<p>The studio model of education incorporated in to many design-oriented HCI programs in the past two decades brings a number of objectives to programs implementing it. One objective is the building of a “bridge” between pedagogy and practice, preparing students for the differing realities between academia, and the constraints imposed in an organizational setting. The bridge also encourages the development of a student’s design philosophy, allowing them to acknowledge and understand their conceptions of design which influence decisions in project-processes, and the projected communities they may navigate towards in practice. This study addresses the dimensions of design philosophy held by students educated in these programs, and how such philosophies are engaged and shaped further in practice. Through a qualitative interview approach, this study presents 9 dimensions of design philosophy through the accounts of 10 students and practitioners, reflecting on their education and practice. Using existing work studying the flow of competence between practitioners and organizations, the discussion of the dimensions presented provides four ways in which the philosophies of practitioners may encounter tensions in practice. This research proposes future work on how the studio model in HCI pedagogy may better prepare students for enacting their philosophies, and further reflecting on the shaping of that philosophy through felt contrasts between education and practice. </p>

Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.

The researcher’s intent in this study was to understand users’ performance, specifically in terms of time, error and workload, in different display conditions while manipulating a space-time cube visualization. A convergent mixed-method design was applied to allow the researcher to better understand the research problems. In the study, time, error and perceived workload were investigated to test performance to detect if a display condition had a positive or negative influence on users’ abilities to perform a task. The qualitative data explored the differences in users’ experiences with the HoloLens and desktop<br>

Tato práce se zabývá vzájemnou provázaností projektového a procesního řízení. Jejím cílem je zjištění možností přenosu technik a metod aplikovaných při řešení projektů na řešení problémů v procesním řízení. Po prozkoumání společných a rozdílných rysů projektů a procesů jsem v první části práce dospěl k závěru, že mezi instanci procesu a projekt lze, s jistými omezeními, položit ekvivalenci. V následující části práce mapuji vzájemné interakce mezi projektovým a procesním řízením. Jsou zde uvedeny jak obecné způsoby vzájemných interakcí mezi oběma typy řízení, tak vybrané metodiky, které získávají v posledních letech na významu, ale prozatím nepronikly do obecného povědomí (Six Sigma, Lean). Práce má také za úkol čtenáře seznámit s novými trendy v projektovém řízení. Do této oblasti spadá a významnou částí na práci se podílí pojednání o Human Interaction Managementu. Práce se pokouší o zmapování jeho možností a přínosů, ale také možných problémových oblastí, se kterými se může uživatel tohoto přístupu setkat. Do práce jsem zahrnul také seznámení s prvním Human Interaction Management Systémem, který prozatím není běžně dostupný. Cílem bylo přiblížit čtenáři způsob práce s takovýmto systémem, seznámit jej s výhodami tohoto nového přístupu na konkrétní aplikaci, ale také nastínit možná úskalí.

The recent boom of Machine Learning Network Architectures like Generative Adversarial Networks (GAN), Deep Convolution Generative Adversarial Networks (DCGAN), Self Attention Generative Adversarial Networks (SAGAN), Context Conditional Generative Adversarial Networks (CCGAN) and the development of high-performance computing for big data analysis has the potential to be highly beneficial in many domains and fittingly in the early detection of chronic diseases. The clinical heterogeneity of one such chronic auto-immune disease like Systemic Lupus Erythematosus (SLE), also known as Lupus, makes it difficult for medical diagnostics. One major concern is a limited dataset that is available for diagnostics. In this research, we demonstrate the application of Generative Adversarial Networks for data augmentation and improving the error rates of Convolution Neural Networks (CNN). Limited Lupus dataset of 30 typical ’butterfly rash’ images is used as a model to decrease the error rates of a widely accepted CNN architecture like Le-Net. For the Lupus dataset, it can be seen that there is a 73.22% decrease in the error rates of Le-Net. Therefore such an approach can be extended to most recent Neural Network classifiers like ResNet. Additionally, a human perceptual study reveals that the artificial images generated from CCGAN are preferred to closely resemble real Lupus images over the artificial images generated from SAGAN and DCGAN by 45 Amazon MTurk participants. These participants are identified as ’healthcare professionals’ in the Amazon MTurk platform. This research aims to help reduce the time in detection and treatment of Lupus which usually takes 6 to 9 months from its onset.

Traditional computer vision algorithms try to understand the world using visible light cameras. However, there are inherent limitations of this type of data source. First, visible light images are sensitive to illumination changes and background clutter. Second, the 3D structural information of the scene is lost when projecting the 3D world to 2D images. Recovering the 3D information from 2D images is a challenging problem. Range sensors have existed for over thirty years, which capture 3D characteristics of the scene. However, earlier range sensors were either too expensive, difficult to use in human environments, slow at acquiring data, or provided a poor estimation of distance. Recently, the easy access to the RGBD data at real-time frame rate is leading to a revolution in perception and inspired many new research using RGBD data. I propose algorithms to detect persons and understand the activities using RGBD data. I demonstrate the solutions to many computer vision problems may be improved with the added depth channel. The 3D structural information may give rise to algorithms with real-time and view-invariant properties in a faster and easier fashion. When both data sources are available, the features extracted from the depth channel may be combined with traditional features computed from RGB channels to generate more robust systems with enhanced recognition abilities, which may be able to deal with more challenging scenarios. As a starting point, the first problem is to find the persons of various poses in the scene, including moving or static persons. Localizing humans from RGB images is limited by the lighting conditions and background clutter. Depth image gives alternative ways to find the humans in the scene. In the past, detection of humans from range data is usually achieved by tracking, which does not work for indoor person detection. In this thesis, I propose a model based approach to detect the persons using the structural information embedded in the depth image. I propose a 2D head contour model and a 3D head surface model to look for the head-shoulder part of the person. Then, a segmentation scheme is proposed to segment the full human body from the background and extract the contour. I also give a tracking algorithm based on the detection result. I further research on recognizing human actions and activities. I propose two features for recognizing human activities. The first feature is drawn from the skeletal joint locations estimated from a depth image. It is a compact representation of the human posture called histograms of 3D joint locations (HOJ3D). This representation is view-invariant and the whole algorithm runs at real-time. This feature may benefit many applications to get a fast estimation of the posture and action of the human subject. The second feature is a spatio-temporal feature for depth video, which is called Depth Cuboid Similarity Feature (DCSF). The interest points are extracted using an algorithm that effectively suppresses the noise and finds salient human motions. DCSF is extracted centered on each interest point, which forms the description of the video contents. This descriptor can be used to recognize the activities with no dependence on skeleton information or pre-processing steps such as motion segmentation, tracking, or even image de-noising or hole-filling. It is more flexible and widely applicable to many scenarios. Finally, all the features herein developed are combined to solve a novel problem: first-person human activity recognition using RGBD data. Traditional activity recognition algorithms focus on recognizing activities from a third-person perspective. I propose to recognize activities from a first-person perspective with RGBD data. This task is very novel and extremely challenging due to the large amount of camera motion either due to self exploration or the response of the interaction. I extracted 3D optical flow features as the motion descriptor, 3D skeletal joints features as posture descriptors, spatio-temporal features as local appearance descriptors to describe the first-person videos. To address the ego-motion of the camera, I propose an attention mask to guide the recognition procedures and separate the features on the ego-motion region and independent-motion region. The 3D features are very useful at summarizing the discerning information of the activities. In addition, the combination of the 3D features with existing 2D features brings more robust recognition results and make the algorithm capable of dealing with more challenging cases. / text

This thesis introduces a new approach to designing a Message Handling Assistant (MA). It presents a model of an MA and an intention extraction function for text messages, such as emails and Newsgroups articles. Based on a speech act theory and the belief-desire-intention (BDI) theory of rational agency, we define a generic MA. By interpreting intuitive descriptions of the desired behaviours of an MA using the BDI theory and speech act theory, we conjecture that intentions of messages alone provide enough information needed to capture user models and to reason how messages should be processed. To identify intentions of messages written in natural language, we develop a model of an intention extraction function that maps messages to intentions. This function is modelled in two steps. First, each sentence in a message is converted into a tuple (performative, proposition) using a dialogue act classifier. Second, the sender's intentions are formulated from the tuples using constraints for felicitous human communication. As an investigation of the use of machine learning technologies for designing the intention extraction function, four dialog act classifiers are implemented and evaluated on Newsgroups articles. The thesis also proposes a semantic communication framework, which integrates the agent and Internet technologies for automatic message composing and ontology exchange services.

The tremendous growth of information on the Internet has been above our ability to process. A recommender system, which filters out useful information and generate recommendations, has been introduced to help users overcome the information overload problem and has been widely applied in an ever-increasing number of e-commercial websites. Collaborative filtering and content-based recommendation methods are two major approaches used in recommender systems. The collaborative filtering predicts items which a particular user prefers by using a database about the past preferences of users with similar interests. The content-based method analyses the content of the objects to generate a representative list of the user’s interests, and then compares the similarity of item descriptions. These two methods have some drawbacks in dealing with situations such as sparse data and cold start problems. Recently, hybrid methods combining collaborative filtering and content-based methods have been proposed to overcome these limitations. However, personalized recommender system attempt to penetrate people’s various demand and generate the tailored recommendations. A highly effective and personalised recommender system may still face new challenges including interestdrifting and multicriteria optimisation. For example, a user’s interest may change over time. They may no longer like a item which was strongly preferred. Another example is that a person’s preference is varying and always has multiple criteria. Classic collaborative filtering uses a single overall rating for prediction. It does not properly reflect the opinion on a item and the reason why people rated this item high or low. Unfortunately, the current recommender systems do not consider these important factors. First, we proposed a novel hybrid recommender system to overcome interest-drifting by embedding the time-sensitive functions into the recommendation process. The experimental results show that the intergraded approach with interest-drifting can constantly perform better and provide users with higher quality recommendations. Meanwhile, the experimental results on different size of training dataset show that our algorithm can boost the prediction accuracy for all configurations. The contributions of this proposed algorithm are in two main aspects. First, using time function to reflect users’ intersts changing in order to achieve higher quality of recommendations. Second, using intergraded methods to solve some problems such as sparsity and cold start. Then we developed a new technique to aggregate the multicriteria ratings for predicting more accurate recommendations. The results show that our algorithms outperforms the traditional collaborative filtering recommender system on both accuracy of predicting ratings and accuracy of recommendations. The one of contributions in this proposed method is that we introduced the multicriteria concept into recommender systems to reflect the users’ opinion more accurate. Another contribution is that we develop a linear method to aggregate multicriteria to single rating for higher quality of recommendations. Our experiments demonstrate that the recommendation achieved better performances when interest-drifting and multicriteria ratings were considered. The significance of our research study is that we consider incorporating interest-drifting, and multicriteria ratings into a recommender system to generate personalised and effective recommendations.

abstract: Debugging is a boring, tedious, time consuming but inevitable step of software development and debugging multiple threaded applications with user interactions is even more complicated. Since concurrency and synchronism are normal features in Android mobile applications, the order of thread execution may vary in every run even with the same input. To make things worse, the target erroneous cases may happen just in a few specific runs. Besides, the randomness of user interactions makes the whole debugging procedure more unpredictable. Thus, debugging a multiple threaded application is a tough and challenging task. This thesis introduces a replay mechanism for debugging user interactive multiple threaded Android applications. The approach is based on the 'Lamport Clock' concept, 'Event Driven' implementation and 'Client-Server' architecture. The debugger tool described in this thesis provides a user controlled debugging environment where users or developers are allowed to use modified record application to generate a log file. During the record time, all the necessary events like thread creation, synchronization and user input are recorded. Therefore, based on the information contained in the generated log files, the debugger tool can replay the application off-line since log files provide the deterministic order of execution. In this case, user or developers can replay an application as many times as they need to pinpoint the errors in the applications. / Dissertation/Thesis / M.S. Computer Science 2012

Abstract Product development (PD) has an important role as a key competitive factor in business environments. The capacity of designers and other stakeholders to perceive and process product related information is burdened by the increasing complexity of products and the high demands of working life. Therefore, companies need new human-centred perspectives and methods of balancing and enhancing their overall PD processes in order to develop successful products. The main motive for this research arises from the fact that ergonomics design research has been scarce from the process-oriented and systemic methods perspective. It has mainly focused on the methods, such as those needed in user interface design, and the usability and safety testing of products. The purpose of this dissertation is to consider the PD work system from the macroergonomics perspective. Macroergonomics is a top-down sociotechnical systems approach that is concerned with the analysis, design and evaluation of work systems. Nowadays, the individual user context is the dominating source of product requirements, but the designers’ work system has significant influence on its outcome as well. As an open work system, PD covers the use and design contexts of a product, not only at the individual, but also at the social and system levels. In this dissertation, the use and design contexts of products are examined through six individual studies, which were carried out during a demanding PD project of a new simulation game. In this design process, from the initial state to the goal state, macroergonomics was used as the main theoretical guideline. In many companies, PD processes are considered and developed mainly from the project management or technological points of view. However, because of the increasing complexity and systemic nature of products, PD organisations, too, will have to become more participatory, more networked and more systems oriented. As the main findings, this dissertation indicates that the macroergonomic approach can enrich the PD process and its outcomes by emphasising the balance between the technical and social subsystems of PD work system. The emerging complexity of products must be controlled from the entire PD work system, not the individual context of use only. The research introduces a new PD work system model that includes both the design and use contexts of products and demonstrates their analogical sociotechnical structures. The value of this dissertation for the industry is that companies can overcome certain emerging challenges of PD by applying the introduced macroergonomic principles. The findings of the research may encompass the re-designing of the current PD process in a company. Instead of shutting their eyes to the complexity of the surrounding world, companies should consider it as the macroergonomic PD work system and be more aware about the overall product requirements.