We present an alternative video-making framework for children with tools that integrate video capture with movie production. We propose different forms of interaction with physical artifacts to capture storytelling. Play interactions as input to video editing systems assuage the interface complexities of film construction in commercial software. We aim to motivate young users in telling their stories, extracting meaning from their experiences by capturing supporting video to accompany their stories, and driving reflection on the outcomes of their movies. We report on our design process over the course of four research projects that span from a graphical user interface to a physical instantiation of video. We interface the digital and physical realms using tangible metaphors for digital data, providing a spontaneous and collaborative approach to video composition. We evaluate our systems during observations with 4- to 14-year-old users and analyze their different approaches to capturing, collecting, editing, and performing visual and sound clips.

This study focuses on the application of neural network techniques to adaptive remeshing of an idealized squareshaped structure and individual triangle by using triangular elements. The backpropagation learning algorithm is implemented by a supervised training technique to deal with the problem of remeshing structural elements in a structural analysis. A recent study introduced a structural remeshing that incorporated a finite-element analysis with an adaptive mesh generation technique. The main objective of the study is to demonstrate how neural networks can be employed to remesh structural elements without using numerically intensive computations. One essential requirement of this approach is the selection of feasible and appropriate training and testing data. The exploration of neural network in remeshing structural elements is a fundamental technique that looks beyond the finite-element and adaptive mesh generation techniques. It also demonstrates the capability of neural network to represent n-dimensional space and track each individual characteristic in that separate space. In general, an overview is presented, and the potential of neural networks to use the backpropagation algorithm instead of the more conventional approach of numerical methods is demonstrated.

A temporal logic for representing and reasoning on a robotic domain is presented. Actions are represented by describing what is true while the action itself is occurring, and plans are constructed by temporally relating actions and world states. The temporal language is a member of the family of Description Logics, which are characterized by high expressivity combined with good computational properties. The logic is used to organize the domain actions and plans in a taxonomy. The classification and recognition tasks, together with the subsumption task form the basis for action management. An action/plan description can be automatically classified into a taxonomy; an action/plan instance can be recognized to take place at a certain moment from the observation of what is happening in the world during a time interval.

Although many approaches to digital ink recognition have been proposed, most lack the flexibility and adaptability to provide acceptable recognition rates across a variety of problem spaces. This project uses a systematic approach of data mining analysis to build a gesture recognizer for sketched diagrams. A wide range of algorithms was tested, and those with the best performance were chosen for further tuning and analysis. Our resulting recognizer, RATA.Gesture, is an ensemble of four algorithms. We evaluated it against four popular gesture recognizers with three data sets; one of our own and two from other projects. Except for recognizer–data set pairs (e.g., PaleoSketch recognizer and PaleoSketch data set) the results show that it outperforms the other recognizers. This demonstrates the potential of this approach to produce flexible and accurate recognizers.

Manufacturing industries are looking for efficient assembly planners that can swiftly develop a practically feasible assembly sequence while keeping costs and time to a minimum. Most assembly sequence planners rely on part relations in the virtual environment. Nowadays, tools and robotic grippers perform most of the assembly tasks. Ignoring the critical aspect renders solutions practically infeasible. Additionally, it is vital to test the feasibility of positioning and assembling components while employing robotic grippers and tools prior to their implementation. This paper presents a novel concept named by considering both part and tool geometry to propose “tool integrated assembly interference matrices” (TIAIMs) and a “tool integrated axis-aligned bounding box” (TIAABB) to generate practically feasible assembly sequence plans. Furthermore, the part-concatenation technique is used to determine the best assembly sequence plans for an actual mechanical component. The results show that the proposed approach effectively and efficiently deals with real-life industrial problems.

There is an increasing demand for humanitarian aid around the world. At the same time, the number of makerspaces has been growing exponentially. Recently, the humanitarian sector has become interested in how these new design spaces can help crisis-affected populations. Despite the emergence of humanitarian makerspaces, there is little research to date that documents their outcomes and impacts. A multi-case study approach is taken to analyze three makerspaces that support migrants, refugees, and asylum seekers in Greece. A maturity grid tool is used to show that humanitarian makerspaces are driving impact in six key areas. The study underlines how these makerspaces support different design activities and have different outcomes. It also considers the challenges which are preventing humanitarian makerspaces from achieving their ultimate goals, drawing attention to the need for an enabling ecosystem in both the local and humanitarian context. This research brings clarity to the poorly understood phenomenon of humanitarian makerspaces and highlights the important role of design in humanitarian interventions. It also reveals practical insights for humanitarian organizations who are considering setting up makerspaces in crisis-affected communities.

Design is situated. “Situatedness” in designing entails the explicit consideration of the state of the environment, the knowledge and experiences of the designer, and the interactions between the designer and the environment during designing. Central to the notion of situatedness is the notion of design situation and constructive memory. A design situation models a particular state of interaction between a design agent and the environment at a particular point in time. Memory construction occurs whenever a design agent uses past experiences and knowledge within the current design environment in a situated manner. This paper is concerned with the development of an agent-based computational design tool that takes into consideration the notion of situatedness in designing. A key element of this tool is a constructive memory system that supports the dynamic nature of designing. Memories of past experiences are constructed as required by the current situation, and past experiences are refined for future utility according to the current interactions between the agent and the environment. This latter case of knowledge improvement is illustrated through a series of experiments that demonstrates the effect of grounding on the operating modes and responses of a constructive memory system.

A system that integrates design and planning for mechanical assemblies is presented. The system integrates neural network computing that captures designer's design concept and rule-based system to generate a task-level assembly plan automatically. The design concept is expressed by a standard pattern format representing qualitative assembly information. A neural network model together with feature-based model translates the input pattern into a preliminary boundary representation (B-rep). Based on a refinement B-rep assembly representation, assembly plans are generated for practical use in a single-robot assembly workcell. A feasible assembly plan that minimizes tool changes and subassembly reorientations is generated from the system. A robust part collision detection algorithm to generate the precedence relationships among the assembly's components is included in the system. By contrast with many assembly planning systems that used a prolonged question-and-answering session or required knowledge beyond what is typically available in the design database, an automated assembly planning system presented here draws input relationships directly from the conceptual design and the geometry of the assembly. The system developed under this study extracts all reasoning information from the product model and permits the components to be assembled in a multitude of directions. Several experiments illustrate the effectiveness of the designed assembly planning system.

This paper presents research in the development of heuristic evolutionary algorithms (EAs) for generating and exploring differentiated force-based structures. The algorithm is weighted toward design exploration of topological differentiation while including specific structural and material constraints. An embryological EA model is employed to “grow” networks of mass-spring elements achieving desired mesh densities that resolve themselves in tensile force (form-active) equilibrium. The primal quadrilateral quadrisection method serves as the foundation for a range of extensible subdivision methods. Unique to this research, the quad is addressed as a “cell” rather than a topological or geometric construct, allowing for the contents of the cell to vary in number of mass-spring elements and orientation. In this research, this approach has been termed the quadrilateral quadrisection with n variable topological transformation method. This research culminates with the introduction of a method for grafting meshes where emergent features from the evolved meshes can be transposed and replicated in an explicit yet informed manner. The EA and grafting methods function within a Java-based software called springFORM, developed in previous research, which utilizes a mass-spring based library for solving force equilibrium and allows for both active (manual) and algorithmic topology manipulation. In application to a specific complex tensile mesh, the design framework, which combines the generative EA and mesh grafting method, is shown to produce emergent and highly differentiated topological arrangements that negotiate the specific relationships among a desired maximal mesh density, geometric patterning, and equalized force distribution.

This paper describes how a computational system for designing can learn useful, reusable, generalized search strategy rules from its own experience of designing. It can then apply this experience to transform the design process from search based (knowledge lean) to knowledge based (knowledge rich). The domain of application is the design of spatial layouts for architectural design. The processes of designing and learning are tightly coupled.

Designers who are experts in a given design domain are well known to be able to Immediately focus on “good designs,” suggesting that they may have learned additional constraints while exploring the design space based on some functional aspects. These constraints, which are often implicit, result in a redefinition of the design space, and may be crucial for discovering chunks or interrelations among the design variables. Here we propose a machine-learning approach for discovering such constraints in supervised design tasks. We develop models for specifying design function in situations where the design has a given structure or embodiment, in terms of a set of performance metrics that evaluate a given design. The functionally feasible regions, which are those parts of the design space that demonstrate high levels of performance, can now be learned using any general purpose function approximator. We demonstrate this process using examples from the design of simple locking mechanisms, and as in human experience, we show that the quality of the constraints learned improves with greater exposure in the design space. Next, we consider changing the embodiment and suggest that similar embodiments may have similar abstractions. To explore convergence, we also investigate the variability in time and error rates where the experiential patterns are significantly different. In the process, we also consider the situation where certain functionally feasible regions may encode lower dimensional manifolds and how this may relate to cognitive chunking.

Several industrial systems are characterized by high nonlinearities with wide operating ranges and large set point changes. Identification and representation of these systems represent a challenge, especially for control engineers. Multimodel technique is one effective approach that can be used to describe nonlinear systems through the combination of several submodels, where each is contributing to the output with a certain degree of validity. One major concern in this technique, especially for systems with unknown operating conditions, is the partitioning of the system's operating space and thus the identification of different submodels. This paper proposes a three-stage approach to obtain a multimodel representation of a nonlinear system. A reinforced combinatorial particle swarm optimization and hybrid K-means are used to determine the number of submodels and their respective parameters. The proposed method automatically optimizes the number of submodels with respect to the submodel complexity. This allows operating space partition and generation of a parsimonious number of submodels without prior knowledge. The application of this approach on several examples, including a continuous stirred tank reactor, demonstrates its effectiveness.

This study investigates the use of similarities in the form of analogy, metaphor, and simile by students and reviewers in an undergraduate architectural design review. In contrast to studies conducted in vitro settings, this study emphasizes the importance of studying analogies, metaphors, and similes in a natural setting. All similarity relationships were coded according to their type, the level of expertise, range, frequency, goal, value judgment, and depth. The results indicate that analogies, metaphors, and similes were used spontaneously and without any difficulty by both reviewers and students. Reviewers, however, were almost twice as likely to evoke similarities. Metaphor was the most frequently used similarity relationship among the three. It was found that there was a significant relationship between the level of expertise and type of similarity, with students more likely to use analogies and less likely to use similes. It was also found that goal is the most important factor, with a significant relation to all other variables, and that embodiment is often invoked in both students’ and reviewers’ metaphors. We conclude that design education should take full advantage of students’ natural ability to benefit from similarity relationships.

Shape queries based on shape embedding under a given Euclidean, affine, or linear transformation are absent from current CAD systems. The only systems that have attempted to implement shape embedding are the shape grammar interpreters albeit with promising but inconclusive results. The work here identifies all possible 14 cases of shape embedding with respect to the number of available registration points, four for determinate cases and ten for indeterminate ones, and an approach is sketched to take on the complexities underlying the indeterminate cases. All visual calculations are done with shapes consisting of straight lines in the Euclidean plane within the algebra Uij for i = 1 the dimension of lines and j = 2 the dimension of space in which the lines are defined, transformed and combined. Aspects of interface design and integration to current work design workflows are deliberately left aside.

Traditional failure modes and effects analysis (FMEA) methods lack sufficient semantics and structure to provide full traceability between the failure modes and the effects of the complex system. To overcome this limitation, this paper proposes a formal failure knowledge representation model combined with the structural decomposition of the complex system. The model defines the failure modes as the inherent properties of the physical entities at different hierarchical levels, and employs the individual color, unified color, and Boolean matrix of the polychromatic sets to represent the failure modes in terms of their interrelationships and their relations to the physical system. This method is a structure-based modeling technique that provides a simple, yet comprehensive framework to organize the failure modes and their causes and effects more systematically and completely. Using the iterative search process operated on the reasoning matrices, the end effects on the entire system can be achieved automatically, which allows for the consideration of both the single and multiple failures. An example is embedded in the description of the methodology for better understanding. Because of the powerful mathematical modeling capability of the polychromatic sets, the approach presented in this paper makes significant progress in FMEA formalization.

Design has always been seen as an inherently human activity and hard to automate. It requires a lot of traits that are seldom attributable to machines or algorithms. Consequently, the act of designing is also hard to assess. In particular in an educational context, the assessment of progress of design tasks performed by individuals or teams is difficult, and often only the outcome of the task is assessed or graded. There is a need to better understand, and potentially quantify, design progress. Natural Language Processing (NLP) is one way of doing so. With the advancement in NLP research, some of its models are adopted into the field of design to quantify a design class performance. To quantify and visualize design progress, the NLP models are often deployed to analyze written documentation collected from the class participants at fixed time intervals through the span of a course. This paper will explore several ways of using NLP in assessing design progress, analyze its advantages and shortcomings, and present a case study to demonstrate its application. The paper concludes with some guidelines and recommendations for future development.

When developing an artifact, designers must first understand the problem. This includes the benefits that the artifact must deliver and the user variation that is present. Each user has a unique set of human factors, preferences, personal knowledge, and solution constraints that could potentially influence the characteristics of the artifact. Currently, there is little work supporting the process of how to formally generate user-specific design specifications, resulting in ad hoc or a priori decisions when generating design specifications. Further, because most design processes generate design specifications manually, the number of design specifications is not typically addressed at the user level. This research presents an affordance-based approach for use in the early stages of design to help designers establish user-specific design specifications. This information can then be used in the creation of a system or set of systems that meets the demands of both the user(s) and the organization that is developing the artifact. An affordance-based approach is leveraged because it maintains the relational field of view among the user, existing artifacts, and the artifact(s) being designed. Once individual design specifications are generated, designers can use this information in later stages of the design process.

Parkinson's disease and Alzheimer's disease are progressive nervous system disorders that affect physical and cognitive capacities of individuals, including memory loss, motion impairment, or problem-solving dysfunctions. Leisure activities are associated with reducing the risk of dementia and are preventive policies for delaying the cognitive impairment in later stages of those neurodegenerative diseases. Electronic games related to cognitive abilities are an easy and inexpensive alternative for stimulating brain activity in this kind of patients. The previous research demonstrated the acceptance of these activities in the environment of Connected TV when playing at home and in daily care centers. Interaction in Connected TV applications has its own particularities that influence the design of the interface, including the viewing distance, the type of interaction through a remote control or other techniques, the size of the screen, or the collectiveness of consumption. Iterative testing with patients of these groups revealed how the physical characteristics and cognitive impairment of these concrete end-users affect the human–computer interaction, offering guidelines and recommendations in good practices for the Smart TV interface design. On the other hand, data analytics extracted from the interaction and evolution of the game offer important information enabling the creation of estimation prediction models about the cognitive state of the patient.

The purpose of this paper is to investigate whether knowledge transformers that are featured in the learning process are also present in the creative process. First, this was achieved by reviewing accounts of inventions and discoveries with the view of explaining them in terms of knowledge transformers. Second, this was achieved by reviewing models and theories of creativity and identifying the existence of the knowledge transformers. The investigation shows that there is some evidence to show that the creative process can be explained through knowledge transformers. Hence, it is suggested that one of links between learning and creativity is through the knowledge transformers.