Understanding and capturing people’s privacy policies in a mobile social networking application

A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the extent that these concerns are adequately addressed. In this article, we report on our work on PeopleFinder, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends, family, and colleagues). The objective of our work has been to better understand people’s attitudes and behaviors towards privacy as they interact with such an application, and to explore technologies that empower users to more effectively and efficiently specify their privacy preferences (or “policies”). These technologies include user interfaces for specifying rules and auditing disclosures, as well as machine learning techniques to refine user policies based on their feedback. We present evaluations of these technologies in the context of one laboratory study and three field studies.

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Inverse multi-objective robust evolutionary design

In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.

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Ethical regulations on robotics in Europe

There are only a few ethical regulations that deal explicitly with robots, in contrast to a vast number of regulations, which may be applied. We will focus on ethical issues with regard to “responsibility and autonomous robots”, “machines as a replacement for humans”, and “tele-presence”. Furthermore we will examine examples from special fields of application (medicine and healthcare, armed forces, and entertainment). We do not claim to present a complete list of ethical issue nor of regulations in the field of robotics, but we will demonstrate that there are legal challenges with regard to these issues.

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A Subgradient Descent Algorithm for Optimization of Initially Controllable Flow Shop Systems

We consider an optimization problem for deterministic flow shop systems processing identical jobs. The service times are initially controllable; they can only be set before processing the first job, and cannot be altered between processes. We derive some waiting and completion time characteristics for fixed service time flow shop systems, independent of the cost formulation. Exploiting these characteristics, an equivalent convex optimization problem, which is non-differentiable, is derived along with its subgradient descent solution algorithm. This algorithm not only eliminates the need for convex programming solvers but also allows for the solution of larger systems due to its smaller memory requirements. Significant improvements in solution times are also observed in the numerical examples.

Using trust assumptions with security requirements

Assumptions are frequently made during requirements analysis of a system about the trustworthiness of its various components (including human components). These trust assumptions, whether implicit or explicit, affect the scope of the analysis, derivation of security requirements, and in some cases how functionality is realized. This paper presents trust assumptions in the context of analysis of security requirements. A running example shows how trust assumptions can be used by a requirements engineer to help define and limit the scope of analysis and to document the decisions made during the process. The paper concludes with a case study examining the impact of trust assumptions on software that uses the secure electronic transaction specification.

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Managing Next Generation Networks and Services: A Report on APNOMS 2007

This article presents a report on APNOMS 2007, which was held October 10–12, 2007 in Sapporo, Japan. The theme of APNOMS 2007 was “Managing the Next Generation Networks and Services.”

How relative absolute can be: SUMI and the impact of the nature of the task in measuring perceived software usability

This paper addresses the possibility of measuring perceived usability in an absolute way. It studies the impact of the nature of the tasks performed in perceived software usability evaluation, using for this purpose the subjective evaluation of an application’s performance via the Software Usability Measurement Inventory (SUMI). The paper reports on the post-hoc analysis of data from a productivity study for testing the effect of changes in the graphical user interface (GUI) of a market leading drafting application. Even though one would expect similar evaluations of an application’s usability for same releases, the analysis reveals that the output of this subjective appreciation is context sensitive and therefore mediated by the research design. Our study unmasked a significant interaction between the nature of the tasks used for the usability evaluation and how users evaluate the performance of this application. This interaction challenges the concept of absolute benchmarking in subjective usability evaluation, as some software evaluation methods aspire to provide, since subjective measurement of software quality will be affected most likely by the nature of the testing materials used for the evaluation.

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Multimodal support to group dynamics

The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole. A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.

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ONTRACK: Dynamically adapting music playback to support navigation

Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation that demonstrated the approach’s efficacy: users were able to complete tasks within a reasonable time and their subjective feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite effectively navigate complicated routes.

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COMUNICAR: designing a multimedia, context-aware human-machine interface for cars

The European Union co-funded COMUNICAR (communication multimedia unit inside car) project designed and developed an integrated multimedia human–machine interface (HMI) able to manage a wide variety of driver information systems (from entertainment to safety). COMUNICAR proposed an innovative information provision paradigm, in which the on-vehicle HMI is able to tailor the delivery of the information in real time according to the actual driving context and the drivers workload. COMUNICAR adopted a user-centred design process involving an iterative development based on extensive user tests since the early phases of the project. This approach was particularly useful to define and improve the layout of the user interface and specify the rules that decide the scheduling and the modalities of the delivery of the information messages to the driver. This paper introduces the COMUNICAR concept and the user-centred flow of design. Then, a concrete case of user-test driven, iterative improvement of a systems functionality is presented. We also briefly describe two software tools that we have designed to enhance the development process in a user-centred perspective. Finally, the future evolution of the concept of smart and safe information scheduling is sketched and discussed.

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Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing

Support vector machines (SVMs) have been promising methods for classification and regression analysis due to their solid mathematical foundations, which include two desirable properties: margin maximization and nonlinear classification using kernels. However, despite these prominent properties, SVMs are usually not chosen for large-scale data mining problems because their training complexity is highly dependent on the data set size. Unlike traditional pattern recognition and machine learning, real-world data mining applications often involve huge numbers of data records. Thus it is too expensive to perform multiple scans on the entire data set, and it is also infeasible to put the data set in memory. This paper presents a method, Clustering-Based SVM (CB-SVM), that maximizes the SVM performance for very large data sets given a limited amount of resource, e.g., memory. CB-SVM applies a hierarchical micro-clustering algorithm that scans the entire data set only once to provide an SVM with high quality samples. These samples carry statistical summaries of the data and maximize the benefit of learning. Our analyses show that the training complexity of CB-SVM is quadratically dependent on the number of support vectors, which is usually much less than that of the entire data set. Our experiments on synthetic and real-world data sets show that CB-SVM is highly scalable for very large data sets and very accurate in terms of classification. A preliminary version of the paper, “Classifying Large Data Sets Using SVM with Hierarchical Clusters”, by H. Yu, J. Yang, and J. Han, appeared in Proc. 2003 Int. Conf. on Knowledge Discovery in Databases (KDD'03), Washington, DC, August 2003. However, this submission has substantially extended the previous paper and contains new and major-value added technical contribution in comparison with the conference publication.

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Hitchcock’s (2001) treatment of singular and general causation

Hitchcock (2001a) argues that the distinction between singular and general causation conflates the two distinctions ‘actual causation vs. causal tendencies’ and ‘wide vs. narrow causation’. Based on a recent regularity account of causation I will show that Hitchcock’s introduction of the two distinctions is an unnecessary multiplication of causal concepts.

Interacting with mobile services: an evaluation of camera-phones and visual tags

We present a study of using camera-phones and visual-tags to access mobile services. Firstly, a user-experience study is described in which participants were both observed learning to interact with a prototype mobile service and interviewed about their experiences. Secondly, a pointing-device task is presented in which quantitative data was gathered regarding the speed and accuracy with which participants aimed and clicked on visual-tags using camera-phones. We found that participants’ attitudes to visual-tag-based applications were broadly positive, although they had several important reservations about camera-phone technology more generally. Data from our pointing-device task demonstrated that novice users were able to aim and click on visual-tags quickly (well under 3 s per pointing-device trial on average) and accurately (almost all meeting our defined speed/accuracy tradeoff of 6% error-rate). Based on our findings, design lessons for camera-phone and visual-tag applications are presented.

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The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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Multilingual language resources and interoperability

This article introduces the topic of “Multilingual language resources and interoperability”. We start with a taxonomy and parameters for classifying language resources. Later we provide examples and issues of interoperatability, and resource architectures to solve such issues. Finally we discuss aspects of linguistic formalisms and interoperability.

Evolving strategy for a probabilistic game of imperfect information using genetic programming

We provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. Ant Wars contestants are virtual ants collecting food on a grid board in the presence of a competing ant. BrilliAnt has been evolved through a competitive one-population coevolution using genetic programming and fitnessless selection. In this paper, we detail the evolutionary setup that lead to BrilliAnt’s emergence, assess its direct and indirect human-competitiveness, and describe the behavioral patterns observed in its strategy.

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Mining top-K frequent itemsets from data streams

Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to set a bound on the result size. We study the problem of mining top K frequent itemsets in data streams. We introduce a method based on the Chernoff bound with a guarantee of the output quality and also a bound on the memory usage. We also propose an algorithm based on the Lossy Counting Algorithm. In most of the experiments of the two proposed algorithms, we obtain perfect solutions and the memory space occupied by our algorithms is very small. Besides, we also propose the adapted approach of these two algorithms in order to handle the case when we are interested in mining the data in a sliding window. The experiments show that the results are accurate.

Legal safeguards for privacy and data protection in ambient intelligence

To get the maximum benefit from ambient intelligence (AmI), we need to anticipate and react to possible drawbacks and threats emerging from the new technologies in order to devise appropriate safeguards. The SWAMI project took a precautionary approach in its exploration of the privacy risks in AmI and sought ways to reduce them. It constructed four “dark scenarios” showing possible negative implications of AmI, notably for privacy protection. Legal analysis of the depicted futures showed the shortcomings of the current legal framework in being able to provide adequate privacy protection in the AmI environment. In this paper, the authors, building upon their involvement in SWAMI research as well as the further advancement of EU privacy analysis, identify various outstanding issues regarding the legal framework that still need to be resolved in order to deal with AmI in an equitable and efficacious way. This article points out some of the lacunae in the legal framework and postulates several privacy-specific safeguards aimed at overcoming them.

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An exploratory study for analyzing interactional processes of group discussion: the case of a focus group interview

The purposes of this study are (a) to establish a measurement for evaluating conversational impressions of group discussions, and (b) to make an exploratory investigation on their interactional processes which may affect to form those impressions. The impression rating and factor analysis undertaken first give us four factors concerning conversational impressions of “focus group interviews (FGIs)”: conversational activeness, conversational sequencing, the attitudes of participants and the relationships of participants. In relation to the factors of conversational activeness and conversational sequencing in particular, the microanalysis of four selected topical scenes from our database further shows that the behavior of the moderator and the interviewees is organized not independently but with reference to each other. The study thus emphasizes the importance of the integration of quantitative and qualitative approaches towards human interactions.

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