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1.
Quantitative usability requirements are a critical but challenging, and hence an often neglected aspect of a usability engineering process. A case study is described where quantitative usability requirements played a key role in the development of a new user interface of a mobile phone. Within the practical constraints of the project, existing methods for determining usability requirements and evaluating the extent to which these are met, could not be applied as such, therefore tailored methods had to be developed. These methods and their applications are discussed. 相似文献
2.
As an effective technique for feature extraction and pattern classification Fisher linear discriminant (FLD) has been successfully applied in many fields. However, for a task with very high-dimensional data such as face images,
conventional FLD technique encounters a fundamental difficulty caused by singular within-class scatter matrix. To avoid the
trouble, many improvements on the feature extraction aspect of FLD have been proposed. In contrast, studies on the pattern
classification aspect of FLD are quiet few. In this paper, we will focus our attention on the possible improvement on the
pattern classification aspect of FLD by presenting a novel linear discriminant criterion called maximum scatter difference (MSD). Theoretical analysis demonstrates that MSD criterion is a generalization of Fisher discriminant criterion, and is
the asymptotic form of discriminant criterion: large margin linear projection. The performance of MSD classifier is tested in face recognition. Experiments performed on the ORL, Yale, FERET and AR databases
show that MSD classifier can compete with top-performance linear classifiers such as linear support vector machines, and is better than or equivalent to combinations of well known facial feature extraction methods, such as eigenfaces, Fisherfaces, orthogonal complementary space, nullspace, direct linear discriminant analysis, and the nearest neighbor classifier.
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3.
Visual media data such as an image is the raw data representation for many important applications. Reducing the dimensionality of raw visual media data is desirable since high dimensionality degrades not only the effectiveness but also the efficiency of visual recognition algorithms. We present a comparative study on spatial interest pixels (SIPs), including eight-way (a novel SIP detector), Harris, and Lucas‐Kanade, whose extraction is considered as an important step in reducing the dimensionality of visual media data. With extensive case studies, we have shown the usefulness of SIPs as low-level features of visual media data. A class-preserving dimension reduction algorithm (using GSVD) is applied to further reduce the dimension of feature vectors based on SIPs. The experiments showed its superiority over PCA. 相似文献
4.
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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5.
Awareness systems have attracted significant research interest for their potential to support interpersonal relationships.
Investigations of awareness systems for the domestic environment have suggested that such systems can help individuals stay
in touch with dear friends or family and provide affective benefits to their users. Our research provides empirical evidence
to refine and substantiate such suggestions. We report our experience with designing and evaluating the ASTRA awareness system,
for connecting households and mobile family members. We introduce the concept of connectedness and its measurement through
the Affective Benefits and Costs of communication questionnaire (ABC-Q). We inform results that testify the benefits of sharing
experiences at the moment they happen without interrupting potential receivers. Finally, we document the role that lightweight,
picture-based communication can play in the range of communication media available.
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6.
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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7.
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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8.
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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9.
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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10.
The Stanford Event Heap has been shown to provide appropriate support for constructing interactive workspace applications.
Given this success it is natural to consider the Event Heap as a platform to support other classes of Ubiquitous Computing
applications. In this paper we argue that the distributed, spontaneous nature of these applications places additional demands
on the Event Heap that require extensions to both the engineering and API. Suitable extensions are described and their use
to support a typical Ubicomp application is discussed.
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11.
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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12.
Ohne Zusammenfassung
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13.
The paper reflects on the unique experience of social and technological development in Lithuania since the regaining of independence
as a newly reshaped society constructing a distinctive competitive IST-based model at global level. This has presented Lithuanian
pattern of how to integrate different experiences and relations between generations in implementing complex information society
approaches. The resulting programme in general is linked to the Lisbon objectives of the European Union. The experience of
transitional countries in Europe, each different but facing some common problems, may be useful to developing countries in
Africa.
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14.
Ranked models in the form of linear transformations of multivariate feature vectors on a line can be found on the basis of
a priori given order within particular pairs of objects or events. Such ranked transformations are designed to preserve given
sequential order. In this way, the sequential patterns inside sets of the feature vectors can be discovered and modelled.
Attention is paid here to combining problems of sequential patterns modelling and recognition with feature selection. The
feature selection problem is aimed at the best representation of the sequential patterns. The convex and piecewise linear
(CPL) criterion functions are used here both for designing ranked linear models and for feature selection.
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15.
Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these
algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive
information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one
to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods,
instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving
clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general
features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better
protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated.
It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well
as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to
which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements.
In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then,
we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations
about future work and promising directions in the context of privacy preservation in data mining are discussed.
*The work reported in this paper has been partially supported by the EU under the IST Project CODMINE and by the Sponsors of
CERIAS.
Editor: Geoff Webb
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16.
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 P eopleF inder, 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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17.
Nowadays data mining plays an important role in decision making. Since many organizations do not possess the in-house expertise
of data mining, it is beneficial to outsource data mining tasks to external service providers. However, most organizations
hesitate to do so due to the concern of loss of business intelligence and customer privacy. In this paper, we present a Bloom
filter based solution to enable organizations to outsource their tasks of mining association rules, at the same time, protect
their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off the
storage requirement.
This research was supported by the USA National Science Foundation Grants CCR-0310974 and IIS-0546027.
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18.
In this paper, we present a new model for time-series forecasting using radial basis functions (RBFs) as a unit of artificial neural networks (ANNs), which allows the inclusion of exogenous information (EI) without additional pre-processing. We begin by summarizing the most well-known EI techniques used ad hoc, i.e., principal component analysis (PCA) and independent component analysis (ICA). We analyze the advantages and disadvantages of these techniques in time-series forecasting using Spanish bank and company stocks. Then, we describe a new hybrid model for time-series forecasting which combines ANNs with genetic algorithms (GAs). We also describe the possibilities when implementing the model on parallel processing systems. 相似文献
19.
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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20.
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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