A New Quantum Lower Bound Method, with Applications to Direct Product Theorems and Time-Space Tradeoffs We give a new version of the adversary method for proving lower bounds on quantum query algorithms. The new method is based on analyzing the eigenspace structure of the problem at hand. We use it to prove a new and optimal strong direct product theorem for 2-sided error quantum algorithms computing k independent instances of a symmetric Boolean function: if the algorithm uses significantly less than k times the number of queries needed for one instance of the function, then its success probability is exponentially small in k. We also use the polynomial method to prove a direct product theorem for 1-sided error algorithms for k threshold functions with a stronger bound on the success probability. Finally, we present a quantum algorithm for evaluating solutions to systems of linear inequalities, and use our direct product theorems to show that the time-space tradeoff of this algorithm is close to optimal.A. In this paper we described a new version of the adversary method for quantum query lower bounds, based on analyzing the eigenspace structure of the problem we want to lower bound. We proved two new quantum direct product theorems, the first using the new adversary method, the second using the polynomial method:. • For every symmetric function f , every 2-sided error quantum algorithm for f (k) using fewer than αkQ 2 (f ) queries has success probability at most 2 − (k) . • For every t-threshold function f , every 1-sided error quantum algorithm for f (k) using fewer than αkQ 2 (f ) queries has success probability at most 2 − (kt) .. Both results are tight up to constant factors. From these results we derived the following time-space tradeoffs for quantum algorithms that evaluate a system Ax ≥ b of N linear inequalities (where A is a fixed N × N matrix of nonnegative integers, x, b are variable, b i ≤ t for all i, and the algorithm has to determine the truth-value for each of the N inequalities):. • Every T -query, S-space 2-sided error quantum algorithm for evaluating Ax ≥ b satisfies T 2 S = (tN 3 ) if S ≤ N/t, and satisfies T S = (N 2 ) if S > N/t. We gave an algorithm matching these bounds up to polylogarithmic factors. • Every T -query, S-space 1-sided error quantum algorithm for evaluating Ax ≥ b satisfies T 2 S = (t 2 N 3 ) if t ≤ S ≤ N/t 2 , and satisfies T S = (N 2 ) if S > N/t 2 .. We do not have a matching algorithm in the first case and conjecture that this bound is not tight.

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On the potential advantages of exploiting behavioural information for contract-based service discovery and composition The importance of service contracts providing a suitably synthetic description of software services is widely accepted. While different types of information -ranging from extrafunctional properties to ontological annotations to behavioural descriptions -have been proposed to be included in service contracts, no widely accepted de facto standard has yet emerged for describing service contracts, except for signature information. The lack of a de facto standard is inhibiting large scale deployment of techniques and tools supporting enhanced discovery and composition of services. In this paper we discuss the potentially huge advantages of exploiting behavioural information for service discovery and composition, and relate them to the cost of generating such information and to the needed trade-off between expressiveness and cost and value of analysing such information. On such ground, we also discuss the potential suitability of some well-known modelling approaches to become the de facto standard to represent service behaviour in contracts, also in view of contextual factors (such as required know-how and current employment). Service-oriented computing [30] builds on the very notion of (software) service. Simply stated, a software service is a software component that can be discovered and used on the net, via public domain standards, by other software components. Business processes play a crucial role in the integration of services both within and across enterprise boundaries. The push towards business process automation -strongly motivated by the enormous opportunities in terms of cost and quality of service provision -calls for effective solutions to the problem of integrating different enterprise applications involved in such processes. Service-oriented computing is considered today to be one of the most promising approaches to address the automation of business processes and, more in general, the integration of software systems. Clearly, the possibility of easily integrating software services has opened a new horizon in software development, introducing at the same time new technological challenges which mainly originate from the difficulties of guaranteeing the correct interoperation of remote services developed and provided by different parties.. One of the crucial aspects of services in general is that consumers use services while having little or no knowledge of how those services are implemented. Car repairing or air transportation are just two examples of services that are used every day by customers with little or no knowledge of how those services are implemented. Indeed customers typically choose to use a service on the basis of the service contract which is (more or less explicitly) exposed by the service provider. Informally, a service contract should describe all the terms of usage of the service, including cost, warranties, possible compensations, as well as all the other aspects that could possibly be object of controversy between the customer and the provider. The role of service contracts is even more critical in the case of software services, where both service provision and usage are performed by automata. The need of including signature information in software service contracts to enable interoperability among services is universally accepted. WSDL (Web Service Description Language [46] ) is the de facto standard to define the (syntax of the) functionalities featured by a Web service. While many proposals have been put forward to include other types of information in service contracts -ranging from extra-functional properties to ontological annotations to behavioural descriptions, no widely accepted de facto standard has yet emerged for describing service contracts, beyond signature information.. In this paper we will discuss the potentially huge advantages of exploiting behavioural information for service discovery and composition, and relate them to the cost of generating such information and to the needed trade-off between expressiveness and cost and value of analysing such information.. It is worth saying that while we will also mention some of the issues related to including ontology annotations in service contracts, we will not touch other important aspects of service contracts, such as the many proposals for including QoS information and the definition of new types of Service Level Agreements (capable of suitably formalising different aspects of service provision, such as access cost, warranties, or compensations), nor the orthogonal issues related to contract validation and monitoring. We have started our discussion from the observation that the lack of behavioural information in service contracts inhibits the possibility of guaranteeing that service interactions will really work. This is due to the fact that the order in which the operations of services are to be invoked can lead to locks in the services' interaction (e.g., see [10, 8] ). According to one of the key principles of service orientation, providers publish only the contracts of their services, and hence if no behavioural information is included in service contracts, no a priori analysis can be performed to guarantee the correct interoperation of service compositions. We have then discussed "how much" behavioural information could be included in a service contract, in particular with reference to the need of a trade-off between expressiveness, and cost and value of analysing such information. In this perspective, we have then discussed the potential suitability for service contracts of three well-known approaches to modelling software behaviour: finite state machines, behavioural types, and workflows. We have also mentioned the importance of other factors that will contribute to determining whether or not behavioural information will be eventually included in service contracts, and which formalism will be chosen for that end. Those factors include the amount of know-how required by developers for handling behavioural information, the availability of engineered tools supporting the generation and the analysis of behavioural information, and of course the economical advantages for service providers of investing in the management of behavioural information.. At the end of Section 4, we have argued that the impressive spread of WSBPEL is suggesting the possible emergence of workflows as a de facto reference for describing service behaviour. It is worth stressing here that while such a possible trend could push the adoption of workflows to represent service behaviour in contracts, it would not per se require all analysis and verification tools to work necessarily on workflows. The plethora of techniques developed for Petri nets, behavioural types or finite state machines could of course be exploited to analyse abstractions of the workflows, obtained by means of suitable translators as sketched in Fig. 5 . However, as already discussed in Section 3, to avoid requiring developers to own specific know-how of those techniques, the whole conversion and analysis process should be made transparent to developers by suitable engineered tools. In the remainder of this section, we will try to highlight some of the potentially huge advantages of exploiting behavioural information in the development of software services. Consider the typical situation of a service developer wishing to implement a new service by suitably orchestrating two available types of services. As a first step, the developer needs to locate two actual services that provide the desired functionalities. To do this, the developer may query one of the service registries available on the net, as illustrated in Fig. 6 .. By issuing two queries (one for each type of service needed), the developer will fetch two sets of contracts of candidate services, that is, of services whose contracts match the specified requirements. At this point, the developer can analyse the contracts received in order to choose one service per set and start the design of the orchestrated service. As we already mentioned, without any information on the interaction behaviour of the chosen services, the developer cannot formally guarantee a priori that the designed orchestration will not lock. The common practice is to perform a testing phase on the implemented prototype, and to face during maintenance all problems that will possibly occur after deployment.. If instead contracts would include a formal description of service behaviour, a rigorous behavioural analysis could be performed before implementing and deploying the orchestration service. While the introduction of such an analysis phase would increase the labour needed to reach the implementation phase, such an additional cost would be largely compensated by a reduced need of testing and by a considerable reduction of maintenance costs, as illustrated in parts (a) and (b) of Fig. 7 . The reason is that, as we have discussed in Section 4, a rigorous behavioural analysis can permit to verify a priori safety properties of the composed services (such as lock-freedom), thus preventing -at design time -a considerable number of problems that would otherwise occur without such a verification phase. The potential advantages of introducing a behavioural analysis phase are even more evident if we consider the availability of engineered tools automating the required behavioural analysis. In such a scenario, the reduction of testing and maintenance costs would not be accompanied by additional labour cost for the (automated) behavioural analysis, as illustrated in part (c) of Fig. 7 . It is worth stressing that the hypothesised availability of engineered tools featuring behaviour analyses -considered in the last scenario -is not unrealistic. Indeed, the considerable body of basic research on the behaviour of software systems has set firm theoretical grounds that make it already possible the development of effective analysis tools, as we briefly discussed in Section 4.. To overcome the current limitations of currently available service registries, which do not take into account behavioural information, various behaviour-aware matchmaking prototypes for service discovery have been proposed over the last few years (e.g. [7, 4, 39, 18] ). A very interesting feature of some of these prototypes is the possibility of handling behavioural queries. Simply stated, clients of registries may specify in the query the desired behaviour of the services they are looking for. The possibility of issuing behavioural queries turns out to be extremely valuable for instance when in need of substituting (either for technical reasons or for policy reasons due to changes in business partnerships) one or more services that are part of an orchestration. Indeed, the possibility of issuing a behavioural query specifying the interactive behaviour of the service to be replaced, and of discovering this way a new service featuring such a behaviour may dramatically reduce the cost of service substitution. Some of the proposed behaviour-aware matchmaking prototypes feature non-trivial behavioural analyses (e.g., bisimulation equivalence over Petri nets [3, 12, 19] ) to implement strong matchings based on behavioural congruences, that are particularly valuable for modular service composition as they guarantee that the returned services can replace the old service without altering the behaviour of the overall orchestration. Also the notion of operating guideline of a service S, introduced in [20] to efficiently characterize the set of services that are behaviourally compatible with S (that is, whose composition with S is dead-lock free), can be directly applied to evaluate behavioural queries specifying the interactive behaviour of the service to be replaced as well as to verify service replaceability.. The main problem in implementing behaviour-aware analysis is, however, efficiency. Even considering the possibility of embedding behaviour-aware engines in UDDI registries -as in the approach taken by Srinivasan et al. in [35] to feature ontology-aware service matchmaking -the burden of performing non-trivial behavioural analyses spoils the scalability of service discovery with respect to the number of contracts stored in the registry. It is worth observing that no suitable indexing technique for behavioural descriptions has been proposed yet to face this scalability issue. While indexing is commonly employed to achieve scalable query-answering systems -Web search engines are the best known example -the definition of indexing over complex terms representing service behaviour is still an open research issue.. A suitable trade-off between accuracy and efficiency is hence needed in order to introduce behaviour-aware mechanisms in the service discovery process in a sustainable manner -that is, without degrading the latter to the point of spoiling its actual usability. In this perspective a possible approach to the problem may be to add on the matcher side suitable light-weight behaviour-aware mechanisms capable of efficiently performing simple checks on quite abstract behaviour (e.g., via finite state automata or even finite execution traces), and to delegate possible further, more sophisticated analyses to the client side. The potential value of such an early light-weight behavioural analysis can be measured in terms of the ratio between the introduced overhead and the percentage of contracts filtered out by such analysis (viz., (|A| − |B|)/|A| in Fig. 8) .. We have tried to highlight several important advantages of including behavioural information in service contracts, and we have tried to highlight some of the available results and some of the needed trade-offs that can make such an effort feasible. We insist however that several contextual factors (shared know-how, availability of engineered tools, investment/profit ratio) will play a crucial role in determining whether behavioural information will eventually enter as a first-class citizen in the service-oriented world.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Image Restoration for Quantifying TFT-LCD Defect Levels However, while these functions have been verified in the laboratory, further consideration is needed in order to apply them to real systems in the field.To begin with, we should correct the distortion occurring through the capturing of panels. Distortion can cause the defect level in the observed image to differ from that in the panel. There are several known methods to restore the observed image in general vision systems. However, TFT-LCD panel images have a unique background degradation composed of background non-uniformity and vignetting effect which cannot easily be restored through traditional methods. Therefore, in this paper we present a new method to correct background degradation of TFT-LCD panel images using principal component analysis (PCA). Experimental results show that our method properly restores the given observed images and the transformed shape of muras closely approaches the original undistorted shape. Since TFT-LCD has become a widely used technology, many manufacturers have shown significant interest in improving product quality. In addition to efforts to manufacture flawless panels, manufacturers have also concentrated on completely locating all muras in the panels already manufactured with the help of human inspectors. Recently, some algorithms which automatically detect muras have been introduced and applied [13] , [16] . However, these steps have not eliminated the need for human inspectors because this inspection technology lacks the objective criterion to correctly identify real defects to the degree possible by human inspectors.. Some trials to quantify the defect levels based on human perception have taken place [11] , [12] , [18] . Researchers conducted experiments measuring the degree to which humans can recognize given muras. These muras were ideally modeled defects synthesized with several features such as contrast, size, and edge sharpness. The mean visibility of the muras was then estimated. Next, the determinant line or surface to separate the real defects from the others in the feature space was identified, and index functions considering this information were generated. This paper is the ongoing work of quantifying line muras [18] and so will focus only on line muras. Even though the verification of the index function is thoroughly performed, the application of this function in practice faces a problem: automatic defect detection systems generally inspect the image obtained by taking a picture of an object, and during this process distortion can be introduced to the observed image. Distortion can transform the shape of defect as shown in Fig. 1 and yield an incorrect identification of the defect. Therefore, the removal of the distortion in the obtained image is necessary in order for the index function to be applicable in the field.. In this paper, we first summarize the process of quantifying TFT-LCD defect levels in Sect. 2. In Sects. 3 and 4, we describe the process of image restoration for TFT-LCD panel images, including several traditional methods appropriate for removing distortion which generally occurs in vision systems and a new method for correcting background degradation in panel images. Next, we show experimental results for the verification of image restoration in Sect. 5. Finally, the conclusions from this research are described in Sect. 6. In this paper, we described overall restoration methods applicable to TFT-LCD panel images and suggested a new method to estimate a reference image for correcting background degradation. With the support of industry, we constructed a system using the same hardware as used in the field and obtained 175 test TFT-LCD panel images from the system. Experimental results showed that our restoration method was able to properly restore the distorted images in the given test set. The feature values of distorted muras were restored to nearly those of their corresponding pseudo muras. In addition, the identification results differing from the pseudo muras due to distortion were corrected to match, except in some cases of severely distorted muras. We focused on only the pseudo line mura in this experiment, but our method can be applied to the other types of mura such as spot mura and area mura regardless of the shape of mura. In addition, it is also expected that our method can properly work for typical defects in the manufacturing process because the experiment was conducted using the same system currently in place in the field.

B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O B O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


The Adaptive Distributed Source Coding of Multi-View Images in Camera Sensor Networks We show that distributed source coding of multi-view images in camera sensor networks (CSNs) using adaptive modules can come close to the Slepian-Wolf bound. In a systematic scenario with limited node abilities, work by Slepian and Wolf suggest that it is possible to encode statistically dependent signals in a distributed manner to the same rate as with a system where the signals are jointly encoded. We considered three nodes (PN, CN and CNs), which are statistically depended. Different distributed architecture solutions are proposed based on a parent node and child node framework. A PN sends the whole image whereas a CNs/CN only partially, using an adaptive coding based on adaptive module-operation at a rate close to theoretical bound -H(CNs|PN)/H(CN|PN,CNs). CNs sends sub-sampled image and encodes the rest of image, however CN encodes all image. In other words, the proposed scheme allows independent encoding and jointly decoding of views. Experimental results show performance close to the information-theoretic limit. Furthermore, good performance of the proposed architecture with adaptive scheme shows significant improvement over previous work. key words: distributed source coding, camera sensor networks, multi-view images, Slepian-Wolf theory, parent node, child node Multi-view images of a scene can be used for several applications ranging from free viewpoint television (FTV) [1] to surveillance. In FTV, the user can freely control the viewpoint position of any dynamic real-world scene. This system can cover a limited space. To expand the coverage to a wider area, a distributed sensor network can be used. In a paradigm of Smart Dust, hundreds or thousands of sensor nodes of cubic millimeter dimension can be scattered about any desired environment. It is used for such tasks as surveillance, widespread environmental sampling, security, and health monitoring and it is known to make human life safer and easier [2] , [3] . CSN (Camera Sensor Network) [4] was introduced as an extension to the FTV system, and which has hundreds of cameras, distributed throughout the environment. It performs different tasks, such as data fusion for further processing, and arbitrary viewpoint generation. In this research, we are interested in data fusion application of CSNs. CSN presents a significant trade-off between powers consumed by processing versus communication. ComManuscript received January 24, 2005. Manuscript revised April 22, 2005 . Final manuscript received June 15, 2005 . † The author is with Information Technology Center, Nagoya University, Nagoya-shi, 464-8601 Japan.. † † The authors are with the Department of Electrical Engineering and Computer Science, Graduate School of Engineering, Nagoya University, Nagoya-shi, 464-8603 Japan.. a) E-mail: mehrdad@itc.nagoya-u.ac.jp DOI: 10.1093/ietfec/e88-a. 10 .2835 munication power costs can vastly exceed today's powerefficient processor demands. As a result, in general, developers then strive to process information locally to reduce the data transmitted. The distributed architecture was designed to help the sensor network capitalize on the collective behavior of these complex systems by increasing communication load only when doing so is optimal. Due to the enormous size of multi-view images in CSNs, coding is one of the challenges to build such applications. Multi-view images are usually highly correlated in spatial domain and therefore spatial redundancy can be removed by encoding the information "differentially" with respect to an appropriate "reference" [5] . The disadvantage of this method is an extra overhead in communication between those nodes. In a scenario with limited processing and communication abilities, this method is not preferable. Work by Slepian and Wolf [6] shows that even if the sources are encoded independently, they can be fully reconstructed under certain conditions. In other words, the Slepian-Wolf theorem suggests that it is possible to encode statistically dependent signals in a distributed manner to the same rate as with a system where the signals are jointly encoded. Therefore, distributed source coding is preferable if there is a major constraint on individual camera node performance (i.e., energy, which is consumed by sensing and communication operations). However, approaching the Slepian-Wolf bound is still a challenging issue.. Some works have been carried out [7] - [12] in designing a distributed source coding but the performance is not close to information theoretic bound. Aaron et al [13] proposed compression with side information using turbo codes. This method approaches the theoretic bound proposed by Slepian-Wolf for video sequences; however it resembles our method in a different way. In the following of the work by Aaron, Zhu et al. [12] proposed a distributed compression method for a large number of cameras using WynerZiv [14] , [15] coding, which is a lossy scheme and cannot be compare with the proposed method in this paper.. This paper introduces a lossless adaptive distributed source coding method [16] , [17] without inter-node communication for multi-view images in CSNs; similar to the distributed source coding method proposed in [18] - [20] based on module-operation and a parent node (PN) and child node (CN) framework. A parent node encodes the whole image whereas a child node only partially. The coding scheme allows independent encoding and jointly decoding of each view. To perform the decoding task, disparity esti-mation is employed to compensate the scene geometry [21] , [22] to provide the side information. Experimental results show performance close to the limit of information theory. Furthermore, the distributed source coding with adaptive scheme shows significant improvement over previous work [18] , [19] .. The remainder of the paper is organized as follows: Sect. 2 defines CSNs and its configuration. Section 3 describes the coding architectures. Coding method is explained in Sect. 4. Section 5 shows the experimental results. Finally, Sect. 6 and Sect. 7 are discussion and conclusions of this research. This paper introduced an encoder/joint decoder scheme based on adaptive module-operation for asymmetric distributed source coding of multi-view images in camera sensor networks considering the spatial frequency of the area where the encoding algorithm is applied. The CN/CNs is encoded without knowledge of other nodes. At the encoder the desired quality can be controlled by the "D" value. The output is the module image (i.e., coset image). The decoder performs an inverse module-operation by estimating the "D" value using the received encoded CN/CNs and the side information provided by geometry compensation. The adaptive distributed source coding can approach to the SlepianWolf bound by controlling the quality of encoding. Furthermore, its performance has a significant improvement in comparison with conventional coding scheme, with smaller average "D" value. The coding scheme can be applied to random arrangement and more than three nodes architecture, by having cameras parameters, and carefully deciding the block sizes and the "D" values at encoder. In addition, a suitable compression scheme is needed for coset images, which is considered as future research issues. The adaptive distributed source coding in CSNs can generally be evaluated in terms of quality and Slepian-Wolf bound performances. In this section, in addition to aforementioned terms, the proposed method is discussed accord-ing to its compatibility for a generalized coding architecture more than three nodes, random arrangement of cameras in CSNs, and further compression of coset images in the following.. • Generalization on more than three nodes:. The coding scheme can be applied to more than three nodes architecture, by carefully deciding the block sizes and the "D" values at encoder. The Larger block size and the smaller "D" values should be used if the number of nodes in cluster is increased, or the camera interval decreased.. • Random arrangement of cameras: The adaptive distributed source coding can be applied to the random arrangement of cameras. Not that, in such applications, cameras viewing range overlapped parts in a cluster should be found by using the camera parameters [18] .. • Compression of coset images: In addition, further compression scheme is needed for coset images, which is considered as future research issues. Normal JPEG compression of coset images is useful as it shown in [19] , [20] , however, we believe a better compression of coset images can be developed based on the coset image statistics.

O O B I I O B I O B I I O O O B I I O B I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I B I I B I B I B I B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O B I O O O O O B I I O O O O O O O O O O O B I O O B I O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O B I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


The power of amnesia: Learning probabilistic automata with variable memory length We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Suffix Automata (PSA). Though hardness results are known for learning distributions generated by general probabilistic automata, we prove that the algorithm we present can efficiently learn distributions generated by PSAs. In particular, we show that for any target PSA, the KL-divergence between the distribution generated by the target and the distribution generated by the hypothesis the learning algorithm outputs, can be made small with high confidence in polynomial time and sample complexity. The learning algorithm is motivated by applications in human-machine interaction, Here we present two applications of the algorithm. In the first one we apply the algorithm in order to construct a model of the English language, and use this model to correct corrupted text. In the second application we construct a simple stochastic model for E.coli DNA. Statistical modeling of complex sequences is a fundamental goal of machine learning due to its wide variety of natural applications. The most noticeable examples of such applications are statistical models in human communication such as natural language, handwriting and speech (Jelinek, 1985 , Nadas, 1984 , and statistical models of biological sequences such as DNA and proteins (Krogh, Mian & Haussler, 1993) .. These kinds of complex sequences clearly do not have any simple underlying statistical source since they are generated by natural sources. However, they typically exhibit the following statistical property, which we refer to as the short memory property. If we consider the (empirical) probability distribution on the next symbol given the preceding subsequence of some given length, then there exists a length L (the memory length) such that the conditional probability distribution does not change substantially if we condition it on preceding subsequences of length greater than L.. This observation lead Shannon, in his seminal paper (Shannon, 1951) , to suggest modeling such sequences by Markov chains of order L > 1, where the order is the memory length of the model. Alternatively, such sequences may be modeled by Hidden Markov Models (HMMs) which are more complex distribution generators and hence may capture additional properties of natural sequences. These statistical models define rich families of sequence distributions and moreover, they give efficient procedures both for generating sequences and for computing their probabilities. However, both models have severe drawbacks. The size of Markov chains grows exponentially with their order, and hence only very low order Markov chains can be considered in practical applications. Such low order Markov chains might be very poor approximators of the relevant sequences. In the case of HMMs, there are known hardness results concerning their learnability which we discuss in Section 1.1.. In this paper we propose a simple stochastic model and describe its learning algorithm. It has been observed that in many natural sequences, the memory length depends on the context and is notfixed. The model we suggest is hence a variant of order L Markov chains, in which the order, or equivalently, the memory, is variable. We describe this model using a subclass of Probabilistic Finite Automata (PFA), which we name Probabilistic Suffix Automata (PSA) .. Each state in a PSA is labeled by a string over an alphabet ~. The transition function between the states is defined based on these string labels, so that a walk on the underlying graph of the automaton, related to a given sequence, always ends in a state labeled by a suffix of the sequence. The lengths of the strings labeling the states are bounded by some upper bound L, but different states may be labeled by strings of different length, and are viewed as having varying memory length. When a PSA generates a sequence, the probability distribution on the next symbol generated is completely defined given the previously generated subsequence of length at most L. Hence, as mentioned above, the probability distributions these automata generate can be equivalently generated by Markov chains of order L, but the description using a PSA may be much more succinct. Since the size of order L markov chains is exponential in L, their estimation requires data length and time exponential in L.. In our learning model we assume that the learning algorithm is given a sample (consisting either of several sample sequences or of a single sample sequence) generated by an unknown target PSA M of some bounded size. The algorithm is required to output a hypothesis machine 21~/, which is not necessarily a PSA but which has the following properties. M can be used both to efficiently generate a distribution which is similar to the one generated by M, and given any sequence s, it can efficiently compute the probability assigned to s by this distribution.. Several measures of the quality of a hypothesis can be considered. Since we are mainly interested in models for statistical classification and pattern recognition, the most natural measure is the Kullback-Leibler (KL) divergence. Our results hold equally well for the variation (L1) distance and other norms, which are upper bounded by the KL-divergence. Since the KL-divergence between Markov sources grows linearly with the length of the sequence, the appropriate measure is the KL-divergence per symbol. Therefore, we define an e-good hypothesis to be an hypothesis which has at most e KL-divergence per symbol to the target source.. In particular, the hypothesis our algorithm outputs, belongs to a class of probabilistic machines named Probabilistic Suffix Trees (PST). The learning algorithm grows such a suffix tree starting from a single root node, and adaptively adds nodes (strings) for which there is strong evidence in the sample that they significantly affect the prediction properties of the tree.. We show that every distribution generated by a PSA can equivalently be generated by a PST which is not much larger. The converse is not true in general. We can however characterize the family of PSTs for which the converse claim holds, and in general, it is always the case that for every PST there exists a not much larger PFA that generates an equivalent distribution. There are some contexts in which PSAs are preferable, and some in which PSTs are preferable, and therefore we use both representation in the paper. For example, PSAs are more efficient generators of distributions, and since they are probabilistic automata, their well defined state space and transition function can be exploited by dynamic programming algorithms which are used for solving many practical problems. In addition, there is a natural notion of the stationary distribution on the states of a PSA which PSTs lack. On the other hand, PSTs sometimes have more succinct representations than the equivalent PSAs, and there is a natural notion of growing them.. Stated formally, our main theoretical result is the following. If both a bound L, on the memory length of the target PSA, and a bound n, on the number of states the target PSA has, are known, then for every given 0 < e < 1 and 0 < ~ < 1, our learning algorithm outputs an e-good hypothesis PST, with confidence I -5, in time polynomial in L, •, I~1, !, and ½. Furthermore, such a hypothesis can be obtained from a single sample sequence if the sequence length is also polynomial in a parameter related to the rate in which the target machine converges to its stationary distribution. Despite an intractability result concerning the learnability of distributions generated by Probabilistic Finite Automata (Kearns, et al., 1994) , that is described in Section 1.1, our restricted model can be learned in a PAC-like sense efficiently. This has not been shown so far for any of the more popular sequence modeling algorithms.. We present two applications of the learning algorithm. In the first application we apply the algorithm in order to construct a model of the English language, and use this model to correct corrupted text. In the second application we cons~uct a simple stochastic model for E.coli DNA. Combined with a learning algorithm for a different subclass of probabilistic automata (Ron, Singer & Tishby, 1995) , the algorithm presented here is part of a complete cursive handwriting recognition system . The most powerful (and perhaps most popular) model used in modeling natural sequences is the Hidden Markov Model (HMM). A detailed tutorial on the theory of HMMs as well as selected applications in speech recognition is given by Rabiner (Rabiner, 1989) . A commonly used procedure for learning an HMM from a given sample is a maximum likelihood parameter estimation procedure that is based on the Baum-Welch method (Baume, et al., 1970 , Baume, 1972 (which is a special case of the EM (Expectation-Maximization) algorithm (Dempster, Laired & Rubin, 1977) ). However, this algorithm is guaranteed to converge only to a local maximum, and thus we are not assured that the hypothesis it outputs can serve as a good approximation for the target distribution. One might hope that the problem can be overcome by improving the algorithm used or by finding a new approach. Unfortunately, there is strong evidence that the problem cannot be solved efficiently.. Abe and Warmuth (Abe & Warmuth, 1992 ) study the problem of training HMMs. The HMM training problem is the problem of approximating an arbitrary, unknown source distribution by distributions generated by HMMs. They prove that HMMs are not trainable in time polynomial in the alphabet size, unless RP --NP. Gillman and Sipser (Gillman & Sipser, 1994 ) study the problem of exactly inferring an (ergodic) HMM over a binary alphabet when the inference algorithm can query a probability oracle for the longterm probability of any binary string. They prove that inference is hard: any algorithm for inference must make exponentially many oracle calls. Their method is information theoretic and does not depend on separation assumptions for any complexity classes.. Natural simpler alternatives, which are often used as well, are order L Markov chains (Shannon, 1951 , Good, 1969 , also known as n-gram models. As noted earlier, the size of an order L Markov chain is exponential in L and hence, if we want to capture more than very short term memory dependencies in the sequences, of substantial length in the sequences, then these models are clearly not practical.. HSffgen (HSffgen, 1993 ) studies families of distributions related to the ones studied in this paper, but his algorithms depend exponentially and not polynomially on the order, or memory length, of the distributions. Freund et. al. (Freund, et al., 1993) point out that their result for learning typical deterministic finite automata from random walks without membership queries, can be extended to learning typical PFAs. Unfortunately, there is strong evidence indicating that the problem of learning general PFAs is hard. Kearns et. al. (Kearns, et al., 1994) show that PFAs are not efficiently learnable under the assumption that there is no efficient algorithm for learning noisy parity functions in the PAC model.. The machines used as our hypothesis representation, namely Probabilistic Suffix Trees (PSTs), were introduced (in a slightly different form) in (Rissanen, 1983) and have been used for other tasks such as universal data compression (Rissanen, 1983 , Rissanen, 1986 , Weinberger, Lempel & Ziv, 1982 , Willems, Shtarkov & Tjalkens, 1993 .. Perhaps the strongest among these results (which has been brought to our attention after the completion of this work) and which is most tightly related to our result is (Willems, Shtarkov & Tj alkens, 1993) . This paper describes an efficient sequential procedure for universal data compression for PSTs by using a larger model class. This algorithm can be viewed as a distribution learning algorithm but the hypothesis it produces is not a PST or a PSA and hence cannot be used for many applications. Willems et. aL show that their algorithm can be modified to give the minimum description length PST. However, in case the source generating the examples is a PST, they are able to show that this PST convergence only in the limit of infinite sequence length to that source.. Vitter and Krishnan (Vitter & Krishnan, 1991 , Krishnan & Vitter, 1993 ) adapt a version of the Ziv-Lempel data compression algorithm (Ziv & Lempel, 1978) to get a page prefetching algorithm, where the sequence of page accesses is assumed to be generated by a PFA. They show that the page fault rate of their algorithm converges to the page fault rate of the best algorithm that has full knowledge of the source. This is true for almost all page access sequences (in the limit of the sequence length). Laird and Saul (Laird & Saul, 1994) describe a prediction algorithm which is similar in spirit to our algorithm and is based on the Markov tree or Directed Acyclic Word Graph approach which is used for data compression (Blumer, 1990) . They do not analyze the correctnes of the algorithm formally, but present several applications of the algorithm.

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Overcoming the Lack of Screen Space on Mobile Computers Abstract: One difficulty for interface design on mobile computers is lack of screen space caused by their small size. This paper describes a small pilot study and two formal experiments that investigate the usability of sonically-enhanced buttons of different sizes. The underlying hypothesis being that presenting information about the buttons in sound would increase their usability and allow their size to be reduced. An experimental interface was created that ran on a 3Com Palm III mobile computer and used a simple calculator-style interface to enter data. The buttons of the calculator were changed in size between 464, 868 and 16616 pixels and used a range of different types of sound from basic to complex. Results showed that sounds significantly improved usability for both standard and small button sizes -more data could be entered with sonically-enhanced buttons and subjective workload reduced. More sophisticated sounds that presented more information about the state of the buttons were shown to be more effective than the standard Palm III sounds. The results showed that if sound was added to buttons then they could be reduced in size from 16616 to 868 pixels without much loss in quantitative performance. This reduction in size, however, caused a significant increase in subjective workload. Results also showed that when a mobile device was used in a more realistic situation (whilst walking outside) usability was significantly reduced (with increased workload and less data entered) than when used in a usability laboratory. These studies show that sound can be beneficial for usability and that care must be taken to do testing in realistic environments to get a good measure of mobile device usability. Mobile computing devices are becoming extremely popular. Mobile telephones, Personal Digital Assistants (PDAs) and hand-held computers are one of the fastest growth areas of computing. One problem with these devices is that there is a very limited amount of screen space on which to display information: the screen cannot physically be made bigger as the devices must be able to fit into the hand or pocket to be easily carried. Small screens can easily become cluttered with information and widgets (buttons, menus, windows, etc.), and this presents a difficult challenge for interface designers.. The graphical techniques for designing interfaces on desktop systems do not apply well to handheld devices. Screen resources are limited; often screens are black and white to reduce cost and power consumption. Memory and processing power are much reduced from desktop systems. However, in many cases widgets have been taken straight from standard desktop graphical interfaces (where screen space and other resources are not a problem) and applied directly to mobile devices. This has resulted in devices that are hard to use, with small text that is hard to read, cramped graphics and little contextual information. For example, early versions of Microsoft Windows CE used many features from Windows 95. As Zuberec [1] says, ''Windows CE designers have begun to realise that the device form factors and the tasks that the users need to accomplish should dictate the degree of the desktop behaviour retained''.. It is therefore clear that taking the desktop interface and implementing it on a mobile device does not work well; other methods must be investigated to make mobile interfaces more usable. One possibility to solve the problems with widgets could be to substitute non-speech sound for visual feedback so that smaller versions could be used. This would mean that the clutter on the display could be reduced and/or allow more information to be presented on the display. This must be done in a way that maintains usability otherwise these smaller widgets will render the device unusable. This paper presents initial work to investigate if sounds can be used in this way.. Another reason for using sound is that if users are performing tasks whilst walking or driving, they cannot devote all of their visual attention to the mobile device. Visual attentional resources must remain with the main task for safety. It is therefore hard to design a visual interface that can work under these circumstances. An alternative, sonically-enhanced interface would require less visual attention and therefore potentially interfere less in the main activity in which the user is engaged.. Many mobile computers can make simple sounds already -a selling feature of many mobile telephones is the range of ringing tones that they can make. Apart from the ringing tones, the audio capabilities are little used -many mobile telephones beep to confirm a button press and some PDAs do the same to confirm hitting buttons with a stylus. Sounds could be used for much more than this to improve the whole interaction with the device and the information it contains. This paper proposes some simple uses of sound that could significantly improve usability. The next step in this research is to look at presenting much more complex, dynamic information in sound. Many mobile devices display only static data -address books, calendars, etc. There is much potential for presenting more dynamic data using sound, for example stock market information or monitoring remote information and devices by telemetry [25] . Users would then be able to get at this from wherever they were.. Presenting dynamic information on devices with small displays is difficult. Techniques for large displays again do not transfer well to small ones. For example, on a large graphical display a strip-chart or line graph might be used so that users can see a change in one or more variables over time. This is very effective as the user can see general trends, compare one graph against another and also find current values. Utilising this type of presentation on a small display device is difficult, as the screen is too small to provide much history so that trends are hard to follow. If the user has the device to his/her ear, then no visual display is possible at all.. One way to present this type of information is using sound. Research has shown that presenting graphs in sound (for example mapping the Y axis value to pitch and X to time -this then allows you to hear the graph as a variation in pitch over time) for blind people allows them to extract much of the information needed from the graph [26] -maximum and minimum points, rates of change, etc. We are working on solutions based on non-speech sounds to present the trend information with speech to present current values when required. This will allow mobile devices to expand beyond their current limitations of static information to become devices that can present useful dynamic data.. An important future experiment would be to run a Fitt's law study of the effects of sound on target selection time. Sound is likely to change performance, for example the sound played when the stylus is on the button should aid targetting. The effects of sound could be quantified by such a study, as there is currently no information on this. The pilot study and experiments described here have looked in detail at the use of sound in data entry type applications on mobile computer interfaces with different button sizes. Lack of screen space is a major interface design problem for mobile computers and if sound can improve performance then it is one way of overcoming this. The results have shown that adding sound to buttons on a 3Com Palm III can, in general, significantly improve usability by decreasing workload and increasing the amount of data that can be entered.. The results showed that if sound is added to buttons on a Palm style interface then they can be reduced in size from 5 mm 2 to 2.5 mm 2 without much loss in quantitative performance when used for data entry tasks. This reduction in size, however, has a significant effect on the qualitative aspects of the interaction by increasing subjective workload. Going to buttons less than 2.5 mm 2 is likely to cause a fall in quantitative performance for data entry as well as a further significant increase in workload. As a general design guideline buttons should not be reduced to below 2.5 mm 2 and sound must be added to offset the size reduction.. If small buttons are to be used in the display of a mobile device then sounds can be included to raise levels of usability. If sounds are not used then performance will be significantly impacted. This is an important but simple design guideline that mobile device designers can use to improve usability and quality of their systems. The results of this pilot study generally indicate that enhancing the buttons on a handheld computer such as the Palm III with sound could significantly increase their usability. This was only a pilot study and its aim was to initially explore the possibilities of sound and therefore was not fully controlled, but some broad conclusions can be drawn. The sounds were, in general, useful with the enhanced sounds the same or better than the basic ones. In particular, the buttons with sound allowed more data to be entered than those without, and the small buttons with enhanced sound significantly more than the silent or basic sound versions.. The small buttons with enhanced sounds were not found to be as usable as the standard buttons without sound in the data entry task here. The problem was that the small buttons were just too small. The stylus tip almost completely obscured the buttons when pressing them and the buttons did not highlight as there were not enough pixels to put into reverse video. These buttons are perhaps too small to be used reliably in a data entry application such as the one used on here, and whatever was done to them would not make them effective. That the small buttons were very difficult to use is vividly demonstrated in the workload data in Figs 3 and 4 . The average overall workload scores of the three treatments in each condition (taking an average of the overall workload for each button size) were very significantly higher for the small buttons (small buttons workload = 12.05, standard buttons = 5.6, T 5 = 13.33, p = 0.00004). This suggested that buttons of this size are really not practical in the type of interface tested here. This would be even worse if participants were trying to operate the interface on the move and were therefore not able to hold the device steady. Button size is an important factor in the design of the display of a mobile device -if a designer makes them too small this could have a major impact on usability.. In some circumstances it may be important to get more information on screen, rather than lower the error rate, for example in a search task or when navigating through large number of menus. In this case a user might be prepared to accept less usable buttons because of the advantages of getting more of them on screen at one time. This pilot study gives some data on the costs of the trade-offs that can be made and their likely results on performance.. The results from the experiment show that sound can have important effects on usability in mobile computing devices. The results broadly confirmed those of the pilot study but now under more formal experimental conditions. The qualitative results showed that sound had a big effect on workload for both button sizes in the experimental task. In almost all of the categories workload was significantly reduced when sound was present. The results also showed that the sounds did not annoy the participants, in fact they rated the annoyance as significantly less when sounds were present. They also rated the sounds as preferable to the silent buttons. This indicated that sound can improve the qualitative experience users have with mobile devices.. With the small buttons no difference in subjective physical effort between the sound and no sound conditions was detected by the experiment (as in the pilot study). With the standard buttons sound reduced the physical effort required for the task. Any reduced effect with the small buttons may perhaps be explained by the fact that they were harder to press with the pen because they were small and addition of sound did not help. The effect of size outweighed the effect of sound. This would suggest that it would be very difficult to make these buttons easier to use as usability problems are dominated by size of target (as would be suggested by studies into Fitts Law, for example).. The quantitative results also back up the hypothesis that sounds improve usability as participants were able to enter significantly more codes when sounds were present for both button sizes in the experimental task. The sounds helped users target the buttons better; know when they had been pressed correctly and when they had been mis-hit. This made them significantly easier to operate. A difference between the number of codes entered with standard sized silent buttons and small sonically-enhanced ones was still found but it had been reduced to just 3 codes over the 14 minutes of the two trials. Depending on the situation in which a mobile device might be used, this difference could be acceptable given the amount of screen space that would be released for more information.. These results give interface designers a simple way to increase the usability of buttons (one of the most common widgets) of different sizes in mobile devices in the data entry type of task studied here. They also show that using small buttons can reduce the usability of an interface. Designers must take care over the buttons (and other targets) that users must select -if they have to be small then sounds could be used to raise usability levels.. The results for Experiment 2 are generally in-line with those of Experiment 1. Again sound had a positive impact on the quantitative and qualitative aspects of usability for both button sizes. The small buttons were again the hardest to use. In this experiment there was no measured difference in the number of codes entered between the standard buttons with no sound and the small buttons with sound (on average 28 codes were entered with the small buttons with sound and 32 with the large silent buttons). This suggests that the small buttons with sound may have reached a similar level of performance to the large silent buttons for data entry. A further study would be needed to investigate this in more detail but it begins to show that the use of sound can offset a reduction in widget size. However, the workload scores were still significantly higher for the small buttons, so this result must be used with care. It does indicate that sound can improve the performance of small buttons when on the move, but users have to invest more effort to use the small buttons at this higher level. Why did the participants not walk further when using the small buttons with sound? The number of laps walked was not a very sensitive measure (as mentioned above, there was only a difference of two laps between the best and worst condition) perhaps because the length of each lap was long compared to the time taken for the experimental tasks. It proved to be useful in the more real world effects it had on workload and performance but did not cause much differentiation between participants. Further investigation of this as a measure must be undertaken to understand it more. One other possibility is that the participants had to balance two taskswalking and data entry. They may have invested more effort in walking (and so walked at almost the same rate as with the standard buttons) and consequently less in the data entry (and so recorded lower scores than in Experiment 1). This is realistic as, if users were using their PDA's whilst crossing roads, avoiding cars, etc. in everyday life, they must, for safety, concentrate on the walking and not on their PDA. The results of the NASA TLX workload tests for the standard and small buttons can be seen in Fig. 10 . Paired T-tests were used for the analysis. There was again a significant difference between the workload scores for the small and standard buttons (see Table 3 ). The average overall workload for the standard buttons was 8.5 and for the small buttons was 11.9 (T 5 = 7.36, p = 0.0007).. Results for the numbers of codes typed with the different buttons sizes can be seen in Fig. 11 . There were significantly more codes entered with the sonically-enhanced buttons for both button sizes (standard buttons: T 15 ; = 4.94, p = 0.0002, small buttons: T 15 = 4.35, p = 0.0006). In this experiment no significant difference was detected between the small buttons with sound and the standard silent buttons (T 15 = 2.08, p = 0.055). On average 28 codes were entered with the small buttons with sound and 32 with the large silent buttons (see Table 4 ). There has been little previous research directly in the area of sound in mobile computing devices. Some of the research that has been done on different selection techniques using pen and a touch tablets applies to mobile interfaces. For example, Ren et al. [2] [3] [4] looked at six different selection strategies to see which was most effective (see Table 1 ).. In Ren et al.'s experiments, participants had to select individual targets that appeared in different locations on screen as fast as possible. Their results showed that the Land-on 2 strategy gave the best balance between speed of selection and error rate. This strategy has some problems in real situations as, if there were many targets close together, one might inadvertently select the wrong one by moving over it on the way to the target required.. Not all of these strategies are currently implemented on mobile devices (for example, the space strategies relied on an electromagnetic tablet which could sense the stylus when it was above the surface -no current mobile computers work in this way). Many mobile devices implement just the Land-on 1 or Take-off 1 strategies (in the work described below the Take-off 1 strategy was used as the 3Com Palm III only provides this technique).. Ren et al. did not consider the effect of sound on selection strategies. Sound can improve targeting and therefore could affect the usability of the different selection strategies. Sound could help with highlighting when the stylus is on or off a target or when nearing a target (such as in the Space strategies). The work below will investigate the effect of sound in the Take-off 1 strategy to see its usefulness.. One problem when selecting items with a stylus is that there is no tactile feedback. With keys on a normal keyboard you can feel that you have pressed them, with a stylus or finger on a touch screen it is hard to know if you have hit the target item or not. Much previous work has shown that touch screens can be made more usable (users type faster and more accurately) if sounds are added to indicate key clicks (Lewis et al. [5] review this area). This suggests that sounds could be used to improve the performance of the stylus driven interfaces used by many mobile computers. This work did not address all of the information that could be presented in sound about a button -only the confirmation of key clicks. The work presented here will build upon this but present more information in sound as to the state of the button and whether the press was successful or not.. Some work has gone into the investigation of the size of targets on touch screens. Hall et al. [6] showed that for seated users to reach greater than Take-off 1 Target is highlighted when stylus is touching it, selection is made when pen is taken off the target Take-off 2 Similar to Take-off 1, but the target is selected when the stylus is removed from any point on the screen (either inside or outside the target area) Space 1 Pen approaches from above, target highlights when stylus is within 1 cm above the target. Selection occurs as soon as the stylus lands on the target Space 2. Similar to Space 1, except that the selection is made when the pen lands anywhere on the screen (either inside or outside the target area). 99% target selection accuracy required targets of 26 mm 2 and for standing users 30 mm 2 . This size was heavily affected by the size of users fingers; using a stylus (which has a much smaller tip than a finger) to select targets allows much smaller sized targets. One interesting thing to note is that users needed larger targets when standing than when sitting -it was more difficult to hit small targets when standing. This was probably because it was hard for users to rest and steady their arms when standing, but this is actually more realistic in many respects (for example, when pressing on the screen of an information kiosk or on the display of a mobile device). The work presented below investigates much smaller buttons than those investigated by Hall et al. -the size of buttons on the real display of a current mobile device were used.

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B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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Applying the extended mass-constraint EM algorithm to image retrieval We extend the mass-constraint data clustering and vector quantization algorithm to estimate Gaussian Mixture Models (GMMs) as image features applying to the image retrieval problems. The GMM feature is an alternative method to histograms to represent data density distributions. Histograms are well known for their advantages including rotation invariance, low calculation load, and so on. The GMM maintains the rotation invariance properties; moreover, it addresses the high-dimensional problems due to which histograms usually suffer inefficiency problems. The extended mass-constraint (EMass) GMM estimation algorithm is compared with the typical Expectation-Maximization(EM) algorithm, and the deterministic annealing EM (DAEM) algorithm. The three algorithms are applied to train a GMM for a set of simulation data, and compared with the log-likelihood values. From the comparison results, we know that DAEM still has strong dependence on initial data point selection, which is the main problem we need to solve by taking advantage of the deterministic annealing methods. Thus the DAEM algorithm is not chosen to estimate GMM density functions for image retrieval. The EM and EMass algorithms are then applied to train GMMs from image RGB color features for the purpose of image retrieval. Finally the GMM features are combined with the Local Binary Pattern (LBP) features to achieve higher precision retrieval. After we compare the precision/recall curves and mean average precisions achieved by two algorithms, we conclude that the extended mass-constraint algorithm is a better solution for GMM estimation, and combining the GMM and Local Binary Pattern (LBP) provides a new promising feature for image retrieval. Image retrieval is important for a number of applications, such as publishing and advertising, multimedia database management systems and so on [1] . Image retrieval encompasses two basic steps: image indexing, and matching. We are usually given a query image and an image database with all target images. Indexing is to extract image features from each image, both query and target images. Low-level image features, such as color, texture, and shape features, are often used for retrieval matching purposes. Central to an image retrieval system is image matching, where the query image is matched to several target images in the image database. The matching criterion is based on a term: similarity, which evaluates how similar the retrieved target images and the query image are. Similarity between two images is calculated based on the extracted indexing features. Histograms [2] are one of the most popular features for images. The histogram provides advantages such as low computation load, and rotation invariance. Grey level histogram is a small size feature, and thus retrieval applications by grey level histograms are very fast. Color histogram, however, usually is in high dimension, and retrieval is comparatively much slower. The Gaussian Mixture Model (GMM) [3] [4] [5] [6] is introduced to approximate the histograms features, while the feature space is compact with components' weights, means and covariance matrix.. Using GMMs to represent features has been widely followed for different applications in many research areas. The Expectation-maximization (EM) algorithm [7] is the dominant algorithm for estimating GMMs. The EM algorithm iteratively updates GMM parameters by maintaining a non-decreasing likelihood function. The EM algorithm does not guarantee to obtain the global maximum, because usually the likelihood function is not strictly concave. Under such cases, the non-decreasing EM algorithm will result in either a local maximum or a global maximum depending on the initial values.. In order to estimate a better maximum, deterministic annealing algorithm is applied to estimate the GMMs from data. The deterministic annealing method was first introduced for data clustering and vector quantization [8] . The deterministic annealing EM (DAEM) algorithm has been proposed for GMM parameter estimation problems [9] . In this paper, we make a comparison between the EM and DAEM algorithms for data modeling. Based on the comparison results, we conclude that DAEM is not an appropriate solution for data modeling by GMMs. We extend the deterministic annealing clustering and vector quantization algorithm in [8] for GMM estimation. The extended algorithm is compared with the EM algorithm by likelihood, and both are applied to and tested for image retrieval.. This paper is organized as follows. We briefly review how to apply the EM and DAEM algorithms to Gaussian mixture model estimation in Section 2. The extended mass-constraint EM algorithm is introduced in Section 3. Experimental results of each algorithm are shown in Section 4. Conclusions and ideas for future work arising from the paper are addressed in Section 5. In this paper, we introduced an extended mass-constraint algorithm for training Gaussian mixture models from a set of data. The EMass algorithm has the promising property of avoiding local maxima problems, and achieves a higher log-likelihood than the EM algorithm. The experimental results show that the EMass algorithm also provides higher average precision when applied to image retrieval applications. Our future work will focus on providing detailed performance analysis on the relationship between log-likelihood and precision.. A tighter approximation for calculating the distance between two Gaussian Mixture models is also open for solution. This calculation can be accurately approximated by the Monte Carlo method [13] but with a heavy computational overhead. Both the effectiveness and the efficiency remain as important issues to be solved in the future work in this part.

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