Reading Between the Lines of Qualitative Data – How to Detect Hidden Structure Based on Codes

While qualitative research is experiencing broad acceptance in the information systems discipline, growing volumes of heterogeneous data pose challenges to manual qualitative analysis. We introduce an unsupervised machine learning approach based on graph partitioning to detect hidden information and structure in qualitative data samples. With the clustering technique, we map coded data to a graph and formulate a partitioning problem which is solved by integer linear programming. As a result, clusters of information sources are identified based on similarities given in the coded data. We demonstrate the approaches’ ability to detect hidden information in coded qualitative data by application on coded interview transcripts. With the approach, we draw on a technique from the operations research discipline and expand the repertoire of approaches being used to analyze qualitative data in the context of information systems

Codes of Commitment to Crime and Resistance: Determining Social and Cultural Factors over the Behaviors of Italian Mafia Women

This article categorizes thirty-three women in four main Italian Mafia groups and explores social and cultural behaviors of these women. This study introduces the feminist theory of belief and action. The theoretical inquiry investigates the sometimes conflicting behaviors of women when they are subject to systematic oppression. I argue that there is a cultural polarization among the categorized sub-groups. Conservative radicals give their support to the Mafia while defectors and rebels resist the Mafia. After testing the theory, I assert that emancipation of women depends on the strength of their beliefs to perform actions against the Mafiosi culture

How does identity influence creative photography

The purpose of this research project was to explore the origins of the photographer’s identity and how this is evidenced in his/her photographic work. The findings and conclusions derived from this examination were assessed in terms of their relevance and value for photography in education in order to contribute to an innovative theoretical approach for reading, understanding and analysing a photograph. The study’s aim has been to identify significant connections between the photographer’s identity and his/her photographic work and develop an innovative framework based on teaching photography as an art form comparable to Fine Art. Finally, it merged the two fields of my interest in teaching and practicing photography in a multi-phase action based experience. In order to build on an innovative approach to the teaching of photography, this study drew on several theories, concepts and ideas related to philosophy and the theoretical analysis of photography within current discourses. The results illuminated that there is no literature review on the relation of photography with the artist’s identity in analysing a photograph either in English or Greek sources. This art and education research process was conducted under the practice-based methodology of A/r/tography. Through this methodology I was implicated in three different roles, those of the artist, the researcher and the teacher, and linked them with their actual function within the project process (Irwin, 2006). As an artist I observed the participants’ actions, behaviours and situations within their environment and private space and gathered information about their real life experiences and knowledge that constructed their identity and influenced their creative work. As a researcher I collected all the necessary material from literature review as well as the participants’ real life and work settings. Finally, my role as a researcher-teacher was to analyse and evaluate the findings and come to the conclusion of the approach for teaching photography to the students. As a part of the research, eight contemporary photographers (including the self-agent) were interviewed. The sample comprised of four Cypriot and four Greek professional photographers. I was the only participant who was also a teacher. All participants were selected after evaluating the evidence of their identity in their personal work. During evaluation I considered any information about the artists that suggested any influence of their identity in their images. The findings of the participants’ interviews linked and correlated with the literature review filled the scholarly gap about the construction of identity and its influence on the photographer’s inspiration and creative work. This new line of thought suggested a need to revise the syllabus in photography education not only to the basic principles of photography but also to reading, understanding and analysing a photograph. This awareness has led to an innovative framework for teaching photography

Discovery of Linguistic Relations Using Lexical Attraction

This work has been motivated by two long term goals: to understand how humans learn language and to build programs that can understand language. Using a representation that makes the relevant features explicit is a prerequisite for successful learning and understanding. Therefore, I chose to represent relations between individual words explicitly in my model. Lexical attraction is defined as the likelihood of such relations. I introduce a new class of probabilistic language models named lexical attraction models which can represent long distance relations between words and I formalize this new class of models using information theory. Within the framework of lexical attraction, I developed an unsupervised language acquisition program that learns to identify linguistic relations in a given sentence. The only explicitly represented linguistic knowledge in the program is lexical attraction. There is no initial grammar or lexicon built in and the only input is raw text. Learning and processing are interdigitated. The processor uses the regularities detected by the learner to impose structure on the input. This structure enables the learner to detect higher level regularities. Using this bootstrapping procedure, the program was trained on 100 million words of Associated Press material and was able to achieve 60% precision and 50% recall in finding relations between content-words. Using knowledge of lexical attraction, the program can identify the correct relations in syntactically ambiguous sentences such as ``I saw the Statue of Liberty flying over New York.''Comment: dissertation, 56 page

A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization

Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviours with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterise several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localisation. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localisation experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers