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In this thesis, a tag-based approach is proposed for predicting mashup patterns, thus deriving inspiration for potential new mashups from the community's consensus. The proposed approach applies association rule mining techniques to discover relationships between APIs and mashups based on their annotated tags. The importance of the mined relationships is advocated as a valuable source for recommending mashup candidates while mitigating common problems in recommender systems. The proposed methodology is evaluated through experimentation using a real-life dataset. Results show that the proposed mining approach achieves prediction accuracy with 60% precision and 79% recall improvement over a direct string matching approach that lacks the mining information. / Master of Science

Durch die wachsende Bedeutung des Onlinehandels und der Zunahme an Benutzer-generierten Inhalten werden neue Ansätze benötigt, um Konsumenten bei ihrer Entscheidungsfindung zu unterstützen. Wie Studien zeigen, werden im Onlinehandel häufig Kundenrezensionen und Gesamtbewertungen eingesetzt. Allerdings sind diese beiden Werkzeuge für die Entscheidungsfindung von Konsumenten nur begrenzt hilfreich. Gesamtbewertungen zeigen zwar eine oberflächliche Zufriedenheit der Kunden, geben jedoch keine Auskunft über die Bewertung bestimmter Produktaspekte, z.B. den Tragekomfort von Kopfhörern. Diese Aspekte werden von Kunden häufig in Rezensionen beschrieben, welche jedoch aufgrund ihrer unstrukturierten Weise nicht automatisiert aufbereitet werden können. Konsumenten sind daher gezwungen Rezensionen zu lesen und die darin diskutierten Merkmale manuell zu extrahieren. Die vorliegende Arbeit leistet mehrere Beiträge zur Adressierung des oben genannten Problems und beschäftigt sich dabei mit der Konzeptionierung, Evaluierung und Dienst-orientierten Bereitstellung einer interaktiven Entscheidungshilfe für den E-Commerce. Zunächst wird anhand einer empirischen Untersuchung der umsatzstärksten Onlineshops aus Deutschland der aktuelle Einsatz von Social Media Features analysiert. Dabei zeigt sich, dass die o.g. Problematik von keinem untersuchten Onlineshop adressiert wird. Ein weiterer Beitrag ist der Entwurf sowie die prototypische Implementierung einer interaktiven Entscheidungshilfe mit der Bezeichnung Rated Tags. Rated Tags erlaubt die Benutzer-generierte Definition von bewertbaren Schlagwörtern (Tags) und kombiniert dabei Methoden aus den Bereichen Social Tagging und Bewertungssysteme. Eine nachfolgende Evaluierung des Konzepts im Rahmen einer Anwenderstudie zeigt, dass der Einsatz von Rated Tags die Entscheidungsqualität verbessern sowie den Entscheidungsaufwand von Konsumenten reduzieren kann. Zur Optimierung des Lösungsansatzes wird dann ein Ensemble-Klassifikator aus dem Bereich des überwachten Lernens zur semiautomatisierten Vereinheitlichung von semantisch ähnlichen Tags entworfen, prototypisch implementiert und evaluiert. Die Ergebnisse der Evaluierung zeigen, dass die Leistung des Klassifikators den aktuellen Stand der Technik übersteigt. Als Abschluss der Arbeit wird ein Modell mit der Bezeichnung Rated Tags as a Service vorgestellt, welches die Service-orientierte Bereitstellung des Rated Tags-Ansatzes für Onlineshops oder Bewertungsportale beschreibt.

Les recherches autours la localisation d’étiquettes RFID dans des environnements intérieurs (indoor) ont suggéré plusieurs algorithmes et techniques. Identifier des étiquettes RFID dans les environnements intérieurs avec une parfaite précision n’est pas une tâche facile pour de nombreuses applications dans l'industrie, les supermarchés ou encore dans la logistique. Les systèmes de localisation RFID ont évolué en raison de l’augmentation de la popularité des applications qui nécessitent ce type de ressource. En effet, de nombreux procédés ont manifesté le besoin d'identifier et de localiser des étiquettes RFID en mouvement pour des raisons de traçabilité ou d'autres besoins . Entre autres, les supermarchés, les magasins, les hôpitaux, ou lignes de production de l'industrie peuvent bénéficier de ce type de système. Cependant, la plupart des techniques sont théoriques et ne tiennent pas compte de l'influence des problèmes de signalisation de cas réels en intérieur. De plus, bien que le problème de la localisation et identification soit traitée dans de nombreux autres travaux, les solutions proposées restent encore insuffisantes en termes de précision. Dans cette thèse, nous proposons une solution pour identifier des éléments avec des étiquettes RFID qui sont en mouvement, en les séparant des autres tags dits statiques. Notre méthode est basée sur la configuration classique d'un scénario de ligne de production typiquement utilisé dans l'industrie. A la fin du processus d'identification la méthode détermine les étiquettes RFID en mouvement et les étiquettes RFID statiques. Notre méthode d'identification a deux variantes. Dans un premier temps, nous proposons un procédé hybride qui opère au niveau de la couche d'application, ce qui lui permet d'être discret et personnalisable. Dans la seconde variante, la couche physique est associée à la solution initiale afin de limiter et d'optimiser la zone d'identification. Pour la validation de la méthode, nous avons éffectué plusieurs expériences sur des applications réelles de type industriel ou logistique, dans différentes conditions d'environnement intérieur et en utilisant des paramètres par défaut. Les résultats des tests ont montré que notre méthode donne d’excellentes performances lorsqu'elle est appliquée dans les processus industriels. / Indoor localization research has generated many of potential techniques and algorithms. Identifying RFID tags in an indoor environment with perfect accuracy is not a trivial task for many applications in industry, a marketplace, or a supply chain. Localization systems have been maturing as the number of applications requiring such capabilities has increased in popularity\cite{29} \cite{20}. In businesses, these applications allow intelligent process manufacturing, increasing productivity and enabling reliable product flows. Based on these systems, many processes need to localize and identify the movement of RFID tags. Marketplaces, shops, hospitals, or simple conveyor belts for supply chains can benefit from these tracking systems. However, most of these techniques do not take into account the influence of the real constraints to which the radio signal of the physical system is subject. Although this localization problem has been addressed in lots of treatments, it remains unsolved for systems that need precision. In this thesis, our solution involves identifying RFID tags in movement from others referred to as RFID static tags. We have based our methodology on the classical configuration supply chains scenario involved in industrial and market warehouse use cases. We completely identify which RFID tags are moving and which tags are placed outside and static by the time the conveyor belt supply chain process has ended. We developed our approach by proposing an identification method with two main variations. In its first variant, the method is hybrid and works directly in the application layer, which allows a non-intrusive, custom made and articulate RFID tag identification and classification. In its second variant, we also associate our solution with a physical layer constraint, to limit and optimize the identification zone.The evaluation of the performance shows an excellent precision in a practical industrial operational zone, validated with lots of experiments conducted in a real indoor application under different conditions and using classical setup devices.

The Social Web, or Web 2.0, has recently gained popularity because of its low cost and ease of use. Social tagging sites (e.g. Flickr and YouTube) offer new principles for end-users to publish and classify their content (data). Tagging systems contain free-keywords (tags) generated by end-users to annotate and categorise data. Lack of semantics is the main drawback in social tagging due to the use of unstructured vocabulary. Therefore, tagging systems suffer from shortcomings such as low precision, lack of collocation, synonymy, multilinguality, and use of shorthands. Consequently, relevant contents are not visible, and thus not retrievable while searching in tag-based systems. On the other hand, the Semantic Web, so-called Web 3.0, provides a rich semantic infrastructure. Ontologies are the key enabling technology for the Semantic Web. Ontologies can be integrated with the Social Web to overcome the lack of semantics in tagging systems. In the work presented in this thesis, we build an architecture to address a number of tagging systems drawbacks. In particular, we make use of the controlled vocabularies presented by ontologies to improve the information retrieval in tag-based systems. Based on the tags provided by the end-users, we introduce the idea of adding “system tags” from semantic, as well as social, resources. The “system tags” are comprehensive and wide-ranging in comparison with the limited “user tags”. The system tags are used to fill the gap between the user tags and the search terms used for searching in the tag-based systems. We restricted the scope of our work to tackle the following tagging systems shortcomings: - The lack of semantic relations between user tags and search terms (e.g. synonymy, hypernymy), - The lack of translation mediums between user tags and search terms (multilinguality), - The lack of context to define the emergent shorthand writing user tags. To address the first shortcoming, we use the WordNet ontology as a semantic lingual resource from where system tags are extracted. For the second shortcoming, we use the MultiWordNet ontology to recognise the cross-languages linkages between different languages. Finally, to address the third shortcoming, we use tag clusters that are obtained from the Social Web to create a context for defining the meaning of shorthand writing tags. A prototype for our architecture was implemented. In the prototype system, we built our own database to host videos that we imported from real tag-based system (YouTube). The user tags associated with these videos were also imported and stored in the database. For each user tag, our algorithm adds a number of system tags that came from either semantic ontologies (WordNet or MultiWordNet), or from tag clusters that are imported from the Flickr website. Therefore, each system tag added to annotate the imported videos has a relationship with one of the user tags on that video. The relationship might be one of the following: synonymy, hypernymy, similar term, related term, translation, or clustering relation. To evaluate the suitability of our proposed system tags, we developed an online environment where participants submit search terms and retrieve two groups of videos to be evaluated. Each group is produced from one distinct type of tags; user tags or system tags. The videos in the two groups are produced from the same database and are evaluated by the same participants in order to have a consistent and reliable evaluation. Since the user tags are used nowadays for searching the real tag-based systems, we consider its efficiency as a criterion (reference) to which we compare the efficiency of the new system tags. In order to compare the relevancy between the search terms and each group of retrieved videos, we carried out a statistical approach. According to Wilcoxon Signed-Rank test, there was no significant difference between using either system tags or user tags. The findings revealed that the use of the system tags in the search is as efficient as the use of the user tags; both types of tags produce different results, but at the same level of relevance to the submitted search terms.

The Social Web or ‘Web 2.0’ is focused on the interaction and collaboration between web sites users. It is credited for the existence of tagging systems, amongst other things such as blogs and Wikis. Tagging systems like YouTube and Flickr offer their users the simplicity and freedom in creating and sharing their own contents and thus folksonomy is a very active research area where many improvements are presented to overcome existing disadvantages such as the lack of semantic meaning, ambiguity, and inconsistency. TE is a tagging system proposing solutions to the problems of multilingualism, lack of semantic meaning and shorthand writing (which is very common in the social web) through the aid of semantic and social resources. The current research is presenting an addition to the TE system in the form of an embedded stemming component to provide a solution to the different lexical form problems. Prior to this, the TE system had to be explored thoroughly and then its efficiency had to be determined in order to decide on the practicality of embedding any additional components as enhancements to the performance. Deciding on this involved analysing the algorithm efficiency using an analytical approach to determine its time and space complexity. The TE had a time growth rate of O (N²) which is polynomial, thus the algorithm is considered efficient. Nonetheless, recommended modifications like patch SQL execution can improve this. Regarding space complexity, the number of tags per photo represents the problem size which, if it grows, will increase linearly the required memory space. Based on the findings above, the TE system is re-implemented on Flickr instead of YouTube, because of a recent YouTube restriction, which is of greater benefit in multi languages tagging system since the language barrier is meaningless in this case. The re-implementation is achieved using ‘flickrj’ (Java Interface for Flickr APIs). Next, the stemming component is added to perform tags normalisation prior to the ontologies querying. The component is embedded using the Java encoding of the porter 2 stemmer which support many languages including Italian. The impact of the stemming component on the performance of the TE system in terms of the size of the index table and the number of retrieved results is investigated using an experiment that showed a reduction of 48% in the size of the index table. This also means that search queries have less system tags to compare them against the search keywords and this can speed up the search. Furthermore, the experiment runs similar search trails on two versions of the TE systems one without the stemming component and the other with the stemming component and found out that the latter produced more results on the conditions of working with valid words and valid stems. The embedding of the stemming component in the new TE system has lessened the effect of the storage overhead needed for the generated system tags by their reduction for the size of the index table which make the system suited for many applications such as text classification, summarization, email filtering, machine translation…etc.

Digital images are being created and uploaded online in large numbers and this can be attributed to three main interconnected factors: a change in attitudes towards photography and its role in society; technological advancements in the camera industry; and changes in web technology. Many of these digital images are being uploaded to Flickr, one of the most popular of the new web 2.0 image management and sharing applications. Flickr supports secure storage, sharing, online communities, and tagging. Tagging is intended to aid with the organisation, description, and retrieval of images, and as tagging in Flickr generally relates to personal images (e.g., photographs), the tags assigned are highly subjective. Previous research has investigated motivations to upload and tag images in web 2.0 image management and sharing applications, and types of tags used in web 2.0 image management and sharing applications, and a limited number of studies have attempted to correlate the two, however no research has attempted to correlate the two whilst also taking into account the subjective nature of image tagging. Identifying the discrepancies between why people want to use Flickr, and how they use it can help system designers and users to get the best out of these applications. This thesis compares users’ motivations to upload and tag their images in Flickr with how they tag their images in practice. The study used a quantitative survey methodology consisting of a semi-structured questionnaire to explore user motivations. Tagging practices were investigated via a manual tag classification scheme applied to automatically extracted Flickr tags. The questionnaire results show that Flickr users are primarily motivated to upload their images to Flickr for the purposes of social-communication (i.e., to draw attention to their images for comments and feedback and to express and present aspects of their personality and identity) and for socialorganisation (i.e., so other people can access and view the images uploaded). However, tagging images in Flickr is not associated with the motivation of social-organisation and is instead more closely aligned to social-communication, and self-organisation (i.e., as a way of organising images for personal search and retrieval). Self-communication (i.e., documenting and recording for memory and personal reflection) was not found to be a popular motivation for either uploading or tagging images. Flickr users that are motivated to upload their images for the purposes of self-organisation have the clearest tagging practice and they predominantly use tags that are only meaningful to themselves. Gender, pro account status, number of images, and number of contacts are also strong predictors of tagging practice. However, overall, tagging practice is more closely associated with image content than with the motivation the user has. Although the results show that socialcommunication is the most prominent factor in motivating users to upload their images and in motivating users to tag their images, the findings reveal that users who are motivated to upload their images for the purposes of social-organisation are not using the system to its full potential.

Chan Ching-Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 109-123). / Abstracts in English and Chinese. / Thesis committee --- p.i / Statement --- p.ii / Abstract --- p.iii / Acknowledgements --- p.vi / Abbreviations --- p.vii / Table of contents --- p.xi / List of figures --- p.xiv / List of tables --- p.xv / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- Literature review --- p.3 / Chapter 2.1 --- History --- p.3 / Chapter 2.2 --- The living environment and life cycles of Cordyccps --- p.3 / Chapter 2.3 --- Chemical constituents of Cordyceps --- p.4 / Chapter 2.3.1 --- Determintaion of active ingredients in Cordyceps --- p.6 / Chapter 2.4 --- Therapeutic Functions --- p.8 / Chapter 2.4.1 --- Cardiovascular and circulatory functions --- p.8 / Chapter 2.4.1.1 --- Effects on Cholesterol and lipid metabolism --- p.8 / Chapter 2.4.1.2 --- Dilation of vasculature and cerebolature --- p.8 / Chapter 2.4.2 --- Respiratory functions --- p.9 / Chapter 2.4.3 --- Renai functions --- p.9 / Chapter 2.4.3.1 --- Effccts on chronic renal failure patients --- p.9 / Chapter 2.4.3.2 --- Protective effects on kidney toxicity --- p.9 / Chapter 2.4.4 --- Hepatic functions --- p.10 / Chapter 2.4.4.1 --- Effect on hepatitis B patients --- p.10 / Chapter 2.4.4.2 --- Energy state of liver --- p.10 / Chapter 2.4.5 --- Aging and senescence: Longevity enhancement --- p.11 / Chapter 2.4.5.1 --- Senescence --- p.11 / Chapter 2.4.5.2 --- Antioxidant effects --- p.11 / Chapter 2.4.6 --- Immune functions --- p.12 / Chapter 2.4.6.1 --- Enhancing immune system --- p.12 / Chapter 2.4.6.2 --- Anti-tumor effects --- p.12 / Chapter 2.4.7 --- Reproductive effects --- p.13 / Chapter 2.4.8 --- Hyperglycemic effects --- p.13 / Chapter 2.5 --- Cultivation --- p.15 / Chapter 2.5.1 --- Carbon and nitrogen sources --- p.15 / Chapter 2.5.2 --- Initial pH and temperature --- p.16 / Chapter 2.5.3 --- Bioelements --- p.16 / Chapter 2.5.4 --- Agitation intensity --- p.16 / Chapter 2.5.5 --- Aeration rate --- p.18 / Chapter 2.6 --- Fungal genetics --- p.19 / Chapter 2.6.1 --- EST approach --- p.19 / Chapter 2.7 --- Proteomic studies --- p.21 / Chapter 2.7.1 --- 2D gel electrophoresis --- p.21 / Chapter 2.7.2 --- Mass spectrometry --- p.22 / Chapter 2.7.3 --- Limitations and improvements --- p.22 / Chapter 2.7.4 --- Multiple spots for the same proteins --- p.24 / Chapter 2.7.5 --- Fungal proteomics --- p.24 / Chapter 2.7.5.1 --- Extraction method --- p.24 / Chapter 2.7.5.2 --- Combined uses of EST sequences and amino acid sequences --- p.26 / Chapter 2.7.5.3 --- Glycosylation --- p.26 / Chapter 3. --- Materials and methods --- p.28 / Chapter 3.1 --- Genomic Studies --- p.28 / Chapter 3.1.1 --- Strains and growth conditions --- p.28 / Chapter 3.1.2 --- Total RNA Extraction --- p.28 / Chapter 3.1.3 --- Isolation of mRNA --- p.30 / Chapter 3.1.4 --- cDNA Library Construction --- p.30 / Chapter 3.1.5 --- PCR Screening --- p.31 / Chapter 3.1.6 --- EST sequencing --- p.31 / Chapter 3.1.7 --- EST assembling and annotation --- p.32 / Chapter 3.2 --- Proteomic Studies --- p.33 / Chapter 3.2.1 --- Sample Preparation --- p.33 / Chapter 3.2.2 --- Quantitation --- p.34 / Chapter 3.2.3 --- 2-D PAGE --- p.34 / Chapter 3.2.4 --- In-gel digestion and peptide extraction --- p.36 / Chapter 3.2.5 --- MALDI-TOF MS Analysis --- p.37 / Chapter 3.3 --- Determination of adenosine using RP-HPLC --- p.38 / Chapter 4. --- Result --- p.39 / Chapter 4.1 --- Genomic studies --- p.39 / Chapter 4.1.1 --- cDNA library --- p.39 / Chapter 4.1.2 --- cDNA sequence analysis --- p.39 / Chapter 4.1.3 --- Functional annotation and analysis --- p.42 / Chapter 4.2 --- Proteomic --- p.67 / Chapter 4.2.1 --- 2D analysis and resolution --- p.67 / Chapter 4.2.2 --- Protein identification and annotation --- p.74 / Chapter 4.2.3 --- Image analysis --- p.81 / Chapter 4.3 --- Presence of adenosine (HPLC) --- p.84 / Chapter 5. --- Discussion and conclusion --- p.91 / Chapter 5.1 --- Genomic studies --- p.91 / Chapter 5.2 --- Presence of adenosine --- p.95 / Chapter 5.3 --- Proteomic --- p.96 / Chapter 5.3.1 --- Protein with increasing expression level --- p.97 / Chapter 5.3.1.1 --- MEI5 (Spot1084) --- p.97 / Chapter 5.3.1.2 --- Hsp70 and hsp60 (Spot 894 & 903) --- p.97 / Chapter 5.3.1.3 --- GRP 78 (Spot 1085) --- p.98 / Chapter 5.3.1.4 --- Ubiquitin (Spot1071) --- p.98 / Chapter 5.3.1.5 --- "Serine-tRNA ligase, glutaminyl-tRNA synthase (Spot 1037, 924)" --- p.99 / Chapter 5.3.1.6 --- 2-isopropylmalate synthase (Spot 862) --- p.99 / Chapter 5.3.1.7 --- Acyl-CoA oxidase 3 (Spot 882) --- p.100 / Chapter 5.3.1.8 --- ATP synthase beta chain (Spot 937) --- p.100 / Chapter 5.3.2 --- Proteins with decreasing expression --- p.101 / Chapter 5.3.2.1 --- 14-3-3 protein (spot 1080) --- p.101 / Chapter 5.3.2.2 --- Actin (Spot 945) --- p.101 / Chapter 5.3.2.3 --- GTP binding protein SPI1 (Spot1031) --- p.102 / Chapter 5.3.2.4 --- Hoclp (Spot 972) --- p.102 / Chapter 5.3.2.5 --- Rchl8p (Spot 983) --- p.103 / Chapter 5.3.2.6 --- Formaldehyde dehydrogenase (Spot 958) --- p.103 / Chapter 5.3.2.7 --- V-type ATPase (Spot 961) --- p.104 / Chapter 5.3.2.8 --- Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (Spot 987) --- p.104 / Chapter 5.3.2.9 --- Idp3p (Spot 929) --- p.105 / References

Traditional clustering methods, where documents are represented by term frequency vectors, are not very suitable for Lithuanian document clustering as there is no any freely available morphological analyzer or stemmer to make compact term dictionaries. It is still possible though to cluster Lithuanian documents using loose term dictionaries, but as Lithuanian is a highly synthetic language significant increase in resources and possibly inaccurate or distorted results must be taken into account. In this master thesis a clustering method for personally classified documents is deve­loped to overcome shortcomings of traditional document clustering stated above. In a new method documents are represented by tag frequency vectors, pair-wise similarities are measured by cosine coefficient and clustering itself is performed using experimentally selected bisecting K‑means algorithm. Experiments comparing developed method with traditional document clustering using loose term dictionary showed that former copes better with large document collections and/or large cluster number. At the same time subjective clustering estimation showed that even when new method demonstrates larger entropy and lower purity values, it still overcomes traditional method by clustering sense.

Zugl.: Halle (Saale), Univ., Diss., 2006