Formal analysis of human operator behavioural patterns in interactive surveillance systems

An important area of Human Reliability Assessment in interactive systems is the ability to understand the causes of human error and to model their occurrence. This paper investigates a new approach to analysis of task failures based on patterns of operator behaviour, in contrast with more traditional event-based approaches. It considers, as a case study, a formal model of an Air Traffic Control system operator’s task which incorporates a simple model of the high-level cognitive processes involved. The cognitive model is formalised in the CSP process algebra. Various patterns of behaviour that could lead to task failure are described using temporal logic. Then a model-checking technique is used to verify whether the set of selected behavioural patterns is sound and complete with respect to the definition of task failure. The decomposition is shown to be incomplete and a new behavioural pattern is identified, which appears to have been overlooked in the informal analysis of the problem. This illustrates how formal analysis of operator models can yield fresh insights into how failures may arise in interactive systems.

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Efficient parallel processing with spin-wave nanoarchitectures

In this paper, we study the algorithm design aspects of three newly developed spin-wave architectures. The architectures are capable of simultaneously transmitting multiple signals using different frequencies, and allow for concurrent read/write operations. Using such features, we show a number of parallel and fault-tolerant routing schemes and introduce a set of generic parallel processing techniques that can be used for design of fast algorithms on these spin-wave architectures. We also present a set of application examples to illustrate the operation of the proposed generic parallel techniques.

Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the same time simpler models with respect to the single objective based approach.

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DHT routing analysis in a logarithmically transformed space

This paper presents an analytical model that helps understanding the common foundations of routing in DHTs and provides means for analytical comparison of different systems and different parameter combinations. In the proposed model, a logarithmic transformation is applied to the metric space embedding node identifiers. We show that in this transformed space - similarly to short-range connections in the real metric space - long-range connections have linear properties: connections are uniformly distributed and routing via long-range contacts progresses linearly toward the target. Using this transformation model, we introduce a λ long-range connection density parameter to characterize DHT routing and analyze common properties and differences between existing DHT routing mechanisms. For the the two extreme DHT families (“most random” and completely deterministic), we also present a detailed stochastic analysis of routing in the transformed space and express analytically the expected value of the number of routing hops.

A Self-selection Technique for Flooding and Routing in Wireless Ad-hoc Networks

There is a fundamental difference between wireless and wired networks, since the latter employ point-to-point communication while the former use broadcast transmission as the communication primitive. In this paper, we describe an algorithm, called self-selection, which takes advantage of broadcast communication to efficiently implement the basic operation of selecting a node possessing some desired properties among all the neighbors of the requestor. Self-selection employs a prioritized transmission back-off delay scheme in which each node’s delay of transmitting a signal is dependent on the probability of the node’s ability to best perform a pertinent task, and in turn, enables the node to autonomously select itself for the task. We demonstrate the benefits of self-selection in two basic wireless ad hoc network communication algorithms: flooding and routing. By relating back-off delay to the signal strength of a received packet, we design an efficient variant of conventional flooding called Signal Strength Aware Flooding. By using distance-to-destination to derive back-off delay, we design a novel and fault-tolerant wireless ad hoc network routing protocol named Self-Selective Routing.

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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Prototypical implementation of location-aware services based on a middleware architecture for super-distributed RFID tag infrastructures

We provide evidence of the feasibility and effectiveness of a middleware architecture for mobile devices (MoDs), which employs dense distributions of small computerized entities for providing fault-tolerant location-aware services. We do so by describing exemplary implementations based on radio frequency identification as an enabling technology. Firstly, we present prototypical implementations of the hardware abstraction layer and of selected core middleware services. The latter enable a MoD to store and retrieve data and position information in physical places in a fault-tolerant manner, and to identify places based on a location abstraction which is robust against failure of individual tags. Secondly, we investigate the feasibility of some higher-level services and applications by developing and evaluating prototypical systems for tracing and tracking, self-positioning, and collaborative map-making.

A new model for time-series forecasting using radial basis functions and exogenous data

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.

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A review of procedures to evolve quantum algorithms

There exist quantum algorithms that are more efficient than their classical counterparts; such algorithms were invented by Shor in 1994 and then Grover in 1996. A lack of invention since Grover’s algorithm has been commonly attributed to the non-intuitive nature of quantum algorithms to the classically trained person. Thus, the idea of using computers to automatically generate quantum algorithms based on an evolutionary model emerged. A limitation of this approach is that quantum computers do not yet exist and quantum simulation on a classical machine has an exponential order overhead. Nevertheless, early research into evolving quantum algorithms has shown promise. This paper provides an introduction into quantum and evolutionary algorithms for the computer scientist not familiar with these fields. The exciting field of using evolutionary algorithms to evolve quantum algorithms is then reviewed.

A Framework for Evaluating Privacy Preserving Data Mining Algorithms<Superscript>*</Superscript>

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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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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E-redesigning of society: towards experiential connectivity of generations in Lithuania

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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Probe Station Placement for Robust Monitoring of Networks

We address the problem of selecting probe station locations from where probes can be sent to monitor all the nodes in the network. Probe station placement involves instrumentation overhead. Hence, the number of probe stations should be minimal to reduce the deployment cost. Also, probe station placement should be such that the network can be monitored even in the presence of failures. We present algorithms to select locations of probe stations so that the entire network can be monitored for computing various performance metrics. We aim to find a minimal set of probe station nodes so as to minimize the instrumentation overhead. The algorithm presented provides robust monitoring in presence of node failures. We then present algorithms to make the solution resilient to probe station failures, and to deal with weakly connected nodes. We provide an experimental evaluation of the proposed algorithms through simulation results.

Adaptive channel allocation for large-scale streaming content delivery systems

Scheduling algorithms are used in content delivery systems to control the resource allocation rate. They not only improve system efficiency, but also increase user satisfaction. Lower renege rate and less waiting time for users are the main goals for a scheduling algorithm. Among existing algorithms, On-Demand strategy does not perform well, while rate control channel allocation policies performs much better. Pure-Rate-Control (PRC) and Multiple-Service-Class (MSC) belong to the rate control algorithms. MSC performs well, but a drawback is that it uses the Hot Index, which is hard to decide and has significant effects on the performance. In order to solve this problem and to improve the overall system performance, two new algorithms, Modified MSC(MMSC) and Adaptive Algorithm (AA), are proposed in this paper. Both of them solved the problem of MSC very well and improved the overall performance. For example, the renege rate of AA is about 5.4% less than that of MMSC, and about 9.8% less than that of MSC.

Transmission of layered video streaming via multi-path on ad hoc networks

Mobile ad hoc networks without centralized infrastructure change their topology rapidly because of node mobility, making multimedia applications difficult to run across wireless networks. Moreover, video transmission over ad hoc networks causes frequent transmission loss of video packets owing to end-to-end transmission with a number of wireless links, and requires essential bandwidth and restricted delay to provide quality-guaranteed display. This paper presents an architecture supporting transmission of multiple video streams in ad hoc networks by establishing multiple routing paths to provide extra video coding and transport schemes. This study also proposes an on-demand multicast routing protocol to transport layered video streams. The multicast routing protocol transmits layered video streaming based on a weight criterion, which is derived according to the number of receivers, delay and expiration time of a route. A simulation is performed herein to indicate the viability and performance of the proposed approach. The simulation results demonstrate that the proposed transport scheme is more effective than other video transport schemes with single or multiple paths.

Trace anomalies as precursors of field failures: an empirical study

Reproducing and learning from failures in deployed software is costly and difficult. Those activities can be facilitated, however, if the circumstances leading to a failure can be recognized and properly captured. To anticipate failures we propose to monitor system field behavior for simple trace instances that deviate from a baseline behavior experienced in-house. In this work, we empirically investigate the effectiveness of various simple anomaly detection schemes to identify the conditions that precede failures in deployed software. The results of our experiment provide a preliminary assessment of these schemes, and expose the tradeoffs between different anomaly detection algorithms applied to several types of observable attributes under varying levels of in-house testing.

Supporting ordering and consistency in a distributed Event Heap for Ubiquitous Computing

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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Connecting the family with awareness systems

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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The election problem in asynchronous distributed systems with bounded faulty processes

Determining the “weakest” failure detectors is a central topic in solving many agreement problems such as Consensus, Non-Blocking Atomic Commit and Election in asynchronous distributed systems. So far, this has been studied extensively for several such fundamental problems. It is stated that Perfect Failure Detector P is the weakest failure detector to solve the Election problem with any number of faulty processes. In this paper, we introduce Modal failure detector M and show that to solve Election, M is the weakest failure detector to solve election when the number of faulty processes is less than ⌈n/2⌉. We also show that it is strictly weaker than P.

Analytical modeling of interconnection networks in heterogeneous multi-cluster systems

The study of interconnection networks is important because the overall performance of a distributed system is often critically hinged on the effectiveness of its interconnection network. This paper addresses the problem of interconnection networks performance modeling of large-scale distributed systems with emphases on heterogeneous multi-cluster computing systems. We present an analytical model to predict message latency in multi-cluster systems in the presence of node, network and system organization heterogeneity. The model is validated through comprehensive simulation, which demonstrates that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.