Adaptation of robot’s perception of fuzzy linguistic information by evaluating vocal cues for controlling a robot manipulator

This article proposes a method for adapting a robot’s perception of fuzzy linguistic information by evaluating vocal cues. The robot’s perception of fuzzy linguistic information such as “very little” depends on the environmental arrangements and the user’s expectations. Therefore, the robot’s perception of the corresponding environment is modified by acquiring the user’s perception through vocal cues. Fuzzy linguistic information related to primitive movements is evaluated by a behavior evaluation network (BEN). A vocal cue evaluation system (VCES) is used to evaluate the vocal cues for modifying the BEN. The user’s satisfactory level for the robot’s movements and the user’s willingness to change the robot’s perception are identified based on a series of vocal cues to improve the adaptation process. A situation of cooperative rearrangement of the user’s working space is used to illustrate the proposed system by a PA-10 robot manipulator.     

Rapid behavior adaptation for human-centered robots in a dynamic environment based on the integration of primitive confidences on multi-sensor elements

This article presents a method for tele-operated mobile robots to rapidly adapt to behavior policies. Since real-time adaptation requires frequent observations of sensors and the behavior of users, rapid policy adaptation cannot be achieved when significant data are not differentiated from insignificant data in every process cycle. Our method solves this problem by evaluating the significance of data for learning based on changes in the degree of confidence. A small change in the degree of confidence can be regarded as reflecting insignificant data for learning (that data can be discarded). Accordingly, the system can avoid having to store experience data too frequently, and the robot can adapt more rapidly to changes in the user’s policy. In this article, we confirm that by taking advantage of a significance evaluation not only of proposition of behavior, but also of each proposition of each piece of sensor-level data, a robot can rapidly adapt to a user’s policy. We discuss the results of two experiments in static and dynamic environments, in both of which the user switched policies between “avoid” and “approach.”     

Entertainment capture through heart rate activity in physical interactive playgrounds

An approach for capturing and modeling individual entertainment (“fun”) preferences is applied to users of the innovative Playware playground, an interactive physical playground inspired by computer games, in this study. The goal is to construct, using representative statistics computed from children’s physiological signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose children’s heart rate (HR) signals, and their expressed preferences of how much “fun” particular game variants are, are obtained from experiments using games implemented on the Playware playground. A comprehensive statistical analysis shows that children’s reported entertainment preferences correlate well with specific features of the HR signal. Neuro-evolution techniques combined with feature set selection methods permit the construction of user models that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and two control tasks requiring physical activity. The best network is able to correctly match expressed preferences in 64% of cases on previously unseen data (p−value 6 · 10⁻⁵). The generality of the methodology, its limitations, its usability as a real-time feedback mechanism for entertainment augmentation and as a validation tool are discussed.     

Opinion helpfulness prediction in the presence of “words of few mouths”

This paper identifies a widely existing phenomenon in social media content, which we call the “words of few mouths” phenomenon. This phenomenon challenges the development of recommender systems based on users’ online opinions by presenting additional sources of uncertainty. In the context of predicting the “helpfulness” of a review document based on users’ online votes on other reviews (where a user’s vote on a review is either HELPFUL or UNHELPFUL), the “words of few mouths” phenomenon corresponds to the case where a large fraction of the reviews are each voted only by very few users. Focusing on the “review helpfulness prediction” problem, we illustrate the challenges associated with the “words of few mouths” phenomenon in the training of a review helpfulness predictor. We advocate probabilistic approaches for recommender system development in the presence of “words of few mouths”. More concretely, we propose a probabilistic metric as the training target for conventional machine learning based predictors. Our empirical study using Support Vector Regression (SVR) augmented with the proposed probability metric demonstrates advantages of incorporating probabilistic methods in the training of the predictors. In addition to this “partially probabilistic” approach, we also develop a logistic regression based probabilistic model and correspondingly a learning algorithm for review helpfulness prediction. We demonstrate experimentally the superior performance of the logistic regression method over SVR, the prior art in review helpfulness prediction.     

From Popularity to Personality — A Heuristic Music Recommendation Method for Niche Market

In most current recommender systems, the goal to accurately predict what people want leads to the tendency to recommend popular items, which is less helpful in revealing user’s personality, especially to new users. In this paper, we propose a heuristic music recommendation method for niche market by focusing on how to identify user’s personality as soon as possible. Instead of trying to improve algorithm's performance on new users by recommending the most popular items, we work on how to make them “familiar” with the system earlier. The method is more suitable for brand-new users, and gives a hint to solve the cold start problem. In real applications it is better to combine it with a traditional approach.     

Human action annotation,modeling and analysis based on implicit user interaction

This paper proposes an integrated framework for analyzing human actions in video streams. Despite most current approaches that are just based on automatic spatiotemporal analysis of sequences, the proposed method introduces the implicit user-in-the-loop concept for dynamically mining semantics and annotating video streams. This work sets a new and ambitious goal: to recognize, model and properly use “average user’s” selections, preferences and perception, for dynamically extracting content semantics. The proposed approach is expected to add significant value to hundreds of billions of non-annotated or inadequately annotated video streams existing in the Web, file servers, databases etc. Furthermore expert annotators can gain important knowledge relevant to user preferences, selections, styles of searching and perception.     

Trust, privacy, and legal protection in the use of software with surreptitiously installed operations: An empirical evaluation

The class of software which is “surreptitiously installed on a user’s computer and monitors a user’s activity and reports back to a third party on that behavior” is referred to as spyware “(Stafford and Urbaczewski in Communications of the AIS 14:291–306, 2004)”. It is a strategic imperative that software vendors, who either embed surreptitious data collection and other operations in legitimate software applications or whose software is unwittingly used as a delivery vehicle for surreptitious operations, understand users’ perceptions of trust, privacy, and legal protection of such software to remain competitive. This paper develops and tests a research model to explore application software users’ perceptions in the use of software with embedded surreptitious operations. An experiment was undertaken to examine whether the presence of spyware in application software impacts users’ perceptions and beliefs about trustworthiness of the application software, privacy control of the software vendor, United States legal protection, and overall trust of the software vendor. The results indicate users of software with spyware, versus users of software without spyware, have lower trust perceptions of a software vendor. Further examination of trustworthiness as a multi-dimensional construct reveals a software vendor’s competence in appropriately using private user information collected and the user’s belief that the vendor will abide by acceptable principles in information exchange are important influences in gaining users’ overall trust in a vendor. User trust in software utilization is critical for a software vendor’s success because without it, users may avoid a vendor’s software should the presence of spyware be discovered. Software vendors should respond to the strategic necessity to gain users’ trust. Vendors must institute proactive and protective measures to demonstrate that their software should be trusted. These protections could take the form of technological approaches or government legislation, or both.

Formation conditions of mutual adaptation in human-agent collaborative interaction

When an adaptive agent works with a human user in a collaborative task, in order to enable flexible instructions to be issued by ordinary people, it is believed that a mutual adaptation phenomenon can enable the agent to handle flexible mapping relations between the human user’s instructions and the agent’s actions. To elucidate the conditions required to induce the mutual adaptation phenomenon, we designed an appropriate experimental environment called “WAITER” (Waiter Agent Interactive Training Experimental Restaurant) and conducted two experiments in this environment. The experimental results suggest that the proposed conditions can induce the mutual adaptation phenomenon.     

Discovering frequent user--environment interactions in intelligent environments

Intelligent Environments are expected to act proactively, anticipating the user’s needs and preferences. To do that, the environment must somehow obtain knowledge of those need and preferences, but unlike current computing systems, in Intelligent Environments, the user ideally should be released from the burden of providing information or programming any device as much as possible. Therefore, automated learning of a user’s most common behaviors becomes an important step towards allowing an environment to provide highly personalized services. In this article, we present a system that takes information collected by sensors as a starting point and then discovers frequent relationships between actions carried out by the user. The algorithm developed to discover such patterns is supported by a language to represent those patterns and a system of interaction that provides the user the option to fine tune their preferences in a natural way, just by speaking to the system.     

On neuroscience-based informational technologies for authentic models of individual mind

Progress of neuroscience and information technologies tentatively makes possible actual implementation of old ideas to enable individual human being informational individuality with ability of autonomous existence (“life”) in computers. The idea has numerous reflections in science fiction and mass media communications. It seems, that just now computer technologies and understanding of brain mechanisms have attained a level, which makes this idea to be feasible and contemporary. The authentic model of an individual can consist of neurocomputer-based expert system, endowed with multi-media abilities. The system simulates its user, reproducing a kind of an Alter Ego for the user. The simulation ability of the system is being fed into it as a result of a thorough knowledge engineering process, which includes multiple psychological tests and psycho-physical measurements. The alter ego multi-media system is self-adjusted in a process of a permanent day to day dialog with its unique user. As a result it acquires real characters of user’s individuality. The user can authorize use of the system, adjusted to simulate user’s features to represent user in communication with other persons. In this case the system operates in an Alter Homo mode, enabling the user with communication abilities a good deal superior to human ones. The text was submitted by the authors in English.     

Plan recognition for interface agents

Interface agents are computer programs that provide personalized assistance to a user dealing with computer based applications. By understanding the tasks the user performs in a software application an interface agent could be aware of the context that represents the user’s focus of attention at each particular moment. With this purpose, plan recognition aims at identifying the plans or goals of a user from the tasks he (for simplicity, we use “he” to refer to the user, but we do not mean any distinctions about sexes) performs. A prerequisite for the recognition of plans is knowledge of a user’s possible tasks and the combination of these tasks in complex task sequences, which describes typical user behavior. Plan recognition will enable an interface agent to reason about what the user might do next so that it can determine how to assist him. In this work we present the state of the art in Plan Recognition, paying special attention to the features that make it useful to interface agents. These features include the ability to deal with uncertainty, multiple plans, multiple interleaved goals, overloaded tasks, noisy tasks, interruptions and the capability to adapt to a particular user.     

Browsing the environment with the SNAP&TELL wearable computer system

This paper provides an overview of a multi-modal wearable computer system, SNAP&TELL. The system performs real-time gesture tracking, combined with audio-based control commands, in order to recognize objects in an environment, including outdoor landmarks. The system uses a single camera to capture images, which are then processed to perform color segmentation, fingertip shape analysis, robust tracking, and invariant object recognition, in order to quickly identify the objects encircled and SNAPped by the user’s pointing gesture. In addition, the system returns an audio narration, TELLing the user information concerning the object’s classification, historical facts, usage, etc. This system provides enabling technology for the design of intelligent assistants to support “Web-On-The-World” applications, with potential uses such as travel assistance, business advertisement, the design of smart living and working spaces, and pervasive wireless services and internet vehicles. An erratum to this article can be found at An erratum to this article can be found at     

Getting closer: tailored human–computer speech dialog

This paper presents an advanced call center, which adapts presentation and interaction strategy to properties of the caller such as age, gender, and emotional state. User studies on interactive voice response (IVR) systems have shown that these properties can be used effectively to “tailor” services to users or user groups who do not maintain personal preferences, e.g., because they do not use the service on a regular basis. The adopted approach to achieve individualization of services, without being able to personalize them, is based on the analysis of a caller’s voice. This paper shows how this approach benefits service providers by being able to target entertainment and recommendation options. It also shows how this analysis at the same time benefits the customer, as it can increase accessibility of IVR systems to user segments which have particular expectations or which do not cope well with a “one size fits all” system. The paper summarizes the authors’ current work on component technologies, such as emotion detection, age and gender recognition on telephony speech, and presents results of usability and acceptability tests as well as an architecture to integrate these technologies in future multi-modal contact centers. It is envisioned that these will eventually serve customers with an avatar representation of an agent and tailored interaction strategies, matching powerful output capabilities with advanced analysis of the user’s input.

A multi-agent based user context Bayesian neural network analysis system

The increasing user mobility demands placed upon IT services necessitates an environment that enables users to access optimal services at any time and in any place. This study presents research conducted to develop a system that is capable of analyzing user IT service patterns and tendencies and provides the necessary service resources by sharing each user’s context information. First, each user’s context information is gathered to provide the multi-agent software training data necessary to describe user operations in a hybrid peer-to-peer (P2P) structured communication environment. Next, the data collected about each user’s mobile device is analyzed through a Bayesian based neural network system to identify the user’s tendency and extract essential service information. This information provides a communication configuration allowing the user access to the best communication service between the user’s mobile device and the local server at any time and in any place, thereby enhancing the ubiquitous computing environment.     

Learning opponent’s preferences for effective negotiation: an approach based on concept learning

We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user’s preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user’s preferences, but also its opponent’s preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer’s needs and to offer services that respect consumer’s preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer’s preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer’s preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.     

Better Living Through Geometry

Mark Weiser described ubiquitous computing as “invisible, everywhere computing that does not live on a personal device of any sort, but is in the woodwork everywhere”[1]. The EasyLiving project is concerned with development of an architecture and technologies for ubiquitous computing environments that allow the dynamic aggregation of diverse I/O devices into a single coherent user experience. Though the need for research in distributed computing, perception and interfaces is widely recognised, the importance of an explicit geometric world model for enhancing the user’s experience of a ubiquitous computing system has not been well-articulated. This paper describes three scenarios that benefit from geometric context and introduces the EasyLiving Geometric Model.     

Using affective parameters in a content-based recommender system for images

There is an increasing amount of multimedia content available to end users. Recommender systems help these end users by selecting a small but relevant subset of items for each user based on her/his preferences. This paper investigates the influence of affective metadata (metadata that describe the user’s emotions) on the performance of a content-based recommender (CBR) system for images. The underlying assumption is that affective parameters are more closely related to the user’s experience than generic metadata (e.g. genre) and are thus more suitable for separating the relevant items from the non-relevant. We propose a novel affective modeling approach based on users’ emotive responses. We performed a user-interaction session and compared the performance of the recommender system with affective versus generic metadata. The results of the statistical analysis showed that the proposed affective parameters yield a significant improvement in the performance of the recommender system.     

Rudolph M. Schindler—Proportion,Scale and the “Row”

Jin-Ho Park interprets Schindler’s “reference frames in space” as set forth in his 1916 lecture note on mathematics, proportion, and architecture, in the context of Robinson’s1898–99 articles in the Architectural Record. Schindler’s unpublished, handwritten notes provide a source for his concern for “rhythmic” dimensioning in architecture. He uses a system in which rectangular dimensions are arranged in “rows.” Architectural examples of Schindler’s Shampay, Braxton-Shore and How Houses illustrate the principles.     

“Those found responsible have been sacked”: some observations on the usefulness of error

Erik Hollnagel’s body of work in the past three decades has molded much of the current research approach to system safety, particularly notions of “error”. Hollnagel regards “error” as a dead-end and avoids using the term. This position is consistent with Rasmussen’s claim that there is no scientifically stable category of human performance that can be described as “error”. While this systems view is undoubtedly correct, “error” persists. Organizations, especially formal business, political, and regulatory structures, use “error” as if it were a stable category of human performance. They apply the term to performances associated with undesired outcomes, tabulate occurrences of “error”, and justify control and sanctions through “error”. Although a compelling argument can be made for Hollnagel’s view, it is clear that notions of “error” are socially and organizationally productive. The persistence of “error” in management and regulatory circles reflects its value as a means for social control.