Advanced techniques for the creation and propagation of modules in cartesian genetic programming The choice of an appropriate hardware representation model is key to successful evolution of digital circuits. One of the most popular models is cartesian genetic programming, which encodes an array of logic gates into a chromosome. While several smaller circuits have been successfully evolved on this model, it lacks scalability. A recent approach towards scalable hardware evolution is based on the automated creation of modules from primitive gates.In this paper, we present two novel approaches for module creation, an age-based and a cone-based technique. Further, we detail a cone-based crossover operator for use with cartesian genetic programming. We evaluate the different techniques and compare them with related work. The results show that age-based module creation is highly effective, while cone-based approaches are only beneficial for regularly structured, multiple output functions such as multipliers. Evolvable hardware [5, 9] combines evolutionary algorithms with reconfigurable hardware in order to construct smaller, more robust, or even self-adaptive and self-optimizing hardware systems. The common denominator of all evolvable hardware approaches is the application of evolutionary techniques directly at the hardware level. Here, hardware means both digital and analog electronic circuits, and the hardware level comprises all models of hardware, from configuration bitstreams for reprogrammable devices over netlists of gates to behavioral descriptions. Evolutionary techniques have been shown to be able to generate astonishing circuits that are totally different from classically engineered circuits, and sometimes even superior [24] . Moreover, for applications with time-varying specifications very promising initial results have been achieved that indicate the potential of evolutionary techniques to construct selfadapting systems. Examples include evolved controllers for prosthetic hands and robot navigation [8] .. The choice of a suitable hardware representation model, i.e., the encoding of a hardware circuit into a chromosome, is key to a successful application of evolutionary design techniques. On one hand, the representation determines the size and the structure of the search space which affects the efficiency of the evolutionary operators. Using a low-level representation, e.g., logic gates and wires, the chromosome length grows rapidly with the circuit size. This might render evolutionary techniques infeasible and is known as the scalability problem. On the other hand, high-level representations, e.g., behavioral descriptions, require substantially more effort to map an individual to the target hardware. The majority of related work in evolvable hardware focused on creating combinational Boolean functions. Typically, the chromosome encodes a netlist with a number of logic nodes and an interconnect. A rather popular representation model arranges the logic gates in an array which resembles the architecture of current programmable hardware devices, such as field-programmable gate arrays (FPGAs). As most authors implemented versions of genetic programming on this model, the approach has been termed cartesian genetic programming (CGP).. For larger and real-world applications, the CGP model in combination with fine-grained logic lacks scalability. The excessive chromosome lengths lead to extremely large search spaces. To tackle the problem of scalability, a number of approaches have been proposed which can be classified along three dimensions: The first dimension corresponds to the level of the hardware representation. To improve scalability we should give up too hardware-friendly structural models and move towards behavioral models. Orthogonally to that, we see the dimension of object granularity. We can evolve hardware using gates and wires, arithmetic functions and buses, and eventually complete intellectual property cores and networks on chip. The third dimension relates to incorporating knowledge into the evolutionary process. We can either evolve in a blind manner or try to leverage domain or problem-specific knowledge. An example for using problemspecific knowledge is demonstrated in [26] . There, the fact is exploited that multiplier circuits usually compute products in the first few logic levels and add partial sums in later levels. Consequently, the representation model was constrained such that and gates were preferred in the first columns of the logic array and adders in the remaining columns.. The main contribution of this paper is the introduction of novel techniques for identifying and dealing with modules, leveraging a previously discussed approach for module creation in the CGP model [28] . We present and evaluate two module creation techniques, called age-based and conebased module creation. Further, we develop and evaluate a novel cone-based crossover operator to be used with a genetic algorithm on the CGP model. In a sense, we address all three dimensions discussed above. First, we are giving up the structural, hardware-friendly aspects of the CGP model. Second, modules are building blocks of coarser granularity, albeit generated in an automated fashion. Finally, our conebased approach represents circuit design knowledge.. The paper is structured as follows: In Section 2 we introduce the cartesian genetic programming model as well as its extension to automated module creation and review related work on these models. Our novel methods for improving the automated module creation and propagation process are presented in Section 3. Section 4 describes the experimental setup to evaluate the proposed techniques and presents and discusses our empirical findings. Finally, we summarize and point to future work in Section 5. In this section we review related work on genetic programming models for digital circuit evolution. In particular, we discuss cartesian genetic programs for representing digital circuits and their extension to automated module creation. Further, we point to the fundamental trade-off between an efficient evolution and the effort required to map evolved circuits to real hardware.

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


Analysis and measurement of session setup delay and jitter in VoWLAN using composite metrics In this paper we look at the signaling for session mobility using the Session Initiation Protocol (SIP) and the Session Description Protocol (SDP). Bearing in mind that perceived voice quality is sensitive mostly to delay variation (also called jitter), we work on bounding that value for providing a better voice Quality of Service over Wireless LAN (WLAN). For mobility we use a hybrid SIP-MIP stack. The basic idea is to combine the strengths of MIP for doing fast handovers and the strengths of SIP for powerful session adaptation capabilities. Numerical calculation figures as well as real implementation results are provided. It can be observed that the session update cycle during mobility can be modified in such a way that session delay variation bounds achieved are quite low and there are almost no spikes. We use composite performance metrics that we defined in previous work to analyze the correlation between the data patterns for session signaling delay in the classical case and our proposed case to better highlight the achieved performance delta. We have shown that it is possible to use a different signaling mechanism in open access WLAN-based networks for a better performance. When replacing classical P2P negotiation via SIP messages with resource control at the access point, all the mobile stations needs to do is to re-compute their session resources after a handover and then launch an updated stream. This technique yields lower delay values and more strongly bounded jitter. The key issue here is fewer bytes and messages for session setup or update in the parameter injection scheme compared to negotiation. Jitter reduction from around 23 milliseconds on the average down to around 11.5 milliseconds yields a much better perceived audio quality for VoWLAN. Optimizing session setup delay cycle times and the variation of those cycle times (what we call session jitter) contributes to stabilizing voice-over-WLAN and makes it an attractive application and service for virtual operators and service providers. Furthermore, the metrics used in our previous publications allow us to graphically convey the system behavior and pattern in session delay distribution and bounds. The demonstration was performed for a pedestrian speed experiment, but the work can be extended to low and intermediate speed vehicular scenarios.. Future work includes activities such as using more metrics e.g. by Andersson et al, Brannstrom et al, and Åhlund et al [4] that variously combine jitter and delay in order to formally represent each signaling scheme and evaluate it. In addition, scalability tests for VoWLAN can be done to see how different signaling schemes perform with increasing load. A wide range of papers has been analyzed in parallel to conducting our work. In fact, we observed that as SIP is adopted in various access technologies, it faces a challenge in each access technology it is deployed in. VoIP over 3G is thoroughly discussed by Prasad et al in [6] . The authors analyze the different phases in setting up a SIP-based voice session and explore the different cases via numerical analysis in order to obtain an estimate on what it would cost in terms of time and resources to establish an appropriate VoWLAN or video-over-WLAN session in 3G networks.. The authors in [13] explore statistical multiplexing and try to suppress the large overhead in VoWLAN. They perform voice multiplexing using a polling mechanism in the contention-free period and a deterministic priority access for voice traffic in the contention period. They also work on reducing the overhead for voice traffic.. In [14] McGovern et al. address the problem of link adaptation using media codecs in WLAN. Different codecs are believed to result in different levels of congestion, and thus the system the authors propose switches back and forth between codecs to reduce the measured or perceived congestion level.. Bacciu et al. [12] presents a fuzzy approach to determine a soft admission control mechanism for Voice-over-IP services over Wireless LANs. A framework is defined where the provider may express the network status and the client their preferences by the means of an approach based on Fuzzy Set Theory.. Brännström el al. [4] proposes a mobility support system integrating the benefits of application-layer SIP mobility with network-layer MIP mobility where a cross-layer information system exchanges context for mobility adaptation.. Åhlund et al. [31] proposes a Running Variance Metric performance metric of wireless local area network used to predict relative traffic loads of available access points at the network layer.. The different approaches in this section either take a step in introducing combined metrics in mobility scenarios or try to solve the congestion and bad coupling of voice traffic and IEEE 802.11. We move on in the next sections to show how hybrid SIP-MIP mobility is used on a single stack with access point controllers brokering for airtime resources after every handover, and then measure the system performance. Assessing system performance in the case of SIP classical session negotiation over MIP, and then with parameter injection [3] using specialized limited signaling and more intensive computations, allows us to show a delta in performance. The key that enabled our quantitatively improved result is the architecture with proper placement of components, and specialized signaling that lead to lower session update delays and thus lower errors and shorter occupation of the airtime needed for VoWLAN session initiation or resumption.

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


On the length of integers in telescopers for proper hypergeometric terms We show that the number of digits in the integers of a creative telescoping relation of expected minimal order for a bivariate proper hypergeometric term has essentially cubic growth with the problem size. For telescopers of higher order but lower degree we obtain a quintic bound. Experiments suggest that these bounds are tight. As applications of our results, we give an improved bound on the maximal possible integer root of the leading coefficient of a telescoper, and the first discussion of the bit complexity of creative telescoping. Creative telescoping is a backbone of symbolic summation. It permits the construction of recurrence equations for definite sums. In its classical version, it is applied to sums whose summands are hypergeometric terms. This situation was intensively studied during the 1990s (see Petkovšek et al. [12] and the references given there for an overview on the classical results). While during the first decade of this century most research in the area focussed on generalizing creative telescoping to sums whose summands are more complicated (see, for instance, the survey articles of Koutschan [9] and Schneider [13] and the references given there), the hypergeometric case is recently getting back into the focus. There is now a general interest in getting a better understanding of the sizes of the output of summation algorithms, and of the amount of time spent on the computation. First complexity estimates for summation (and integration) algorithms were given by [14] and [5] . More recent works include the articles by [1] and [3, 4] . In the present paper, we continue these investigations. We work out bounds for the length of the integers that may appear in the output of creative telescoping algorithms, complementing earlier results given for the order and the degree of creative telescoping relations. As corollaries of our bounds, we obtain a new bound on the maximal integer root of the leading coefficient as well as a first bound on the bit complexity of creative telescoping.. Throughout this article, we consider a proper hypergeometric term. where p ∈ [n, k], M ∈ AE is fixed, x, y, a m , a To avoid discussion of degenerate cases, we assume throughout that h is not a rational function. The assumption that there are exactly M Gamma-terms for each of the four types is without loss of generality, because we can always add further terms Γ(0n + 0k + 1) without changing h.. A creative telescoping relation for h is a pair (L, C), where L = ℓ 0 + ℓ 1 S n + · · · + ℓ r S r n ∈ [n][S n ] \ {0} is a nonzero recurrence operator in n, free of k, and C ∈ É(n, k) a bivariate rational function in n and k (which may well be zero), with the property L(h) = (S k − 1)(Ch).. The symbols S n and S k refer to the usual shift operators n n+1, k k+1, respectively. The operator L is called a telescoper for h, and C is called a certificate for L and h. Note that with h non-rational, and C nonzero, Ch is also non-rational, in particular, not constant. Therefore, (S k − 1)(Ch) is nonzero. From the equality above, we thus have L(h) nonzero, or specifically, L nonzero. In short, when h is non-rational, we can be sure that every nontrivial pair (L, C) must have a nontrivial L.. If h has finite support, i.e., for every n ∈ AE there are only finitely many k with h(n, k) = 0, and if Ch is well-defined for all n, k, then a telescoper L annihilates the definite hypergeometric sum H(n) := k h(n, k). If not, a creative telescoping relation still gives rise to an inhomogeneous recurrence for finite definite sums such as n k=0 h(n, k) or 2n k=n h(n, k). See Petkovšek et al. [12] for details.. The classical Zeilberger algorithm [18, 19, 12 ] finds a creative telescoping relation for any given proper hypergeometric term h. This algorithm is based on Gosper's algorithm [6] for indefinite hypergeometric summation and delivers a creative telescoping relation (L, C) for which the order r of L is minimal. An alternative algorithm proposed by Apagodu and Zeilberger (2005) does not use Gosper's algorithm during the computation but only in its correctness proof. This algorithm also finds creative telescoping relations for proper hypergeometric terms, but unlike Zeilberger's original algorithm there is no guarantee that the telescoper has minimal possible order. The key observation behind the algorithm of Apagodu and Zeilberger is that L = ℓ 0 + ℓ 1 S n + · · · + ℓ r S r n ∈ É[n][S n ] is a telescoper for h if there exists some polynomial Y ∈ É[n, k] with the property. where. Here and below we write x m := x(x + 1) · · · (x + m − 1) and x m := x(x − 1) · · · (x − m + 1) to denote the rising and falling factorial, respectively. A certificate is then given by. These results are due to Apagodu and Zeilberger (2005) . For a justification of the formulas, see either their article, or, with the notation we are using here, the paper by [3] . The following definition contains certain quantities in terms of which bounds on the size of the telescoper of h can be formulated.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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


Unified approach to coefficient-based regularized regression ℓ 2 -empirical covering number Learning rates a b s t r a c tIn this paper, we consider the coefficient-based regularized least-squares regression problem with the l q -regularizer (1 ≤ q ≤ 2) and data dependent hypothesis spaces.Algorithms in data dependent hypothesis spaces perform well with the property of flexibility. We conduct a unified error analysis by a stepping stone technique. An empirical covering number technique is also employed in our study to improve sample error. Comparing with existing results, we make a few improvements: First, we obtain a significantly sharper learning rate that can be arbitrarily close to O(m −1 ) under reasonable conditions, which is regarded as the best learning rate in learning theory. Second, our results cover the case q = 1, which is novel. Finally, our results hold under very general conditions. In this paper, we study coefficient-based regularization learning algorithms in a least-squares regression setting. Let X be a compact metric space, Y = R and ρ be an unknown distribution on Z := X × Y . Regression algorithms aim at producing functions to approximate the regression function f ρ given by. where ρ(·|x) is the conditional distribution of ρ at x ∈ X .. We consider a learning algorithm generated by a coefficient-based regularization scheme in a reproducing kernel Hilbert space (RKHS) H K [1] associated with a Mercer kernel K , where K : X × X → R is a continuous, symmetric and positive semi-definite function. H K is the completion of the linear span of functions {K x = K (x, ·) : x ∈ X }, with the inner product for fundamental functions given by ⟨K x , K y ⟩ K = K (x, y). That is,. Moreover, following the reproducing property f (x) = ⟨f , K x ⟩ K , we know that for every f ∈ H K , ‖f ‖ ∞ ≤ κ‖f ‖ K where κ = sup x∈X √ K (x, x). Given a sample z := {(x i , y i )} m i=1 drawn independently according to ρ, we learn the regression function by. where.  , η = η(m) > 0 is a regularization parameter, 1 ≤ q ≤ 2 and. Throughout the paper, we assume that ρ(·|x) is supported on [−M, M], for some M > 0 and each x ∈ X . Under this assumption, it is natural to apply a projection operator, which was introduced into learning algorithms to improve learning rates in [2] [3] [4] .

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 B I 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 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 B I O O O O O


Model-based concerns mashups for mobile hypermedia Mobile (or Physical) Hypermedia combines the navigational style typical of Web applications with the functionality of location and context-aware software. Users explore digital and physical relationships while accessing to information about their actual location, e.g. the object in front of them. Similar to "conventional" Web applications one might suffer usability problems when dealing with multiple informational concerns, but the situation gets worse because of screen size issues, the need to avoid user distraction, etc. In this paper we outline our modelbased approach for building mobile hypermedia applications by combining ("mashing up") information corresponding to multiple concerns in a modular, usable way. Architectural issues are discussed and a simple example is presented together with its implementation. Physical Hypermedia (PH) [16] introduces the well-known and intuitive navigation-by-links style of Web software into mobile and ubiquitous systems. In a PH application, the mobile user access to information items both physically and digitally. When facing an application-aware real-world object or place, information about that object is presented in the user's device; this information may also encompass services corresponding to the actual place and links to other related objects (digital and physical). Both types of links connect corresponding nodes in the same way that a hypermedia network does. However two important differences can be noted:. • Some hypermedia nodes are "activated" by the user's presence. When the user stands in front of a recognized physical object, the digital information about it is displayed. • Navigating to a physical node is not an atomic operation. Different from the Web, where traversing a link implies opening the target node, a physical link implies traversing a physical area to reach the target. This creates new scenarios, where the user for example may not reach the target because he got lost or changed his mind while walking.. As an example, consider a user visiting a city with the aid of a digital tour guide: when he stands in front of a point of interest (e.g. the Cathedral) the page on his web browser is updated to show information related to it. This means that a digital navigation has been automatically performed by standing in front of a physical object that is known by the application 1 . While standing in front of the Cathedral, the user can navigate through a digital information space as in a "standard" hypermedia application. Besides, a PH application may also display anchors for physical links; when the user clicks on the anchor, he expresses his intention to walk to the link target, which is also a physical object. As a result he may get a map showing his current location, the target's location and a path connecting both places.. In Figure 1 we illustrate this example. In the upper part of the web page we show digital information and in the lower sector the "spatial" information. Figure 1 .a shows the information exhibited when the user is standing in front of the Cathedral. In Figure 1 .b the user navigates digitally, while still standing in the same place (notice that the spatial information did not change). Finally in Figure 1 .c the user decides to walk to Dardo Rocha Museum, which makes the system react by showing a map. In [3] the authors present a conceptual framework to generate context-triggered adaptation actions and a model-driven approach to create Web applications. This approach is based on WebML which enables the automatic generation of adaptive applications by means of a CASE tool. Our work is similar as it supports building mobile and context-aware hypermedia software; it supports also a clear separation of applicative concerns (besides the typical conceptual, navigational and presentation). In contrast we still do not have a full model-driven transformational approach to derive the final application.. In [9] the authors present "mashup personalization", which consists on applying mashup techniques to combine context information with an existing web application, to enhance the original application. The authors present a tool called MARGMASH to provide this mechanism which collaborates with Yahoo's pipes to perform the actual mashup. In [8] the authors present a component-based approach to build adaptive web applications. In particular the authors focus on adapting to context by using client-side context information (both from local and remote sensors) and context data found in the server. Each UI component is defined by an XML file (the UISDL descriptor), stating the properties of the component and the events it can trigger. The authors propose an event-based communication between components, managing their connection through listeners, which are configured in another XML document (the XPIL file). It is worth noting that the page processing is done in the client side, by parsing the XPIL file and instantiating the required components. Also in the mashup area, similar to our example application, the authors of [2] present a mashup platform called Telar. The authors implement the mashup client using the Google Web Toolkit, by retrieving the points of interest from the map and looking for them in their mashup up services. To manage different sources of information the mashup server wraps each data provider so that they all conform to the same protocol. Both works share with ours the idea of combining multiple sources of information and services to give the user a better experience. However, these approaches focus on architectural support for connecting already existing components; our project, is more focused on a design approach for conceiving new applications, even though some "legacy" or external ones might be also used. Additionally we stress in this paper the need to clearly separate information and services pertaining to different application concerns to improve usability. Finally, it should be noticed that a full comparison of the complexity of our approach in contrast with other developments is outside the scope of the paper. We plan to make such a comparison in a future publication.

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 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


Self-organised dynamic recognition states for chaotic neural networks Chaos offers several advantages to the Engineer over other non-chaotic dynamics. One is that chaotic systems are often significantly easier to control than other linear or non-linear systems, requiring only small, appropriately timed perturbations to constrain them within specific unstable periodic orbits (UPOs). Another is that chaotic attractors contain an infinite number of these UPOs. If individual UPOs can be made to represent specific internal states of a system, then a chaotic attractor can be turned into an infinite state machine. In this paper we investigate this possibility with respect to chaotic neural networks. We present a method by which a network can self-select UPOs in response to specific input values. These UPOs correspond to network recognition states for these input values. This research is inspired by the discovery that the firing behaviour in biological neural networks is fundamentally chaotic [3] [4] [5] [6] 10] . Although there is little doubt about the presence of chaos in such networks, there is far less Information Sciences 150 (2003) [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73] [74] [75] www.elsevier.com/locate/ins certainty about its role. Some have suggested that biological networks use this chaotic behaviour as a means of storing and retrieving memories [4, 9, [15] [16] [17] . These memories correspond to dynamic ''states'' which are embedded within the chaotic attractor followed by the network. The use of chaos to store and retrieve memories in an artificial neural network offers a number of significant advantages over alternative models. Among them are a dramatically increased capacity for memory storage, and an ability to ''control'' the network using only small perturbations [1] .. A chaotic system can be described by a set of dynamic variables. An n-dimensional plot of an n variable system is called a ''state space''. Each point in the state space corresponds to a unique set of values for the dynamic variables. As the system changes states a continuous trajectory is described through the state space. Chaotic systems that can be modelled by a set of continuous dynamic variables are constrained to a specific sub-area of state space called an ''attractor''. An example of an attractor is given in Fig. 1 . This attractor is followed by R€ o ossler system of equations [12] which is modelled by three dynamic variables.. By definition, a chaotic system can never be in the same state twice on a continuous run. In other words, a single trajectory in state space can never intersect itself. However, as the system follows its attractor it will come very close to points in state space which it had previously visited. Such trajectories are called unstable periodic orbits (UPOs). A chaotic system following a UPO would describe an orbit in state space which almost repeats itself. However, under the influence of chaos, the trajectory would drift away from this almost periodic orbit and continue to track the surface of the attractor. An example of a UPO is illustrated in Fig. 2 .. Techniques have been developed in non-linear dynamical systems theory for controlling a chaotic system so that it will remain on a periodic orbit until the control is removed [2, 5, 11, 13, 14] . These techniques can be divided into two categories: those that rely on some knowledge of the local dynamics of the attractor around the UPO to be stabilised, and those which use delayed feedback as a method of control. With the first category, the control waits for the system state to fall in the neighbourhood of the desired UPO. Once this has happened, the control makes small temporary adjustments to local dynamics of the attractor so that the system trajectory will come back to the same point when it returns to that neighbourhood, thereby stabilising the orbit. The second category of control method uses a feedback mechanism with a delay proportional to the period of the orbit to be stabilised. The control makes small temporary modifications to the dynamics of the system so that the trajectory will return close to a point in state space which it previously visited. This point is determined by the delay.. Within the context of chaotic neural networks, delayed feedback is the most appropriate method of control. There are three main reasons for this that are based on the biological plausibility of the method in question. The first is that the delay feedback method does not rely on a priori knowledge of the local dynamics of the attractor around the UPO to be stabilised. It seems extremely unlikely that biological neural networks are aware of the local dynamics in every UPO. The second is that the delayed feedback method does not specify which UPO is to be stabilised, it simply specifies the period of the required orbit. This suggests an element of self-organisation which is more biologically appealing than the first method. The third reason is that delays are inherent in biological neural networks: the nerve impulse takes time to travel the length of the axon to its target neurons, these neurons, in turn, take time to summate their inputs and produce their response. Consequently, we consider the delayed feedback method to be best suited to the control of chaos in neural networks.. Our approach has been to study small networks consisting of two or three interconnected neurons. We use systems of chaotic equations to model the activation functions of the neurons. Our method for the control of chaos is based on that presented by Kittel et al. [8] and Pyragas [11] . We have presented a first step towards developing a chaotic neural network which is able to select internal dynamic states in response to external input in a self-organised manner. Our results indicate that for the R€ o ossler system it is possible to devise a method of chaos control which self-selects values for s corresponding to the nearest UPO which can be stabilised with relatively small fluctuations in the feedback signal F ðt; sÞ. We have also demonstrated that a network can self-select a UPO in response to specific input values represented by frequency modulations in an external input signal.. The models we have presented here are small and limited. Yet we believe that the underlying principle that UPOs represent memory states and that adaptive feedback delay periods can be used to stabilise them is a very promising area of research in the context of chaotic neural networks. We are currently experimenting with other models which use adaptive feedback delay periods to select UPOs. These models focus on the adaptation of both s n and k n parameters in networks with n delays.

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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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


On classification with missing data using rough-neuro-fuzzy systems The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described. The classic fuzzy system depicted in (Lin and Lee, 1991; Lee and Kwang, 1994; Nauck et al., 1997) as well as later ones, e.g., (Nowicki, 2000; Czogała and Łęski, 2000; Rutkowska and Nowicki, 2000a) are designed to work with some strictly determined set of input values. In the case of classification tasks, the decision is made based on known values of classified object features represented by a vector v = [v 1 , v 2 , . . . , v n ] . The values of features are denoted as the vector v = [v 1 , v 2 , . . . , v n ]. The goal of classification is to determine whether or not the object or state x belongs to class ω j , j = 1, . . . , m. Thus the classifier makes the decision if object x belongs to class ω j (x ∈ ω j ) or not (x / ∈ ω j ). The assumption that the values of all n features are known is not always fulfilled. This occurs, e.g., in the case of medical or industrial diagnosis. In the first case, the lack of information is due to the impossibility of performing some tests with a patient in bad condition or when the test is unacceptable (due to an invasive method, cost or his/her faith). Moreover, some tests can be unnecessary when a classifier could make a certain decision without these test results. In industrial diagnosis, the classifier can work in on-line process monitoring and the process should still works even when some measurements are missing. Moreover, some values can be unnecessary when a classifier could make a certain decision without these values.. Thus, we consider a set of n selected features, denoted as Q, which we use to describe objects (note that the number of real object features is unlimited) and to design the classifiers. When the systems are working, we have the information about the values of n D ≤ n features. The set of features with known values is denoted by D, and the set of features with unknown values is denoted by G. Any information about features from outside the set Q has no significance-it cannot be used by the classifiers. Thus we can write down that D ⊆ Q and G = Q \ D. The number of unknown values is n G = n − n D . In this paper we consider such a case.. Generally, in the case of classification with incomplete data, three approaches are well known in the literature:. • imputation,. • marginalisation,. • rough sets.. In the first approach, unknown values are replaced by estimated ones (Chan et al., 1976; Dixon, 1979; Renz et al., 2002; Tanaka et al., 1996) . The estimated value can be determined by any method, e.g., as the mean of known values of the same feature in other instances, by R.K. Nowicki a neural network, or using the nearest neighbour algorithm (Morin and Raeside, 1981) . In the second approach, features with unknown values are ignored (Cooke et al., 2001; Little and Rubin, 2002) and the problem boils down to classification in a lower-dimensional feature space. Here we can use any classifier, such as neural network (Bishop, 1995; Żurada, 1992) , fuzzy systems (Lin and Lee, 1991; Wang, 1994; Zadeh, 1965; 1975) , a nearest neighbor algorithm, statistical methods (Bishop, 1995; Duda et al., 2001) , the genetic approach (Fogel, 1995; Goldberg, 1989; Michalewicz, 1992) , granular computing (Pedrycz and Bargiela, 2002; Yao and Yao, 2002) , and support vector machines (Kecman, 2001; Surges, 1998) . The third approach relies on rough set theory (Pawlak, 1982; , which is a concept of description of the uncertainty of classified object taking into consideration limited knowledge about the object.. Imputation and marginalisation always lead to an increase in the number of mistakes when the number of unknown values increases. When we use rough set theory, the object can be classified into the positive region of the class (i.e., the object certainly belongs to the class), to the negative region of the class (i.e., the object certainly does not belong to the class), or to the boundary region of the class (i.e., it is not possible to determine if the object belongs to the class or not). The membership to the regions depends on the quality of object description. If the description is good enough, the object belongs to the positive or the negative region. If the description is too weak, then the object belongs to the boundary region.. In this paper we consider a fuzzy classifier which, due to cooperation with rough set theory, will give an answer only if the object description is good enough. Otherwise, it refrains from giving an answer. The classifier will apply well-known fuzzy rules (see Section 2) and, when all n values are known, will work exactly as its fuzzy progenitor. Moreover, we focus only on the logical type of fuzzy reasoning (Driankov et al., 1993; Rutkowska and Nowicki, 2000a; Yager and Filev, 1994; Czogała and Łęski, 2000) . This type of fuzzy system is more appropriate for classification tasks than Mamdani type or TSK systems, which was shown in Rutkowska and Nowicki, 2000a) and confirmed later in (Rutkowski and Cpałka, 2003; 2005) using flexible neuro-fuzzy systems. The learning (rule developing) phase is out of the scope of this paper. We assume that the appropriate fuzzy classifier has been built using any method (Fogel, 1995; Goldberg, 1989; Lin and Lee, 1991; Wang, 1994; Zadeh, 1965; 1975) , and it performs satisfactorily when the values of all n input features (from the set Q) are available.. A similar approach based on Mamdani type reasoning and CA (center average) defuzzification as well as MICOG (modified indexed centre of gravity) defuzzifications (both Mamdani and logical type of reasoning) were studied in (Nowicki, 2008; 2009) , respectively.. The main contribution of this paper is the development of original architectures of rough-neuro-fuzzy classifiers based on logical reasoning and DCOG defuzzification. Experiments illustrate the performance of roughneuro-fuzzy classifiers working in the case of missing features.. In the paper we first study the neuro-fuzzy structures (NFSs) based on genuine (logical) inference (Section 2). Next (Section 3), the rough fuzzy set definition and some aspects of it are presented. Then (Section 4), logical neuro-fuzzy classifiers based on DCOG defuzzification are defined. Finally, logical neuro-fuzzy classifiers are converted into the logical rough-neuro-fuzzy classifier (RNFC), and the correctness of this conversion is proven.. We would like to emphasise here that rough-neurofuzzy classifiers proposed in this paper do not give better results compared with the corresponding neuro-fuzzy classifiers. When all features are available, the performance of both classifiers is the same. However, a great advantage of rough-neuro-fuzzy classifiers lies in unambiguously defined classification in the case of missing features (see Theorem 1 in Section 5 and experiments with discussions in Section 6). In this paper the modification of a neuro-fuzzy classifier has been proposed. Based on the rough fuzzy sets the scope of employing the classifier has been extended to the cases when some input information is missing. The demonstrate that the number of unclassified instances increases dramatically when the input information is incomplete. However, the number of mistakes does no grow. Competitive solutions, e.g., based on the k-nn algorithm, give usually much more correct classifications, but the number of mistakes also increases. The main benefit of the proposed modification is protection against mis- takes in the case of missing input data. The complexity of the proposed solution with S-implications, D-implications and R-implications is similar to that of other fuzzy and neuro-fuzzy systems, which has been broadly discussed, e.g., (Marin et al., 2008; Jin, 2000) . In the case of QLimplications, the complexity is 2 N times higher. The paper concludes a trilogy by the author (Nowicki, 2008; 2009) . It presents rough-neuro-fuzzy classifiers that work with missing features but having various properties and structures due to various methods of reasoning and defuzzification used. Users can choose the solution appropriate for his or her problem.

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


FUZZY CLASS LOGISTIC REGRESSION ANALYSIS Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm Wcis most used for estimating the parameters of logistic regression mixture models. In this paper, we propose a new type of fuzzy class model and then derive an algorithm for the parameter estimation of a fuzzy class logistic regression model. The effects of the explanatory variables on the response variables Eire described. The focus is on binary responses for the logistic regression mixture analysis with a fuzzy class model. An algorithm, called a fuzzy CIEISsification maximum likelihood (FGML), is then created. The mean squared error (MSE) based accuracy criterion for the FGML and EM algorithms to the parameter estimation of logistic regression mixture models are compared using the samples drawn from logistic regression mixtures of two classes. Numerical results show that the proposed FGML algorithm presents good eiccuracy and is recommended as a new tool for the parameter estimation of the logistic regression mixture models. The latent class model was first proposed in the 1950s by Lazarsfeld [15] and Anderson [2] . It has been widely used in many areas, for example, in psychology, sociology and economics (see Everitt [8] ). The latent class model has also been effectively used for the analysis of grouped categorical data. Distribution mixtures are used as models in statistical studies and also have applications in clustering (see Everitt and Hand [10] and McLachlan and Ba,sford [19] ). A mixture of multivariate multinomial distributions is used as a latent class model for categorical data.. Since Zadeh [27] proposed fuzzy set theory which produced the idea of partial membership described by a membership function, fuzziness has received more attention. The use of fuzzy sets can provide a method for extending a latent class model into a fuzzy class model. This allows embedding fuzzy classification methods into fuzzy class models for categorical or discrete data. Recently, Yang and Yu [25] proposed an approach to estimate the parameters in fuzzy class models using a fiizzy clustering algorithm for the analysis of grouped categorical data. They demonstrated that their algorithm for estimating the parameters of multivariate multinomial mixtures based on fuzzy class models is more accuracy and robustness than those based on latent class models.. In this paper, we will propose a fuzzy class logistic regression model and then derive an algorithm for this regression analysis to describe the effects of the explanatory variables on the response variables. This paper focuses on binary responses for logistic regression mixture analysis with latent and fuzzy class models. The remainder of the paper is organized as follows. Section 2 introduces the logistic regression model based on the logit transformation of a proportion which is the most important one of the generalized linear model. In Section 3, we review the latent class logistic regression model and also give its most used Expectation-Maximazation (EM) algorithm. In Section 4, we propose the fuzzy logistic regression model and then derive its corresponding algorithm called a fuzzy classification maximum hkelihood (FCML). Section 5 gives numerical comparisons between the EM and FCML algorithms. Conclusions are made in Section 6. In this paper, cluster analysis on latent class and fuzzy class logistic regression models for categorical response data was studied. It is known that the EM algorithm had been used for the latent class logistic regression model. In general, the latent class variables are considered as non-overlapping clustering and crisp class variables with a probability sense. However, some concepts in the social and behavioral sciences may not be well defined with vagueness and also with overlapping 778 M.S. Yang & H.-M. Chen clustering cases. To consider overlapping clustering with vagueness, we extended latent class variables to fuzzy class variables based on the fuzzy set concept in this paper. We then extended the log-hkelihood equation of EM for the latent class logistic regression model to the fuzzy log-likelihood equation for the fuzzy class logistic regression model. Based on the extended objective function, we created an FCML algorithm and then used the algorithm to the parameter estimation for a fuzzy class logistic regression model. According to numerical results, we found that the FCML algorithm can be well used as a tool for estimating the parameters of logistic regression mixture models. In van Rees et al. [21] , they used latent class models to analyze the relation between the response variable of reading behavior and explanatory variables of readers' background such as Age, Education, Gender, Activity and Time Costs. In marketing research, market segmentation has become a central concern of top management and strategic plarmers (see Datta [5] ). Cluster analysis has been considered in market segmentation (see Green and Krieger [11] , Hruschka and Natter [12] and Kuo et al. [14] ). To apply the proposed fuzzy class models and the FCML algorithm to market segmentation and on the analysis of the relation between the response variable and explanatory variables in logigstic regression mixture models will be our further research topic.

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


Language operations with regular expressions of polynomial size In the last 20 years, a large body of research on the descriptional complexity of finite automata has been developed. To the authors' knowledge, the first systematic attempt to start a parallel development for the descriptional complexity of regular expressions was presented by Ellul et al. [4] at the workshop "Descriptional Complexity of Formal Systems" (DCFS), in 2002. In particular, they raised the question of determining how basic language operations such as complementation and intersection affect the required regular expression size. For the intersection and shuffle operation, exponential lower bounds are known, and complementation can even incur a doubly-exponential blow-up [5, 6] . In [6] it was shown that the star height of a regular language is at most logarithmic in the minimum regular expression size, and lower bounds are proved by finding families of languages for which the respective language operations give rise to a dramatic increase in star height. In contrast, it is well known that taking language quotients does not increase the star height [3] . This and similar language operations appear to be a natural testing ground for deepening our understanding of the descriptional complexity of regular expressions: Either one has to find some new lower bound techniques, or one has to find a nontrivial implementation of these operations on regular expressions, or both-a straightforward procedure would be to convert the expression into a finite automaton, implement the operation on a finite automaton, and convert back to a regular expression using state elimination. Yet that last step can incur an exponential blow-up in general, even over binary alphabets [6] .

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 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 B I 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


The probability theories for IVFSs and IVIFSs Since the concept of IFS-probability was proposed, the probability theory for IF-events has been constructed. In this paper the concepts of IVFS-probability and IVIFS-probability are proposed for the first time. And the representation theorem for IVFS-probability is given. Furthermore, the relationships between IFS-probability, IVFS-probability and IVIFSprobability are discussed. Since the concept of fuzzy sets was introduced by Zadeh in 1965 [1] , the theories of fuzzy sets and fuzzy systems have been developed rapidly. In 1986, the notion of an intuitionistic fuzzy set was introduced by Atanassov [2] as a generalization of a fuzzy set.. An intuitionistic fuzzy set [2] in X , X ̸ = ∅, is an expression A given by A = {⟨x, (µ A (x), ν A (x))⟩|x ∈ X } with µ A (x), ν A (x) : X → [0, 1] satisfying the condition 0 ≤ µ A (x) + ν A (x) ≤ 1 for all x ∈ X .. The numbers µ A (x) and ν A (x) denote, respectively, the degree of membership and the degree of nonmembership of the element x in the set A. We will denote by IFSs the set of all the intuitionistic fuzzy sets in X . And for simplicity, we define A = (µ A , ν A ). The closed interval [f A (x), g A (x)] denotes the degree of membership of an element x in the set A. We will denote by IVFSs the set of all interval-valued fuzzy sets in X . And for simplicity, we define. Theorem 1 ([4] . (ii) The map g assigns to every IFSs B = {⟨x,. where. Then IFSs and IVFSs are equivalent, and are generalizations of the notion of FSs.. The notion of an interval-valued intuitionistic fuzzy set was introduced for the first time by Atanassov and Gargov [4] as a generalization of an intuitionistic fuzzy set.

O O O O 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 B O O O O O O O O B I O B 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 Common Randomness Capacity of a Pair of Independent Discrete Memoryless Channels Abstract-We study the following problem: two agents Alice and Bob are connected to each other by independent discrete memoryless channels. They wish to generate common randomness, i.e., agree on a common random variable, by communicating interactively over the two channels. Assuming that Alice and Bob are allowed access to independent external random sources at rates (in bits per step of communication) of H A and H B , respectively, we show that they can generate common randomness at a rate of maxfmin[H A + H (W j Q); I (P ; V )] + min[H B + H (V j P ); I (Q; W )]g bits per step, by exploiting the noise on the two channels. Here, V is the channel from Alice to Bob, and W is the channel from Bob to Alice. The maximum is over all probability distributions P and Q on the input alphabets of V and W , respectively. We also prove a strong converse which establishes the above rate as the highest attainable in this situation.Index Terms-Common randomness capacity, generating randomness from noise, interactive communication.

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 B I I I B I


Inferring applications at the network layer using collective traffic statistics In this paper, we propose a novel technique for inferring the distribution of application classes present in the aggregated traffic flows between endpoints, which exploits both the statistics of the traffic flows, and the spatial distribution of those flows across the network. Our method employs a two-step supervised model, where the bootstrapping step provides initial (inaccurate) inference on the traffic application classes, and the graph-based calibration step adjusts the initial inference through the collective spatial traffic distribution. In evaluations using real traffic flow measurements from a large ISP, we show how our method can accurately classify application types within aggregate traffic between endpoints, even without the knowledge of ports and other traffic features. While the bootstrap estimate classifies the aggregates with 80% accuracy, incorporating spatial distributions through calibration increases the accuracy to 92%, i.e., roughly halving the number of errors. Accurate application inference is a prerequisite for many network management tasks, such as QoS, accounting and anomaly detection, etc. Service providers commonly infer application classes by means of traffic flow measurements provided by routers. Using either rule-based or machine learning based approaches [1, 2] , application classes are commonly attributed to flow records on the basis of the reported TCP/UDP port numbers, sometimes in conjunction with traffic features such as total packets, bytes, duration, or combinations thereof. However, the performance of such approaches often degrades when these flow measurements are either inaccurate or incomplete. For example, well-known ports may be mis-used or abused by other applications (e.g., some peer-to-peer applications use TCP port 80 to circumvent firewalls). In some cases, UDP/TCP ports and some or all other transport header information may even be absent from flow records, either because they are offset within the packet by encapsulation protocols and thus not reported (for example with GRE), or because they are obscured by encryption (such as by IPSec).. Together, these factors motivate us to develop a worst-case ap- * The work is supported in part by the National Science Foundation grants CNS-0626808, CNS-0626812, CNS-0905037 and the DTRA grant HDTRA1-09-1-0050.. Copyright is held by the author/owner(s). SIGMETRICS'10, June 14-18, 2010, New York, New York, USA. ACM 978-1-4503-0038-4/10/06. proach to application identification that can function with only what we term the basic flow features that are reported in flow records: source and destination IP addresses, as well as aggregate flow statistics of number of packets, their total bytes, and flow duration. To achieve better accuracy in this scenario, in this paper, we propose a machine learning approach to classify application traffic based upon both the traffic features and the spatial features of the traffic disposition. Such idea is inspired by the observation that the statistics of spatial features of the network flows of a given application, as characterized e.g., by the distribution of the number of different endpoints exchanging traffic with a given endpoint, depend strongly on the application class [3, 4] . Datasets. The datasets used for our study are network flow records from a large ISP over the time period of a year. Flow measurements comprise summary statistics that aggregate information derived from a flow's packet headers (including the key, aggregate packet and byte counts for the flow, and timing information) that are exported as IP flow records to a collector. The flow records are collected by special purpose traffic measurement devices operating at two geographically dispersed sites of the ISP. The datasets contain flow records from approximately 40,000 ISP network endpoints gathered at two sites, representing several hundred Terabytes of network traffic. Serving as the ground truth for both training and testing purposes, the flow records in the dataset are annotated with 12 broad "application class" labels 1 , which are then used to define edge types 2 , i.e., the dominant application between two endpoints. In this paper, we proposed a two-step supervised model for solving the application inference problem. The bootstrapping step provides initial labels of the edges based purely on available traffic statistics and the graph-based calibration step utilizes inherent neighborhood properties of edges to correct the initial labels to achieve a much better accuracy. We evaluated our approach using flow records from a large ISP network. In the application scenario of classifying the port-obfuscated traffic, the calibration step consistently reduced the error rate from the bootstrapping step by 50%. We formally define the application inference problem using both the spatial disposition of traffic between endpoints and the traffic statistics as follows. Let xij := {x. ij } be a set of m attributes associated with each edge eij (between two endpoints i and j). We assume that each edge eij ∈ E belongs to one of K pre-defined types (i.e., application classes), C k , 1 ≤ k ≤ K (with K = 12). However, which class eij belongs to is unknown and to be determined. Let L : E → {C k , 1 ≤ k ≤ K} denote the edge type mapping, L(eij) = C k for some k. Then our goal is to infer this edge type mapping L, given the collection of the edge attribute sets, {xij : eij ∈ E}, and the spatial distribution of edges. To solve this problem, we assume a supervised ma-1 These 12 application classes are: Business, Chat, DNS, FTP, FileSharing, Games, Mail, Multimedia, NetNews, SecurityThreat, VoIP, Web. Details of these application classes and flow-level features are presented in [4] . 2 An edge is defined as all the traffic between two endpoints during the observation period. In this paper, we assume each edge corresponds to only one application classes, which covers more than 99.5% of the cases in our study. In this paper, we propose a streamlined variant of the iterative classical collective classification algorithm [5] , which we call the two-step model. The first step, referred to as bootstrapping, treats edges as i.i.d. and infers edge types according to only the traffic attributes xij associated with each edge, regardless of any spatial disposition information of the edges. The initial labeling from the bootstrapping step isL0(eij ) := f0(xij). Bootstrapping provides us with the initial labels for all edges, though the accuracy of these labels depend on the available traffic information in different application scenarios and hence can be inaccurate in certain situations.. The second step, referred to as graph-based calibration or calibration in short, incorporates the inherent neighborhood and local properties of the edges in TAG to calibrate (re-enforce or re-label) the initial edge labeling provided by the bootstrapping step. For example, given an edge labeled as Games and all the neighborhood edges labeled as Web, the calibration step may change the edge label to Web according to the clustering rule learned from the training data. In addition, other rules describing the attractive/repulsive relationship between heterogeneous application classes can also be employed by the calibration process to revise the initial labeling (details are discussed in [4] ). The calibration process is expressed asL(eij ) := f1. . Therefore, the edge type mapping from the proposed model is expressed as a combination of the bootstrapping step and the calibration step 3 .. Implementation of the two-step model. Both f0 and f1 are trained using Adaboost algorithm with decision stumps as the weak learners 4 . f0 employs the available flow features associated with the edges, depending on the specific application scenarios. Given the fact that an edge may have an unbounded number of neighborhood edges connected to the end nodes, we encode the neighborhood information as histograms, i.e., the percentage of edges connected to each end host that are labeled as C k , which are used as the features for f1. Two additional features used by f1 are the degrees of the two end hosts of each edge.

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 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 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 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 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 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 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


Design of Robust Transceiver for Precoded Uplink MU-MIMO Transmission in Limited Feedback System Abstract This paper proposes a robust transceiver design against the effect of channel state information (CSI) estimation error to optimize precoded uplink (UL) multi-user multipleinput multiple-output (MU-MIMO) transmission in limited feedback system under the consideration of the least-square technique on CSI estimation. To improve this limited feedback precoding, the constrained minimum variance (MV) approach with quadratic form to realize the computationally-efficient optimization problem, advantageously invoking the characteristics of the CSI estimation error, is proposed to suppress the effect of CSI estimation error, multiple user interference and noise. According to the Lagrange multiplier method on this MV approach, the deterministic function to resist uncertain CSI can be obtained to optimize design of the precoder and adaptive matrices jointly. With these optimum adaptive and precoder matrices, an optimum robust weighting matrix can be obtained to facilitate the user-wise detection in precoded UL MU-MIMO system. Performance analysis shows that the proposed robust weighting matrix is an unbiased design and it also can regularize the diagonal loading factor technique, and the detection performance of the proposed robust transceiver design can be predicted simplistically by applying our derived signal-to-interference-plusnoise ratio formulation. Computer simulations are conducted to confirm the efficacy of the proposed design in both perfect and imperfect CSI estimation. An uplink (UL) multi-user multiple-input multiple-output (MU-MIMO) system [1, 2] is an important wireless communication technique for the third generation partnership project (3GPP) long-term evolution (LTE) [3] [4] [5] , where multiple users (i.e., mobile stations) transmit data to a base station (BS). The MU-MIMO system with the concept of space-division multiple access (SDMA) allows multiple users to BS in the same band simultaneously [2] . In the precoded UL MU-MIMO system, Liu gives contributions in [6] that the singular value decomposition (SVD)-assisted detection structure [7, 8] mainly based on the assumption of perfect channel state information (CSI) estimation was developed to suppress the multiple user interference (MUI) caused by other users, where receiver transmits precoding matrices to users in the transmitted side via an error-free feedback link. However, precoding with the entire CSI feedback in [6] is impractical because the receiver sends all of the CSI to users via a limited feedback channel [9] . Additionally, this conventional detection structure [6] performs terribly due to the involvement of an inaccurate SVD [10] suffered from the effect of uncertain CSI and noise.. Recently, to avoid having to provide the entire CSI, the limited feedback precoding was developed [9] , in which the receiver determines the optimal precoder from pre-design codebooks and sends only the index of the selected precoder to users via the limited feedback channel. The feedback related to this index contains fewer bits because codebooks are stored in both the transmitter and receiver for the LTE system [5] . Additionally, robustness against uncertain CSI effect, attracted a lot of attention, was developed in [11] [12] [13] to improve the detection performance of the downlink transmission. For MU-MIMO precoding in the limited feedback precoding, the UL, robustness against the effect of the CSI estimation error, will be the major concerned scenario in this paper. On the other hand, the robust design in the limited feedback system has more complicated optimization problem due to the involvement of the non-linear characteristic in SVD [14, 15] . These motivate us to develop a computationallyefficient convex optimization problem to optimize jointly the design of the adaptive and precoder matrices for the robust transceiver design as follows.. For the precoded UL MU-MIMO transmission [6] , there is no cooperated among the UL users. By utilizing this no cooperation, we develop the user-wise (or called columnwise) detection that all columns (i.e., MUI) not in selected being detected user, constructed from partial columns of overall MU-MIMO channel, are nulled by column-wise weighting matrix iteratively. With the least-square (LS) estimation technique [16] , we can formulate CSI estimation error as a random variable, exploiting property of arbitrary covariance matrix, to resist uncertain CSI for the robust transceiver design. Based on this property, the minimum variance (MV) approach with quadratic form advantageously [17, 18] can realize a computationally-efficient optimization problem [19] to minimize the output power of joint MUI and noise subjecting to constraint while rejects the CSI estimation error. Applying the first-order approximation [19] , the non-linear characteristic of SVD can be tractable in proposed MV approach for the expectation computation to acquire a deterministic function of adaptive matrix when the Lagrange multiplier method [19] is considered. Thus, an optimum adaptive matrix [15] can be obtained by selecting an optimum precoder matrix from codebook according to the minimization optimization problem by exploiting this deterministic function. With the optimum adaptive and precoder matrices, an optimum robust column-wise weighting matrix can be obtained to facilitate the user-wise detection in precoded UL MU-MIMO transmission for limited feedback system. Performance analysis shows that the proposed robust weighting matrix is an unbiased design and it also can regularize the diagonal loading (DL) factor technique, and the detection performance of the proposed robust transceiver design can be predicted simplistically by applying our derived signal-to-interference-plusnoise ratio (SINR) formulation. Computer simulations are conducted to confirm the efficacy of the proposed scheme in both perfect and imperfect CSI estimation. This paper is organized as follows. In Sect. 2, system model and detection scheme is developed to improve UL MU-MIMO precoding in limited feedback system. In Sect. 3, performance analysis is investigated to validate the detection performance of the proposed robust design. In Sect. 4, we conduct computer simulations to confirm the effectiveness of the proposed robust design. In Sect. 5, we present our conclusions. Notation: Matrices and vectors are denoted by upper and lower case boldface letters, respectively. I M is an M × M identity matrix. For vector x, x i is its ith entry. Superscript (·) H , (·) −1 and (·) † represent Hermitian transpose. inverse and pesudoinverse operations, respectively. E{·} is an expectation operation, tr(·) is a trace operation and || · || F represents Frobenius norm. In this paper, we have presented a robust transceiver design with joint suppression of effect of MUI, noise and CSI estimation error in UL MU-MIMO precoding for limited feedback system under considering LS technique on CSI estimation. Since robust transceiver design is difficult to be tractable, the optimization problem with considering design of the precoder and adaptive matrices on the constrained MV approach subjects to constraint the rejection of CSI estimation error developed in the use-wise detection. To validate this user-wise detection for robustness specifically, the performance analysis of robust transceiver design induced by mean characteristic, regularization of the DL factor, SINR formulation and computational complexity has been investigated. In both perfect and imperfect CSI estimation, simulations verify that the proposed RMV-based transceiver design can achieve optimal detection performance due to optimizing design of precoder and adaptive matrices jointly. Therefore, the proposed RMV-based transmission design is a promising solution for practical UL MU-MIMO precoding in limited feedback wireless systems at the mobile station is of major concerns.. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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


Compressing Java class files Java class files are often distributed as jar files, which are collections of individually compressed class files (and possibility other files). Jar files are typically about I/2 the size of the original class files due to compression. This paper examines techniques for compressing (collections of) Java class files. Java class files are generated by Java compilers, are the standard distribution medium for Java programs and are the usual way of providing programs to a Java virtual machine. Java class files contain a substantial amount of symbolic information. In the javac benchmark from SPEC JVM98, only 21% of the uncompressed class file size is actually taken up by the method bytecodes. One purpose of this is to avoid the need to recompile all Java classes that use a class X whenever X is changed. So long as the functionality depended on doesn't change, previously compiled Java classes will work with the new version of X.. Few interesting Java applications are comprised of a single class. Many applications are composed of hundreds or even thousands of classes. Java class files can be collected in jar files, which are collections of compressed Java class files (and possibly other files, such as images). Jar files are used both on disk and for network transmission.. In many applications, Java programs are transmitted across the network. While ample bandwidth is available in some situations, there are many applications in which Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted wthout fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SIGPLAN '99 (PLDI) 5/99 Atlanta, GA, USA 0 1999 ACM l-581 13-093-X/99/0004...$5.00 there are slow modem or mobile communication links in the network. The jar format normally uses the gzip compression mechanism to compress the files in a jar file. This typically provides a factor of 2 compression over standard Java class files. However, the compressed jar files for substantial applications can still be quite large (50-200K is not unusual), and take several minutes to transmit over a slow communication link.. I use a number of approaches to creating smaller files that contain the same information as a jar file: l Developing a more efficient and compact organization of classfile information. l Taking steps to allow gzip to do a better job of compressing the information we use. l Sharing information across class files, to reduce transmission of redundant information.. Although this paper focuses solely on the problem on compressing Java class files, many of the techniques described would be generally useful for developing compact object serialization protocols.

O O B O O B 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 B O O O B O O O B O O O O O O O O O B 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 B O O O O O O O O O O O O O O O O O O O O B 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 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 B 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 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 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 B O O O O O O O O O O O O O O O O B 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 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 O O O O O O O O O O


GHIC: a hierarchical pattern-based clustering algorithm for grouping Web transactions Abstract-Grouping customer transactions into segments may help understand customers better. The marketing literature has concentrated on identifying important segmentation variables (e.g., customer loyalty) and on using cluster analysis and mixture models for segmentation. The data mining literature has provided various clustering algorithms for segmentation without focusing specifically on clustering customer transactions. Building on the notion that observable customer transactions are generated by latent behavioral traits, in this paper, we investigate using a pattern-based clustering approach to grouping customer transactions. We define an objective function that we maximize in order to achieve a good clustering of customer transactions and present an algorithm, GHIC, that groups customer transactions such that itemsets generated from each cluster, while similar to each other, are different from ones generated from others. We present experimental results from user-centric Web usage data that demonstrates that GHIC generates a highly effective clustering of transactions. As argued in this paper, the existence of natural categories of customer behavior is intuitive and these categories influence the transactions/data observed. We suggest that pattern-based clustering techniques, such as the one described in this paper, may be effective in learning such natural categories and can enable firms to understand their customers better and build more accurate customer models. In this paper, we presented a new approach, GHIC, for pattern-based clustering of Web transactions and demonstrated that the technique performs effectively, compared to some traditional techniques. A notable strength of our approach is the ability to explain the clusters and the differences between clusters and our experiments demonstrate this to be the case. There are several opportunities for future work based on these ideas. Additional difference/similarity metrics and global objective functions could be examined and there may be opportunities to develop better heuristics for such functions. Further, new representations for patterns could result in the development of further novel PBC techniques. The approach in [4] considers two objects to be similar if they exhibit a coherent pattern on a subset of dimensions. The definition of a pattern is based on the correlation between attributes of objects to be clustered, while this paper uses itemsets as the pattern representation. Hence, within the clustering methods dealing with categorical data those, using large items/itemsets and association rules are more closely related to this paper. In [1] , frequent itemsets are used to construct a weighted hypergraph and a hypergraph partitioning algorithm from [2] is used to partition the items. The result is a clustering of items (not transactions) that occur together. Suggesting that there should be many large items within a cluster and little overlapping of such items across clusters, [5] introduces a clustering criterion and notes that, for transactions that come naturally in the collection of items, it is more meaningful to use item/rule-based methods. A similar problem is addressed in [6] and our work is most similar to [5] and [6] in that we do not use any pairwise distance function. We define a global goal and use this goal to guide the clustering process. Compared to [5] and [6] , our method takes a new perspective of associating itemsets with behavior patterns and using that concept to guide the clustering process. Notably, unlike previous work, we allow a set of itemsets to describe a cluster instead of just a set of items.

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