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Online credit card fraud presents a significant challenge in the field of eCommerce. In 2012 alone, the total loss due to credit card fraud in the US amounted to $ 54 billion. Especially online games merchants have difficulties applying standard fraud detection algorithms to achieve timely and accurate detection. This paper describes the Special constrains of this domain and highlights the reasons why conventional algorithms are not quite effective to deal with this problem. Our suggested solution for the problem originates from the fields of feature construction joined with the field of temporal sequence data mining. We present Feature construction techniques, which are able to create discriminative features based on a sequence of transaction and are able to incorporate the time into the classification process. In addition to that, a framework is presented that allows for an automated and adaptive change of features in case the underlying pattern is changing.
Vehicles have been so far improved in terms of energy-efficiency and safety mainly by optimising the engine and the power train. However, there are opportunities to increase energy-efficiency and safety by adapting the individual driving behaviour in the given driving situation. In this paper, an improved rule match algorithm is introduced, which is used in the expert system of a human-centred driving system. The goal of the driving system is to optimise the driving behaviour in terms of energy-efficiency and safety by giving recommendations to the driver. The improved rule match algorithm checks the incoming information against the driving rules to recognise any breakings of a driving rule. The needed information is obtained by monitoring the driver, the current driving situation as well as the car, using in-vehicle sensors and serial-bus systems. On the basis of the detected broken driving rules, the expert system will create individual recommendations in terms of energy-efficiency and safety, which will allow eliminating bad driving habits, while considering the driver needs.
The recent years and especially the Internet have changed the ways in which data is stored. It is now common to store data in the form of transactions, together with ist creation time-stamp. These transactions can often be attributed to Logical units, e.g., all transactions that belong to one customer. These groups, we refer to them as data sequences, have a more complex structure than tuple-based data. This makes it more difficult to find discriminatory patterns for classification purposes. However, the complex structure potentially enables us to track behaviour and its change over the course of time. This is quite interesting, especially in the e-commerce area, in which classification of a sequence of customer actions is still a challenging task for data miners. However, before standard algorithms such as Decision Trees, Neural Nets, Naive Bayes or Bayesian Belief Networks can be applied on sequential data, preparations are required in order to capture the information stored within the sequences. Therefore, this work presents a systematic approach on how to reveal sequence patterns among data and how to construct powerful features out of the primitive sequence attributes. This is achieved by sequence aggregation and the incorporation of time dimension into the feature construction step. The proposed algorithm is described in detail and applied on a real-life data set, which demonstrates the ability of the proposed algorithm to boost the classification performance of well-known data mining algorithms for binary classification tasks.
Prominent theories of action recognition suggest that during the recognition of actions the physical patterns of the action is associated with only one action interpretation (e.g., a person waving his arm is recognized as waving). In contrast to this view, studies examining the visual categorization of objects show that objects are recognized in multiple ways (e.g., a VW Beetle can be recognized as a car or a beetle) and that categorization performance is based on the visual and motor movement similarity between objects. Here, we studied whether we find evidence for multiple levels of categorization for social interactions (physical interactions with another person, e.g., handshakes). To do so, we compared visual categorization of objects and social interactions (Experiments 1 and 2) in a grouping task and assessed the usefulness of motor and visual cues (Experiments 3, 4, and 5) for object and social interaction categorization. Additionally, we measured recognition performance associated with recognizing objects and social interactions at different categorization levels (Experiment 6). We found that basic level object categories were associated with a clear recognition advantage compared to subordinate recognition but basic level social interaction categories provided only a little recognition advantage. Moreover, basic level object categories were more strongly associated with similar visual and motor cues than basic level social interaction categories. The results suggest that cognitive categories underlying the recognition of objects and social interactions are associated with different performances. These results are in line with the idea that the same action can be associated with several action interpretations (e.g., a person waving his arm can be recognized as waving or greeting).
Putting actions in context: visual action adaptation aftereffects are modulated by social contexts
(2014)
The social context in which an action is embedded provides important information for the interpretation of an action. Is this social context integrated during the visual recognition of an action? We used a behavioural visual adaptation paradigm to address this question and measured participants’ perceptual bias of a test action after they were adapted to one of two adaptors (adaptation after-effect). The action adaptation after effect was measured for the same set of adaptors in two different social contexts. Our results indicate that the size of the adaptation effect varied with social context (social context modulation) although the physical appearance of the adaptors remained unchanged. Three additional experiments provided evidence that the observed social context modulation of the adaptation effect are owed to the adaptation of visual action recognition processes. We found that adaptation is critical for the social context modulation (experiment 2). Moreover, the effect is not mediated by emotional content of the action alone (experiment 3) and visual information about the action seems to be critical for the emergence of action adaptation effects (experiment 4). Taken together these results suggest that processes underlying visual action recognition are sensitive to the social context of an action.
Services Oriented Architectures (SOA) have emerged as a useful framework for developing interoperable, large-scale systems, typically implemented using the Web Services (WS) standards. However, the maintenance and evolution of SOA systems present many challenges. SmartLife applications are intelligent user-centered systems and a special class of SOA systems that present even greater challenges for a software maintainer. Ontologies and ontological modeling can be used to support the evolution of SOA systems. This paper describes the development of a SOA evolution ontology and its use to develop an ontological model of a SOA system. The ontology is based on a standard SOA ontology. The ontological model can be used to provide semantic and visual support for software maintainers during routine maintenance tasks. We discuss a case study to illustrate this approach, as well as the strengths and limitations.
Many companies practice performance management in the framework of a heterogeneous, grown mix of numerous separate decisions, instruments, processes and systems and not in terms of a strategically and systematically planned management system. Due to the inefficiency of the above mentioned performance management style, a holistic and integrated approach is a key factor. Performance management must be able to meet central objectives and requirements and set the groundwork for long-term corporate success. This article presents a central approach of the conception of holistic and long-term performance management. The five equal part disciplines are illustrated and demonstrate the issue and composition complexity of a performance management due to their characteristics and combination. The objective of this article is to display and communicate the performance management issue and its context through an easily comprehensible system without following a general recipe.
This is a report from a one-day fourth international workshop on "Information Systems in Distributed Environments" (ISDE), which was organized in conjunction with the OnTheMove Federated Conferences & Workshops (OTM 2014) October 29-30, 2014, Amantea, Calabria, Italy. The main focus of this event was to provide a venue for the discussion of challenges related to the development, operation, and maintenance of distributed information systems, and their creation in the context of global development projects. Further dissemination of research results will lead to an improvement of distributed information system development and deployment across the globe.
A behavior marker for measuring non-technical skills of software professionals : an empirical study
(2015)
Managers recognize that software development teams need to be developed. Although technical skills are necessary, non-technical (NT) skills are equally, if not more, necessary for project success. Currently, there are no proven tools to measure the NT skills of software developers or software development teams. Behavioral markers (observable behaviors that have positive or negative impacts on individual or team performance) are successfully used by airline and medical industries to measure NT skill performance. This research developed and validated a behavior marker tool rated video clips of software development teams. The initial results show that the behavior marker tool can be reliably used with minimal training.
Context: Companies increasingly strive to adapt to market and ecosystem changes in real time. Gauging and understanding team performance in such changing environments present a major challenge.
Objective: This paper aims to understand how software developers experience the continuous adaptation of performance in a modern, highly volatile environment using Lean and Agile software development methodology. This understanding can be used as a basis for guiding formation and maintenance of high-performing teams, to inform performance improvement initiatives, and to improve working conditions for software developers.
Method: A qualitative multiple-case study using thematic interviews was conducted with 16 experienced practitioners in five organisations.
Results: We generated a grounded theory, Performance Alignment Work, showing how software developers experience performance. We found 33 major categories of performance factors and relationships between the factors. A cross-case comparison revealed similarities and differences between different kinds and different sizes of organisations.
Conclusions: Based on our study, software teams are engaged in a constant cycle of interpreting their own performance and negotiating its alignment with other stakeholders. While differences across organisational sizes exist, a common set of performance experiences is present despite differences in context variables. Enhancing performance experiences requires integration of soft factors, such as communication, team spirit, team identity, and values, into the overall development process. Our findings suggest a view of software development and software team performance that centres around behavioural and social sciences.