung sind zudem u. a. ESG-Risiken und Silent Cyber-Risiken zu beachten. Aus der Analyse der Auswirkungen der digitalen Transformation auf die Unternehmensorganisation geht hervor, dass ĂŒber den Einsatz digitaler Innovationen innerhalb des Backoffice die Realisation von Effizienzgewinnen sowie das Entgegenwirken eines Personalmangels möglich ist. DarĂŒber hinaus wird in den Modulen der Einfluss des Faktors Mensch auf die Cyber-Sicherheit hervorgehoben. WĂ€hrend dieser einerseits als âschwĂ€chstes Gliedâ und potenzielles Angriffsziel im Sicherheitskonstrukt der Unternehmen dargestellt wird, ist andererseits das Potenzial der BeschĂ€ftigten zur FrĂŒhwarnung zu berĂŒcksichtigen