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Information Technology is an academic discipline that is well recognized by the academic community. There is an increasing number of schools offering degrees in Information Technology and has there is an official curriculum published with the ACM/IEEE computing Curriculum. A concern with Information Technology as an academic discipline is that it does not have a clearly defined set of research issues which are not studied by any other discipline. One way to propose this set of issues is to perform a “bottom-up” analysis and gather research in IT that has already been published. This research can then be analyzed for recurring themes. This research describes a repository of graduate level work in the form of master's degree theses and projects and doctoral dissertations. A keyword analyses was done on the publications gathered, and it was confirmed that a set of themes could be found. As a demonstration of the viability of this approach the methodology has identified five initial themes. A larger sample is required to define a definitive set of themes for the IT discipline.

Denna studie presenterar Part-of-Speech tagging som metod för identifiering av nyckelord samt en marknadsanalys för en konverserande robot att leda språkkaféer. Resultatet evaluerades med hjälp av svar från enkäter utskickade till 30 anonyma personer med svenska som modersmål. Resultatet visar att metoden är rimlig och kan implementeras i en konverserande robot för att öka dess förståelse av det talade språket som förekommer inom språkkaféer. Marknadsanalysen indikerar att det existerar en marknad för den konverserande roboten. Roboten behöver dock förbättras för att kunna bli en ersättning för mänskliga språkledare inom språkkaféer. / This study presents Part-of-Speech tagging as a method for keyword spotting as well as a market research for a conversational robot to lead a language café. The results are evaluated using the answers from 30 anonymous Swedish native speakers. The results show that the method is plausible and could be implemented in a conversational robot to increase its understanding of the spoken language in a language café. The market research indicates that there is a market for the conversational robot. The conversional robot needs, however, improvements to successfully become a substitute for human language teachers in language cafés.

Conformité Européenne (CE) approval is a complex task for producers in Europe. The producers need to search for necessary standard documents and do the tests by themselves. CE-CHECK is a website which provides document searching service, and the company engineers want to use machine learning methods to analysis the documents and the results can improve the searching system. The first task is to construct an auto keyword extraction system to analysis the standard documents. This paper performed three different machine learning methods: Conditional Random Field (CRF), joint-layer Recurrent Neural Network (RNN), and double directional Long Short-Term Memory network (Bi-LSTM), for this task and tested their performances. CRF is a traditional probabilistic model which is widely used in sequential processing. RNN and LSTM are neural network models which show impressive performance on Natural Language processing in recent years. The result of the tests was that Bi-LSTM had the best performance: the keyword extraction recall was 76.97% while RNN was 72.99% and CRF was 70.18%. In conclusion, Bi-LSTM is the best model for this keyword extraction task, and the accuracy is high enough to provide a reliable result. The model also has good robustness that it have excellent performance on documents in different fields. Bi-LSTM model can analysis all documents in less than five minutes while manual works need months, so it saved both time and cost. The results can be used in searching system and further document analysis. / Att få Conformité Européenne (CE)-godkännande är en komplicerad process för producenter i Europa. Producenterna måste söka efter nödvändiga dokument för standarder samt utföra olika tester själva. CE-CHECK är en hemsida som erbjuder söktjänster för dokument. Företagets ingenjörer vill använda maskininlärningsmetoder för att analysera dokumenten då resultaten kan förbättra söksystemet. Den första uppgiften är att konstruera ett system som automatiskt extraherar nyckelord för att analysera dokument för standarder. Detta examensarbete använde tre olika maskininlärningsmetoder och testade deras prestanda: Conditional Random Field (CRF), joint-layer Recurrent Neural Network (RNN), samt Double directional Long Short-Term Memory network (Bi-LSTM). CRF är en traditionell probabilistisk modell som ofta används inom behandling av sekventiella data. RNN och LSTM är neurala nätverksmodeller som har visat imponerande resultat inom språkteknologi de senaste åren. Resultatet av undersökningen var att Bi-LSTM presterade bäst. Modellen lyckades extrahera 76.97% av nyckelorden medan resultatet för RNN var 72.99% och för CRF var det 70.18%. Slutsatsen blev således att Bi-LSTM är det bästa valet av modell för denna uppgift och dess exakthet är tillräckligt god för att producera pålitliga resultat. Modellen är även robust då den visar goda resultat på dokument från olika forskningsområden. Bi-LSTM kan analysera alla dokument på mindre än fem minuter medan manuellt arbete skulle kräva månader. Den minskar således både tidsåtgång och kostnad. Resultaten kan användas både i söksystem samt i vidare analys av dokument.

AbstractArbetet, som är skrivet av Martina Filling och Therese Rasmusson, handlar om synen på lärarens förändrade fostransroll mellan 1946 och 1962. Titeln på arbetet är En förändrad lärarroll och syftet är att undersöka vilka olika perspektiv som existerade i diskussionen om lärarrollens fostransuppdrag mellan 1946 och 1962. För att uppnå ett resultat och dra en slutsats har två tidskrifter använts som källor, vilka publicerades under den tidsperiod undersökningen ämnar undersöka. Utvalda artiklar ur tidskrifterna granskades och sammanfördes till en textanalys. Materialet för textanalysen består av lärartidningarna Skola och samhälle och Svensk lärartidning. Resultatet i studien visar på att debatten kring lärarens fostransroll har varit livlig och delad. Agan som bestraffningsmedel var omtvistat, då vissa ville ha kvar systemet och andra ville hitta nya metoder som ersättning. Lärarna kände en rädsla över att förlora den självklara ledarrollen efter agans avskaffande och samtidigt diskuterade experterna pedagogiska verktyg till hur läraren kunde agera i fostransuppdraget.Nyckelord: Aga, auktoritet, disciplin, fostran, omsorg. / AbstractThe work, which was written by Martina Filing Therese Rasmussen, is about the perception of the teacher changed fostransroll between 1946 and 1962. The title of the work is a changing roles and the purpose is to explore the different perspectives that exist in the discussion of teacher roles task of character formation between 1946 and 1962. In order to achieve a result and draw a conclusion, the two journals used as sources, which were published during the period of investigation to conduct an examination. Selected articles from the journals were reviewed and were brought to a text analysis. The material on text analysis consists of teacher newspapers School and Society and the Swedish Teachers Journal. The results of the study show that the debate about teacher fostransroll has been lively and shared. Spanking as punishment medium was contentious, as some wanted to maintain the system and wanted to find new methods of compensation. The teachers felt a fear of losing the natural leadership role after the abolition spankings while experts debated educational tools to how the teacher could act in fostransuppdraget.Keyword: Aga, authority, discipline, education, social services.

In Mpuntuo: A Keyword Approach, Malmö University Communication for Development Master’s Degree candidate, Benita Uttenthal presents research exploring indigenous knowledge of the term development using an extended case study method of the critical case of the Ghanaian Ashanti community of Akrofuom, from which her family originates. Inspired by Raymond Williams’ classic work, Keywords, which was uniquely applied in Andrew Kipnis’ Suzhi: A Keyword Approach, Uttenthal embarks on a keyword study of the Ashanti term Mpuntuo, which is commonly translated in English as Development. The primary purpose of this investigation is to determine a working definition of development from the indigenous perspective of the citizens of Akrofuom. The research is intended to ignite discourse on the stagnation and seeming regression of development processes in the Akrofuom society. The guiding questions for this research are:●What does Mpuntuo mean both denotatively and connotatively?●With what do the people of Akrofuom associate the concept of Mpuntuo?●Does Mpuntuo transport meanings that are implicit and that you have to be a native speaker to understand?● How is the word used in everyday speech and other contexts?●What wider conclusions about 'development' can be drawn from a social, cultural and political analysis of the Mpuntuo concept?This qualitative study, which employs semi-structured interviews, group discussions and discourse analysis, allows for in-depth and reflexive engagement with the research environment.Ultimately, the research revealed that lack of participation in change processes in the Akrofuom case are having an adverse and depressing impact on the society leading to regression or under development.

Folksonomier, system som låter användare klassificera innehåll, blir allt vanligare på webben. Typiskt sker denna klassificering genom att användare fritt beskriver innehållsobjekt med hjälp av nyckelord.Denna uppsats presenterar en underökning av hur nyckelord förhåller sig till varandra hierarkiskt, inom ett folksonomisystem. Undersökningen är baserad på ett webbgränssnitt, där besökare kunde förfina eller förkasta förslag på hierarkier av nyckelord. Dessa förslag genererades utifrån av ett verkligt folksonomisystem.Efter analys av 400 inkomna svar dras slutsatsen att flera aspekter av metodologin måste förfinas för att tydliga resultat ska kunna uppnås. Förslag på sådana förändringar presenteras slutligen. / Folksonomies, systems for user classified content, are becoming more common on the web. Typically, these classification systems let users describe content objects by assigning them keywords ("tags").This thesis presents a study on how keywords in a folksonomy system relate hierarchically. The study is based on a web interface, where visitors could refine or reject suggestions of hierarchies of keywords. Suggestions where generated from a real folksonomy system.After analysis of 400 responses, the conclusion is made that to reach clear results, several aspects of the methodology have to be modified. The thesis ends with presenting possible ways to achieve this.

Keyword Spotting in general means identifying a keyword in a verbal or written document. In this research a novel approach in designing a simple spoken Keyword Spotting/Recognition system based on Template Matching is proposed, which is different from the Hidden Markov Model based systems that are most widely used today. The system can be used equally efficiently on any language as it does not rely on an underlying language model or grammatical constraints. The proposed method for keyword spotting is based on a modified version of classical Dynamic Time Warping which has been a primary method for measuring the similarity between two sequences varying in time. For processing, a speech signal is divided into small stationary frames. Each frame is represented in terms of a quantized feature vector. Both the keyword and the  speech  utterance  are  represented  in  terms  of  1‐dimensional  codebook  indices.  The  utterance is divided into segments and the warped distance is computed for each segment and compared against the test keyword. A distortion score for each segment is computed as likelihood measure of the keyword. The proposed algorithm is designed to take advantage of multiple instances of test keyword (if available) by merging the score for all keywords used.   The training method for the proposed system is completely unsupervised, i.e., it requires neither a language model nor phoneme model for keyword spotting. Prior unsupervised training algorithms were based on computing Gaussian Posteriorgrams making the training process complex but the proposed algorithm requires minimal training data and the system can also be trained to perform on a different environment (language, noise level, recording medium etc.) by  re‐training the original cluster on additional data.  Techniques for designing a model keyword from multiple instances of the test keyword are discussed. System performance over variations of different parameters like number of clusters, number of instance of keyword available, etc were studied in order to optimize the speed and accuracy of the system. The system performance was evaluated for fourteen different keywords from the Call - Home and the Switchboard speech corpus. Results varied for different keywords and a maximum accuracy of 90% was obtained which is comparable to other methods using the same time warping algorithms on Gaussian Posteriorgrams. Results are compared for different parameters variation with suggestion of possible improvements. / Electrical and Computer Engineering

Yes / Yes / The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion.

Past few years have witnessed a dramatic shift for IT infrastructures from a self-sustained model to a centralized and multi-tenant elastic computing paradigm -- Cloud Computing, which significantly reshapes the landscape of existing data utilization services. In truth, public cloud service providers (CSPs), e.g. Google, Amazon, offer us unprecedented benefits, such as ubiquitous and flexible access, considerable capital expenditure savings and on-demand resource allocation. Cloud has become the virtual ``brain" as well to support and propel many important applications and system designs, for example, artificial intelligence, Internet of Things, and so forth; on the flip side, security and privacy are among the primary concerns with the adoption of cloud-based data services in that the user loses control of her/his outsourced data. Encrypting the sensitive user information certainly ensures the confidentiality. However, encryption places an extra layer of ambiguity and its direct use may be at odds with the practical requirements and defeat the purpose of cloud computing technology. We believe that security in nature should not be in contravention of the cloud outsourcing model. Rather, it is expected to complement the current achievements to further fuel the wide adoption of the public cloud service. This, in turn, requires us not to decouple them from the very beginning of the system design. Drawing the successes and failures from both academia and industry, we attempt to answer the challenges of realizing efficient and useful secure data services in the public cloud. In particular, we pay attention to security and privacy in two essential functions of the cloud ``brain", i.e. data storage and processing. Our first work centers on the secure chunk-based deduplication of encrypted data for cloud backup and achieves the performance comparable to the plaintext cloud storage deduplication while effectively mitigating the information leakage from the low-entropy chunks. On the other hand, we comprehensively study the promising yet challenging issue of search over encrypted data in the cloud environment, which allows a user to delegate her/his search task to a CSP server that hosts a collection of encrypted files while still guaranteeing some measure of query privacy. In order to accomplish this grand vision, we explore both software-based secure computation research that often relies on cryptography and concentrates on algorithmic design and theoretical proof, and trusted execution solutions that depend on hardware-based isolation and trusted computing. Hopefully, through the lens of our efforts, insights could be furnished into future research in the related areas. / Ph. D. / Past few years have witnessed a dramatic shift for IT infrastructures from a self-sustained model to a centralized and multi-tenant elastic computing paradigm – Cloud Computing, which significantly reshapes the landscape of existing data utilization services. In truth, public cloud service providers (CSPs), e.g. Google, Amazon, offer us unprecedented benefits, such as ubiquitous and flexible access, considerable capital expenditure savings and on-demand resource allocation. Cloud has become the virtual “brain” as well to support and propel many important applications and system designs, for example, artificial intelligence, Internet of Things, and so forth; on the flip side, security and privacy are among the primary concerns with the adoption of cloud-based data services in that the user loses control of her/his outsourced data. Encryption definitely provides strong protection to user sensitive data, but it also disables the direct use of cloud data services and may defeat the purpose of cloud computing technology. We believe that security in nature should not be in contravention of the cloud outsourcing model. Rather, it is expected to complement the current achievements to further fuel the wide adoption of the public cloud service. This, in turn, requires us not to decouple them from the very beginning of the system design. Drawing the successes and failures from both academia and industry, we attempt to answer the challenges of realizing efficient and useful secure data services in the public cloud. In particular, we pay attention to security and privacy in two essential functions of the cloud “brain”, i.e. data storage and processing. The first part of this research aims to provide a privacy-preserving data deduplication scheme with the performance comparable to the existing cloud backup storage deduplication. In the second part, we attempt to secure the fundamental information retrieval functions and offer effective solutions in various contexts of cloud data services.