Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.

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.

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.

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.

Oppimisen kontekstuaalisuutta tunnistamassa : aikuisopiskelijan oppimiseen kytkeytyvät kontekstit verkkopohjaisessa opiskelussa

Summary: Identifying contextuality in learning - contexts linked to adult learners in network-based learning

Deep-hole drilling process, monitoring system and sensors modifications

 In the drilling processes and especially deep-hole drilling process, the monitoring system and having control on mechanical parameters (e.g. Force, Torque,Vibration and Acoustic emission) are essential. The main focus of this thesis work is to study the characteristics of deep-hole drilling process, and optimize the monitoring system for controlling the process. The vibration is considered as a major defect area of the deep-hole drilling process which often leads to breakage of the drill, therefore by vibration analysis and optimizing the workpiecefixture, this area is studied by finite element method and the suggestions are explained. By study on a present monitoring system, and searching on the new sensor products, the modifications and recommendations are suggested for optimize the present monitoring system for excellent performance in deep-hole drilling process research and measurements.

Value added logistics in supply and demand chain. Smile. Part 2. eBusiness in a service business. Case: A maintenance and operations network in forest industry

VVALOSADE is a research project of professor Anita Lukka's VALORE research team in the Lappeenranta University of Technology. The VALOSADE includes the ELO technology program of Tekes. SMILE is one of four subprojects of the VALOSADE. The SMILE study focuses on the case of the company network that is composed of small and micro-sized mechanical maintenance service providers and forest industry as large-scale customers. The basic principle of the SMILE study is the communication and ebusiness in supply and demand networks. The aim of the study is to develop ebusiness strategy, ebusiness model and e-processes among the SME local service providers, and onthe other hand, between the local service provider network and the forest industry customers in a maintenance and operations service business. A literature review, interviews and benchmarking are used as research methods in this qualitative case study. The first SMILE report, 'Ebusiness between Global Company and Its Local SME Supplier Network', concentrated on creating background for the SMILE study by studying general trends of ebusiness in supply chains and networks of different industries. This second phase of the study concentrates on case network background, such as business relationships, information systems and business objectives; core processes in maintenance and operations service network; development needs in communication among the network participants; and ICT solutions to respond needs in changing environment. In the theory part of the report, different ebusiness models and frameworks are introduced. Those models and frameworks are compared to empirical case data. From that analysis of the empirical data, therecommendations for the development of the network information system are derived. In process industry such as the forest industry, it is crucial to achieve a high level of operational efficiency and reliability, which sets up great requirements for maintenance and operations. Therefore, partnerships or strategic alliances are needed between the network participants. In partnerships and alliances, deep communication is important, and therefore the information systems in the network also are critical. Communication, coordination and collaboration will increase in the case network in the future, because network resources must be optimised to improve competitive capability of the forest industry customers and theefficiency of their service providers. At present, ebusiness systems are not usual in this maintenance network. A network information system among the forest industry customers and their local service providers actually is the only genuinenetwork information system in this total network. However, the utilisation of that system has been quite insignificant. The current system does not add value enough either to the customers or to the local service providers. At present, thenetwork information system is the infomediary that share static information forthe network partners. The network information system should be the transaction intermediary, which integrates internal processes of the network companies; the network information system, which provides common standardised processes for thelocal service providers; and the infomediary, which share static and dynamic information on right time, on right partner, on right costs, on right format and on right quality. This study provides recommendations how to develop this system in the future to add value to the network companies. Ebusiness scenarios, vision, objectives, strategies, application architecture, ebusiness model, core processes and development strategy must be considered when the network information system will be developed in the next development step. The core processes in the case network are demand/capacity management, customer/supplier relationship management, service delivery management, knowledge management and cash flow management. Most benefits from ebusiness solutions come from the electrifying of operational level processes, such as service delivery management and cash flow management.

Cultural aspects of globalizing university-industry knowledge interaction in China

Previous studies of the local involvement of multinational corporation (MNC) subsidiaries focus on host-country firms and local business partners such as suppliers and customers. The role of host-country universities in the same context of innovation networks is neglected. Furthermore, there are many organizational culture- and knowledge-related differences between universities and companies, and this is likely to pose additional challenges for successful collaboration. Early university-industry (U-I) studies have primarily been limited within a national boundary, being concerned with a single level of culture (i.e., at an organizational level) and one-way knowledge transfer from university to industry. Research on more dynamic knowledge interaction in multinational settings is lacking. This is particularly true in the business context of China. In today’s globalizing and rapidly changing organizations, addressing cultural differences and clashes is an everyday reality, and inter-cultural U-I collaboration is becoming a key asset for gaining global competitiveness. This study deals with Finnish MNC subsidiaries’ research collaboration with Chinese universities. It aims to explore the essence of such U-I collaboration and knowledge interaction, uncovering the deep functioning mechanisms of culture underlying effective collaborative knowledge creation and innovation. The study reviews critically different bodies of literature including knowledge management theories and studies, U-I collaboration and knowledge interaction, and cross-cultural research in terms of organizational knowledge generation and utilization. It adopts a case study strategy with qualitative research methods, and data is collected through in-depth interviews and participant observation. The study presents the following major findings: 1. In the light of a comprehensive analysis of U-I collaboration, an effective matching strategy is proposed, in the assumption that good alignment of knowledge interaction strategies and approaches with their corresponding knowledge type, capability development and research task may greatly enhance the effectiveness of cross-cultural U-I collaboration and knowledge interaction. 2. It is proposed that in the Chinese MNC context more dynamic types of knowledge interaction like knowledge co-creation should be of key concern particularly when dealing simultaneously with multi-disciplinary applied research of human factors and technologies. U-I knowledge interaction, otherwise, pays attention only to the study of one-way technology and knowledge transfer. 3. It is posited that the influence of culture on collaborative knowledge interaction can be studied in a valuable way when knowledge-related variables are simultaneously taken into account. A systematic analysis of the role of knowledge in cross-cultural knowledge interaction could best be approached from multi-aspects of knowledge including not only nature, characteristics and types of knowledge but also the process of knowledge (e.g., intensifications of knowledge interaction). 4. The study demonstrates the significant role of aspects of the host-country culture (e.g., Chinese guanxi) in U-I collaboration and knowledge interaction. This is evident, for instance, in issues related to interpersonal relationships and trust, true interest and the relatedness of the research, mutual commitment and learning, communication intensity and interaction, and awareness of cultural and knowledge-related differences between collaboration partners. Theoretical and practical implications of the findings are suggested and discussed.

Fedora 4.0 Deep Dive

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Luonnonhaltijat ‒ haltijoita luonnonympäristössä? : Nature spirits : continuity and change : Conference of Belief Narrative Network of ISFNR, Zugdidi, Georgia 1.‒4.10.2014

Konferenssiraportti

On a practical realization of Kolb's experiential learning cycle

This study aims to extend prior knowledge on the learning and developmental outcomes of the experiential learning cycle of David Kolb by the analysis of its practical realization at Team Academy. The study is based on the constructivist approach to learning and considers, among others, the concepts of autonomy support, Nonaka and Takeuchi's knowledge creation model, Luft and Ingham's Johari Window and Deci and Ryan's Self-determination theory. For the investigation deep interviews were carried out with the participants of Team Academy, both learners and coaches. Taking the interview results and the above described theories into consideration this study concludes that experiential learning results not only in effective learning, but also in a remarkable soft skill acquisition, self-development and increase in motivation with an internal locus of causality. Real-life projects permit the learners to experience real challenges. By the practical activities and teamwork they also get the possibility to find out their personal strengths, weaknesses and unique capacities.

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.

University students’ regulation of learning and text processing – examples from medical and teacher education

The general aim of the thesis was to study university students’ learning from the perspective of regulation of learning and text processing. The data were collected from the two academic disciplines of medical and teacher education, which share the features of highly scheduled study, a multidisciplinary character, a complex relationship between theory and practice and a professional nature. Contemporary information society poses new challenges for learning, as it is not possible to learn all the information needed in a profession during a study programme. Therefore, it is increasingly important to learn how to think and learn independently, how to recognise gaps in and update one’s knowledge and how to deal with the huge amount of constantly changing information. In other words, it is critical to regulate one’s learning and to process text effectively. The thesis comprises five sub-studies that employed cross-sectional, longitudinal and experimental designs and multiple methods, from surveys to eye tracking. Study I examined the connections between students’ study orientations and the ways they regulate their learning. In total, 410 second-, fourth- and sixth-year medical students from two Finnish medical schools participated in the study by completing a questionnaire measuring both general study orientations and regulation strategies. The students were generally deeply oriented towards their studies. However, they regulated their studying externally. Several interesting and theoretically reasonable connections between the variables were found. For instance, self-regulation was positively correlated with deep orientation and achievement orientation and was negatively correlated with non-commitment. However, external regulation was likewise positively correlated with deep orientation and achievement orientation but also with surface orientation and systematic orientation. It is argued that external regulation might function as an effective coping strategy in the cognitively loaded medical curriculum. Study II focused on medical students’ regulation of learning and their conceptions of the learning environment in an innovative medical course where traditional lectures were combined wth problem-based learning (PBL) group work. First-year medical and dental students (N = 153) completed a questionnaire assessing their regulation strategies of learning and views about the PBL group work. The results indicated that external regulation and self-regulation of the learning content were the most typical regulation strategies among the participants. In line with previous studies, self-regulation wasconnected with study success. Strictly organised PBL sessions were not considered as useful as lectures, although the students’ views of the teacher/tutor and the group were mainly positive. Therefore, developers of teaching methods are challenged to think of new solutions that facilitate reflection of one’s learning and that improve the development of self-regulation. In Study III, a person-centred approach to studying regulation strategies was employed, in contrast to the traditional variable-centred approach used in Study I and Study II. The aim of Study III was to identify different regulation strategy profiles among medical students (N = 162) across time and to examine to what extent these profiles predict study success in preclinical studies. Four regulation strategy profiles were identified, and connections with study success were found. Students with the lowest self-regulation and with an increasing lack of regulation performed worse than the other groups. As the person-centred approach enables us to individualise students with diverse regulation patterns, it could be used in supporting student learning and in facilitating the early diagnosis of learning difficulties. In Study IV, 91 student teachers participated in a pre-test/post-test design where they answered open-ended questions about a complex science concept both before and after reading either a traditional, expository science text or a refutational text that prompted the reader to change his/her beliefs according to scientific beliefs about the phenomenon. The student teachers completed a questionnaire concerning their regulation and processing strategies. The results showed that the students’ understanding improved after text reading intervention and that refutational text promoted understanding better than the traditional text. Additionally, regulation and processing strategies were found to be connected with understanding the science phenomenon. A weak trend showed that weaker learners would benefit more from the refutational text. It seems that learners with effective learning strategies are able to pick out the relevant content regardless of the text type, whereas weaker learners might benefit from refutational parts that contrast the most typical misconceptions with scientific views. The purpose of Study V was to use eye tracking to determine how third-year medical studets (n = 39) and internal medicine residents (n = 13) read and solve patient case texts. The results revealed differences between medical students and residents in processing patient case texts; compared to the students, the residents were more accurate in their diagnoses and processed the texts significantly faster and with a lower number of fixations. Different reading patterns were also found. The observed differences between medical students and residents in processing patient case texts could be used in medical education to model expert reasoning and to teach how a good medical text should be constructed. The main findings of the thesis indicate that even among very selected student populations, such as high-achieving medical students or student teachers, there seems to be a lot of variation in regulation strategies of learning and text processing. As these learning strategies are related to successful studying, students enter educational programmes with rather different chances of managing and achieving success. Further, the ways of engaging in learning seldom centre on a single strategy or approach; rather, students seem to combine several strategies to a certain degree. Sometimes, it can be a matter of perspective of which way of learning can be considered best; therefore, the reality of studying in higher education is often more complicated than the simplistic view of self-regulation as a good quality and external regulation as a harmful quality. The beginning of university studies may be stressful for many, as the gap between high school and university studies is huge and those strategies that were adequate during high school might not work as well in higher education. Therefore, it is important to map students’ learning strategies and to encourage them to engage in using high-quality learning strategies from the beginning. Instead of separate courses on learning skills, the integration of these skills into course contents should be considered. Furthermore, learning complex scientific phenomena could be facilitated by paying attention to high-quality learning materials and texts and other support from the learning environment also in the university. Eye tracking seems to have great potential in evaluating performance and growing diagnostic expertise in text processing, although more research using texts as stimulus is needed. Both medical and teacher education programmes and the professions themselves are challenging in terms of their multidisciplinary nature and increasing amounts of information and therefore require good lifelong learning skills during the study period and later in work life.