Demonstrating the Cognitive Plausibility of Interactive System Specifications

Much of the behaviour of an interactive system is determined by its user population. This paper describes how assumptions about the user can be brought into system models in order to reason about their behaviour. We describe a system model containing reasonable assumptions about the user as being ‘cognitively plausible’. Before asserting the plausibility of a model however we must first be able to make the assumptions made in that model inspectable. There is a tension between the inspectability of user assumptions and the tractability of models; inspectable models tend to not be very tractable and vice versa. We describe how we can get round this tension, by deriving tractable models from explicit user assumptions. The resulting models may not of themselves be very inspectable to human-factors workers, but the process by which they are derived is inspectable. Hence we claim that we can have both tractability and inspectability. We exemplify our claims using a simple cognitive model and ‘Meeting Maker’, an interactive electronic diary system. Received March 2000 / Accepted in revised form July 2000     

User-Centric Learning and Evaluation of Interactive Segmentation Systems

Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user??a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.     

An educational tool for interactive parallel and distributed processing

In this article we try to describe how the modular interactive tiles system (MITS) can be a valuable tool for introducing students to interactive parallel and distributed processing programming. This is done by providing a handson educational tool that allows a change in the representation of abstract problems related to designing interactive parallel and distributed systems. Indeed, the MITS seems to bring a series of goals into education, such as parallel programming, distributedness, communication protocols, master dependency, software behavioral models, adaptive interactivity, feedback, connectivity, topology, island modeling, and user and multi-user interaction which can rarely be found in other tools. Finally, we introduce the system of modular interactive tiles as a tool for easy, fast, and flexible hands-on exploration of these issues, and through examples we show how to implement interactive parallel and distributed processing with different behavioral software models such as open loop, randomness-based, rule-based, user interaction-based, and AI- and ALife-based software.     

Combining human error verification and timing analysis: a case study on an infusion pump

The design of a human–computer interactive system can be unacceptable for a range of reasons. User performance concerns, for example the likelihood of user errors and time needed for a user to complete tasks, are important areas of consideration. For safety-critical systems it is vital that tools are available to support the analysis of such properties before expensive design commitment has been made. In this work, we give a unified formal verification framework for integrating two kinds of analysis: (1) predicting bounds for task-completion times via exhaustive state-space exploration, and (2) detecting user-error related design issues. The framework is based on a generic model of cognitively plausible behaviour that captures assumptions about cognitive behaviour decided through a process of interdisciplinary negotiation. Assumptions made in an analysis, including those relating to the performance consequences of users recovering from likely errors, are also investigated in this framework. We further present a novel way of exploring the consequences of cognitive mismatches, on both correctness and performance grounds. We illustrate our analysis approach with a realistic medical device scenario: programming an infusion pump. We explore an initial pump design and then two variations based on features found in real designs, illustrating how the approach identifies both timing and human error issues.     

Interactivity and memory: Information processing of interactive versus non-interactive content

Existing research on the effects of interactivity tends to treat it as a global characteristic of the interface. However, not all content on an interface is endowed with interactive features. Therefore, it is important to explore how interactivity affects the cognitive processing of those particular content that is presented with interactive features and the surrounding content without these features. With this objective, a between-subjects experiment was conducted to understand how levels of interactivity affect information processing among users of an e-commerce website. The major findings of the study are: 1) Higher interactivity enhances recognition as well as recall memory of interactive content, but diminishes recognition and recall memory of non-interactive content; 2) Individuals spend the least amount of time on the interface with high interactivity, while most of this time is spent on interactive part rather than non-interactive part of the interface; 3) A moderate level of interactivity attracts most user attention to product information. The findings about interactivity’s effect have implications for cue-summation theory and limited capacity theory. The experiment’s results also hold practical implications for e-tailers.     

Transportation ontology definition and application for the content personalization of user interfaces

Ontologies have been largely exploited in many domains and studies. In this paper, we present a new application of a domain ontology for generating personalized user interfaces for transportation interactive systems. The concepts, relationships and axioms of transportation ontology are exploited during the semi-automatic generation of personalized user interfaces. Personalization deals with the capacity of adaptation of a user interface, reflecting what is known about the user and the domain application. It can be performed on the interface container presentation (e.g., layout, colors, sizes) and in the content provided in their input/output (e.g., data, information, document). In this paper, the transportation ontology is used to provide the content personalization. This paper presents the ontology and how it is used for the personalization of user interfaces for developing transportation interactive systems by model-driven engineering.     

一个面向对象的交互式创作工具OOIAT的设计与实现

在开放交互式创伤工具过程中,常常会遇到这样的难题：如何表示和建立用户模型及操作的对象,并向用户提供可直接操作的交互方式;如何有效地表示和管理对象之间的关系。,为此,我们设计并实现了一个创伤工具OOIAT,它提供了一个可扩充的,用于表示用户模型并集成了事件模型的对象集,使用户只需定义对象,不必了解对象如何传递
动作,如何实现其功能,就能够处理在这些对象上的交互式直接创伤。     

Gesture-based interactive augmented reality content authoring system using HMD

This paper proposes an augmented reality content authoring system that enables ordinary users who do not have programming capabilities to easily apply interactive features to virtual objects on a marker via gestures. The purpose of this system is to simplify augmented reality (AR) technology usage for ordinary users, especially parents and preschool children who are unfamiliar with AR technology. The system provides an immersive AR environment with a head-mounted display and recognizes users’ gestures via an RGB-D camera. Users can freely create the AR content that they will be using without any special programming ability simply by connecting virtual objects stored in a database to the system. Following recognition of the marker via the system’s RGB-D camera worn by the user, he/she can apply various interactive features to the marker-based AR content using simple gestures. Interactive features applied to AR content can enlarge, shrink, rotate, and transfer virtual objects with hand gestures. In addition to this gesture-interactive feature, the proposed system also allows for tangible interaction using markers. The AR content that the user edits is stored in a database, and is retrieved whenever the markers are recognized. The results of comparative experiments conducted indicate that the proposed system is easier to use and has a higher interaction satisfaction level than AR environments such as fixed-monitor and touch-based interaction on mobile screens.     

User-controllable personalization: A case study with SetFusion

In this research we investigated the role of user controllability on personalized systems by implementing and studying a novel interactive recommender interface, SetFusion. We examined whether allowing the user to control the process of fusing or integrating different algorithms (i.e., different sources of relevance) resulted in increased engagement and a better user experience. The essential contribution of this research stems from the results of a user study (N=40) of controllability in a scenario where users could fuse different recommendation approaches, with the possibility of inspecting and filtering the items recommended. First, we introduce an interactive Venn diagram visualization, which combined with sliders, can provide an efficient visual paradigm for information filtering. Second, we provide a three-fold evaluation of the user experience: objective metrics, subjective user perception, and behavioral measures. Through the analysis of these metrics, we confirmed results from recent studies, such as the effect of trusting propensity on accepting the recommendations and also unveiled the importance of features such as being a native speaker. Our results present several implications for the design and implementation of user-controllable personalized systems.     

Evaluating the Impact of User Characteristics and Different Layouts on an Interactive Visualization for Decision Making

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be designed to better fit each user's specific needs. Most studies to date, however, have looked at static visualizations. Studies considering interactive visualizations have only looked at a limited number of user characteristics, and consider either low‐level tasks (e.g., value retrieval), or high‐level tasks (in particular: discovery), but not both. This paper contributes to this line of work by looking at the impact of a large set of user characteristics on user performance with interactive visualizations, for both low and high‐level tasks. We focus on interactive visualizations that support decision making, exemplified by a visualization known as Value Charts. We include in the study two versions of ValueCharts that differ in terms of layout, to ascertain whether layout mediates the impact of individual differences and could be considered as a form of personalization. Our key findings are that (i) performance with low and high‐level tasks is affected by different user characteristics, and (ii) users with low visual working memory perform better with a horizontal layout. We discuss how these findings can inform the provision of personalized support to visualization processing.     

INTELLIGENT SYNTAX-DIRECTED EDITOR THAT MODELS USER PROFICIENCY

Current interactive software systems try to offer many features and a user-friendly interface to aid the user. However, where one system may suit one user, another user may find it difficult to use, because each user is different. User modeling techniques have been applied to make a system suitable for different users. This paper surveys research works for developing interactive systems that use user modeling techniques. After presenting the systems and techniques, we introduce a system called the Intelligent Syntax-Directed Editor (ISE) as an example to show how this approach can benefit the user as an intelligent tool. The ISE assists the user in (1) using the editor more efficiently through ISE's active suggestions and on-line help and (2) program development through the syntax-directed editor. In applying user modeling techniques, ISE builds a profile/model of the user and, based on this model, ISE offers a suitable amount of help and advice depending on the proficiency of the user toward the system. The ISE is implemented on a VAX/780 computer to demonstrate its ability to volunteer advice     

The role of user models in cooperative interactive systems

For interactive systems to communicate in a cooperative manner, they must have knowledge about their users. This article explores the role of user models in such systems, with the goal of identifying when and how user models may be useful in a cooperative interactive system. User models are classified by the types of knowledge they contain, several user modelling characteristics that serve as dimension for an additional classification of user models are presented, and user model representations are discussed. These topics help to characterize the space of user modelling in cooperative interactive systems-addressing how they can be used-but do not fully address when it is appropriate to include a user model in an interactive system. Thus, a set of design considerations for user models is presented, while a final example illustrates how these topics influence the user model for a hypothetical investment consulting system.     

融合注意力机制的增强受限玻尔兹曼机驱动的交互式分布估计算法

面向用户生成内容(User generated content, UGC)的进化搜索在大数据及个性化服务领域已引起广泛关注, 其关键在于基于多源异构用户生成内容构建用户认知偏好模型, 进而设计高效的进化搜索机制. 针对此, 提出融合注意力机制(Attention mechanism, AM)的受限玻尔兹曼机(Restricted Boltzmann machine, RBM)偏好认知代理模型构建机制, 并应用于交互式分布估计算法(Interactive estimation of distribution algorithm, IEDA), 设计含用户生成内容的个性化进化搜索策略. 基于用户群体提供的文本评论, 以及搜索物品的类别文本, 构建无监督受限玻尔兹曼机模型提取广义特征; 设计注意力机制, 融合广义特征, 获取对用户认知偏好高度相关特征的集成; 利用该特征再次训练受限玻尔兹曼机, 实现对用户偏好认知代理模型的构建; 根据用户偏好认知代理模型, 给出交互式分布估计算法概率更新模型以及物品适应度评价函数, 实现物品个性化进化搜索. 算法在亚马逊个性化搜索实例的应用验证了用户认知偏好模型的可靠性, 以及个性化进化搜索的有效性.     

Data-driven models for timing feedback responses in a Map Task dialogue system

Traditional dialogue systems use a fixed silence threshold to detect the end of users’ turns. Such a simplistic model can result in system behaviour that is both interruptive and unresponsive, which in turn affects user experience. Various studies have observed that human interlocutors take cues from speaker behaviour, such as prosody, syntax, and gestures, to coordinate smooth exchange of speaking turns. However, little effort has been made towards implementing these models in dialogue systems and verifying how well they model the turn-taking behaviour in human–computer interactions. We present a data-driven approach to building models for online detection of suitable feedback response locations in the user's speech. We first collected human–computer interaction data using a spoken dialogue system that can perform the Map Task with users (albeit using a trick). On this data, we trained various models that use automatically extractable prosodic, contextual and lexico-syntactic features for detecting response locations. Next, we implemented a trained model in the same dialogue system and evaluated it in interactions with users. The subjective and objective measures from the user evaluation confirm that a model trained on speaker behavioural cues offers both smoother turn-transitions and more responsive system behaviour.     

Manifestations of user personality in website choice and behaviour on online social networks

Individual differences in personality affect users’ online activities as much as they do in the offline world. This work, based on a sample of over a third of a million users, examines how users’ behaviour in the online environment, captured by their website choices and Facebook profile features, relates to their personality, as measured by the standard Five Factor Model personality questionnaire. Results show that there are psychologically meaningful links between users’ personalities, their website preferences and Facebook profile features. We show how website audiences differ in terms of their personality, present the relationships between personality and Facebook profile features, and show how an individual’s personality can be predicted from Facebook profile features. We conclude that predicting a user’s personality profile can be applied to personalize content, optimize search results, and improve online advertising.     

Supporting content discovery and organization in networks of contents and users

The design of approaches for supporting the user in the navigation of a variety of contents is an interesting area of research with many potential applications. In particular, interactive television (iTV) offers users the opportunity of accessing a huge amount of contents, ranging from general to specialized ones. As a consequence, the exploration of such contents must be supported in some way. In this paper, we present an innovative approach that integrates some recent methodologies and technologies developed in different areas of Artificial Intelligence and the Web: user-model-based adaptation, social networking, semantic annotation, and content sharing. We show how the integration of these technologies can provide interesting opportunities for a new approach to content navigation and discovery based on the possibility of exploring personalized networks of contents, users and concepts. Also, we focus on the specific goal of designing a platform for accessing iTV contents with the aim of providing the user with many alternative ways of exploring and discovering potentially interesting videos. After discussing the application, integrated in a project by Telecom Italia for a new paradigm of iTV, we preset the architecture we designed for integrating the methodologies discussed above.     

Knowledge-based interaction protocols for intelligent interactive environments

Intelligent interactive environments are systems whose main features are related to intelligence, interactivity and location. Intelligent interactive environments are implemented in a variety of platforms, such as distributed sensors, actuators and processors for ambient intelligence; distributed and mobile reasoning entities in virtual worlds and augmented reality; and distributed, multiuser information systems such as social networks and service-oriented systems directed to the dissemination of public services and retailing. In the present article, we introduce JamSession, a tool to specify and execute workflow choreographies, as well as to monitor and control heterogeneous resources, which can be specially useful for the specification and implementation of the coordination of real-time, multimodal and multimedia activities. JamSession is used to specify and execute knowledge-based interaction protocols, which are formally grounded and can be formally analysed and verified using a straightforward and user-friendly graphical language. A knowledge-based interaction protocol determines how disparate resources can work cooperatively to build complex behaviour in intelligent interactive environments.