Ammattikorkeakoulun oppimisverkostossa kehittyy uudenlaista oppimista

Summary: New forms of learning develop within the learning networks of polytechnics

Luottamuksen ilmeneminen alueellisissa yhteistyöverkostoissa

Appearance of trust in regional, co-operative networks In our times, the value of social networks has been widely acknowledged. One can say that it is important for private persons to get networked, whilst it is even a must for companies and organizations in business life. This doctor's thesis examines three co-operative regional networks. Networks are located in Western Uusimaa (Länsi-Uusimaa) region in southernmost Finland, and they had both public organizations and private companies as participants (later called ‘players’). Initially, all of them were co-financed from public funds, and two of them are still operational while writing this. The main target of these networks has been to act as learning networks. The learning network stands for an ensemble of research and development units and workplaces constituting a common forum for learning. The main focus in this study has been on qualitative and structural characteristics of the networks, and how they are relating with intrinsic trust. In addition to the development of trust, it has been studied, at what level organizational learning within the networks takes place, and lastly, what kind of factors facilitate the development of social capital. The theoretical framework for the study is built on analysing trust and social capital. It is a 'mission impossible' to find single definitions for such major concepts. In this study, from the research questions' point of view it has been more relevant to concentrate on the aspects of networking and the relationships between the participating organizations. The total view in this study is very network-centric, and therefore those theories which have similar point of view have been prioritized. Such is the theory about structural holes by Ronald S. Burt (1992). It has been widely applied; especially his views on constraints affecting players in networks. The purpose of this study has not been to create new theories or to analyse and compare thoroughly the existing theoretical trends. Instead, the existing theories have provided the study with conceptual tools, which have been utilized for supporting the empirical results. The aim has been to create an explanatory case study consisting relevant discussion on the relationship between the network characteristics and the appearance of trust. The conceptual categorization for confidence vs. trust created by Niklas Luhmann (1979) is another important theoretical building block. In most cases, co-operation in networks is initiated by people already trusting in each other and willing to work together. However, personal trust is not sufficient in the long run to sustain the co-operation within the network: more abstract systemic trust described by Luhmann must also emerge. In the networks with different structures and at different development phases, these forms of trust appear at different levels. In this study, Luhmann’s systemic trust as a term has been replaced by the concept of 'trust in network as a system'. Structural characteristics of a network (density, centrality, structural holes etc.) have been selected to explain the creation of social capital and trust. The ability to adapt new information is essential for the development of social capital. Qualitative analysis for development phase has been used, and the Learning Network Maturity Test by Leenamaija Otala (2000) and her work have been applied. Thus, the qualitative characteristics and the structural characteristics of the networks are utilized together, when the creation of social capital and appearance of trust are assessed. Social Network Analysis, questionnaires and interviews have been the research methods. Quantitative and qualitative data have been combined. There is a similarity in viewpoints to research data with Extensive Case Study method, in which different cases are searched by exploring various cases and comparing certain common features between them and generic models. Development of trust, social capital and organizational learning has been explained in the study by comparing the networks in hand. Being a case study, it doesn't have targets to provide with general results and findings like conventional surveys. However, in this work phenomena and mechanisms related to them are interpreted from the empirical data. Key finding of this study is that the networks with high structural equality and clear target setting enable building trust to the network as a system. When systemic trust is present, e.g. changes in personnel involved in the co-operation won't hinder the network from remaining operational. On the other hand, if the players are not well motivated to co-operate, if the network is extremely centralized structurally, or if the network has players holding very much more beneficial position compared to the others, systemic trust won't develop: trust tends to remain at the personal level, and is directed to some players only. Such networks won't generate results and benefits to its players, and most probably they won’t live very long. In other words, learning networks cannot solely be based on willingness to learn, but also on willingness to co-operate.

Inter organizational learning within and by innovation networks

The thesis deals with the phenomenon of learning between organizations in innovation networks that develop new products, services or processes. Inter organizational learning is studied especially at the level of the network. The role of the network can be seen as twofold: either the network is a context for inter organizational learning, if the learner is something else than the network (organization, group, individual), or the network itself is the learner. Innovations are regarded as a primary source of competitiveness and renewal in organizations. Networking has become increasingly common particularly because of the possibility to extend the resource base of the organization through partnerships and to concentrate on core competencies. Especially in innovation activities, networks provide the possibility to answer the complex needs of the customers faster and to share the costs and risks of the development work. Networked innovation activities are often organized in practice as distributed virtual teams, either within one organization or as cross organizational co operation. The role of technology is considered in the research mainly as an enabling tool for collaboration and learning. Learning has been recognized as one important collaborative process in networks or as a motivation for networking. It is even more important in the innovation context as an enabler of renewal, since the essence of the innovation process is creating new knowledge, processes, products and services. The thesis aims at providing enhanced understanding about the inter organizational learning phenomenon in and by innovation networks, especially concentrating on the network level. The perspectives used in the research are the theoretical viewpoints and concepts, challenges, and solutions for learning. The methods used in the study are literature reviews and empirical research carried out with semi structured interviews analyzed with qualitative content analysis. The empirical research concentrates on two different areas, firstly on the theoretical approaches to learning that are relevant to innovation networks, secondly on learning in virtual innovation teams. As a result, the research identifies insights and implications for learning in innovation networks from several viewpoints on organizational learning. Using multiple perspectives allows drawing a many sided picture of the learning phenomenon that is valuable because of the versatility and complexity of situations and challenges of learning in the context of innovation and networks. The research results also show some of the challenges of learning and possible solutions for supporting especially network level learning.

Designing new learning experiences? : exploring corporate E-learners’ self-regulated learning

The context of this study is corporate e-learning, with an explicit focus on how digital learning design can facilitate self-regulated learning (SRL). The field of e-learning is growing rapidly. An increasing number of corporations use digital technology and elearning for training their work force and customers. E-learning may offer economic benefits, as well as opportunities for interaction and communication that traditional teaching cannot provide. However, the evolving variety of digital learning contexts makes new demands on learners, requiring them to develop strategies to adapt and cope with novel learning tools. This study derives from the need to learn more about learning experiences in digital contexts in order to be able to design these properly for learning. The research question targets how the design of an e-learning course influences participants’ self-regulated learning actions and intentions. SRL involves learners’ ability to exercise agency in their learning. Micro-level SRL processes were targeted by exploring behaviour, cognition, and affect/motivation in relation to the design of the digital context. Two iterations of an e-learning course were tested on two groups of participants (N=17). However, the exploration of SRL extends beyond the educational design research perspective of comparing the effects of the changes to the course designs. The study was conducted in a laboratory with each participant individually. Multiple types of data were collected. However, the results presented in this thesis are based on screen observations (including eye tracking) and video-stimulated recall interviews. These data were integrated in order to achieve a broad perspective on SRL. The most essential change evident in the second course iteration was the addition of feedback during practice and the final test. Without feedback on actions there was an observable difference between those who were instruction-directed and those who were self-directed in manipulating the context and, thus, persisted whenever faced with problems. In the second course iteration, including the feedback, this kind of difference was not found. Feedback provided the tipping point for participants to regulate their learning by identifying their knowledge gaps and to explore the learning context in a targeted manner. Furthermore, the course content was consistently seen from a pragmatic perspective, which influenced the participants’ choice of actions, showing that real life relevance is an important need of corporate learners. This also relates to assessment and the consideration of its purpose in relation to participants’ work situation. The rigidity of the multiple choice questions, focusing on the memorisation of details, influenced the participants to adapt to an approach for surface learning. It also caused frustration in cases where the participants’ epistemic beliefs were incompatible with this kind of assessment style. Triggers of positive and negative emotions could be categorized into four levels: personal factors, instructional design of content, interface design of context, and technical solution. In summary, the key design choices for creating a positive learning experience involve feedback, flexibility, functionality, fun, and freedom. The design of the context impacts regulation of behaviour, cognition, as well as affect and motivation. The learners’ awareness of these areas of regulation in relation to learning in a specific context is their ability for design-based epistemic metareflection. I describe this metareflection as knowing how to manipulate the context behaviourally for maximum learning, being metacognitively aware of one’s learning process, and being aware of how emotions can be regulated to maintain volitional control of the learning situation. Attention needs to be paid to how the design of a digital learning context supports learners’ metareflective development as digital learners. Every digital context has its own affordances and constraints, which influence the possibilities for micro-level SRL processes. Empowering learners in developing their ability for design-based epistemic metareflection is, therefore, essential for building their digital literacy in relation to these affordances and constraints. It was evident that the implementation of e-learning in the workplace is not unproblematic and needs new ways of thinking about learning and how we create learning spaces. Digital contexts bring a new culture of learning that demands attitude change in how we value knowledge, measure it, define who owns it, and who creates it. Based on the results, I argue that digital solutions for corporate learning ought to be built as an integrated system that facilitates socio-cultural connectivism within the corporation. The focus needs to shift from designing static e-learning material to managing networks of social meaning negotiation as part of a holistic corporate learning ecology.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Structure learning of context-specific graphical models

Abstract The ultimate problem considered in this thesis is modeling a high-dimensional joint distribution over a set of discrete variables. For this purpose, we consider classes of context-specific graphical models and the main emphasis is on learning the structure of such models from data. Traditional graphical models compactly represent a joint distribution through a factorization justi ed by statements of conditional independence which are encoded by a graph structure. Context-speci c independence is a natural generalization of conditional independence that only holds in a certain context, speci ed by the conditioning variables. We introduce context-speci c generalizations of both Bayesian networks and Markov networks by including statements of context-specific independence which can be encoded as a part of the model structures. For the purpose of learning context-speci c model structures from data, we derive score functions, based on results from Bayesian statistics, by which the plausibility of a structure is assessed. To identify high-scoring structures, we construct stochastic and deterministic search algorithms designed to exploit the structural decomposition of our score functions. Numerical experiments on synthetic and real-world data show that the increased exibility of context-specific structures can more accurately emulate the dependence structure among the variables and thereby improve the predictive accuracy of the models.

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.