An overview of rough set semantics for modal and quantifier logics In this paper, we would like to present some logics with semantics based on rough set theory and related notions. These logics are mainly divided into two classes. One is the class of modal logics and the other is that of quantifier logics. For the former, the approximation space is based on a set of possible worlds, whereas in the latter, we consider the set of variable assignments as the universe of approximation. In addition to surveying some well-known results about the links between logics and rough set notions, we also develop some new applied logics inspired by rough set theory. The rough set theory is invented by Pawlak 3, 4, 5 to account for the definability of a concept in terms of some elementary ones in an approximation space. It captures and formalizes the basic phenomenon of information granulation. The finer the granulation is, the more concepts are definable in it. For those concepts not definable in an approximation space, the lower and upper approximations for them can be defined. These approximations construct a representation of the given concept in the approximation space. Pawlak claims that knowledge is deep-seated in the classificatory abilities of human beings and other species, so rough set theory is a framework for discussions about knowledge, in particular when imprecise knowledge is of primary concern( 4 , p.2). Thus the theory is in particular effective in extracting knowledge from data tables and it has been successfully and widely applied to domains such as intelligent data analysis (data mining and knowledge discovery in database), decision making, machine learning, pattern recognition and conflict analysis 6, 7, 8, 9, 10 . An important common feature of these applications is the classification of objects and the representation of the classificatory knowledge in rough set notions. On the other hand, philosophers and logicians have spent centuries of time in developing formal tools for the representation and reasoning of knowledge. The results are various of classical and nonclassical logics 11 . In particular, first order logic 12 and modal logics 13 have been widely used as a formalism for knowledge representation in artificial intelligence and an analysis tool in computer science 14, 15 .. Since set theory and logic systems are strongly coupled in the development of modern logic, it is expected that the rough set-based knowledge representation framework and the logic-based one have close relationship. This expectation is verified almost from the very beginning stage of the invention of rough set theory. The most well-known relationship between rough set theory and logics is the connection of approximation space with possible world semantics for the modal epistemic logic S5. The connection is so obvious that it is difficult to point out who is the first one discovering it. However, undoubtedly, E. Or lowska explores the connection in full details and develop different knowledge representation logics in a sequence of papers 16, 17, 18, 19, 20, 21, 22 . There are of course other works along the same direction, for example, those reported in 23, 24, 25, 26 . The relationship between more general rough set model and modal logics have also been examined in 27, 28, 29 . As for the first order logic, different logical systems incorporating rough quantifiers and approximation operators have been developed 30, 31, 32, 33, 34, 35, 36, 37, 38 . The common semantic intuition behind these logics is to view the first order interpretation as an approximation space. These results all show the cross fertilization between rough set theory and logics, so it is worthwhile to investigate the further relationship between different generalizations of rough set models and logic systems. Based on this background, the purpose of this paper is twofold. The first is to survey and present the current results in a uniform way. The second is to fill some missing links between the existing general rough set models and logical formalisms and develop further some applied logics inspired by rough set theory.. In what follows, we will first review the rough set theory and some of its main generalizations. Then the paradigms of possible worlds as approximation space and variable assignments as approximation space are discussed respectively in the following two sections. Finally, some concluding remarks are given.. where [x] R denotes the equivalence class containing x. RX and RX are called the R-lower and R-upper approximation of X respectively. From a practical viewpoint, R can be considered as an indiscernibility relation, so for a given concept X, we can only know that X contains at least all elements in RX and does not contain any element outside RX. The pair (RX, RX) is called the rough approximation of X and any such pair is called a rough set. The pair (U, R) defined as above is thus called a Pawlak approximation space(PAS).. For a given approximation space (U, R), the equivalence classes of R and the empty set are called the elementary sets , and the union of some elementary sets is called a composed set . The composed sets are those definable in the space. Note that both lower and upper approximations of a concept are composed sets, so they can be seen as the definable approximations of the concept.. A direct generalization of the above-mentioned idea is to relax the constraints on R. To allow R to be an arbitrary binary relation, we can get different useful generalizations of the Pawlak rough set model. For example, the case where R is a tolerance (reflexive and symmetry) relation has been considered in 39 . To distinguish the Pawlak approximation space and the generalized one, we will refer the latter as relational approximation space(RAS). When (U, R) is an RAS, the lower and upper approximations of a set X are modified as. Even further generalization of RAS is possible. The most well-known one is the neighborhood systems proposed by Lin 40 . A neighborhood system(NS) is a pair (U, N ), where N : U → 2 2 U satisfies the following constraints:. The universe U is open if U ∈ N (x) for all x ∈ U , or equivalently, N (x) = ∅ for all x ∈ U . The lower and upper approximations of a set X in an NS (U, N ) is based on the definition of interior and closure in topology 41 .. Given an RAS (U, R), we can define an NS (U, N ) by. so the latter is indeed a generalization of the former.

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


On preserving privacy in content-oriented networks The recent literature has hailed the benefits of content-oriented network architectures. However, such designs pose a threat to privacy by revealing a user's content requests. In this paper, we study how to ameliorate privacy in such designs. We present an approach that does not require any special infrastructure or shared secrets between the publishers and consumers of content. In lieu of any informational asymmetry, the approach leverages computational asymmetry by forcing the adversary to perform sizable computations to reconstruct each request. This approach does not provide ideal privacy, but makes it hard for an adversary to effectively monitor the content requests of a large number of users. Content-oriented networking improves the availability of data (by integrating caching and replication into the network) and simplifies ensuring the integrity and provenance of content (by moving from securing the communication channel to securing the content). Indeed, security has been a primary driver in many cleanslate content-oriented architectural proposals [19, 20, 29] .. These benefits come with a cost. Because the basic functions of content-oriented networks operate at the content level, not the bit level, nodes in the infrastructure know the names of the content users request. An adversary that can compromise the security of this infrastructure (here we have in mind a nation-state or other Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without 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. formidable institution that can exert control over the infrastructure) can learn the names of the content a user requests. In contrast, in today's infrastructure, the exact nature of the data requested is only visible to the server providing that data, and the adversary would have to compromise all servers or break end-to-end cryptography, not just the network infrastructure, to learn the content being requested by a user. Thus, content-oriented networks may represent a fundamental shift in the degree of communication privacy.. Many countries already censor Internet usage, and giving them a network architecture that provides far greater access to user requests would further tighten their grip on information flow. In particular, had content-oriented networks been in use, blocking information during the uprisings in the Middle East would have been much an easier task for governments.. Our aim in this paper is tip the balance of privacy in contentoriented networks back toward the network user. We are not striving for ideal privacy (because we don't think it possible), just a significant improvement over what current content-oriented designs would give. Compared to traditional networks, content-oriented networks have the potential to expose users to a range of new privacy vulnerabilities without an obvious recourse. In this paper we introduced a technique by which users can protect their privacy despite a lack of specialized network mechanisms or shared secrets by leveraging computational asymmetry. Our analysis suggests this approach may have promise in an environment where substantial storage infrastructure is available, as we expect will be the case.. We only scratched the surface of the privacy implications of content-oriented networking, and we expect the area to require more efforts to raise the privacy bar to the level of today's Internet. Specifically, further study is needed in reader anonymity-the sort of privacy that systems like Tor provide. While there are clumsy ways of achieving such functionality in content-oriented networks even today, there is an urgent need for approaches that achieve the same degree of reader anonymity, and crucially, pseudonymity, without resorting to a host-oriented overlays. In this section we analyze two key aspects of our design. First, how hard is it for the adversary to perform a privacy-threatening attack at scale? That is, what is the cost to the adversary to decode chunks observed in the network in an attempt to match the chunks to its watchlist or do a content-analysis attack? Second, even if an adversary successfully mounts an online name-watchlist attack or does post-hoc analysis after a content-analysis attack, can the adversary non-repudiably link the deciphered content to a user? We offer preliminary answers to these two questions, and discuss the implications. User, storage, and query privacy research each has a long history, encompassing numerous distinct strands. There are many possible axes along which to categorize prior work but here we consider only prior work with a primary goal of a) privacy, b) censorship resistance, or c) plausible deniability for users. However, we note it is typical for these systems to have more than a single goal; many of them protect users against a broad set of threats with a panoply of mechanisms. We note the literature is replete with approaches for steganographic communication, though steganographic encodings are generally domain-specific, and none to our knowledge directly apply to content-oriented networks. Privacy. Chaum's mix-nets [9] forms the foundation for several systems providing user anonymity in public networks. Tor [13, 17] is a widely used instantiation of the technique, though many similar approaches provide specific benefits over it, including e.g., Tarzan [16] (peer-to-peer overlay nodes), Freedom [33] (cover traffic), Anonymizer [2] (centralized performance). While all these approaches provide privacy of the end user's identity, they are not designed for an environment in which content itself is central to the architecture and is the target of censors.. Private Information Retrieval (PIR) and Oblivious Transfer (OT) ensure privacy of the queries themselves. In OT schemes [5, 23, 24] a client can request a piece of data from a server while ensuring that neither the server learns what was requested nor the client learns anything about data that wasn't requested. PIR schemes [7, 10, 21, 27] relax this requirement by only requiring that the server not learn what the client requested. These mechanisms require specialized server-side support and do not fit with our scenario. Censorship Resistance. Eternity service [1] , Freehaven [12] , and Freenet [11] have sought to enable the persistent and anonymous publication of content via a global storage service that, through replication and oblivious storage mechanisms, make it difficult to censor. Tangler [32] and Dagster [28] take a complementary approach, aiming to "entangle" the on-disk representation of benign content with that of potentially-objectionable content, making it harder to selectively censor content [4] . Mnemosyne [18] aimed to provide an oblivious storage service via which users could store their content without revealing the content or even its presence. Plausible Deniability. To provide deniability in storage there are popular systems (e.g., TrueCrypt [30] ) using an approach known as deniable encryption [8] , which allows a single, opaque ciphertext to decrypt to multiple plaintexts (presumably one of which is benign). To provide lower-cost query anonymity, researchers developed a variant of PIR called Repudiable Information Retrieval, in which a server cannot learn definitively the content requested [3] . Off-therecord messaging provides deniability for instant messaging: users can communicate securely and authentically, but their communication is repudiable after the fact [6] .

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


On the stability and dissipation of wall boundary conditions for compressible flows Characteristic formulations for boundary conditions have demonstrated their effectiveness to handle inlets and outlets, especially to avoid acoustic wave reflections. At walls, however, most authors use simple Dirichlet or Neumann boundary conditions, where the normal velocity (or pressure gradient) is set to zero. This paper demonstrates that there are significant differences between characteristic and Dirichlet methods at a wall and that simulations are more stable when using walls modelled with a characteristic wave decomposition. The derivation of characteristic methods yields an additional boundary term in the continuity equation, which explains their increased stability. This term also allows to handle the two acoustic waves going towards and away from the wall in a consistent manner.Those observations are first confirmed by one-dimensional simulations and stability matrix analysis of acoustic modes in cavities. Finally, a two-dimensional test case shows the validity of the demonstration in multi-dimensional configurations. Many aspects of turbulent compressible flows can be simulated with the help of Large Eddy Simulation (LES), which solves the filtered unsteady Navier-Stokes (NS) equations [1, 2, 3, 4] .. The increasing available computational power allows to carry out LES in more and more complex flows and geometries [6, 7, 8, 9, 10] . One of the key issues to ensure the quality of the resolution is the accuracy of the numerical scheme used to discretize the convective terms [11, 12, 13, 14] . Families of discretizations -such as compact schemes [15, 16, 17] or finite differences with summation-by-parts (SBP) properties [18] on structured grids, or TaylorGalerkin (TG) schemes [12, 19] on unstructured meshes -have demonstrated their abilities to give satisfactory results. The treatment of the boundary conditions (BC) has also been a subject of intense research [20, 21, 22, 23, 24, 25] since it can dramatically deteriorate both the accuracy and robustness of the calculations. Non-reflecting BC based on characteristic treatment (wave analysis at boundaries) have been developed and are now commonly used to model inlets and outlets of the computational domain and their influence on acoustics is now well identified [26, 27, 28, 29] . Despite the proven effectiveness of characteristic formulations, walls are still often represented with Dirichlet BC, where the velocity is set to zero and density at the wall is obtained either using the continuity equation without characteristic treatment or specifying a zero pressure gradient. The behaviour of wall BC is a critical issue to study acoustic damping in cavities (such as combustion chambers) and controlling the dissipation and dispersion of acoustic waves at walls is required in many present research fields such as combustion instability studies [14, 30, 31] . This paper presents an analysis of wall BC using both Dirichlet and characteristic methods. Despite the apparent simplicity of specifying a wall BC, results show that characteristic methods differ from Dirichlet techniques at walls and provide more stable schemes. This is confirmed by a linear stability analysis incorporating wall BC. Throughout the article, wall means any rigid obstacle in the computational domain (on the sides or inside the domain).. The formalism used for characteristic BC is first recalled (section 2) and compared to Dirichlet BC in the case of a solid wall (section 3). Characteristic formulations lead to the introduction of an additional BC term (called the ABC term), supposed to be zero in the continuous problem but not in its discretized counterpart. This term increases the stability of the scheme, while not degrading its accuracy. A simple test case (one-dimensional acoustic cavity) is then used to study the influence of the additional term (section 4). Simulations and matrix analyses confirm the theoretical observations, showing the importance of considering characteristic treatment for the hyperbolic part of wall BC (section 5). Finally, both wall BC are used to simulate a longitudinal / transversal 2D acoustic mode in an infinite duct to demonstrate the conclusions are also valid in multidimensional applications (section 6). Building boundary conditions at walls in compressible simulation is a difficult topic if one wants to obtain both robustness and limited dissipation. This paper provides a comparison between two methods to treat solid walls in compressible flow simulations. The first is based on Dirichlet (or Neumann) formulation, which consists in imposing a zero normal velocity (or a zero pressure gradient) at the wall, while the second is based on characteristic wave decomposition (NSCBC method). Even though they are supposed to be physically equivalent, they do not solve the same discretized equations. Theoretical analysis gives rise to additional pressure terms in the continuity equation in the case of characteristic BC, called here ABC terms. After discretization, it is shown that ABC terms have a stabilizing effect, which is a very desirable property in non-linear simulations. Numerical calculations and amplification matrix eigenvalues confirm the increased robustness of NSCBC formulation.. While it does not seem to be a serious issue, the stabilizing effect can be too strong when dealing with higher-order methods because of the dissipation induced by ABC terms. In such a case, higher-order approximations have to be considered, which is not a problem when the mesh is structured but can be a harder task for unstructured meshes.. In terms of applications, these results show that Dirichlet methods at walls should not be used in configurations where the acoustic damping of cavities is computed. These methods will lead to wrong damping coefficients and sometimes to numerical instabilities. Using characteristic formulation such as NSCBC leads to stable schemes but also to a dissipation level which is controlled by the order of accuracy used for wall derivatives much more than by the precision of the numerical scheme within the domain. This means that wall treatments will be very important in such compressible flows. In this section, the influence of the ABC terms is studied using two methods: (1) ! "! #! $! %! &!! '()*+,- ." .& ! & " ')(--/)( ! "! #! $! %! &!! '()*+,- ." .& ! & " ')(--/)(! "!! #!! $!! %!! &!! '!! (!! )*+,-./ 0"!! 0&! ! &! "!! )+*//1+* ! " #! #" $! %&'()*+ ,# ,!-" ! !-" # #-" %'&++.'& /01&234 560782+)9.8():

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 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 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 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


Cross-Sector Competition in Telecommunications Cross-sector competition in the information and communications technology sectors (ICT sectors) constitutes a key strategic challenge for telecommunications companies. Due to increasing convergence, value creation is resulting in a greater degree of interaction. The diversification potential of telecommunications businesses is therefore changing with respect to associated ICT sectors, such as hardware, software and media. This article analyses cross-sector competition in the telecommunications industry on the basis of the diversification activities of ICT companies. A concentration of competitive interdependence in the ICT sectors is demonstrated using a cluster analysis of 34,142 companies. The cross-sector activities of telecommunications companies are investigated using contingency and dependency analyses, and the diversification-related competition in the telecommunications sector is also analysed. With regard to the telecommunications sector, particularly high level cross-sector competition with the media industry is identified, as well as strong diversification activities in the software sector. The results are used to derive the potentials and risks that have a significant bearing on the structure of the cross-sector competitive environment of telecommunications companies. The competitive environment of a company is significantly shaped by the sector to which it belongs (Porter (1980, pg.5) , that is, by the group of companies that produce closely-related substitutes. The commercial environment of telecommunications companies (TCCs) is also affected considerably by cross-sector competitive and cooperative relationships in the formerly mostly independent software, hardware, media and telecommunications sectors. These so-called convergence phenomena (Stieglitz 2003, pg. 25; Katz 1996 Katz , pg. 1079 Katz -1095 continue to represent a major strategic challenge for the TCCs. Internet telephony is an obvious, contemporary example. Whilst in the past speech services were predominantly offered by TCCs on the basis of circuit switching technology, software and internet providers are now in a position to address this market. The result of this convergence is a greater degree of interaction between the companies of the relevant sectors with regard to the adding of value (Zerdick et al. 2000, pg. 130-135) . The diversification activities of a company determine the sectors in which it is competitively active and also indicate whether competitive advantages can be generated by integrating different product-specific, value-creating processes (Ansoff 1966, pg. 149-135; Porter 1985, pg. 317-363) .. Articles published on cross-sector competition in the information and communications technology sectors (ICT sectors) predominantly use qualitative and argument-based deductive methodologies to demonstrate the consequences of increasing integration in the value creation activities of ICT companies and to determine the strategic implications for companies. As suggested in articles by Mayring (2001) and by Srnka and Koeszegi (2007) , we make use of quantitative analysis procedures to generalise the already available qualitative results and broaden the investigation (generalisation model). By means of an examination of the hypotheses used (and to an extent contradictorily discussed) by the quoted authors, our work tests the available qualitative descriptions of cross-sector competition in the telecommunications industry. Our findings show that the results of qualitative studies can only be partially generalised and lead to the company strategic implications outlined in Section 4.. 2 Cross-sector competition in the telecommunications industry

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


Approximation Algorithms for the Euclidean Traveling Salesman Problem with Discrete and Continuous Neighborhoods In the Euclidean traveling salesman problem with discrete neighborhoods, we are given a set of points P in the plane and a set of n connected regions (neighborhoods), each containing at least one point of P . We seek to find a tour of minimum length which visits at least one point in each region. We give (i) an O(α)-approximation algorithm for the case when the regions are disjoint and α-fat, with possibly varying size; (ii) an O(α 3 )-approximation algorithm for intersecting α-fat regions with comparable diameters. These results also apply to the case with continuous neighborhoods, where the sought TSP tour can hit each region at any point. We also give (iii) a simple O(log n)-approximation algorithm for continuous non-fat neighborhoods. The most distinguishing features of these algorithms are their simplicity and low running-time complexities. In the TSP with neighborhoods problem we are given n connected subsets (regions or neighborhoods) of the Euclidean plane, and a set of specified points P. The objective is to find a tour of minimum length which visits each region in one of the specified points 1 . This generalizes the classical Euclidean TSP with applications in VLSI-design, and other routing-related applications (see e.g. [14, 16] ). The problem is also a special case of the more general Euclidean Group-TSP problem, in which the specified regions are not necessarily connected, and hence can be arbitrary subsets of points in the plane. In this paper, we will impose the connectivity requirement and consider two variants of the problem (see Figure 1 ): (i) the continuous case, in which P is the whole plane, i.e., the sought TSP tour can hit each region at any of its points, and (ii) the discrete case, in which P is a finite set of points intersecting all regions, and the tour is allowed to hit the region only in one of the specified points. Henceforth, we shall refer to the former problem as Continuous TSPN and for the latter as Discrete TSPN.. Although the problem has been extensively studied in the last decade after Arkin and Hassin [1] introduced it in 1994, still large discrepancies remain between known inapproximability and approximation ratios for various cases. For the most general case in which the subsets are unrestricted, and the metric is not necessarily Euclidean, the gap is almost closed: Garg et al. [9] gave a randomized O((log N ) 2 log k log n)-approximation algorithm for the group Steiner tree problem, the variant in which we are given a graph with N vertices and a set of n groups with at most k vertices per group, and seek a minimum cost Steiner tree connecting to at least one point from each group 2 . This approximation ratio can be improved to O(log N log k log n) using the results of Fakcharoenphol et al. [8] . Slavík [18] showed that the problem can be approximated within O(k). On the negative side, Halperin and Krauthgamer [11] gave an inapproximability threshold of Ω(log 2− n) for any fixed > 0.. With this being the situation for the general case, recent research has considered the cases where the given subsets are connected regions in the plane. Typically, the putting the discrete restriction on the problem makes it harder. Safra and Schwartz [17] showed that the discrete problem is NP-hard to approximate, in the general case, within any constant factor, and is APX-hard if each subset forms a connected region in the plane. For connected polygonal regions, Mata and Mitchell [12] gave an O(log n)-approximation in O(N 5 )-time based on"guillotine rectangular subdivision", where N is the total number of vertices of the polygons. Gudmundsson and Levcopoulos [10] reduced the running time to O(N 2 log N ).

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 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 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


QMachine:

commodity

supercomputing

in

web

browsers

Background:

Ongoing

advancements

in

cloud

computing

provide

novel

opportunities

in

scientific

computing,

especially

for

distributed

workflows.

Modern

web

browsers

can

now

be

used

as

high-performance

workstations

for

querying,

processing,

and

visualizing

genomics'

"Big

Data"

from

sources

like

The

Cancer

Genome

Atlas

(TCGA)

and

the

International

Cancer

Genome

Consortium

(ICGC)

without

local

software

installation

or

configuration.

The

design

of

QMachine

(QM)

was

driven

by

the

opportunity

to

use

this

pervasive

computing

model

in

the

context

of

the

Web

of

Linked

Data

in

Biomedicine.QM

is

an

open-sourced,

publicly

available

web

service

that

acts

as

a

messaging

system

for

posting

tasks

and

retrieving

results

over

HTTP.

The

illustrative

application

described

here

distributes

the

analyses

of

Streptococcus

pneumoniae

genomes

for

shared

suffixes.

Because

all

analytical

and

data

retrieval

tasks

are

executed

by

volunteer

machines,

few

server

resources

are

required.

Any

modern

web

browser

can

submit

those

tasks

and/or

volunteer

to

execute

them

without

installing

any

extra

plugins

or

programs.

A

client

library

provides

high-level

distribution

templates

including

MapReduce.

This

stark

departure

from

the

current

reliance

on

expensive

server

hardware

running

"download

and

install"

software

has

already

gathered

substantial

community

interest,

as

QM

received

more

than

million

API

calls

from

countries

in

months.

Conclusions:

QM

was

found

adequate

to

deliver

the

sort

of

scalable

bioinformatics

solutions

that

computation-and

data-intensive

workflows

require.

Paradoxically,

the

sandboxed

execution

of

code

by

web

browsers

was

also

found

to

enable

them,

as

compute

nodes,

to

address

critical

privacy

concerns

that

characterize

biomedical

environments.Keywords:

Cloud

computing,

Crowdsourcing,

Distributed

computing,

JavaScript,

MapReduce,

PaaS,

Sequence

analysis,

Web

service

Availability

A

supporting

QM

deployment

is

available

at

and

its

source

code

is

available

at

The

illustrative

examples

and

their

dependencies

are

provided

for

live

demonstration

at

html

along

with

a

screencast

and

archived

genomic

data.

QMachine

was

developed

to

respond

to

the

challenges

of

-and

to

capitalize

on

the

opportunities

of

-bioinformatics

applications

encountered

in

biomedical

environments.

For

more

than

a

decade,

volunteer

computing

has

enticed

computational

biology

as

a

scalable

and

cost-effective

high-performance

computing

solution.

QM

essentially

ports

that

solution

to

the

modern

computational

landscape,

which

is

increasingly

dominated

by

mobile

hardware

platforms

and

the

use

of

the

web

browser

as

the

universal

software

platform.

The

features

of

the

modern

web

browser

go

beyond

those

that

make

it

a

highperformance

computational

environment

with

advanced

communication

layers;

they

also

include

the

transformative

feature

that

computations

run

in

a

robust

sandbox

that

prevents

access

to

the

underlying

machine's

potentially

sensitive

filesystem.

QM

also

responds

to

another

modern

trend

towards

engaging

HPC

resources

through

the

use

of

the

MapReduce

programming

pattern,

rather

than

through

direct

interactions

with

compute

nodes.

The

sequence

analysis

application

that

illustrates

the

use

of

QM

in

this

report

offers

the

sort

of

immediate

utility

that

would

benefit

bioinformatics

applications

in

Medical

Genomics.

It

is

argued,

however,

that

QM,

as

an

"of

the

Web"

distributed

computing

system,

may

be

just

as

useful

in

the

identification

of

the

fundamental

features

of

pervasive

web

computing.

QMachine

is

a

web

service

for

executing

distributed

workflows

that

can

use

ordinary

web

browsers

as

the

ephemeral

compute

nodes

of

a

crowdsourced

supercomputer.

The

idea

here

is

simple:

commodity

computers

equipped

with

web

browsers

join

an

abstract

machine

by

visiting

a

website,

and

they

unjoin

by

navigating

to

a

different

site

or

by

closing

the

browser.

While

a

browser

remains

on

the

site,

it

reacts

to

input

from

the

user

and

from

the

site's

backend

infrastructure

by

executing

JS,

which

provides

the

abstract

machine

with

some

potential

to

perform

computations.

At

any

instant,

the

net

computational

potential

available

to

a

high-traffic

website

falls

well

within

the

HPC

range,

as

shown

in

Figure

QM

enables

this

potential

to

be

harnessed

with

no

nominal

cost

through

volunteer

computing.

The

architecture

of

a

QMachine

detailed

in

Figure

follows

the

general

pattern

of

Web

technologies

by

using

the

server

side

exclusively

for

persistent

representation

and

leaving

the

rest

of

the

program

logic

to

run

on

the

client

side.

QM

uses

a

key-value

architecture

to

orchestrate

volunteering

client

machines

in

a

manner

that

maximizes

the

distribution

of

the

computational

resources

required

for

data

transfer

and

subsequent

data

processing.

This

orchestration

is

highlighted

in

Figure

QM

distributes

not

only

the

compute

cycles

needed

to

execute

the

n

different

procedures

..,n

but

also

the

bandwidth

needed

the

retrieve

the

corresponding

input

data

(D

..,n

being

processed

from

their

respective

URLs

(d

This

design

is

motivated

by

the

constraints

of

biological

applications

such

as

next

generation

sequencing

in

which

the

limiting

factor

is

more

often

the

available

memory

than

the

processor

speed..

The

operation

of

QM

relies

on

the

creation

of

unique

identifiers

to

define

"boxes"

that

are

then

shared

with

the

volunteering

browsers

in

a

manner

resembling

traditional

API

keys.

This

operation

will

be

described

in

a

series

of

four

examples

that

increase

in

complexity,

beginning

with

the

remote

execution

of

a

simple

algebraic

operation,

followed

by

distribution

of

the

same

operation

as

a

parallel

(map)

transformation

of

the

elements

of

an

array

and

distribution

again

as

part

of

a

MapReduce

procedure;

finally,

the

parallel

execution

of

a

real-world

genomic

sequence

analysis

in

which

both

the

code

and

the

data

needed

to

perform

the

analysis

are

invoked

by

a

single

submitter

but

then

entirely

resolved

and

executed

asynchronously

by

multiple

volunteer

browsers.

The

final,

real-world

example

distributes

both

the

processing

and

networking

loads,

as

described

in

Figure

It

illustrates

the

ability

of

volunteer

nodes

to

call

code

and

data

from

multiple

sources

which

are

independently

developed

and

maintained.

This

illustrative

series

is

also

available

as

a

YouTube

webcast

at

QM

provides

a

distributed

computing

platform

as

a

web

service

(PaaS).

Its

architecture

(see

Figure

in

Results)

combines

the

general

pattern

of

Web

technologies

with

the

model

used

by

modern

social

networking

sites

by

decoupling

the

presentation/analytical

layer

from

the

persistent

representation

layer

so

that

the

former

runs

on

the

client

side

as

a

web

app

that

consumes

an

application

programming

interface

(API)

presented

by

the

latter

on

the

server

side.

QM

also

decouples

the

presentation

and

analytical

layers

of

the

web

app

so

that

third

parties

may

embed

the

QM

web

service

as

part

of

their

own

web

apps..

To

provide

this

functionality,

QM

contains

three

main

components:

an

API

server,

a

web

server,

and

a

website.

The

API

and

web

servers

are

written

completely

in

JS,

and

the

website

is

written

in

CSS,

and

JS.

Nothing

about

QM's

design

or

interface

binds

it

to

a

particular

development

stack,

but

our

desire

to

construct

the

project

as

a

true

Web

Computing

"device"

motivated

us

to

implement

as

much

of

the

code

in

JS

as

possible.

The

strategy

paid

unexpected

dividends,

as

well;

the

server-side

components

are

free

from

assumptions

about

the

hardware

and

operating

systems

on

which

they

run,

which

vastly

simplifies

deployment

to

the

cloud

via

Platform-asa-Service

(PaaS)

This

report

also

presents

code

examples

(see

Results)

which

can

be

run

from

any

website

that

embeds

the

QM

web

service.

The

examples

are

all

written

in

JS,

but

some

of

them

also

make

use

of

CoffeeScript,

"a

little

language

that

compiles

into

JavaScript"

Many

common

scientific

languages

can

be

translated

to

and

from

JS,

and

a

comprehensive

list

of

projects

for

this

purpose

is

available

at

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Effect of management support, training, and user involvement on system usage and satisfaction in Kuwait The purpose of this paper is to investigate organizational factors and human motivations affecting IS/IT usage and user's satisfaction in an Arabic country Design/methodology/approa ch The study develops a research model that links three organizational factors (top management support, availability of training, and user's involvement) to IS/IT usage and enduser's satisfaction via the mediation of TAM beliefs (usefulness and ease of use) by 382 IS users. The model was examined through the LISREL.The results indicate that IS/IT usage and user satisfaction is largely influenced by perceived usefulness. Among the organizational factors, top management support was found to have the strongest effect on IS/IT usage and user's satisfaction, followed by availability of training and user involvement.This study focuses on perceptions of respondents from the public organizations who have high experience with IS/IT. Theses perceptions are based on a one-time survey. For better validity a longitudinal study with additional variables is needed.This study is useful for practitioners who may learn how to increase and boost system usage and user's satisfaction in the Arab region. What is original/value of paper Contributes to highlights motives of IT/IS usage and user' satisfaction in the Arab world, and to educate researchers 2 about how IS/IT is perceived by end-users in Kuwait. The role of Information Systems and Information Technology (hereafter IS/IT) in providing businesses with a competitive edge has been the subject of much debate. However, it has been argued that IS/IT's utilization, not the IS/IT solution, is what provides the competitive advantage to businesses. While most previous research has been carried out in the technologically developed world, see literature review done by Legris et al., 2003 , little is known about IS/IT acceptance in the Arab region. In fact, of the 124 research studies in IT adoption covered in three literature review papers that appeared in leading MIS journals between 1991 and 2004, no study took place in the Arab world. We define IS/IT acceptance in this paper as the potential user's predisposition toward personally using a specific system and being satisfied about it. In order to adequately evaluate acceptance, multiple constructs were considered. Usage and user satisfaction are commonly used to measure information success (DeLone and McLean, 1992; Briggs et al., 2008; Rouibah, 2009; Lee et al., 2009) . User satisfaction was found to be the most widely used surrogate of computing success (Srite et al., 2007; Lee et al., 2009) . Past studies on IS/IT acceptance advocate the existence of cultural and management differences between Western and Arab cultures (Rouibah, 2009 ) and other cultures (McCoy et al., 2007) . Most of the findings on the relationship between IS/IT usage and its satisfaction are based on research in technologically developed countries (e.g. DeLone and McLean, 1992; Igbaria et al., 1997; Gelderman, 1998; Gahtani and King, 1999; Adamson and Shine, 2003; Lee et al., 2009) , with the exception of a few studies in less developed countries (Khalil and Elkordy, 1999; Aladwani, 2002; Al-Gahtani, 2004; Rouibah, 2009) . Previous studies have also found a wide variation in the predicted effects in various studies with different types of users, systems and cultures (Legris et al., 2003; McCoy et al., 2007) . This study aims to study factors that influence IS/IT acceptance (determinant and consequences of use) in Kuwaiti public organizations. While there are differences between private and public organizations, so far much of the empirical research in the area of IS/IT and its impacts on organizations have occurred in the private sector of well developed countries. This study targets several benefits. First, among other Arabic countries, Kuwait has achieved the highest IS/IT utilization. Therefore, this study would orient decision makers on how to achieve sustainable benefits from IS/IT investment. A recent study has shown a substantial growth (9.4%) in IT investment in the Gulf region in the last three years and is expected to reach $7.9 billion by 2009 (Research Institute for Digital Economy, 2006) . Second, although reasons for this investment vary such as e-government initiatives and promotion of ecommerce, but the overriding belief suggested that knowledgeable workers will be more productive by using IS/IT. Third, the study aims to outlines the conditions under which IS/IT usage and satisfaction could be increased to generate significant performance gains (Mahmood and Mann, 2000) , and avoid failure (Anonymous 2008) . Fourth, the complexity of IS/IT acceptance has became an important issue for academic researchers and practitioners over the world (Legris et al., 2003) . Fifth, it is expected that this study would advance the knowledge about IS/IT acceptance in the Arab region. It is also expected that it will contribute to educate IS/IT researchers about how IS/IT is perceived by end-users in a region where less is known about IS/IT acceptance. Section 2 discusses the relevant literature review on IS/IT acceptance, the foundation of the research model, and the current state of IS/IT in the Arab world. Section 3 introduces the proposed research model. Section 4 presents the research methodology. Section 4 describes the results and their implications while the last section, Section 5, summarizes the findings and points out future research directions. Information usage This study revealed that three external variables have only an indirect influence on current IS, through the mediation of PU. The indirect influence is mediated by the role of usefulness. This means that availability of training, management support and user involvement contribute to increase IS usage only if the system is perceived as useful. This result confirmed the previously discussed theoretical expectation (see Table 1 ). In particular, studies have found that organizational support influences IS usage (Karahanna et al., 1999; Straub et al., 2001; Anandarajan et al., 2002; Al-Gahtani, 2004 ); availability of training influences IS usage (Igbaria et al., 1995; Igbaria et al., 1997; Al-Gahtani and King, 1999; Al-Gahtani, 2004; Gallivan et al., 2005) ; user involvement influences IS usage (Igbaria et al., 1997) . Usefulness exerts the strongest effect on current usage followed by top management support and user training. Moreover, our study was in agreement with Al-Gahtani (2004) regarding the effect of management support on current usage which was found to be larger than that of availability of training. Unlike our results, Al-Gahtani and King (1999) in UK (a study performed in a Western culture) found, however, that user training has more effect on system usage than does management support.. Our findings also extend those of Petter (2008) who identified three strategies for managing user expectations. These strategies included leadership (part of top management support), user involvement, and trust. Unlike our findings, Rouibah (2009) found that ease of use of instant messaging in Kuwait exerts the strongest effect on current usage, while Igbaria et al., (1995) found user training to exert the strongest effect on current usage followed by usefulness for PC usage in the USA. Differences between our results and previous studies could be explained by the type of used systems (general IS, instant messaging, and PCs), type of settings (social usage, private vs. public organization) and culture (Arab vs. Western).. In addition, our results confirm the indirect effect of "facilitating conditions" of the unified theory of acceptance and use of technology on system usage in the USA (Venkatesh et al., 2003) . Venkatesh et al., (2003) measured facilitating conditions by availability of resources, knowledge, and assistance to use the system. Our results also extended this concept to also include user involvement, besides management support and availability of training. Although the analysis revealed significant positive associations between different latent constructs (see Table 4 ), SEM was employed to explore the causal interrelationship between variables of the model. The results of the multivariate test of the structural model are presented in Table 5 and Figure 2 . The path coefficients (γ from external variable to latent and β from latent to latent) were examined.

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


Joint Pricing and Inventory Control with a Markovian Demand Model We consider the joint pricing and inventory control problem for a single product with a finite horizon and periodic review. The demand distribution in each period is determined by an exogenous Markov chain. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. The surplus costs as well as fixed and variable costs are state dependent. We show the existence of an optimal (s, S, p)-type feedback policy for the additive demand model. We extend the model to the case of emergency orders and also incorporate capacity and service level constraints. We compute the optimal policy for a class of Markovian demand and illustrate the benefits of dynamic pricing over fixed pricing strategies through numerical examples. The results indicate that it is more beneficial to implement the dynamic pricing strategy in a Markovian demand environment with a high fixed ordering cost or with high demand uncertainty. The joint pricing and inventory control problem has been studied by many researchers in the operations management literature, starting with the work of Whitin (1955) . The basic idea is to integrate the pricing decision with the replenishment policy when managing product inventory.. In this problem, retailers act as price setters and can adjust prices dynamically to influence demand and potentially gain higher profits. Other well-known examples in the service industry are found in revenue management, which has been adopted by all major airlines, many hotel chains and car rental companies. (Kimes 1989 ).. Most of the recent papers that address the pricing and inventory control coordination problem with periodic review assume that demand in different periods are independent random variables. In practice, demand usually fluctuates and depends on many exogenous factors such as economic conditions, natural disasters, strikes, etc. In addition, when a competitor introduces a new product to the market, some customers may switch to the new product and consequently, the retailer's average demand may drop dramatically during some periods. In these cases, a "state-dependent" demand model seems to be more appropriate to capture such randomly changing environmental factors. Furthermore, if demand is highly price-sensitive, retailers could combine pricing decisions with replenishment planning and use price as an effective tool to hedge against demand uncertainty. Therefore there is a need to consider the joint pricing and inventory control problem in a fluctuating demand environment, and a Markovian demand modeling approach provides an effective mechanism to address this problem.. The purpose of this paper is to characterize the structure of the optimal replenishment and pricing decisions with a Markovian demand model, and to illustrate the benefits of dynamic pricing strategies through numerical examples. Specifically, we consider a single product, periodic review system with a finite horizon, where demand is price-dependent and its distribution at each time period is determined by an exogenous Markov chain. The ordering cost consists of a fixed cost and a variable cost, and all the cost parameters are state and time dependent.. Under the assumptions of an additive demand function and full backlogging, we establish the structure of an optimal Markov (feedback) policy. We also present an algorithm to compute and analyze this policy.. There are two streams of literature that are related to our paper. The first stream is the coordination of pricing and inventory control with independent demand, as mentioned above. In this stream of research, demand is a random variable that depends on price. Under the assumption that unsatisfied demand in each period is fully backlogged, Federgruen and Heching (1999) and Simchi-Levi (2004a, 2004b) have considered periodic review models with both finite and infinite horizons. In Federgruen and Heching (1999) , the ordering cost is proportional to the order quantity, and there is no setup cost. They prove a base-stock list price policy is optimal. In this policy, the optimal replenishment policy in each period is characterized by an order-up-to level, and the optimal price depends on the initial inventory level at the beginning of the period. Furthermore, the optimal price is a nondecreasing function of the initial inventory level. Simchi-Levi (2004a, 2004b) include a fixed ordering cost in their models. They prove an (s, S, p)-type policy is optimal for the finite horizon model with additive demand, and a stationary (s, S, p) policy is optimal for the discounted and average profit models with general demand functions in the infinite horizon problem. In such a policy, the period inventory is managed using the classical (s, S) policy, and the optimal price depends on the inventory position at the beginning of the period. Feng and Chen (2004) consider a long-run average profit model with periodic review and an infinite horizon. The optimality of an (s, S, p)-type policy is also established.. When unsatisfied demands are assumed to be lost, Polatoglu and Sahin (2000) characterize the form of the optimal replenishment policy under a general price-demand relationship and provide a sufficient condition for it to be of the (s, S)-type. For a finite horizon system, Chen, Ray and Song (2003) and Huh and Janakiraman (2005) have proved the optimality of an (s, S, p) policy under assumptions of stationary parameters and a salvage value that is equal to the unit purchasing cost. In the area of continuous review models, Feng and Chen (2003) assume demand follows a Poisson process with price-sensitive intensities, while Chen, Wu and Yao (2004) model the demand process as a Brownian motion with a drift rate that is a function of price. Furthermore, in terms of techniques to prove the optimality of an (s, S, p)-type policy, Chen and Simchi-Levi (2004a) , and Chen, Ray and Song (2003) use induction and the dynamic programming formulations, which are similar to that in Scarf (1960) in the classic stochastic inventory control problem. Huh and Janakiraman (2005) propose an alternative approach for the optimality proof, which is based on the method used in Veinott (1966) . For a review of other work in the pricing and inventory literature, the reader is referred to Petruzzi and Dada (1999) , Elmaghraby and Keskinocak (2003) and Chan et al. (2004) .. The second stream of related literature is the inventory control problem with a Markovian demand model. Song and Zipkin (1993) present a Markovian-modulated model to capture the fluctuating demand environment. Specifically, they assume that demand in each period follows a Poisson process whose rate depends on the demand state. Sethi and Cheng (1997) analyze a general finite horizon inventory model with a Markovian demand process. They show that under certain technical assumptions, the optimal policy for the finite horizon problem is still of (s, S) type, with s and S dependent on the demand state and the time remaining. Cheng and Sethi (1999) extend their previous work to the lost sales case and establish the optimality of (s, S)-type policies based on certain weak conditions on the holding, shortage and unit ordering costs. Another paper that is related to ours is Beyer, Sethi and Taksar (1998) , which establishes the existence and verification theorems of an optimal feedback policy.. Recently, Gayon et al. (2004) consider a Markov Modulated Poisson Process that is similar to Song and Zipkin (1993) , except that the fluctuating intensities are functions of price. The unit ordering cost is given and there is no fixed ordering cost. All the shortages are lost. They generalize some structural results in Li (1998) and prove an optimal base-stock policy exists for the discounted infinite horizon Markov decision process. The base-stock policy is similar to that in Federgruen and Heching (1999) .. To the best of our knowledge, this is the first paper in the literature to address the joint pricing and inventory control problem with a Markovian demand in a periodic-review system and a fixed ordering cost. This paper makes the following contributions. First, under the assumptions of an additive demand function and full backlogging, we establish the optimality of a feedback policy of (s, S, p)-type. Second, we extend the basic model to the case when the unsatisfied demand at the end of a period is filled by an emergency order. Under certain practical assumptions on the holding cost, the regular and emergency ordering cost functions, we prove the state-dependent (s, S, p) policy is still optimal for the case with additive demand.. Third, we provide another extension by incorporating the storage and service level constraints to the basic model. Fourth, we develop an algorithm to compute the optimal policy for a class of Markovian demand with an arbitrary probability transition matrix and discrete, uniformly distributed random noise. Finally, we use this algorithm to illustrate the benefits of dynamic pricing over the fixed pricing strategies through extensive numerical examples. The results indicate that it is more beneficial to implement the dynamic pricing strategy in a Markovian demand environment with a high fixed ordering cost or with high demand uncertainty. This paper is organized as follows. In Section 2, we introduce the notations and assumptions used in this paper and develop a general finite horizon inventory model with a Markovian demand process. In Section 3, we state the dynamic programming equations for the problem and establish the existence of an optimal feedback policy. In Section 4, the additive demand function is analyzed and the optimality of an state-dependent (s, S, p) policy is proved. Two extensions of the basic model are presented in Section 5. In Section 6, we discuss the computation of the optimal policy for a class of Markovian demand and present numerical examples to illustrate the benefits of dynamic pricing over fixed pricing. Section 7 concludes the paper and presents future research directions.

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


FINE-GRAINED AND LAYERED OBJECT RECOGNITION This paper presents a novel research on promoting the performance and enriching the functionalities of object recognition. Instead of simply¯tting various data to a few prede¯ned semantic object categories, we propose to generate proper results for di®erent object instances based on their actual visual appearances. The results can be¯ne-grained and layered categorization along with absolute or relative localization. We present a generic model based on structured prediction and an e±cient online learning algorithm to solve it. Experiments on a new benchmark dataset demonstrate the e®ectiveness of our model and its superiority against traditional recognition methods. When talking about object recognition, we are no strangers to the amazing functionality of animals including ourselves, and the challenging problem we are working on for decades in both computational neural science and computer vision. To untangle this general problem, di®erent levels of recognition have been investigated separately, such as categorizing the whole image into prede¯ned groups (e.g. telling which object categories present in each image 28, 37 ), localizing or detecting objects of a speci¯c category 10, 20 (e.g. cars), classifying objects into subcategories (e.g. assigning quantized direction labels to the objects 1 ) , and identifying every single object instance. 12, 14 These e®orts are mostly on the recognition at speci¯c semantic levels. Recently, some researchers have also dug into certain combined recognition problems like concurrent object localization and classi¯cation (or state estimation), 7, 15, 16 which show that recognition problems at di®erent levels are highly dependent and they may help each other. Such a trend inspired us to rethink object recognition in a more problemoriented way: how do humans recognize surrounding objects in their daily lives? What recognition results do they get if they have no designated expectations on the semantic level of them?. The image in Fig. 1 may represent the typical results. Human beings tend to have di®erent interpretations for the three cars when they see them rapidly just as what has been done in their daily lives. For the close car, we can clearly tell its orientation and even its license plate, such detailed recognition results allow us to have an immediate reaction to this car. But for the car far away from us, we may just perceive that it is a car, heading horizontally or vertically without telling its concrete orientation because doing that is both unreliable and unnecessary. We doubt that such a strategy may not only provide us with rich and adaptive interpretation of the visual objects, but also help us remember the di®erent states of objects and robustly categorize them under di®erent circumstances. However, as shown in the middle of the¯gure, existing research in computer vision has never considered about this. Therefore, in this paper we try to make the computers do similar things, and name it \Fine-grained and layered recognition (FGLR)", where \¯ne-grained " means detailed within-category interpretation to distinguish it from traditional categorization, and \layered " indicates that the interpretation is semantically layered, but not biasing towards the most¯ne-grained results.. In the following sections, we¯rst discuss how others' work inspired us and helped us with the implementation details of FGLR, then brie°y introduce the newly built IAIR-CarPed benchmark dataset. After that we present a general solution with details on the modeling, learning, and implementation. Experiment results are shown and analyzed thereafter, followed by the conclusions and discussions. Fine-grained visual categorization has recently been investigated by several research groups. Specially, attributes have been proven to be helpful for category-level recognition, as shown by Lampert et al. 21 who looked into the problem of betweenclass attribute transfer for recognizing new object classes with no training examples, while at the same time Farhardi et al. 9 shifted the goal of recognition from naming to describing, which signi¯cantly improves the categorization performance and also enables learning new categories. Besides attributes-based approaches, there are some other prior works on within-category classi¯cation/ description. The work on multiplicative kernels 38 manages to classify the view angles of cars in a dataset collected from LabelMe database, 25 and another work directly targeting at within-object classi¯cation 1 represents results on three different databases. Though our FGLR also involves describing the attributes of objects and classifying the objects into subcategories, we are neither trying to use the attributes as the middle-level representation for categorization nor aiming at attribute inference or within-category classi¯cation. Instead, our recognition results are layered, which may and also may not be sub-categorial labels with attributes.. Meanwhile, semantic hierarchies have also been widely researched. Marszalek and Schmid 24 proposed a semantic hierarchical classi¯er that uses image labels to promote multiclass object detection performance. Binder et al. 3 tried to classify images into a given, pre-determined taxonomy using a structured learning framework. Their empirical results on Caltech256 and PASCAL VOC2006 data show that the algorithm exploiting the structure of the taxonomy outperforms multi-class classi¯cation approaches. Zweig and Weinshall 40 demonstrated that training a single classi¯er using object hierarchies obtained from publicly available datasets can signi¯cantly improve multi-class object recognition results. Kapoor et al. 18 combined kernels designed at each level of an object class hierarchy to bene¯t both computational e±ciency and performance. However, almost all these existing papers are limiting themselves to the object category and the levels above, but not for exploring object subcategories and attributes to better describe an object instance. There are also some work on exploring both the object-level information and the information of its parts, such as the research on the combination of bottom-up and top-down processes proposed by Yang et al. 36 Though they have combined the information from three di®erent levels, their goal is to interpret the middle level only. Chen et al. 6 aimed at simultaneous object detection, segmentation and part parsing using a rapid inference algorithm on a speci¯cally designed AND/OR graph, however, the three types of outputs are forced to be predicted.. In the learning part, we are facing the same problem of outputting multilabel or structural labels as the above referred work and many other structure learning applications. Speci¯cally, we are interested in a proper structure learning framework which can model the problem in a compact way and solve it e±ciently. Tsochantaridis et al. 27 investigated large margin methods of doing so. Bordes et al. 5 proposed an e±cient online learning algorithm for multiclass classi¯cation problems in a maximum margin discriminative learning framework. Recently, Wu et al. 35 extended this framework to handle more general structural output prediction problems and showed experimental results on digits recognition. Lampert and Blaschko 19 proposed to use the joint kernel directly for structured prediction by an alternative model named joint kernel support estimation (JKSE). Based on these existing approaches, in this paper, we build an online learning structured model for solving the challenging FGLR problem, as it will be presented in detail in the coming sections.. On the side of data representation, we would like to follow the two-layer HOG representation of objects, 10 but without latent part localization for e±ciency. Using histograms of di®erent scales in a¯xed form is also e®ective. 22 Besides that, colors for special parts (like faces) can be informative for recognizing humans and cars, and we got the inspiration of using it from Refs. 22 and 29. The widely used SIFT features 23 are very good at representing rigid objects with view changes, therefore we use it for representing the license plate, which is the key part of the car.

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


Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering Techniques based on agglomerative hierarchical clustering constitute one of the most frequent approaches in unsupervised clustering. Some are based on the single linkage methodology, which has been shown to produce good results with sets of clusters of various sizes and shapes. However, the application of this type of algorithms in a wide variety of fields has posed a number of problems, such as the sensitivity to outliers and fluctuations in the density of data points. Additionally, these algorithms do not usually allow for automatic clustering.In this work we propose a method to improve single linkage hierarchical cluster analysis (HCA), so as to circumvent most of these problems and attain the performance of most sophisticated new approaches. This completely automated method is based on a self-consistent outlier reduction approach, followed by the building-up of a descriptive function. This, in turn, allows to define natural clusters. Finally, the discarded objects may be optionally assigned to these clusters.The validation of the method is carried out by employing widely used data sets available from literature and others for specific purposes created by the authors. Our method is shown to be very efficient in a large variety of situations. Pattern recognition is a primary conceptual activity of the human being. Even without our awareness, clustering on the information that is conveyed to us is constant. This clustering activity is frequently based on a few selected properties and is not exempt from personal prejudice. Naturally, when objects are defined by a significant number of properties which are or have been made quantitative, and it is intended to obtain exempt results (natural clusters), the use of mathematical tools is mandatory. Actually, mathematical tools cannot be completely exempt. Firstly, because many algorithms rely on options made by the user. Also, because algorithms introduce some propensity for certain types of solution.. Identifying natural patterns in data is one of the most important goals of chemometrics. Specifically, clustering techniques are almost indispensable as a tool for data mining.. These techniques are often divided into unsupervised and supervised methodologies. In the former, information stems from the data and there are no pre-classified groups. Hierarchical cluster analysis usually produces a dendrogram, or other type of tree diagrams, as final output [1] [2] [3] . Each association level of the dendrogram represents a partitioning of the data set into a specific number of clusters [1] . Based on the dendrogram, it is possible to additionally define the number of clusters, but this step is often based on common sense relying on the representation of the data structure. In cases for which the data set is analyzed to produce a simple partitioning of the objects, resulting in a set of clusters, the technique is considered nonhierarchical [1] . In contrast, supervised methodologies involve classifying samples into predefined structures. Typically, there is a set of labelled objects (training set) that is used to establish decision rules so as to classify new objects [4] .. In this work we propose an automated approach to find natural clusters, based on a new representation that highlights weak associations between groups. This representation is intended to complement the information provided by the dendrogram and, if the sole purpose is to separate data in an appropriate number of groups, it may even replace the dendrogram.. Situations in which data contain some amount of outliers or less characteristic data are difficult to tackle with most clustering algorithms. The data structure becomes less defined and the number of groups that are formed may be either too low or too high, depending on the actual situation and algorithm.. Identification and removal of outliers, in a preliminary step, is suggested via a self-consistent technique in which system properties are used to make an automatic specification of the necessary parameters. Additionally, the possibility of merging these initially discarded data in the formed clusters is left open. This final step is especially important when, rather than outliers, objects that are discarded correspond to less characteristic zones in the clusters.. The algorithm proposed in this work was applied to a variety of data sets, and results compared with those obtained by other algorithms. These include ROCK [5] , CURE [6] , DBSCAN [7] , CHAMELEON [8] and FAÇADE [9] . We note that such algorithms were proposed as adequate approaches for finding clusters of different sizes, shapes, and densities in the presence of outliers.. ROCK combines, from a conceptual point of view, nearestneighbor, relocation, and hierarchical agglomerative methods [1, 5] . In this algorithm, cluster similarity is based on the number of points from different clusters that have neighbors in common [5, 10] . The same authors developed another algorithm denoted as CURE that combines centroid and single linkage approaches by choosing more than one representative point from each cluster [1, 6] . At each step of the algorithm, the two clusters with the closest pair of representative points (one in each cluster) are merged [6, 10] .. The CHAMELEON algorithm explores dynamic modeling in hierarchical methodology. In its clustering process, two clusters are merged if the respective inter-connectivity and proximity are highly related to the intra-cluster counter part [8, 10] .. A different approach is presented by the DBSCAN algorithm. It makes use of two external parameters, the minimum number of points in the neighborhood of a point, and the radius that defines this neighborhood. Choosing the appropriate parameters, it is then possible to identify objects located in high and low density zones. Neighboring objects in high density zones define clusters [7, 10] .. FAÇADE was proposed recently and is initiated by an outliereliminating process. Subsequently, the data is compressed, preserving original spatial patterns with a smaller number of data points. In this approach, the algorithm constructs a neighborhood graph, in which each sub-graph is regarded as a group. The clustering information of the compressed data is projected onto the original data. Finally, the groups are merged hierarchically according to the connections between each two groups [9] . In this work we have proposed to use information usually obtained in common HCA procedures in order to identify 'natural clusters', without resorting to external parameters. In fact, the procedure described here is completely automated and attains a degree of reliability comparable to the most sophisticated approaches. It complements the information usually obtained from HCA, often summarised in a dendrogram, with a descriptive function that leads to the number of 'natural clusters' and the corresponding separation. This feature is obviously absent in classical HCA. The methodology presented also includes two additional steps. A previous one, in which outliers are removed, which may be applied in a variety of other filtering applications, and an optional classification after the main clustering analysis, that allows to enlarge the clusters with the whole set of objects.. The algorithm presented has been applied to a variety of data sets. The behavior was almost flawless in most of them, with an adequate recognition of the 'natural clusters'.. The descriptive function has shown to be very adequate in the visualisation of large data sets, for which dendrograms become extremely cumbersome.. It was also specifically employed in sets comprising 'diluting' clusters, i.e., clusters that possess scattered objects in their periphery. Recognition has been very satisfactory, in spite of the significant degree of overlap between adjacent clusters.

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


Deterministic 7/8-approximation for the metric maximum TSP We present the first 7/8-approximation algorithm for the maximum traveling salesman problem with triangle inequality. Our algorithm is deterministic. This improves over both the randomized algorithm of Hassin and Rubinstein [3] with expected approximation ratio of 7/8 − O(n −1/2 ) and the deterministic (7/8 − O(n −1/3 ))-approximation algorithm of Chen and Nagoya [1]. In the new algorithm, we extend the approach of processing local configurations using socalled loose-ends, which we introduced in [6] for the asymmetric variant (ATSP). The Traveling Salesman Problem and its variants are among the most intensively researched problems in computer science and arise in a variety of applications. In its classical version, given a set of vertices V and a symmetric weight function w : V 2 → R ≥0 satisfying the triangle inequality one has to find a Hamiltonian cycle of minimum weight.. There are several variants of TSP, e.g. one can look for a Hamiltonian cycle of minimum or maximum weight (MAX-TSP), the weight function can be symmetric or asymmetric, it can satisfy the triangle inequality or not, etc.. In this paper, we are concerned with the MAX-TSP variant, where the weight function is symmetric and satisfies the triangle inequality. This variant is often called the metric MAX-TSP.. MAX-TSP (not necessarily metric) was first considered by Serdyukov in [8] , where he gives a 3 4 -approximation. Next, a 5 6 -approximation algorithm for the metric case was given by Kostochka and Serdyukov [5] . Hassin and Rubinstein [3] used these two algorithms together with new ideas to achieve a randomized approximation algorithm with expected approximation ratio of ( 7 8 − O(n −1/2 )). This algorithm has later been derandomized by Chen and Nagoya [1] , at a cost of a slightly worse approximation factor of ( In this paper, we give a deterministic 7 8 -approximation algorithm for metric MAX-TSP. Our algorithm builds on the ideas of Serdyukov and Kostochka, but is completely different from that of Hassin and Rubinstein. We apply techniques similar to those used earlier in [6] for the directed version of MAX-TSP with triangle inequality.

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