Critical analysis and comparison of data protection techniques for genomics data sets

This work reviews the current literature on protecting genomic information. The goal is to provide insight on how to define a secure file format for such data. We compare the published ideas to the requirements defined by MPEG. We also propose new ideas to secure such data

The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, http://www.guidetopharmacology.org) provides expert-curated molecular interactions between successful and potential drugs and their targets in the human genome. Developed by the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS), this resource, and its earlier incarnation as IUPHAR-DB, is described in our 2014 publication. This update incorporates changes over the intervening seven database releases. The unique model of content capture is based on established and new target class subcommittees collaborating with in-house curators. Most information comes from journal articles, but we now also index kinase cross-screening panels. Targets are specified by UniProtKB IDs. Small molecules are defined by PubChem Compound Identifiers (CIDs); ligand capture also includes peptides and clinical antibodies. We have extended the capture of ligands and targets linked via published quantitative binding data (e.g. Ki, IC50 or Kd). The resulting pharmacological relationship network now defines a data-supported druggable genome encompassing 7% of human proteins. The database also provides an expanded substrate for the biennially published compendium, the Concise Guide to PHARMACOLOGY. This article covers content increase, entity analysis, revised curation strategies, new website features and expanded download options

Ethnographic Narrative Project

This paper details the journey of a first-year teacher. It is a highly reflective exploration of their inner landscape – one that documents the development of the teaching self in relation to students and society at large. Separated into four distinct sections, this work serves as an account of personal motivation to teach, getting to know students beyond the classroom walls, immersion in the community to situate educational work, and a comprehensive reflection upon teaching effectiveness and the evolution of the educating self. Development as a professional educator as stated in Teacher Performance Expectation (TPE) 6 demands continual introspection and proactive adjustments to our practice. The first year of teaching – a stage of initial and potentially immense growth – is especially critical as it sets the tone for the next and many years after. This ethnography interweaves objective analysis and studies internal and external factors and how they influence one another, and honest perceptions, struggles, and realizations as an individual embarks on the journey to becoming a teacher. By documenting my personal experience and performing higher-level analysis, we unveil the varied intricacies, competing demands, and trying moments that constitute the teaching experience. As the year (and, consequently, the ethnography) unfolds, one thing remains clear – teaching is a work of the heart

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

Business Process Management and Process Mining within a Real Business Environment: An Empirical Analysis of Event Logs Data in a Consulting Project

Il presente elaborato esplora l’attitudine delle organizzazioni nei confronti dei processi di business che le sostengono: dalla semi-assenza di struttura, all’organizzazione funzionale, fino all’avvento del Business Process Reengineering e del Business Process Management, nato come superamento dei limiti e delle problematiche del modello precedente. All’interno del ciclo di vita del BPM, trova spazio la metodologia del process mining, che permette un livello di analisi dei processi a partire dagli event data log, ossia dai dati di registrazione degli eventi, che fanno riferimento a tutte quelle attività supportate da un sistema informativo aziendale. Il process mining può essere visto come naturale ponte che collega le discipline del management basate sui processi (ma non data-driven) e i nuovi sviluppi della business intelligence, capaci di gestire e manipolare l’enorme mole di dati a disposizione delle aziende (ma che non sono process-driven). Nella tesi, i requisiti e le tecnologie che abilitano l’utilizzo della disciplina sono descritti, cosi come le tre tecniche che questa abilita: process discovery, conformance checking e process enhancement. Il process mining è stato utilizzato come strumento principale in un progetto di consulenza da HSPI S.p.A. per conto di un importante cliente italiano, fornitore di piattaforme e di soluzioni IT. Il progetto a cui ho preso parte, descritto all’interno dell’elaborato, ha come scopo quello di sostenere l’organizzazione nel suo piano di improvement delle prestazioni interne e ha permesso di verificare l’applicabilità e i limiti delle tecniche di process mining. Infine, nell’appendice finale, è presente un paper da me realizzato, che raccoglie tutte le applicazioni della disciplina in un contesto di business reale, traendo dati e informazioni da working papers, casi aziendali e da canali diretti. Per la sua validità e completezza, questo documento è stata pubblicato nel sito dell'IEEE Task Force on Process Mining

New horizons for female birdsong : evolution, culture and analysis tools : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Ecology at Massey University, Auckland, New Zealand

Published papers appear in Appendix 7.1. and 7.2 respectively under a CC BY 4.0 and CC BY licence: Webb, W. H., Brunton, D. H., Aguirre, J. D., Thomas, D. B., Valcu, M., & Dale, J. (2016). Female song occurs in songbirds with more elaborate female coloration and reduced sexual dichromatism. Frontiers in Ecology and Evolution, 4(22). https://doi.org/10.3389/fevo.2016.00022 Yukio Fukuzawa, Wesley Webb, Matthew Pawley, Michelle Roper, Stephen Marsland, Dianne Brunton, & Andrew Gilman. (2020). Koe: Web-based software to classify acoustic units and analyse sequence structure in animal vocalisations. Methods in Ecology and Evolution, 11(3). https://doi.org/10.1111/2041-210X.13336As a result of male-centric, northern-hemisphere-biased sexual selection theory, elaborate female traits in songbirds have been largely overlooked as unusual or non-functional by-products of male evolution. However, recent research has revealed that female song is present in most surveyed songbirds and was in fact the ancestral condition to the clade. Additionally, a high proportion of songbird species have colourful females, and both song and showy colours have demonstrated female-specific functions in a growing number of species. We have much to learn about the evolution and functions of elaborate female traits in general, and female song in particular. This thesis extends the horizons of female birdsong research in three ways: (1) by revealing the broad-scale evolutionary relationship of female song and plumage elaboration across the songbirds, (2) by developing new accessible tools for the measurement and analysis of song complexity, and (3) by showing—through a detailed field study on a large natural metapopulation—how vocal culture operates differentially in males and females. First, to understand the drivers of elaborate female traits, I tested the evolutionary relationship between female song presence and plumage colouration across the songbirds. I found strong support for a positive evolutionary correlation between traits, with female song more prevalent amongst species with elaborated female plumage. These results suggest that contrary to the idea of trade-off between showy traits, female plumage colouration and female song likely evolved together under similar selection pressures and that their respective functions are reinforcing. Second, I introduce new bioacoustics software, Koe, designed to meet the need for detailed classification and analysis of song complexity. The program enables visualisation, segmentation, rapid classification and analysis of song structure. I demonstrate Koe with a case study of New Zealand bellbird Anthornis melanura song, showcasing the capabilities for large-scale bioacoustics research and its application to female song. Third, I conducted one of the first detailed field-based analyses of female song culture, studying an archipelago metapopulation of New Zealand bellbirds. Comparing between male and female sectors of each population, I found equal syllable diversity, largely separate repertoires, and contrasting patterns of sharing between sites—revealing female dialects and pronounced sex differences in cultural evolution. By combining broad-scale evolutionary approaches, novel song analysis tools, and a detailed field study, this thesis demonstrates that female song can be as much an elaborate signal as male song. I describe how future work can build on these findings to expand understanding of elaborate female traits

An analytical tale of the social media discursive enactment of networked everyday resistance during the #feesmustfall social movement in South Africa

Social media are a space for discussions, debates and deliberations about personality, culture, society, and actual experiences of social actors in South Africa. They offer an unexpected opportunity for the broader consideration and inclusion of community members’ voices in governance decision making and policy processes. They also offer opportunities to engage, mobilise and change people and society in impressive scale, speed and effect: They have mobilising and transformative powers emanating from their interaction with the impetus of the agency of community members seeking better conditions of living. The magnitude of the effects of these powers makes it imperative to have a better understanding of their workings. Social media have been used in numerous social movements as the medium of communication to mobilise, coordinate, and broadcast protests. However, social media were never a guarantee of success as most movements using them did not achieve significant results. Yet, governments in developed and developing countries tend to engage inadequately with social media supported movements. The research problem is that the contribution of social media to the transformation of the social practice of discourse, which causes SSA community members’ agential impetus (collective intentionality for action) to generate a discourse of resistance on social media during social movements, is not well understood. The main research question is: Why are South African community members using social media to enact online discursive resistance during social movements? The aim of the research is to explain, from a critical realism point of view, Sub-Saharan African community members’ emergent usage of social media during social movements, by providing a contextualised social history (a tale) of South African community members’ practice of online discursive enactment of resistance. The emergent usage of social media of concern is conceptualised as “discursive enactment of networked everyday resistance” within a dialectical space of interaction conceptualised as “space of autonomous resistance”; an instance of a communication space allowing for transformative negation to occur. The research follows Bhaskar’s Critical Realism as a philosophical paradigm. Critical Realism seeks to explain phenomena by retroducing (retrospective inference) causal explanations from empirically observable phenomena to the generative mechanisms which caused them. The research was designed as a qualitative, processual and retroductive inquiry based on the Morphogenetic/Morphostasis approach with two phases: an empirical research developing the case of South African community members’ emergent usage of social media during the #feesmustfall social movement, looking for demi-regularities in social media discourse; and a transcendental research reaching into the past to identified significant events, objects and entities which tendencies are responsible for the shape of observed discourse. In the first phase, a case study was developed from data collected on the social media platform Twitter™, documents, and in-depth interviews of South African community members. The data collected were analysed using qualitative content analysis (QCA) and Critical Discourse Analysis (CDA) to unveil demi-regularities; moving from the observable individual strategic orientation of messages to discourses, thus to the tendencies of relational emergent properties of systemic magnitude which structure local discourses and are transformed by them. Then, the social mediainduced morphogenesis or transformation of South African community members’ discursive action was postulated in an analytical history of emergence (or analytical tale) of their usage of social media within a “space of autonomous resistance” during social movements. The findings of the research suggest that South African community members authored 3 discourses of resistance on Twitter™: #feesmustfall discourses of struggle, identity and oppression. They identified as “student qua black-child” stepping into the “Freedom fighter” role against the hegemonic post-apartheid condition curtailing their aspirations. It was found that social media socio-cultural embeddedness and under-design (Western European socio-cultural globalising underpinning features and functional features of the platforms) which interaction with the local socio-cultural mix (postapartheid socio-cultural tendencies for domination/power, spiral of silence, and legitimacy/identification) resulted in misfits and workarounds enhancing individual emotional conflict and aligning towards a socio-cultural opportunistic contingent complementarity integration in the deployment of discourse. That integration was actualised as a mediatization emergent property through asignification/signification of mainstream discourses of liberal democracy, colonial capitalism, national democratic revolution, free and decolonised education, black consciousness and Fallism. That mediatization through re-signification of the struggle for freedom created a communication “space of autonomous resistance” where networked freedom fighters enacted discursive everyday resistance against the hegemonic forces of students’ precariousness. The contribution of the research includes a realist model of social media discursive action (ReMDA); an explanation of South African community members’ deployment of discourse over social media during social movement and telling the tale of the transformation of discursive practices with the advent of social media in South Africa