Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Broadening the interface bandwidth in simulation based training

Currently most computer based simulations rely exclusively on computer generated graphics to create the simulation. When training is involved, the method almost exclusively used to display information to the learner is text displayed on the cathode ray tube. MICROEXPERT Systems is concentrating on broadening the communications bandwidth between the computer and user by employing a novel approach to video image storage combined with sound and voice output. An expert system is used to combine and control the presentation of analog video, sound, and voice output with computer based graphics and text. Researchers are currently involved in the development of several graphics based user interfaces for NASA, the U.S. Army, and the U.S. Navy. Here, the focus is on the human factors considerations, software modules, and hardware components being used to develop these interfaces

Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Augmenting human memory using personal lifelogs

Memory is a key human facility to support life activities, including social interactions, life management and problem solving. Unfortunately, our memory is not perfect. Normal individuals will have occasional memory problems which can be frustrating, while those with memory impairments can often experience a greatly reduced quality of life. Augmenting memory has the potential to make normal individuals more effective, and those with significant memory problems to have a higher general quality of life. Current technologies are now making it possible to automatically capture and store daily life experiences over an extended period, potentially even over a lifetime. This type of data collection, often referred to as a personal life log (PLL), can include data such as continuously captured pictures or videos from a first person perspective, scanned copies of archival material such as books, electronic documents read or created, and emails and SMS messages sent and received, along with context data of time of capture and access and location via GPS sensors. PLLs offer the potential for memory augmentation. Existing work on PLLs has focused on the technologies of data capture and retrieval, but little work has been done to explore how these captured data and retrieval techniques can be applied to actual use by normal people in supporting their memory. In this paper, we explore the needs for augmenting human memory from normal people based on the psychology literature on mechanisms about memory problems, and discuss the possible functions that PLLs can provide to support these memory augmentation needs. Based on this, we also suggest guidelines for data for capture, retrieval needs and computer-based interface design. Finally we introduce our work-in-process prototype PLL search system in the iCLIPS project to give an example of augmenting human memory with PLLs and computer based interfaces

Natural Notation for the Domestic Internet of Things

This study explores the use of natural language to give instructions that might be interpreted by Internet of Things (IoT) devices in a domestic `smart home' environment. We start from the proposition that reminders can be considered as a type of end-user programming, in which the executed actions might be performed either by an automated agent or by the author of the reminder. We conducted an experiment in which people wrote sticky notes specifying future actions in their home. In different conditions, these notes were addressed to themselves, to others, or to a computer agent.We analyse the linguistic features and strategies that are used to achieve these tasks, including the use of graphical resources as an informal visual language. The findings provide a basis for design guidance related to end-user development for the Internet of Things.Comment: Proceedings of the 5th International symposium on End-User Development (IS-EUD), Madrid, Spain, May, 201

ImageSpirit: Verbal Guided Image Parsing

Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixel. In this paper we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted labels as handles, our system empowers a user to verbally refine the results. This enables hands-free parsing of an image into pixel-wise object/attribute labels that correspond to human semantics. Verbally selecting objects of interests enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g. smart phones, Google Glass, living room devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the tradeoffs compared to traditional mouse based interactions, results are reported for both a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit