Reclaiming human machine nature

Extending and modifying his domain of life by artifact production is one of the main characteristics of humankind. From the first hominid, who used a wood stick or a stone for extending his upper limbs and augmenting his gesture strength, to current systems engineers who used technologies for augmenting human cognition, perception and action, extending human body capabilities remains a big issue. From more than fifty years cybernetics, computer and cognitive sciences have imposed only one reductionist model of human machine systems: cognitive systems. Inspired by philosophy, behaviorist psychology and the information treatment metaphor, the cognitive system paradigm requires a function view and a functional analysis in human systems design process. According that design approach, human have been reduced to his metaphysical and functional properties in a new dualism. Human body requirements have been left to physical ergonomics or "physiology". With multidisciplinary convergence, the issues of "human-machine" systems and "human artifacts" evolve. The loss of biological and social boundaries between human organisms and interactive and informational physical artifact questions the current engineering methods and ergonomic design of cognitive systems. New developpment of human machine systems for intensive care, human space activities or bio-engineering sytems requires grounding human systems design on a renewed epistemological framework for future human systems model and evidence based "bio-engineering". In that context, reclaiming human factors, augmented human and human machine nature is a necessityComment: Published in HCI International 2014, Heraklion : Greece (2014

Embodying Design

Rethinking design through the lens of embodied cognition provides a novel way of understanding human interaction with technology. In this book, Christopher Baber uses embodied cognition as a lens through which to view both how designers engage in creative practices and how people use designed artifacts. This view of cognition as enactive, embedded, situated, or distributed, without recourse to internal representations, provides a theoretical grounding that makes possible a richer account of human interaction with technology. This understanding of everyday interactions with things in the world reveals opportunities for design to intervene. Moreover, Baber argues, design is an embodied activity in which the continual engagement between designers and their materials is at the heart of design practice. Baber proposes that design and creativity should be considered in dynamic, rather than discrete, terms and explores “task ecologies”—the concept of environment as it relates to embodied cognition. He uses a theory of affordance as an essential premise for design practice, arguing that affordances are neither form nor function but arise from the dynamics within the human-artifact-environment system. Baber explores agency and intent of smart devices and implications of tangible user interfaces and activity recognition for human-computer interaction. He proposes a systems view of human-artifact-environment interactions—to focus on any one component or pairing misses the subtleties of these interactions. The boundaries between components remain, but the borders that allow exchange of information and action are permeable, which gives rise to synergies and interactions

Towards a Synthesized Decision Support Methodology that Integrates Human Cognition and Data Mining

Developments in information and computing technologies have given rise to Intelligent Decision Support Systems (IDSS). The design of IDSS is largely based on data mining techniques and fuzzy logic. While decision-making is an advanced cognitive process, very little has been done in developing decision support methodologies that help integrate high level cognitive human reasoning and thinking elements within IDSS. This paper proposes a new IDSS methodology that incorporates both data mining techniques and human cognition in the process of decision-making. This proposed methodology involves a phased decision-support process. The initial phase focuses on phrasing a decision based on important criteria or conditions. The second phase involves the machine to analyse the required information from one or more large datasets. The third phase involves human cognition in making intelligent decisions based on key cognitive elements. Furthermore, the proposed methodology is tested on a large data set in the context of elderly care units in Melbourne

Workplace Surfaces as Resource for Social Interactions

Space and spatial arrangements play an important role in our everyday social interactions. The way we use and manage our surrounding space is not coincidental, on the contrary, it reflects the way we think, plan and act. Within collaborative contexts, its ability to support social activities makes space an important component of human cognition in the post-cognitive era. As technology designers, we can learn a lot by rigorously understanding the role of space for the purpose of designing collaborative systems. In this paper, we describe an ethnographic study on the use of workplace surfaces in design studios. We introduce the idea of artful surfaces. Artful surfaces are full of informative, inspirational and creative artefacts that help designers accomplish their everyday design practices. The way these surfaces are created and used could provide information about how designers work. Using examples from our fieldwork, we show that artful surfaces have both functional and inspirational characteristics. We indentify four types of artful surfaces: personal, shared, project-specific and live surfaces. We believe that a greater insight into how these artful surfaces are created and used could lead to better design of novel display technologies to support designers' everyday work

Why don´t you express youself so that I can understand?

Purpose: Create an overall understanding of computers/software and cognitive science. We also want to investigate discrepancies in 4 particular software systems (The discrepancies are between human/computer and NOT between the computer systems). We also want to investigate if eventual discrepancies, or successes, in the programs might have a connection to the human cognition. Meaning; are these systems built in a way that suits the evolutionary cognitive mind? (I.e.: how the human brain/mind works). Finally, with the help of the four systems as practical examples, we wish to indicate the potential for further financial gain when designing software systems as a whole, using a cognitive approach. Methodology: Due to the difficulty in extracting some of the confidential information, we had to write the thesis as an explorative adapted study, relying heavily on interviews, workshops and an explorative case study. The case being the Liverpool Museum project, researching children’s answers of a museum filed trip. We also chose to make two surveys of our own. These will be either added as appendixes, and/or described in the text. Theory: Main: Cognitive Science, focusing on the work by Dave Snowden. Supporting/explaining; Computational complexity, Web scraping, Artificial Intelligence (A.I.), Black Swan and Knowledge Management. Empirical foundation: Primary data consist of interviews, workshops and a survey of LinkedIn.com and Monster.com. Secondary data consists of scientific articles and information from the Internet and an investigation of two confidential search engines. Findings and Conclusions: The investigation of the four search systems illustrates that there is a software design aspect linked to cognitive science. More research is necessary before any clear conclusions can be made, but this thesis implied that a least a part of the investigated discrepancy is caused by neglect of the human cognition when developing software. This also indicates that there is a potential for efficiency impact in financial terms, if considering this in future software development

Developing a framework for qualitative engineering: Research in design and analysis of complex structural systems

The research is focused on automating the evaluation of complex structural systems, whether for the design of a new system or the analysis of an existing one, by developing new structural analysis techniques based on qualitative reasoning. The problem is to identify and better understand: (1) the requirements for the automation of design, and (2) the qualitative reasoning associated with the conceptual development of a complex system. The long-term objective is to develop an integrated design-risk assessment environment for the evaluation of complex structural systems. The scope of this short presentation is to describe the design and cognition components of the research. Design has received special attention in cognitive science because it is now identified as a problem solving activity that is different from other information processing tasks (1). Before an attempt can be made to automate design, a thorough understanding of the underlying design theory and methodology is needed, since the design process is, in many cases, multi-disciplinary, complex in size and motivation, and uses various reasoning processes involving different kinds of knowledge in ways which vary from one context to another. The objective is to unify all the various types of knowledge under one framework of cognition. This presentation focuses on the cognitive science framework that we are using to represent the knowledge aspects associated with the human mind's abstraction abilities and how we apply it to the engineering knowledge and engineering reasoning in design

Business process discovery through conversation log analysis in pluralist and coercive problem contexts

Business process discovery is one of the most fundamental steps of business process management (BPM) lifecycles. Incorrect, misleading or biased results of this stage can cause the whole BPM project to fail or the information systems that are created based on them to have great alignment problems with the reality of the organisation and how people carry out their work. The main problems of the business process discovery phase stem from two main sources. Firstly, the wrong attachment of BPM definitions and business process discovery techniques to the functionalist social paradigm whose only objective is the survival of the organisation through ensuring its efficiency and adaptability like a machine. This attachment to the functionalist paradigm has made BPM definitions to assume that organisations as social systems are in a unitary problem context, which means its constituents have similar beliefs and interests, they share common goals and objectives and they have all been involved in the decision-making. These assumptions are obviously far from the reality of today’s organisations which are normally either in pluralist or coercive problem contexts. The second source of problems in the business process discovery phase are BPM’s definitions and techniques over-reliance on human memory and cognition that has made them suffer, like any other knowledge acquisition technique, from human memory and cognition limitations. Using Design Science Research methodology, this research develops a conceptual framework in which new definitions for business task, business process and business process model in pluralist and coercive problem contexts will be presented. It will also be shown that conversation logs are a good source of information for business process discovery based on the new definitions and that using conversation logs can reduce the limitations caused by human memory and cognition. To develop the new conceptual framework, organisations as social systems have been analysed using the creative holism systems approach, and sound theories such as viable system model (VSM), i* framework, speech act theory, conversation for action diagrams and episodic memory have been leveraged.Based on the conceptual framework that consumes email messages as the conversation log and as its source of information, a method for business process discovery has been developed.Using two case studies it has been demonstrated that the proposed definitions and the developed methods are applicable in unitary, pluralist and coercive problem contexts; and taking advantage of the conversation logs as their information source, they suffer to a lesser extent from human memory and cognition limitations. As a consequence, the resulting business process models created from applying the proposed definitions and methods are closer to the realities of the organisations and can increase the success rate of the business process management projects and reduce the information system’s alignment problems

Efficient Pedestrian Detection in Urban Traffic Scenes

Pedestrians are important participants in urban traffic environments, and thus act as an interesting category of objects for autonomous cars. Automatic pedestrian detection is an essential task for protecting pedestrians from collision. In this thesis, we investigate and develop novel approaches by interpreting spatial and temporal characteristics of pedestrians, in three different aspects: shape, cognition and motion. The special up-right human body shape, especially the geometry of the head and shoulder area, is the most discriminative characteristic for pedestrians from other object categories. Inspired by the success of Haar-like features for detecting human faces, which also exhibit a uniform shape structure, we propose to design particular Haar-like features for pedestrians. Tailored to a pre-defined statistical pedestrian shape model, Haar-like templates with multiple modalities are designed to describe local difference of the shape structure. Cognition theories aim to explain how human visual systems process input visual signals in an accurate and fast way. By emulating the center-surround mechanism in human visual systems, we design multi-channel, multi-direction and multi-scale contrast features, and boost them to respond to the appearance of pedestrians. In this way, our detector is considered as a top-down saliency system. In the last part of this thesis, we exploit the temporal characteristics for moving pedestrians and then employ motion information for feature design, as well as for regions of interest (ROIs) selection. Motion segmentation on optical flow fields enables us to select those blobs most probably containing moving pedestrians; a combination of Histogram of Oriented Gradients (HOG) and motion self difference features further enables robust detection. We test our three approaches on image and video data captured in urban traffic scenes, which are rather challenging due to dynamic and complex backgrounds. The achieved results demonstrate that our approaches reach and surpass state-of-the-art performance, and can also be employed for other applications, such as indoor robotics or public surveillance. In this thesis, we investigate and develop novel approaches by interpreting spatial and temporal characteristics of pedestrians, in three different aspects: shape, cognition and motion. The special up-right human body shape, especially the geometry of the head and shoulder area, is the most discriminative characteristic for pedestrians from other object categories. Inspired by the success of Haar-like features for detecting human faces, which also exhibit a uniform shape structure, we propose to design particular Haar-like features for pedestrians. Tailored to a pre-defined statistical pedestrian shape model, Haar-like templates with multiple modalities are designed to describe local difference of the shape structure. Cognition theories aim to explain how human visual systems process input visual signals in an accurate and fast way. By emulating the center-surround mechanism in human visual systems, we design multi-channel, multi-direction and multi-scale contrast features, and boost them to respond to the appearance of pedestrians. In this way, our detector is considered as a top-down saliency system. In the last part of this thesis, we exploit the temporal characteristics for moving pedestrians and then employ motion information for feature design, as well as for regions of interest (ROIs) selection. Motion segmentation on optical flow fields enables us to select those blobs most probably containing moving pedestrians; a combination of Histogram of Oriented Gradients (HOG) and motion self difference features further enables robust detection. We test our three approaches on image and video data captured in urban traffic scenes, which are rather challenging due to dynamic and complex backgrounds. The achieved results demonstrate that our approaches reach and surpass state-of-the-art performance, and can also be employed for other applications, such as indoor robotics or public surveillance

CDI-Type II: Collaborative Research: Cyber Enhancement of Spatial Cognition for the Visually Impaired

Wayfinding is an essential capability for any person who wishes to have an independent life-style. It requires successful execution of several tasks including navigation and object and place recognition, all of which necessitate accurate assessment of the surrounding environment. For a visually-impaired person these tasks may be exceedingly difficult to accomplish and there are risks associated with failure in any of these. Guide dogs and white canes are widely used for the purpose of navigation and environment sensing, respectively. The former, however, has costly and often prohibitive training requirements, while the latter can only provide cues about obstacles in one\u27s surroundings. Human performance on visual information dependent tasks can be improved by sensing which provides information and environmental cues, such as position, orientation, local geometry, object description, via the use of appropriate sensors and sensor fusion algorithms. Most work on wayfinding aids has focused on outdoor environments and has led to the development of speech-enabled GPS-based navigation systems that provide information describing streets, addresses and points of interest. In contrast, the limited technology that is available for indoor navigation requires significant modification to the building infrastructure, whose high cost has prevented its wide use. This proposal adopts a multi-faceted approach for solving the indoor navigation problem for people with limited vision. It leverages expertise from robotics, computer vision, and blind spatial cognition with behavioral studies on interface design to guide the discovery of information requirements and optimal delivery methods for an indoor navigation system. Designing perception and navigation algorithms, implemented on miniature-size commercially-available hardware, while explicitly considering the spatial cognition capabilities of the visually impaired, will lead to the development of indoor navigation systems that will assist blind people in their wayfinding tasks while facilitating cognitive-map development

Visualizing data: why an (interactive) picture is worth 1000 numbers

lectureOne of the most striking and unique aspects about this moment in human history is the amount of data we are generating. This data holds the promise of unlocking the mysteries of the universe, untangling complex natural and man-made systems, and allowing us to live longer, healthier, and more productive lives. But generating data is only the first step - developing methods to make sense of vast collections of information is now widely considered the major challenge. A key component of addressing this challenge is visualization, which supports sense making by representing data as pictures and supporting exploration through human-computer interactions. Through the design of intuitive representations of data, visualization designers arc able to harness our perceptual system for quickly finding interesting patterns and trends in a sea of data, instead of relying on our more limited memory and cognition. Creating effective visualizations, however, relies on a combination of knowledge about computer science, design, application domains, and how people internalize the meaning of data. This talk discusses what we know about how to design effective, interactive visualizations, as well as some of the open challenges left to solve. It will also provide examples of how scientists use these tools to glean insight from complex data