Spinner, a media collection, interaction and sharing device for early learners is studied. It promotes play and exploration using a novel set of interactive techniques and designs based around a non sequential, contex...
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ISBN:
(纸本)1581134541
Spinner, a media collection, interaction and sharing device for early learners is studied. It promotes play and exploration using a novel set of interactive techniques and designs based around a non sequential, contextual physical interface and a graphical user interface (GUI). It consists of a device, 3 types of input modules and two connector ports for the modules.
We present VMλ, a formalization and implementation of the functional language VML. VML is a programming language proposed by discovery scientists for the purpose of assisting the process of knowledge discovery. It is...
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The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites. Even though expert knowledge is commonly used for this task, it is desirable to create a...
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The Special Interest Group on computerhumaninteraction (SIGCHI) has had a successful history of 20 years of growth in its numbers and influence. To help guide the continued evolution of the academic discipline and p...
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ISBN:
(纸本)1581134541
The Special Interest Group on computerhumaninteraction (SIGCHI) has had a successful history of 20 years of growth in its numbers and influence. To help guide the continued evolution of the academic discipline and professional community, we invite several senior members to offer their visions for what the field of CHI actually accomplished over the past several decades, and what do we still need to accomplish? What do we need to do differently/ better/smarter? What haven't we tried because the technology, the money or the will wasn't there in the past, but perhaps is now? The CHI field is more than just technology. We understand that our work can have a profound effect on individuals, families, neighborhoods, corporations, and countries. We know that we can influence education, commerce, healthcare, and government. How can we contribute to bridging the digital divides in developed and developing countries? What agendas can we offer for the academic, research, industrial, and civic spheres for the next 20 years? How can we be more ambitious? How can we truly serve human needs?
In recent times, there have been significant efforts to develop intelligent and natural interfaces for interaction between human users and computer systems by means of a variety of modes of information (visual, audio,...
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We describe two experiments using three testbeds (real, virtual and vision-only) for comparison of user performance during 3-D peg-in-hole tasks. Tasks are performed using a six-degree-of-freedom (6-DOF) magnetic levi...
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ISBN:
(纸本)0769514898
We describe two experiments using three testbeds (real, virtual and vision-only) for comparison of user performance during 3-D peg-in-hole tasks. Tasks are performed using a six-degree-of-freedom (6-DOF) magnetic levitation haptic device. The experimental design allows a user to experience real and virtual forces using the same device. The first experiment compares real and virtual tasks. In the virtual task, a peg and hole are rendered haptically and visually. During the real task, a physical peg is attached to the underside of the haptic device. A hole in a plate attached to a force/torque sensor receives the peg. The second experiment compares a virtual haptic task to one performed using vision alone. Preliminary results indicate increased task time, more variation in force and position, and more failures occur with the virtual task than with the real task. More variation in force and position, and more failures occur with the vision-only task than with the virtual task. Users apply similar strategies for virtual and real tasks. Virtual haptic display, while worse than reality, contributes significantly to task performance when compared to vision alone.
In this paper, we extend our greedy information algorithm to multi-layered networks for improved feature detection. We have developed a new information theoretic network-growing model called greedy information acquisi...
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In this paper, we extend our greedy information algorithm to multi-layered networks for improved feature detection. We have developed a new information theoretic network-growing model called greedy information acquisition. The method have shown good performance in extracting salient features in input patterns. However, because networks used in the method are single-layered ones, it has shown some difficulty in dealing with complex problems. In this context, we extend our greedy information acquisition method to multi-layered networks. By multi-layered networks, we can solve many complex problems that single-layered networks fail to do. The new algorithm was applied to two problems: the famous vertical-horizontal lines detection and a drive scene classification problem. In both cases, experimental results confirmed that our method could solve complex problems that single-layered networks fail to do. In addition, information maximization makes it possible to extract salient features in input patterns. The new algorithm can certainly contribute to the extension of neural computing.
We propose a new network growing method to detect salient features in input patterns. The new method is based upon a previous network growing model (R. Kamimura and T. Kamimura, 2002) and introduced to overcome some p...
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We propose a new network growing method to detect salient features in input patterns. The new method is based upon a previous network growing model (R. Kamimura and T. Kamimura, 2002) and introduced to overcome some problems in the previous model. We have so far tried to build a model that can learn input patterns as efficiently as possible. To realize this efficiency, we impose upon networks a constraint that only connections into new competitive units must be updated to absorb as much information as possible from outside. However, one of the problems is that the previous improper feature extraction prevents networks from extracting appropriate features in the later learning stages. To overcome this problem, we relax the condition of the previous model, and we permit networks to update all connections for gradual feature extraction at the expense of computational efficiency. We applied the new method to a simple problem that the previous model cannot solve, and information education data analysis. In both problems, we found that the new method can appropriately extract features from input patterns.
We describe our experience of trying to develop a novel application that transforms information management (both coordination-based and personal) from stand-alone resources into resources deeply embedded in email. We ...
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In this paper, we propose a new information theoretic network growing algorithm. The new approach is called greedy information acquisition, because networks try to absorb as much information as possible in every stage...
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In this paper, we propose a new information theoretic network growing algorithm. The new approach is called greedy information acquisition, because networks try to absorb as much information as possible in every stage of learning. In the first stage, two competitive units compete with each other by maximizing mutual information. In the successive stages, new competitive units are gradually added and information is maximized. Through greedy information maximization, different sets of important features in input patterns can be cumulatively discovered in successive stages. We applied our approach to a language classification problem. Experimental results confirmed that different features in input patterns are gradually discovered.
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