Convenience Over Correctness

For decades, the RPC abstraction has been known to be fraught with serious problems related to partial failure, latency, and concurrency. Still, many developers continue to use RPC—some are even developing and open-sourcing new RPC systems—all in the name of convenience. Given all the known problems that plague RPC, and given better alternatives such as REST, should today's distributed systems developers really be choosing convenience over correctness?     

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.

---

Design and evaluation of systems to support interaction capture and retrieval

Although many recent systems have been built to support Information Capture and Retrieval (ICR), these have not generally been successful. This paper presents studies that evaluate two different hypotheses for this failure, firstly that systems fail to address user needs and secondly that they provide only rudimentary support for ICR. Having first presented a taxonomy of different systems built to support ICR, we then describe a study that attempts to identify user needs for ICR. On the basis of that study we carried out two user-oriented evaluations. In the first, we carried out a task-based evaluation of a state-of-the-art ICR system, finding that it failed to provide users with abstract ways to view meetings data, and did not present users with information categories that they considered to be important. In a second study, we introduce a new method for comparative evaluation of different techniques for accessing meetings data. The second study showed that simple interface techniques that extracted key information from meetings were effective in allowing users to extract gist from meetings data. We conclude with a discussion of outstanding issues and future directions for ICR research.     

MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones

Mobile sensing and mapping applications are becoming more prevalent because sensing hardware is becoming more portable and more affordable. However, most of the hardware uses small numbers of fixed sensors that report and share multiple sets of environmental data which raises privacy concerns. Instead, these systems can be decentralized and managed by individuals in their public and private spaces. This paper describes a robust system called MobGeoSens which enables individuals to monitor their local environment (e.g. pollution and temperature) and their private spaces (e.g. activities and health) by using mobile phones in their day to day life. The MobGeoSen is a combination of software components that facilitates the phone’s internal sensing devices (e.g. microphone and camera) and external wireless sensors (e.g. data loggers and GPS receivers) for data collection. It also adds a new dimension of spatial localization to the data collection process and provides the user with both textual and spatial cartographic displays. While collecting the data, individuals can interactively add annotations and photos which are automatically added and integrated in the visualization file/log. This makes it easy to visualize the data, photos and annotations on a spatial and temporal visualization tool. In addition, the paper will present ways in which mobile phones can be used as noise sensors using an on-device microphone. Finally, we present our experiences with school children using the above mentioned system to measure their exposure to environmental pollution.

Robust object matching for persistent tracking with heterogeneous features

This paper addresses the problem of matching vehicles across multiple sightings under variations in illumination and camera poses. Since multiple observations of a vehicle are separated in large temporal and/or spatial gaps, thus prohibiting the use of standard frame-to-frame data association, we employ features extracted over a sequence during one time interval as a vehicle fingerprint that is used to compute the likelihood that two or more sequence observations are from the same or different vehicles. Furthermore, since our domain is aerial video tracking, in order to deal with poor image quality and large resolution and quality variations, our approach employs robust alignment and match measures for different stages of vehicle matching. Most notably, we employ a heterogeneous collection of features such as lines, points, and regions in an integrated matching framework. Heterogeneous features are shown to be important. Line and point features provide accurate localization and are employed for robust alignment across disparate views. The challenges of change in pose, aspect, and appearances across two disparate observations are handled by combining a novel feature-based quasi-rigid alignment with flexible matching between two or more sequences. However, since lines and points are relatively sparse, they are not adequate to delineate the object and provide a comprehensive matching set that covers the complete object. Region features provide a high degree of coverage and are employed for continuous frames to provide a delineation of the vehicle region for subsequent generation of a match measure. Our approach reliably delineates objects by representing regions as robust blob features and matching multiple regions to multiple regions using Earth Mover's Distance (EMD). Extensive experimentation under a variety of real-world scenarios and over hundreds of thousands of Confirmatory Identification (CID) trails has demonstrated about 95 percent accuracy in vehicle reacquisition with both visible and Infrared (IR) imaging cameras.     

Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference

Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. We introduce a new intelligent signal processing method which addresses the difficulties. The method proposed uses conversion into a symbolic representation with a self-organizing map, and grammatical inference with recurrent neural networks. We apply the method to the prediction of daily foreign exchange rates, addressing difficulties with non-stationarity, overfitting, and unequal a priori class probabilities, and we find significant predictability in comprehensive experiments covering 5 different foreign exchange rates. The method correctly predicts the directionof change for the next day with an error rate of 47.1%. The error rate reduces to around 40% when rejecting examples where the system has low confidence in its prediction. We show that the symbolic representation aids the extraction of symbolic knowledge from the trained recurrent neural networks in the form of deterministic finite state automata. These automata explain the operation of the system and are often relatively simple. Automata rules related to well known behavior such as tr end following and mean reversal are extracted.     

Formal Analysis of Multiparty Contract Signing

We analyze the multiparty contract-signing protocols of Garay and MacKenzie (GM) and of Baum and Waidner (BW). We use a finite-state tool, Mocha, which allows specification of protocol properties in a branching-time temporal logic with game semantics. While our analysis does not reveal any errors in the BW protocol, in the GM protocol we discover serious problems with fairness for four signers and an oversight regarding abuse-freeness for three signers. We propose a complete revision of the GM subprotocols in order to restore fairness.     

Data structures for symbolic multi-valued model-checking

Multi-valued logics provide an interesting alternative to classical boolean logic for modeling and reasoning about systems. Such logics can be used for reasoning about partially-specified systems, effectively encode vacuity detection and query-checking problems, help in detecting inconsistencies, and many others.In our earlier work, we identified a useful family of multi-valued logics: those specified over finite distributive lattices where negation preserves involution, i.e., for every element a of the logic. Such structures are called quasi-boolean algebras, and model-checking over these not only extends the domain of applicability of automated reasoning to new problems, but can also speed up solutions to some classical verification problems.Symbolic model-checking over quasi-boolean algebras can be cast in terms of operations over multi-valued sets: sets whose membership functions are multi-valued. In this paper, we propose and empirically evaluate several choices for implementing multi-valued sets with decision diagrams. In particular, we describe two major approaches: (1) representing the multi-valued membership function canonically, using MDDs or ADDs; (2) representing multi-valued sets as a collection of classical sets, using a vector of either MBTDDs or BDDs. The naive implementation of (2) includes having a classical set for each value of the algebra. We exploit a result of lattice theory to reduce the number of such sets that need to be represented.The major contribution of this paper is the evaluation of the different implementations of multi-valued sets, done via a series of experiments and using several case studies.

Augmenting interaction and cognition using agent architectures and technology inspired by psychology and social worlds

Intelligent agents can play a pivotal role in providing both software systems and augmented interfaces, to individual users from all walks of life, to utilise the Internet 24 h a day, 7 days a week (24×7), including interaction with other users, over both wireless and broadband infrastructures. However, traditional approaches to user modelling are not adequate for this purpose, as they mainly account for a generic, approximate, idealised user. New user models are therefore required to be adaptable for each individual and flexible enough to represent the diversity of all users using information technology. Such models should be able to cover all aspects of an individual’s life—those aspects of most interest to the individual user themselves. This paper describes a novel intelligent agent architecture and methodology both called ShadowBoard, based on a complex user model drawn from analytical psychology. An equally novel software tool, called the DigitalFriend based on ShadowBoard, is also introduced. This paper illustrates how aspects of user cognition can be outsourced, using, for example, an internationalised book price quoting agent. The Locales Framework from Computer Supported Co-operative Work is then used to understand the problematic aspects of interaction involved in complex social spaces, identifying specific needs for technology intervention in such social spaces, and to understand how interactions amongst mobile users with different abilities might be technically assisted in such spaces. In this context, the single user-centred multi-agent technology demonstrated in the DigitalFriend is adapted to a multi-user system dubbed ShadowPlaces. The aim of ShadowPlaces is to outsource some of the interaction necessary, for a group of mobile individuals with different abilities to interact cooperatively and effectively in a social world, supported by wireless networks and backed by broadband Internet services. An overview of the user model, the architecture and methodology (ShadowBoard) and the resulting software tool (the DigitalFriend) is presented, and progress on ShadowPlaces—the multi-user version—is outlined.