Automated risk mitigation in business processes

This paper proposes a concrete approach for the automatic mitigation of risks that are detected during process enactment. Given a process model exposed to risks, e.g. a financial process exposed to the risk of approval fraud, we enact this process and as soon as the likelihood of the associated risk(s) is no longer tolerable, we generate a set of possible mitigation actions to reduce the risks' likelihood, ideally annulling the risks altogether. A mitigation action is a sequence of controlled changes applied to the running process instance, taking into account a snapshot of the process resources and data, and the current status of the system in which the process is executed. These actions are proposed as recommendations to help process administrators mitigate process-related risks as soon as they arise. The approach has been implemented in the YAWL environment and its performance evaluated. The results show that it is possible to mitigate process-related risks within a few minutes.

V1-inspired features induce a weighted margin in SVMs

Image representations derived from simplified models of the primary visual cortex (V1), such as HOG and SIFT, elicit good performance in a myriad of visual classification tasks including object recognition/detection, pedestrian detection and facial expression classification. A central question in the vision, learning and neuroscience communities regards why these architectures perform so well. In this paper, we offer a unique perspective to this question by subsuming the role of V1-inspired features directly within a linear support vector machine (SVM). We demonstrate that a specific class of such features in conjunction with a linear SVM can be reinterpreted as inducing a weighted margin on the Kronecker basis expansion of an image. This new viewpoint on the role of V1-inspired features allows us to answer fundamental questions on the uniqueness and redundancies of these features, and offer substantial improvements in terms of computational and storage efficiency.

Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches.

Decentralized detector generation in cooperative intrusion detection system

We consider Cooperative Intrusion Detection System (CIDS) which is a distributed AIS-based (Artificial Immune System) IDS where nodes collaborate over a peer-to-peer overlay network. The AIS uses the negative selection algorithm for the selection of detectors (e.g., vectors of features such as CPU utilization, memory usage and network activity). For better detection performance, selection of all possible detectors for a node is desirable but it may not be feasible due to storage and computational overheads. Limiting the number of detectors on the other hand comes with the danger of missing attacks. We present a scheme for the controlled and decentralized division of detector sets where each IDS is assigned to a region of the feature space. We investigate the trade-off between scalability and robustness of detector sets. We address the problem of self-organization in CIDS so that each node generates a distinct set of the detectors to maximize the coverage of the feature space while pairs of nodes exchange their detector sets to provide a controlled level of redundancy. Our contribution is twofold. First, we use Symmetric Balanced Incomplete Block Design, Generalized Quadrangles and Ramanujan Expander Graph based deterministic techniques from combinatorial design theory and graph theory to decide how many and which detectors are exchanged between which pair of IDS nodes. Second, we use a classical epidemic model (SIR model) to show how properties from deterministic techniques can help us to reduce the attack spread rate.

Hot topic detection in news blogs from the perspective of W2T

News blog hot topics are important for the information recommendation service and marketing. However, information overload and personalized management make the information arrangement more difficult. Moreover, what influences the formation and development of blog hot topics is seldom paid attention to. In order to correctly detect news blog hot topics, the paper first analyzes the development of topics in a new perspective based on W2T (Wisdom Web of Things) methodology. Namely, the characteristics of blog users, context of topic propagation and information granularity are unified to analyze the related problems. Some factors such as the user behavior pattern, network opinion and opinion leader are subsequently identified to be important for the development of topics. Then the topic model based on the view of event reports is constructed. At last, hot topics are identified by the duration, topic novelty, degree of topic growth and degree of user attention. The experimental results show that the proposed method is feasible and effective.

Spotting the difference : identifying player opponent preferences in FPS games

This paper describes a study designed to understand player responses to artificially intelligent opponents in multi-player First Person Shooter games. It examines the player's ability to tell the difference between artificially intelligent opponents and other human players, and investigates the players' perceptions of these opponents. The study examines player preferences in this regard and identifies the significance of the cues and signs players use to categorise an opponent as artificial or human.

Complexity of increasing the secure connectivity in wireless ad hoc networks

We consider the problem of maximizing the secure connectivity in wireless ad hoc networks, and analyze complexity of the post-deployment key establishment process constrained by physical layer properties such as connectivity, energy consumption and interference. Two approaches, based on graph augmentation problems with nonlinear edge costs, are formulated. The first one is based on establishing a secret key using only the links that are already secured by shared keys. This problem is in NP-hard and does not accept polynomial time approximation scheme PTAS since minimum cutsets to be augmented do not admit constant costs. The second one extends the first problem by increasing the power level between a pair of nodes that has a secret key to enable them physically connect. This problem can be formulated as the optimal key establishment problem with interference constraints with bi-objectives: (i) maximizing the concurrent key establishment flow, (ii) minimizing the cost. We prove that both problems are NP-hard and MAX-SNP with a reduction to MAX3SAT problem.

Benefiting white noise in developing feedforward active noise control systems

In many applications of active noise control (ANC), an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. The modelling accuracy and the convergence rate increase when a white noise with larger variance is used, however larger the variance increases the residual noise, which decreases performance of the system. The proposed algorithm uses the advantages of the white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the system. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to adjust the secondary path estimation. Comparative simulation results shown in this paper indicate effectiveness of the proposed method.

Exploring emotional climate in preservice science teacher education

Classroom emotional climates are interrelated with students’ engagement with university courses. Despite growing interest in emotions and emotional climate research, little is known about the ways in which social interactions and different subject matter mediate emotional climates in preservice science teacher education classes. In this study we investigated the emotional climate and associated classroom interactions in a preservice science teacher education class. We were interested in the ways in which salient classroom interactions were related to the emotional climate during lessons centered on debates about science-based issues (e.g., nuclear energy alternatives). Participants used audience response technology to indicate their perceptions of the emotional climate. Analysis of conversation for salient video clips and analysis of non-verbal conduct (acoustic parameters, body movements, and facial expressions) supplemented emotional climate data. One key contribution that this study makes to preservice science teacher education is to identify the micro-processes of successful and unsuccessful class interactions that were associated with positive and neutral emotional climate. The structure of these interactions can inform the practice of other science educators who wish to produce positive emotional climates in their classes. The study also extends and explicates the construct of intensity of emotional climate.

Which dissimilarity is to be used when extracting typologies in sequence analysis? A comparative study

Originally developed in bioinformatics, sequence analysis is being increasingly used in social sciences for the study of life-course processes. The methodology generally employed consists in computing dissimilarities between the trajectories and, if typologies are sought, in clustering the trajectories according to their similarities or dissemblances. The choice of an appropriate dissimilarity measure is a major issue when dealing with sequence analysis for life sequences. Several dissimilarities are available in the literature, but neither of them succeeds to become indisputable. In this paper, instead of deciding upon one dissimilarity measure, we propose to use an optimal convex combination of different dissimilarities. The optimality is automatically determined by the clustering procedure and is defined with respect to the within-class variance.

Designing an innovative approach to engage students in learning science : the evolving case of hybridized writing

Innovations are usually attributed to ideas generated in the minds of individuals. As we reflect upon the evolving design of an online project to engage students in learning science through hybridized writing activities we propose a more distributed view of the process of innovative design. That is, our experience suggests ideas are generated in the activity of interacting with human and material resources that expand and constrain possibilities. This project is innovative in that it is a new educational response to the problem of disengagement of students in science, and has proven to be effective in changing classroom practice and improving students’ scientific literacy. In this chapter, we identify the antecedents and trace the evolution of the project. This account illuminates the innovative design process, presents a summary of the evidence for the effectiveness of the project, and identifies future directions for further development and research. Keywords: Science learning, hybridized writing, case study, innovative approach

Communication choices to engage participation of rural Indonesian craftspeople in development projects

In participatory design projects, maintaining effective communication between facilitator and participant is essential. This paper describes the consideration given to the choice of communication modes to engage participation of rural Indonesian craftspeople over the course of a significant 3 year project that aims to grow their self-determination, design and business skill. We demonstrate the variety and subtlety of oral and written forms of communication used by the facilitator during the project. The culture, the communication skill and the influence of tacit knowledge affect the effectiveness of some modes of communication over the others, as well as the available infrastructure. Considerations are specific to the case of rural Indonesian craftspeople, but general lessons can be drawn.

Spectroscopic correlation analysis of NMR-based metabonomics in exercise science

Spectroscopic studies of complex clinical fluids have led to the application of a more holistic approach to their chemical analysis becoming more popular and widely employed. The efficient and effective interpretation of multidimensional spectroscopic data relies on many chemometric techniques and one such group of tools is represented by so-called correlation analysis methods. Typical of these techniques are two-dimensional correlation analysis and statistical total correlation spectroscopy (STOCSY). Whilst the former has largely been applied to optical spectroscopic analysis, STOCSY was developed and has been applied almost exclusively to NMR metabonomic studies. Using a 1H NMR study of human blood plasma, from subjects recovering from exhaustive exercise trials, the basic concepts and applications of these techniques are examined. Typical information from their application to NMR-based metabonomics is presented and their value in aiding interpretation of NMR data obtained from biological systems is illustrated. Major energy metabolites are identified in the NMR spectra and the dynamics of their appearance and removal from plasma during exercise recovery are illustrated and discussed. The complementary nature of two-dimensional correlation analysis and statistical total correlation spectroscopy are highlighted.

Dynamic weaving in aspect oriented business process management

Reducing complexity in Information Systems is an important topic in both research and industry. One strategy to deal with complexity is separation of concerns, which results in less complex, easily maintainable and more reusable systems. Separation of concerns can be addressed through the Aspect Oriented paradigm. Although this paradigm has been well researched in programming, it is still at the preliminary stage in the area of Business Process Management. While some efforts have been made to extend business process modelling with aspect oriented capability, it has not yet been investigated how aspect oriented business process models should be executed at runtime. In this paper, we propose a generic solution to support execution of aspect oriented business process models based on the principle behind dynamic weaving of aspects. This solution is formally specified using Coloured Petri Nets. The resulting formal specification serves as the blueprint to the implementation of a service module in the framework of a state-of-the-art Business Process Management System. Using this developed artefact, a case study is performed in which two simplified processes from real business in the domain of banking are modelled and executed in an aspect oriented manner. Through this case study, we also demonstrate that adoption of aspect oriented modularization increases the reusability while reducing the complexity of business process models in practice.