語用論(Pragmatics)を考える

The Shallows: What the Internet is Doing to Our Brains

It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

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Computational Thinking 計算論的思考

Ant Colony Optimization (ACO) is a Swarm Intelligence technique which inspired from the foraging behaviour of real ant colonies. The ants deposit pheromone on the ground in order to mark the route for identification of their routes from the nest to food that should be followed by other members of the colony. This ACO exploits an optimization mechanism for solving discrete optimization problems in various engineering domain. From the early nineties, when the first Ant Colony Optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. This paper review varies recent research and implementation of ACO, and proposed a modified ACO model which is applied for network routing problem and compared with existing traditional routing algorithms.

Review: A survey: Ant Colony Optimization based recent research and implementation on several engineering domain

Orality and Literacy

A number of algorithms in the field of artificial intelligence offer poorly interpretable decisions. To disclose the reasoning behind such algorithms, their output can be explained by means of so-called evidence-based (or factual) explanations. Alternatively, contrastive and counterfactual explanations justify why the output of the algorithms is not any different and how it could be changed, respectively. It is of crucial importance to bridge the gap between theoretical approaches to contrastive and counterfactual explanation and the corresponding computational frameworks. In this work we conduct a systematic literature review which provides readers with a thorough and reproducible analysis of the interdisciplinary research field under study. We first examine theoretical foundations of contrastive and counterfactual accounts of explanation. Then, we report the state-of-the-art computational frameworks for contrastive and counterfactual explanation generation. In addition, we analyze how grounded such frameworks are on the insights from the inspected theoretical approaches. As a result, we highlight a variety of properties of the approaches under study and reveal a number of shortcomings thereof. Moreover, we define a taxonomy regarding both theoretical and practical approaches to contrastive and counterfactual explanation.

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A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence

The direct manipulation interaction style of multi-touch technology makes it the ideal mechanism for learning activities from pre-kindergarteners to adolescents. However, most commercial pre-kindergarten applications only support tap and drag operations. This paper investigates pre-kindergarteners׳ (2–3 years of age) ability to perform other gestures on multi-touch surfaces. We found that these infants could effectively perform additional gestures, such as one-finger rotation and two-finger scale up and down, just as well as basic gestures, despite gender and age differences. We also identified cognitive and precision issues that may have an impact on the performance and feasibility of several types of interaction (double tap, long press, scale down and two-finger rotation) and propose a set of design guidelines to mitigate the associated problems and help designers envision effective interaction mechanisms for this challenging age range.

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Multi-touch gestures for pre-kindergarten children

The advent of wearables (e.g., smartwatches, smartglasses, and digital jewelry) anticipates the need for text entry methods on very small devices. We conduct fundamental research on this topic using 3 qwerty-based soft keyboards for 3 different screen sizes, motivated by the extensive training that users have with qwerty keyboards. In addition to ZoomBoard (a soft keyboard for diminutive screens), we propose a callout-based soft keyboard and ZShift, a novel extension of the Shift pointing technique. We conducted a comprehensive user study followed by extensive analyses on performance, usability, and short-term learning. Our results show that different small screen sizes demand different types of assistance. In general, manufacturers can benefit from these findings by selecting an appropriate qwerty soft keyboard for their devices. Ultimately, this work provides designers, researchers, and practitioners with new understanding of qwerty soft keyboard design space and its scalability for tiny touchscreens.

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Text Entry on Tiny QWERTY Soft Keyboards

Play stands as one of the most natural and inherent behavior among the majority of living species, specifically humans and animals. Human play has evolved significantly over the years, and so have done the artifacts which allow us to play: from children playing tag games without any tools other than their bodies, to modern video games using haptic and wearable devices to augment the playful experience. However, this ludic revolution has not been the same for the humans’ closest companions, our pets. Recently, a new discipline inside the human–computer interaction (HCI) community, called animal–computer interaction (ACI), has focused its attention on improving animals’ welfare using technology. Several works in the ACI field rely on playful interfaces to mediate this digital communication between animals and humans. Until now, the development of these interfaces only comprises a single goal or activity, and its adaptation to the animals’ needs requires the developers’ intervention. This work analyzes the existing approaches, proposing a more generic and autonomous system aimed at addressing several aspects of animal welfare at a time: Intelligent Playful Environments for Animals. The great potential of these systems is discussed, explaining how incorporating intelligent capabilities within playful environments could allow learning from the animals’ behavior and automatically adapt the game to the animals’ needs and preferences. The engaging playful activities created with these systems could serve different purposes and eventually improve animals’ quality of life.

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Envisioning Future Playful Interactive Environments for Animals

There is growing interest in the automatic detection of animals’ behaviors and body postures within the field of Animal Computer Interaction, and the benefits this could bring to animal welfare, enabling remote communication, welfare assessment, detection of behavioral patterns, interactive and adaptive systems, etc. Most of the works on animals’ behavior recognition rely on wearable sensors to gather information about the animals’ postures and movements, which are then processed using machine learning techniques. However, non-wearable mechanisms such as depth-based tracking could also make use of machine learning techniques and classifiers for the automatic detection of animals’ behavior. These systems also offer the advantage of working in set-ups in which wearable devices would be difficult to use. This paper presents a depth-based tracking system for the automatic detection of animals’ postures and body parts, as well as an exhaustive evaluation on the performance of several classification algorithms based on both a supervised and a knowledge-based approach. The evaluation of the depth-based tracking system and the different classifiers shows that the system proposed is promising for advancing the research on animals’ behavior recognition within and outside the field of Animal Computer Interaction.

Alejandro Catala

Papers

A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence

Multi-touch gestures for pre-kindergarten children

Text Entry on Tiny QWERTY Soft Keyboards

Envisioning Future Playful Interactive Environments for Animals

Assessing machine learning classifiers for the detection of animals’ behavior using depth-based tracking