Informaation visualisointi vertaistukipalvelussa

Using visualisations to present multidimensional data may help to understand complex relations and to make better decisions. This thesis presents methods for visualising peers based on their similarity. The purpose of the visualisation is to help users of an online peer support service to browse and find relevant peers that are most similar to them. Four nonlinear dimensionality reduction methods are used to produce visualisations from multidimensional data. The Neighbour Retrieval Visualiser (NeRV), Multidimensional Scaling (MDS), the Self-Organising Map (SOM) and the Generative Topographic Mapping (GTM) are presented and compared quantitatively. The results from the comparison suggest that any one of the four methods could be used in such a peer support service. The methods are then used to visualise data in a hypothetical peer support service called the Stress Map. To further test the methods, the visualisations are subjected to a user study. The visualization based on the NeRV algorithm performs best, whereas the visualisations made with the SOM and the GTM are judged less appealing.Moniulotteisen datan visualisointi voi auttaa päätöksenteossa, kun se edellyttää monimutkaisten relaatioiden ymmärtämistä. Tässä diplomityössä on esitelty metodeja, joilla voidaan visualisoida ihmisten samankaltaisuutta. Visualisaatioiden tarkoituksena on auttaa käyttäjiä selaamaan ja löytämään itselleen relevantteja vertaisia, jotka ovat mahdollisimman samankaltaisia heidän kanssaan. Moniulotteinen data visualisoidaan käyttäen neljää epälineaarista dimensionreduktiomenetelmää: Naapurihaun visualisoija (NeRV), moniulotteinen skaalaus (MDS), itseorganisoiva kartta (SOM) ja generatiivinen topografinen kuvaus (GTM). Menetelmien esittelyn jälkeen niitä vertaillaan kvantitatiivisesti. Vertailun tuloksena esitetään, että menetelmät soveltuvat samankaltaisuuden visualisointiin vertaistukipalvelussa. Kuvitteellinen vertaistukipalvelu StressMap esitellään em. menetelmien avulla luotujen visualisaatioiden avulla, jonka jälkeen visualisaatioiden käyttökelpoisuutta testataan käyttäjäkyselyssä. NeRV:iin perustuva visualisaatio pärjää testissä parhaiten, sillä useat käyttäjät vierastavat SOM:illa ja GTM:lla luotuja visualisointeja

A review of RFID based solutions for indoor localization and location-based classification of tags

Wireless communication systems are very used for indoor localization of items. In particular, two main application field can be identified. The former relates to detection or localization of static items. The latter relates to real-time tracking of moving objects, whose movements can be reconstructed over identified timespans. Among the adopted technologies, Radio-Frequency IDentification (RFID), especially if based on cheap passive RFID tags, stands out for its affordability and reasonable efficiency. This aspect makes RFID suitable for both the above-mentioned applications, especially when a large number of objects need to be tagged. The reason lies in a suitable trade-off between low cost for implementing the position sensing system, and its precision and accuracy. However, RFID-based solutions suffer for limited reading range and lower accuracy. Solutions have been proposed by academia and industry. However, a structured analysis of developed solutions, useful for further implementations, is missing. The purpose of this paper is to highlight and review the recently proposed solutions for indoor localization making use of RFID passive tags. The paper focuses on both precise and qualitative location of objects. The form relates to (i) the correct position of tags, namely mapping their right position in a 2D or 3D environment. The latter relates to the classification of tags, namely the identification of the area where the tag is regardless its specific position

Plant sex affects plant-microbiome assemblies of dioecious Populus cathayana trees under different soil nitrogen conditions

Background: Dioecious plants have coevolved with diverse plant microbiomes, which are crucial for the fitness and productivity of their host. Sexual dimorphism in morphology, physiology, or gene expression may relate to different microbial compositions that affect male and female fitness in different environments. However, sex-specific impacts on ecological processes that control the microbiome assembly are not well known. In this study, Populus cathayana males and females were planted in different nitrogen conditions. It was hypothesized that males and females differently affect bacterial and fungal communities in the rhizosphere soil, roots, old leaves, and young leaves. Physiological traits and transcriptome profiles of male and female plants were investigated to reveal potential mechanisms that control the microbiome assembly. Results: Our results showed strong niche differentiation that shapes microbial communities leading to a rapid loss of diversity along a decreasing pH gradient from the rhizosphere soil to leaves. Sex had different impacts on the microbial assembly in each niche. Especially fungal endophytes showed great differences in the community structure, keystone species, and community complexity between P. cathayana males and females. For example, the fungal co-occurrence network was more complex and the alpha diversity was significantly higher in young female leaves compared to young male leaves. Transcriptome profiles revealed substantial differences in plant-pathogen interactions and physiological traits that clearly demonstrated divergent internal environments for endophytes inhabiting males and females. Starch and pH of young leaves significantly affected the abundance of Proteobacteria, while tannin and pH of roots showed significant effects on the abundance of Chloroflexi, Actinobacteria, and Proteobacteria, and on the bacterial Shannon diversity. Conclusion: Our results provided important knowledge for understanding sexual dimorphism that affects microbial assemblies, thus advancing our understanding of plant-microbiome interactions.Peer reviewe

Plant sex affects plant-microbiome assemblies of dioecious Populus cathayana trees under different soil nitrogen conditions

Background: Dioecious plants have coevolved with diverse plant microbiomes, which are crucial for the fitness and productivity of their host. Sexual dimorphism in morphology, physiology, or gene expression may relate to different microbial compositions that affect male and female fitness in different environments. However, sex-specific impacts on ecological processes that control the microbiome assembly are not well known. In this study, Populus cathayana males and females were planted in different nitrogen conditions. It was hypothesized that males and females differently affect bacterial and fungal communities in the rhizosphere soil, roots, old leaves, and young leaves. Physiological traits and transcriptome profiles of male and female plants were investigated to reveal potential mechanisms that control the microbiome assembly. Results: Our results showed strong niche differentiation that shapes microbial communities leading to a rapid loss of diversity along a decreasing pH gradient from the rhizosphere soil to leaves. Sex had different impacts on the microbial assembly in each niche. Especially fungal endophytes showed great differences in the community structure, keystone species, and community complexity between P. cathayana males and females. For example, the fungal co-occurrence network was more complex and the alpha diversity was significantly higher in young female leaves compared to young male leaves. Transcriptome profiles revealed substantial differences in plant-pathogen interactions and physiological traits that clearly demonstrated divergent internal environments for endophytes inhabiting males and females. Starch and pH of young leaves significantly affected the abundance of Proteobacteria, while tannin and pH of roots showed significant effects on the abundance of Chloroflexi, Actinobacteria, and Proteobacteria, and on the bacterial Shannon diversity. Conclusion: Our results provided important knowledge for understanding sexual dimorphism that affects microbial assemblies, thus advancing our understanding of plant-microbiome interactions.Peer reviewe

Learning Kernel-based Approximate Isometries

The increasing availability of public datasets offers an inexperienced opportunity to conduct data-driven studies. Metric Multi-Dimensional Scaling aims to find a low-dimensional embedding of the data, preserving the pairwise dissimilarities amongst the data points in the original space. Along with the visualizability, this dimensionality reduction plays a pivotal role in analyzing and disclosing the hidden structures in the data. This work introduces Sparse Kernel-based Least Squares Multi-Dimensional Scaling approach for exploratory data analysis and, when desirable, data visualization. We assume our embedding map belongs to a Reproducing Kernel Hilbert Space of vector-valued functions which allows for embeddings of previously unseen data. Also, given appropriate positive-definite kernel functions, it extends the applicability of our method to non-numerical data. Furthermore, the framework employs Multiple Kernel Learning for implicitly identifying an effective feature map and, hence, kernel function. Finally, via the use of sparsity-promoting regularizers, the technique is capable of embedding data on a, typically, lowerdimensional manifold by naturally inferring the embedding dimension from the data itself. In the process, key training samples are identified, whose participation in the embedding map\u27s kernel expansion is most influential. As we will show, such influence may be given interesting interpretations in the context of the data at hand. The resulting multi-kernel learning, non-convex framework can be effectively trained via a block coordinate descent approach, which alternates between an accelerated proximal average method-based iterative majorization for learning the kernel expansion coefficients and a simple quadratic program, which deduces the multiple-kernel learning coefficients. Experimental results showcase potential uses of the proposed framework on artificial data as well as real-world datasets, that underline the merits of our embedding framework. Our method discovers genuine hidden structure in the data, that in case of network data, matches the results of well-known Multi- level Modularity Optimization community structure detection algorithm

INVESTIGATING DIFFERENCES IN STRUCTURAL KNOWLEDGE AND METACOGNITIVE PROCESSES AMONG LAY HELPERS ADVANCED STUDENTS AND SENIOR PROFESSIONAL THERAPISTS

Therapist expertise is associated with the use of complex knowledge structures and metacognitive processes. A cross sectional ex-post facto design assessed differences in structural knowledge and metacognitive processes between lay helpers, advanced students, and senior professional therapists. A card sorting task involving 19 therapist intentions was used to assess the following structural knowledge indicators: minutes to complete a card sort, number of card sort categories, and card sort score. Metacognitive processes were assessed using an adaptation of the Metacognitive Awareness Inventory and the Self-reflection subscale of the Self-Reflection and Insight subscales. An inverse U shaped relationship was found in where compared to lay helpers and senior professional therapists; advanced student's had higher card sort scores, indicative of greater consistency with a sample of experienced therapists. Compared to lay helpers and advanced students, senior professional therapists used significantly more time to sort therapist intentions and sorted intentions into a greater number of categories. Relative to metacognitive process, advanced students and senior professional therapists reported significantly greater knowledge of cognition than lay helpers. Also, advanced students also reported greater self-reflection than both lay helpers and senior professional therapists. Discriminant analysis assessed the potential for a linear combination of structural knowledge indicators and metacognitive processes to differentiate participants by level of therapist development. Self-reflection and card sort scores discriminated advanced students from senior professionals, whereas knowledge of cognition and minutes to complete the card sort discriminated experienced professionals from lay helpers. Multidimensional scaling analysis was used to assess the optimal structural configuration of the pooled card sort data and yielded a 4 dimensional solution of the 19 therapist intentions. Results were consistent with Skovholt and Ronnestad's (1992) model of therapist professional development. Results also supported the attenuating effect of ill defined problems on problem solving ability of highly experienced individuals in their respective domain. The study concludes with implications for training, therapy, and research

Concepts in Action

This open access book is a timely contribution in presenting recent issues, approaches, and results that are not only central to the highly interdisciplinary field of concept research but also particularly important to newly emergent paradigms and challenges. The contributors present a unique, holistic picture for the understanding and use of concepts from a wide range of fields including cognitive science, linguistics, philosophy, psychology, artificial intelligence, and computer science. The chapters focus on three distinct points of view that lie at the core of concept research: representation, learning, and application. The contributions present a combination of theoretical, experimental, computational, and applied methods that appeal to students and researchers working in these fields