Learning to Match the Schemas of Data Sources: A Multistrategy Approach Abstract. The problem of integrating data from multiple data sources-either on the Internet or within enterprises-has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are "element location maps to address" and "price maps to listed-price". We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy. The number of structured data sources available online is growing rapidly. Integrating data across such sources holds the potential to better many aspects of our lives, ranging from everyday activities such as reading news and choosing a movie to important tasks such as buying a house and making a business decision. Manually integrating data from multiple sources, however, is extremely labor intensive. Hence, researchers in both the database and AI communities have proposed building data integration systems (e.g., Garcia-Molina et al., 1997; Levy, Rajaraman, & Ordille, 1996; Ives et al., 1999; Lambrecht, Kambhampati, & Gnanaprakasam, 1999; Friedman & Weld, 1997; Knoblock et al., 1998) . Such a system provides a uniform query interface to a multitude of data sources, thereby freeing the user Figure 1 . A data integration system in the real-estate domain.. from the tedious job of accessing the individual sources, querying them, and manually combining the answers.. Consider for example a data integration system that helps users find houses on the realestate market (figure 1). The system provides the uniform interface in terms of a mediated schema that captures the relevant aspects of the real-estate domain. The mediated schema may contain elements such as ADDRESS, PRICE, and DESCRIPTION, listing the house address, price, and description, respectively. The system maintains for each data source a source schema that describes the content of the source. Wrapper programs, attached to each data source, handle data formatting transformations between the local data model and the data model in the integration system.. Given a user query formulated in the mediated schema, such as "find houses with 2 bedrooms priced under $200K", the system translates the query into queries in the source schemas, executes these queries with the help of the wrappers, then combines the data returned by the sources to produce the final answers.. To translate user queries, a data integration system uses semantic mappings between the mediated schema and the local schemas of the data sources. Examples of mappings are "element ADDRESS of the mediated schema maps to element location of the schema of realestate.com" and "PRICE maps to listed-price". Today, such semantic mappings are specified manually by the system builder, in a very labor-intensive and error-prone process. The acquisition of semantic mappings therefore has become a key bottleneck in deploying data integration systems. The emergence of XML (Extensible markup language, 1998) as a standard syntax for sharing data among sources further fuels data sharing applications and underscores the need to develop methods for acquiring semantic mappings. Hence, the development of techniques to automate the mapping process has now become crucial to truly achieve large-scale data integration.. In this paper we describe a machine learning approach to automatically create semantic mappings. Throughout the discussion, we shall assume that the sources present their data in XML and that the mediated-and source schemas are represented with XML DTDs (see Section 2 for more details on XML). The schema-matching problem is then to find semantic correspondences between the elements of the mediated schema and the source DTDs.. The key idea underlying our approach is that after a small set of data sources have been manually mapped to the mediated schema, a schema-matching system should be able to automatically learn from these mappings to successfully propose mappings for subsequent data sources.. Example 1. Consider the data-integration system in figure 1. Suppose we have source realestate.com in figure 2(a), for which we have manually specified the semantic mappings. This amounts to specifying the five arrows in figure 2(b). The first arrow, for example, states that source-schema element listed-price matches mediated-schema element PRICE.. Once we have specified the mappings, a machine learning system can exploit different types of information in the source schema and data, for the purpose of finding future semantic mappings. It can exploit the names of schema elements: knowing that office matches OFFICE-PHONE, it could construct a hypothesis that if an element name contains the word "office", then that element is likely to be OFFICE-PHONE. The system can also look at example phone numbers in the source data (figure 2(c)), and learn the format of phone numbers. It could also learn from word frequencies: it could discover that words such as "fantastic" and "great" appear frequently in house descriptions. Hence, it may hypothesize that if these words appear frequently in the data instances of an element, then that element is likely to be DESCRIPTION. As yet another example, the system could also learn from the characteristics of value distributions: it can look at the average value of an element, and learn that if that value is in the thousands, then the element is more likely to be price than the number of bathrooms.. Once the system has learned from the manually provided mappings, it is then able to find semantic mappings for new data sources. Consider source homes.com in figure 2(d). First, Figure 2 . Source realestate.com provides data in XML format, as shown in (a). Once we have manually provided the mappings between the source schema and the mediated schema (the five arrows in (b)), a system can learn many different matching hypotheses from schemas in (b) and source data in (c), then apply them to predict mappings for a new source homes.com in (d).. the system extracts data from this source to populate a table where each column consists of data instances for a single element of the source schema. Next, the system examines the name and the data instances of each column, and applies the learned hypotheses to predict the matching mediated-schema element. For example, when examining column extra-info, the system could predict that it matches DESCRIPTION, because the data instances in the column frequently contain the words "fantastic" and "great", and therefore are likely to be house descriptions.. In general, there are many different types of information that a schema-matching system can exploit, such as names, formats, word frequencies, positions, and characteristics of value distributions. It is important that the system exploit all such types of information, in order to maximize matching accuracy. Consider element contact-agent of source homes.com ( figure 2(d) ). If the system exploits only the name "contact-agent", it can only infer that the element relates to the agent, and hence matches either AGENT-NAME or AGENT-PHONE. If the system exploits only the data values of the element, it can recognize only that they are phone numbers, and hence the element matches either AGENT-PHONE or OFFICE-PHONE. Only when combining both types of information can the system correctly infer that the element matches AGENT-PHONE.. Clearly, no single learning technique will be able to exploit effectively all types of information. Hence, our work takes a multistrategy learning approach (Michalski & Tecuci, 1994) . To predict mappings for a new data source, we propose applying a multitude of learning modules, called base learners, where each base learner exploits well a certain type of information, then combining the base learners' predictions using a meta-learner. The meta-learner uses the manually mapped sources to learn a set of weights for each base learner. The weights indicate the relative importance of the base learners and can be different for each mediated-schema element, reflecting that different learners may be most appropriate in different cases.. We have developed the LSD (Learning Source Descriptions) system, which employs the above multistrategy learning approach to semi-automatically create semantic mappings. To improve matching accuracy, LSD extends multistrategy learning to exploit domain integrity constraints that appear frequently in database schemas, and to incorporate user feedback on the proposed mappings. It also employs a novel learning technique that utilizes the nested structure inherent in XML data.. In this paper, we focus on describing multistrategy learning, the central component of the LSD system. We discuss additional techniques employed by LSD briefly in Section 4 and in detail in Doan, Domingos, and Halevy (2001) . Specifically, we make the following contributions:. • We propose the use of multistrategy learning for finding semantic mappings. In addition to providing accuracy superior to that of any single learner, the technique has the advantage of being highly extensible when new learners are developed.. • We describe the implementation of multistrategy learning in the LSD system. LSD extends multistrategy learning to exploit domain constraints, user feedback, and XML structure, in order to propose semantic mappings with high accuracy.. • We describe experiments on several real-world data integration domains. The results validate the utility of multistrategy learning, and show that with the current set of learners, LSD already obtains high predictive accuracy of 71-92% across all tested domains.. The paper is organized as follows. The next section defines the schema-matching problem. Section 3 describes our multistrategy learning approach. Section 4 describes the current LSD implementation. Section 5 presents our experiments. Section 6 discusses the limitations of the current LSD system. Section 7 reviews related work. Section 8 discusses future work and concludes. We have described an approach to schema matching that employs and extends machine learning techniques. Our approach utilizes both schema and data from the sources. To match a source-schema element, the system applies a set of learners, each of which looks at the problem from a different perspective, then combines the learners' predictions using a meta-learner. The meta-learner's predictions are further improved using domain constraints and user feedback. We also developed a novel XML learner that exploits the hierarchical structure in XML data to improve matching accuracy. Our experiments show that we can accurately match 71-92% of the tags on several domains.. More broadly, we believe that our approach contributes an important aspect to the development of schema-matching solutions. Given that schema matching is a fundamental step in numerous data management applications (Rahm & Bernstein, 2001) , it is desirable to develop a generic solution that is robust and applicable across domains. In order to be robust, such a solution must have several important properties. First, it must improve over time-knowledge gleaned from previous instances of the schema-matching problem should contribute to solving subsequent instances. Second, it must allow knowledge to be incorporated in an incremental fashion, so that as the user gets to know the domain better, knowledge can be easily modified or added. Third, it must allow multiple types of knowledge to be used to maximize the matching accuracy. Machine learning techniques, and in particular, multistrategy learning, provide a basis for supporting these properties. Hence, while we do not argue that our techniques provide a complete solution to the schema-matching problem, we do believe that we provide an indispensable component of any robust solution to the problem.. We have been extending our multistrategy learning approach to compute non 1-1 mappings , to match ontologies , a crucial task in constructing large-scale knowledge bases and processing information on the Semantic Web, and to develop a formal semantics for mapping . We are continuing our work on these issues, as well as issues outlined in the discussion section. The most up-to-date information on LSD can be found on the website www-faculty.cs.uiuc.edu/∼anhai. The site also stores a public repository of data intended to be used as benchmarks in evaluating schema matching algorithms. We now address the limitations of the current LSD system, as well as issues related to system evaluation.. Accuracy. The first issue to address is whether we can increase the accuracy of LSD beyond the current range of 71-92%. There are several reasons that prevent LSD from correctly matching the remaining 10-30% of the tags. First, some tags (e.g., suburb) cannot be matched because none of the training sources has matching tags that would provide training data. This problem can be handled by adding domain-specific recognizers or importing data from sources outside the domain.. Second, some tags simply require different types of learners. For example, course codes are short alpha-numeric strings that consist of department code followed by course number. As such, a format learner would presumably match them better than any of LSD's current base learners.. Finally, some tags cannot be matched because they are simply ambiguous. For example, given the following source data fragment "course-code: CSE142 section: 2 credits: 3", it is not clear if "credits" refers to the course-or the section credits. Here, the challenge is to provide the user with a possible partial mapping. If our mediated DTD contains a label hierarchy, in which each label (e.g., credit) refers to a concept more general than those of its descendent labels (e.g., course-credit and section-credit) then we can match a tag with the most specific unambiguous label in the hierarchy (in this case, credit), and leave it to the user to choose the appropriate child label.. Efficiency. The training phase of LSD can be carried out offline, so training time is not an issue. In the matching phase, LSD spends most of its time in the constraint handler (typically in the range of seconds to 5 minutes, but sometimes up to 20 minutes in our experiments), though we should note that we did not spend any time on optimizing the code. Since we would like the process of match prediction and incorporation of user feedback to be interactive, we need to ensure that the constraint handler's performance does not become a bottleneck. The most obvious solution is to incorporate some constraints within some early phases to substantially reduce the search space. There are many fairly simple constraints that can be pre-processed, such as constraints on an element being textual or numeric. Another solution is to consider more efficient search techniques (than the A * search technique (Hart, Nilsson, & Raphael, 1972 ) that we currently use), and to tailor them to our context. We describe work related to LSD from several perspectives.. Schema matching. Work on schema matching can be classified into rule-and learner-based approaches. (For a comprehensive survey of schema matching, see Rahm and Bernstein (2001) ). Rule-based approach includes (Milo & Zohar, 1998; Palopoli, Sacca, & Ursino, 1998; Castano & De Antonellis, 1999) . The Transcm system (Milo & Zohar, 1998) performs matching based on the name and structure of schema elements. The Artemis system (Castano & De Antonellis, 1999) uses names, structures, as well as domain types of schema elements to match schemas. In general, rule-based systems utilize only schema information in a hardcoded fashion, whereas our approach exploits both schema and data information, and does so automatically, in an extensible fashion.. In the learner-based approach, the Semint system (Li & Clifton, 2000) uses a neuralnetwork learner. It matches schema elements using properties such as field specifications (e.g., data types and scale) and statistics of data content (e.g., maximum, minimum, and average). Unlike LSD, it does not exploit other types of data information such as word frequencies and field formats. The ILA system (Perkowitz & Etzioni, 1995) matches schemas of two sources based on comparing objects that it knows to be the same in both sources. Both Semint and ILA employ a single type of learner, and therefore have limited applicability. For example, the neural net of Semint does not deal well with textual information, and in many domains it is not possible to find overlaps in the sources, making ILA's method inapplicable. Clifton, Housman, and Rosenthal (1997) describe DELTA, which associates with each attribute a text string that consists of all meta-data on the attribute, then matches attributes based on the similarity of the text strings. The authors also describe a case study using DELTA and Semint and note the complementary nature of the two methods. With LSD, both Semint and DELTA could be plugged in as new base learners, and their predictions would be combined by the meta-learner.. The Clio system (Miller, Haas, & Hernandez, 2000) introduces value correspondences, which specify functional relationships among related elements (e.g., hotel-tax = room-rate * state-tax-rate). Given a set of such correspondences, Clio produces the SQL queries that translate data from one source to the other. A key challenge in creating the queries is to find semantically meaningful ways to relate the data elements in a source, in order to produce data for the other source. For example, given the above value correspondence, Clio must discover that room-rate belongs to a hotel that is located in the state where the tax rate is state-tax-rate. To this end, Clio explores joining elements along foreign-key relationship paths. There can be many foreign keys, thus many possible ways to join. Hence, the problem boils down to searching for the most likely join path. Clio uses several heuristics and user feedback to arrive at the best join path, and thus the best query candidate. Clio is therefore complementary to the current work of ours. It can take the mappings produced by LSD as the value correspondences.. Ontology matching. Many works have addressed ontology matching in the context of ontology design and integration (e.g., Chalupsky, 2000; McGuinness et al., 2000; Noy & Musen, 2000; Mitra, Wiederhold, & Jannink, 1998) . They use a variety of heuristics to match ontology elements. They do not use machine learning and do not exploit information in the data instances. However, most of them (Chalupsky, 2000; McGuinness et al., 2000; Noy & Musen, 2000) have powerful features that allow for efficient user interaction. Such features are important components of a comprehensive solution to matching ontologies and schemas, and hence should be added to LSD in the future. Several recent works have attempted to further automate the ontology matching process. The Anchor-PROMPT system (Noy & Musen, 2001 ) exploits the general heuristic that paths (in the taxonomies or ontology graphs) between matching elements tend to contain other matching elements. The HICAL system (Ryutaro, Hideaki, & Shinichi, 2001 ) exploits the data instances in the overlap between the two taxonomies to infer mappings. Lacher and Groh (2001) computes the similarity between two taxonomic nodes based on their signature TF/IDF vectors, which are computed from the data instances.. In Brazdil and Muggleton (1991) the authors address the important problem of matching the vocabularies of different agents in the context of building an integrated theory from the individual theories. The paper gives an example of a micro-world of family relations. In this world, agent A knows only the relationships father(X,Y), mother(X,Y), and grandfather(X,Y); and can construct the rule grand-father(X,Y):-father(X,Z), father(Z,Y), which defines the grand-father concept based on the examples that the agent has seen. In contrast, agent B knows only the relationships parent(X,Y), male(X), female(X), and grand-father(X,Y); and can construct the rule grand-father(X,Y):-parent(X,Z), male(X), parent(Z,Y), female(Z). Integrating the two rules above would presumably give a rule that better defines the grand-father concept. To do this, however, we must "match" the terms used by the two rules. The authors propose using inductive learning techniques on the examples that are known to both agents to arrive at the mappings.. Combining multiple learners. Multistrategy learning has been researched extensively (Michalski & Tecuci, 1994) , and applied to several other domains (e.g., information extraction (Freitag, 1998) , solving crossword puzzles (Keim et al., 1999) , and identifying phrase structure in NLP (Punyakanok & Roth, 2001) ). In our context, our main innovations are the three-level architecture (base learners, meta-learner and prediction combiner) that allows learning from both schema and data information, the use of integrity constraints to further refine the learner, and the XML learner that exploits the structure of XML documents.. Exploiting domain constraints. Incorporating domain constraints into learners has been considered in several works (e.g., Donoho & Rendell, 1996) , but most consider only certain types of learners and constraints. In contrast, our framework allows arbitrary constraints (as long as they can be verified using the schema and data), and works with any type of learner. This is made possible by using the constraints during the matching phase, to restrict the learner predictions, instead of the usual approach of using constraints during the training phase, to restrict the search space of learned hypotheses.

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


The lightspeed automatic interactive lighting preview system Figure 1: An automatically-generated preview at 914x389 resolution with 13x13 supersampling for a scene featuring 42 spot, environment, and message-passing lights and multiple 20k instruction surface shaders. The upper-left half of the image is rendered with our approach while the lower right is the final RenderMan frame -the seam is barely visible. The error heat map is in percentage of maximum 8-bit pixel value and is mostly due to shadow map artifacts. This scene renders interactively at 4x4 subsampled resolution at 9.2 Hz, while refining to the above antialiased final-quality in 2.7 seconds, compared to 57 minutes in RenderMan.We present an automated approach for high-quality preview of feature-film rendering during lighting design. Similar to previous work, we use a deep-framebuffer shaded on the GPU to achieve interactive performance. Our first contribution is to generate the deep-framebuffer and corresponding shaders automatically through data-flow analysis and compilation of the original scene. Cache compression reduces automatically-generated deep-framebuffers to reasonable size for complex production scenes and shaders. We also propose a new structure, the indirect framebuffer, that decouples shading samples from final pixels and allows a deepframebuffer to handle antialiasing, motion blur and transparency efficiently. Progressive refinement enables fast feedback at coarser resolution. We demonstrate our approach in real-world production. Configuring lights is a critical bottleneck in modern production rendering, and recent advances have sought to provide real-time preview using deep-framebuffers and graphics hardware [Gershbein and Hanrahan 2000; Pellacini et al. 2005] . A deep-framebuffer caches static values such as normals and texture samples in image space, and each time the user updates light parameters, realtime shaders interactively recompute the image from the cache. Unfortunately, these approaches require substantial additional work from shader authors. For example, in the lpics system deployed at Pixar , at least two versions of each shader need to be written in place of just one: the usual RenderMan shader used for the final rendering (with additional code paths to cache data), and a Cg version used for real-time preview.. We alleviate the need to author multiple versions of a shader by automatically translating unmodified RenderMan shaders into realtime shaders and precomputation shaders. This translation is part of a larger process that automatically generates deep-framebuffer data from unmodified existing scenes. In theory, some RenderMan code cannot be translated into GPU shaders, but we have found that, in practice, the dynamic parts of our production shaders translate well.. In contrast to pure static compiler analysis, we use postexecution cache compression to supplement a simple compiler analysis. Cache compression effectively reduces automaticallygenerated deep-framebuffers to reasonable size for complex production shaders.. In addition, transparency, motion blur and antialiasing can be critical to judge appearance. We introduce the indirect framebuffer, which enables these effects without linearly scaling rendering time.. Similar to RenderMan, it decouples shading from visibility, but also precomputes the final weight of each shading sample for the relevant final pixels. Given the complexity of shots that we handle, we also use progressive refinement to offer both interactive feedback (multiple frames per second) and faithful final quality (potentially after a few seconds).. Finally, it is important to facilitate the implementation of new passes in a preview system. We use a computation graph that directly expresses the dependencies and data-flow between passes to implement shadows and translucency.. We describe a full production relighting system that is being deployed in two studios with different rendering workflows. We have introduced a system for the real-time preview of RenderMan scenes during lighting design. Our method automatically specializes shaders into a static RenderMan pass that generates a deepframebuffer, and a dynamic Cg pass that uses the deep-framebuffer to enable real-time preview on a GPU. Cache compression enables automatically generated deep-framebuffers to fit in modest GPU memory for complex production shots. We have introduced the indirect framebuffer which efficiently encodes multisampling for high-quality rendering with transparency and motion blur. Our computation graph-based system architecture is flexible and is amenable to multipass rendering algorithms, which we demonstrate with shadow mapping and subsurface scattering.. We were surprised by the effectiveness of cache compression. Initially, we assumed we would build complex compiler analyses to control cache size. However, due to the data-parallel nature of shading, redundancy abounds, and simple post-processes easily uncover savings which static analysis could not recognize.. As a whole, our system brings a level of automation that greatly simplifies interactive lighting preview and alleviates the need to write and maintain different shaders for final rendering, preprocessing, and preview. However, it does not close the debate between manual instrumentation and automatic specialization. The manual programming of preview shaders can bring an extra level of flexibility, in particular to adapt the level of detail to further accelerate preview, as illustrated in lpics , though Pellacini separately showed that automatic level-of-detail can help [2005] . In the long run, we believe that lighting preview should be addressed in a way similar to traditional programming: automatic tools are provided for compilation and optimization, and the programmer can provide hints or manually optimize and simplify critical portions of the code based on profiling tools.. Still, the greatest limitation to deep-framebuffer rendering is its basis in local shading. As global illumination becomes prevalent in production rendering, the ability to integrate global effects into this system will determine its future success. Fortunately, our techniques are not specific to GPUs. Rather, they are generally useful for reducing complex shading to efficient data-parallel execution, including on future manycore CPUs, and this may ultimately be the avenue through which global effects are most efficiently achieved.

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


Partial isomorphisms over finite fields Abstract. In this paper, we construct a combinatorial algebra of partial isomorphisms that gives rise to a "projective limit" of the centers of the group algebras CGL(n, F q ). It allows us to prove a GL(n, F q )-analogue of a theorem of Farahat and Higman regarding products of conjugacy classes of permutations. In this paper, n is a positive integer; q is a prime power; F q is a finite field with q elements; and GL(n, F q ) is the group of invertible n × n matrices with coefficients in F q , or, equivalently, the group of linear isomorphisms of the F q -vector space (F q ). n . It will be convenient to write gh for the composition of isomorphisms h • g (beware of the order of composition). The matrix of an isomorphism u : V → W between two (F q )-vector spaces and with respect to two bases E = (e 1 , . . . , e n ) of V and F = (f 1 , . . . , f n ) of W is mat E,F (u) = mat F (u(e 1 ), . . . , u(e n )), the vectors being written in columns; then mat E,G (uv) = mat F ,G (v) mat E,F (u).. 1.1. Generic products in group algebras and the approach of Ivanov and Kerov. If G = GL(n, F q ), we shall be interested in the group algebra CG, and more precisely in its center Z(CG). A linear basis of Z(CG) is the set of conjugacy classes of G, viewed as the formal sums of their elements. We want to address the following kind of problem: Problem 1.1. Let a = b = 1 be two elements of (F q ). × , and C a,n and C b,n the conjugacy classes of the diagonal matrices of size n What is the expansion λ∈Λ(a,b) c λ a,b,n C λ in conjugacy classes of the product C a,n * C b,n computed in the center of the group algebra Z(CGL(n, F q ))?. At first sight, this problem might seem to be not so difficult: since D a and D b each leave invariant a subspace of dimension n − 1, the product P = AB of two matrices conjugated to D a and D b has to leave invariant a subspace of dimension at least n − 2, and as we shall see in a moment, this only leaves a few possibilities for the conjugacy class λ of P . Moreover, by looking at how many ways the eigenspaces of the matrices can intersect, one can guess that the coefficients c λ a,b,n count configurations of subspaces in (F q ) n , and are therefore rational functions in q and in its powers q k , up to k = n.. However, it is in practice quite hard to determine the set of possible conjugacy classes Λ(a, b); and also quite hard to compute the structure coefficients. The reader can already have a look at Theorem 4.1, to realize that the result depends in particular on:. • whether q is even or odd;. • in the odd case, whether ab is a square in (F q ) × or not.. Also, it is a priori unclear that the coefficients c λ a,b,n are in fact all polynomials in q n , with rational coefficients independent of n. One might understand intuitively why this is true from the previous informal discussion: every number of subspaces of fixed dimension k in (F q ) n is a polynomial in q n (of degree k), and the same holds when counting "finitedimensional configurations". However, a formal and rigorous proof demands a lot of combinatorial preparations, especially if one wants to compute the actual polynomials. The author conjectured this polynomiality a few years ago, and this will be one of the major result of the paper. We shall prove it in the general setting where D a and D b are replaced by arbitrary matrices A and B that are completed by 1's on the diagonal to obtain matrices of size n. We shall also provide a general framework which reduces all the computations to the case when n = k + l, k and l being the sizes of A and B (k = l = 1 for A = D a and B = D b ). In other words, knowing the structure coefficients c λ A,B,k+l , we shall have at the end of the paper an easy rule to compute all the coefficients c λ A,B,n .. A similar and simpler problem has been solved for permutations by Farahat and Higman in [FH59] , and forty years later, it was given a beautiful explanation by Ivanov and Kerov, see [IK99] . Fix two permutations σ and ρ of size k and l; we also assume σ and ρ without fixed points in [[1, k] ] and in [[1, l] ]. For any n ≥ max(k, l), one can view σ and ρ as elements of the symmetric group of order n, denoted S(n); σ fixes the integers after k and ρ fixes the integer after l. Denote C σ,n and C ρ,n the conjugacy classes of σ and ρ in CS(n). Then, there exists a unique finite set of permutations S(σ, ρ) ⊂ S(k + l), and polynomials p ν σ,ρ (n) with rational coefficients and integer values at integers, such that C σ,n * C ρ,n = ν∈S(σ,ρ) p ν σ,ρ (n) C ν,n .. Using the well-known labeling of conjugacy classes of S(n) by integer partitions of size n, one can of course restate this result with two partitions λ and µ of size k and l and without parts of size 1, and a finite set of partitions of size smaller than k + l and again without parts of size 1: (1) each algebra Z (n) projects onto Z(CS(n)) via a morphism π n ; (2) each algebra has a basis (A λ,n ) λ labelled by partitions λ of size smaller than n, with π n (A λ,n ) = p λ (n) C λ,n ; p λ (n) is an explicit polynomial in n;. (3) the bases A λ,n are compatible, i.e., if |λ| ≤ n, then the map Z (n + 1) → Z (n) sends A λ,n+1 to A λ,n .

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


Tracking Large-Scale Video Remix in Real-World Events Content sharing networks, such as YouTube, contain traces of both explicit online interactions (such as likes, comments, or subscriptions), as well as latent interactions (such as quoting, or remixing, parts of a video). We propose visual memes, or frequently re-posted short video segments, for detecting and monitoring such latent video interactions at scale. Visual memes are extracted by scalable detection algorithms that we develop, with high accuracy. We further augment visual memes with text, via a statistical model of latent topics. We model content interactions on YouTube with visual memes, defining several measures of influence and building predictive models for meme popularity. Experiments are carried out with over 2 million video shots from more than 40,000 videos on two prominent news events in 2009: the election in Iran and the swine flu epidemic. In these two events, a high percentage of videos contain remixed content, and it is apparent that traditional news media and citizen journalists have different roles in disseminating remixed content. We perform two quantitative evaluations for annotating visual memes and predicting their popularity. The proposed joint statistical model of visual memes and words outperforms an alternative concurrence model, with an average error of 2% for predicting meme volume and 17% for predicting meme lifespan. In this paper, we proposed visual memes for tracking and monitoring of real-world events on content sharing sites like YouTube. We described a system for monitoring event traces in social video, and proposed a scalable algorithm for extracting visual memes with high accuracy. Our system shows that detecting visual memes at a large scale is feasible. Furthermore, we have observed significant remix-behavior in videos discussing news events (up to 70% authors and 60% videos), and observed different user roles in propagating information. We designed a cross-modal matching method for annotating visual memes, and have observed that social network and content features both contribute to better predictions of meme popularity. We are releasing an open-source implementation of the meme-detection algorithm, along with the list of YouTube video ids and the meme detection results on the project page [2] .. We would like to point out some limitations of this work. Patterns on two long-running news topics are intended as case studies-there is more work needed before the conclusions generalize to all events or to online video-making in general. The scope and operational definition of video memes in this work is limited to video remixing that largely preserves the visual appearance and semantic content. One can potentially consider other definition of visual memes with larger variations in both appearance and semantics, such as "Dancing Matt", 2 a YouTube phenomena with an iconic person and action performed at different locations. The quantitative evaluation of video meme detection is based on annotations from a single trained annotator; the reliability of the annotation task is unknown.. Future work can take several directions. For example, we may leverage visual memes for better annotations and content search for online videos. We may also examine the sequence and timing of meme shots in relation to popularity and likelihood of a remix. Finally, we are interested in examining visual remixes in video genres other than news.

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Local maximum stable sets in bipartite graphs with uniquely restricted maximum matchings A maximum stable set in a graph G is a stable set of maximum size. S is a local maximum stable set of G, and we write S ∈ (G), if S is a maximum stable set of the subgraph spanned by S ∪ N (S), where N (S) is the neighborhood of S. A matching M is uniquely restricted if its saturated vertices induce a subgraph which has a unique perfect matching, namely M itself. Nemhauser and Trotter Jr. (Math. Programming 8(1975) 232-248), proved that any S ∈ (G) is a subset of a maximum stable set of G. In Levit and Mandrescu (Discrete Appl. Math., 124 (2002) 91-101) we have shown that the family (T ) of a forest T forms a greedoid on its vertex set. In this paper, we demonstrate that for a bipartite graph G, (G) is a greedoid on its vertex set if and only if all its maximum matchings are uniquely restricted. ? Throughout this paper G = (V; E) is a ÿnite, undirected, loopless graph without multiple edges with vertex set V = V (G) and edge set E = E(G). If X ⊂ V , then G[X ] is the subgraph of G induced by X . By G − W we mean the subgraph G[V − W ], if W ⊂ V (G). We also denote by G − F the partial subgraph of G obtained by deleting the edges of F, for F ⊂ E(G), and we write G − e, whenever F = {e}. If X; Y ⊂ V are Fig . 1 . Graphs with diverse proper local maximum stable sets. disjoint and non-empty, then (X; Y ) stands for the set {xy : xy ∈ E; x ∈ X; y ∈ Y }. The neighborhood of a vertex v ∈ V is the set N (v)={w : w ∈ V and vw ∈ E}. If |N (v)|=1, then v is a pendant vertex of G; by pend (G) we designate the set of all pendant vertices of G. Let K n ; C n denote, respectively, the complete graph on n ¿ 1 vertices and the chordless cycle on n ¿ 3 vertices. We write G = (A; B; E) for a bipartite graph having {A; B} as its standard bipartition.. A stable set in G is a set of pairwise non-adjacent vertices. A stable set of maximum size will be referred to as a maximum stable set of G, and the stability number of G, denoted by (G) , is the cardinality of a maximum stable set in G. Let (G) stand for the set of all maximum stable sets of G.. A set A ⊆ V (G) is a local maximum stable set of G if A is a maximum stable set in the subgraph spanned by N [A], i.e., A ∈ (G[N [A]]) [12] . In the sequel, by (G) we denote the set of all local maximum stable sets of the graph G. For instance, any set S ⊆ pend (G) belongs to (G) , while the converse is not generally true; e.g., {a}; {e; d} ∈ (G 2 ) and {e; d} ∩ pend(G 2 ) = ∅ (G 2 is presented in Fig. 1 ).. Not any stable set of a graph G is included in some maximum stable set of G. For example, there is no S ∈ (G 1 ) such that {c; g} ⊂ S, where G 1 is depicted in Fig. 1 . The following theorem due to Nemhauser and Trotter Jr. [16] , shows that some special stable sets can be enlarged to maximum stable sets. Theorem 1.1 (Newhauser and Trotter Jr. [16] ). Any local maximum stable set of a graph is a subset of a maximum stable set.. Let us notice that the formal converse of Theorem 1.1 is trivially true because (G) ⊆ (G). However, there exist graphs in which not all maximum stable sets include non-empty local maximum stable sets di erent from themselves. For example, in Fig. 1 , S 1 = {a; d; f; g} ∈ (G 1 ), but it has no non-empty local maximum stable set di erent from itself, while S 2 = {b; c; e; h} ∈ (G 1 ) includes {b} ∈ (G 1 ). Moreover, there are graphs having no local maximum stable sets but maximum stable sets and the empty set (e.g., C n ; n ¿ 4).. The graph G 2 in Fig. 1 shows another phenomenon, namely, any S ∈ (G 2 ) includes some non-empty local maximum stable set di erent from S, but these local maximum stable sets are of various cardinalities: for {a; c; f} ∈ (G 2 ) only {a} ∈ (G 2 ), while for {b; d; e} ∈ (G 2 ) only {d; e} ∈ (G 2 ). [9] ). A greedoid is a pair (E; F), where F ⊆ 2 E is a set system satisfying the following conditions: (Accessibility) for every non-empty X ∈ F there is an x ∈ X such that X − {x} ∈ F; (Exchange) for X; Y ∈ F; |X | = |Y | + 1, there is an. In [12] we have proved the following result. Theorem 1. 3 . The family of local maximum stable sets of a forest of order at least two forms a greedoid on its vertex set. Theorem 1.3 is not speciÿc for forests. For instance, the family (G) of the graph G in Fig. 2 is a greedoid. Clearly, (G) ⊆ (G) holds for any graph G. It is worth observing that if (G) is a greedoid and S ∈ (G), |S| = k ¿ 2, then by accessibility property, there is a chain such that {x 1 ; x 2 ; : : : ; x j } ∈ (G), for all j ∈ {1; : : : ; k − 1}. Such a chain we call an accessibility chain of S. As an example, for S = {a; c; e} ∈ (G), where G is the graph in Fig. 2 , an accessibility chain is {a} ⊂ {a; e} ⊂ S.. A matching in a graph G = (V; E) is a set of edges M ⊆ E such that no two edges of M share a common vertex. A cycle C is M -alternating if for any two incident edges of C exactly one of them belongs to the matching M , (see [10] ). It is clear that an M -alternating cycle should be of even size.. A matching M in G is called alternating cycle-free if G has no M -alternating cycle. Alternating cycle-free matchings for bipartite graphs were ÿrst deÿned in [10] , where these matchings appear in some matroidal problems, and in [7] as a tool for generating all the maximum matchings of a bipartite graph. This kind of matchings was also investigated in connection with the so-called jump-number problem for partially ordered sets (see [2, 14, 15] . This notion for bipartite graphs was ÿrst introduced in [10] under the name clean matching. It appears also in the context of matrix theory, as a constrained matching (see [6] ).. A matching M of a bipartite graph is uniquely restricted if and only if M is alternating cycle-free (see [10] ). This result was proved for general graphs in [5] .. We denote the size of a maximum matching (a matching of maximum cardinality) by (G) . A perfect matching is a matching saturating all the vertices of the graph.. Let r (G) be the maximum size of a uniquely restricted matching in G. Clearly, 0 6 r (G) 6 (G) holds for any graph G, e.g., r (C 2n ) = n − 1 ¡ n = (C 2n ), while. In this paper, we characterize the bipartite graphs whose families of local maximum stable sets are greedoids. Namely, we prove that for a bipartite graph G; the family (G) is a greedoid on the vertex set of G if and only if all its maximum matchings are uniquely restricted.. Golumbic, Hirst and Lewenstein have shown in [5] that r (G) = (G) holds when G is a tree or it has only odd cycles. In Theorem 3.2, we give a characterization in terms of accessibility chains for another class of graphs enjoying this equality. We have shown that to have all maximum matchings uniquely restricted is necessary and su cient for a bipartite graph G to enjoy the property that (G) is a greedoid.. We have also found the recursive structure of bipartite graphs having a unique perfect matching. It seems to be interesting to describe a recursive structure of general bipartite graphs whose (G) is a greedoid.. A linear time algorithm to decide whether a matching in a bipartite graph is uniquely restricted is presented in [5] . It is also shown there that the problem of ÿnding a maximum uniquely restricted matching is NP-complete for bipartite graphs. These results motivate us to propose another open problem, namely: how to recognize such bipartite graphs that all their maximum matchings are uniquely restricted?

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


Can relevance of images be inferred from eye movements? Query formulation and efficient navigation through data to reach relevant results are undoubtedly major challenges for image or video retrieval. Queries of good quality are typically not available and the search process needs to rely on relevance feedback given by the user, which makes the search process iterative. Giving explicit relevance feedback is laborious, not always easy, and may even be impossible in ubiquitous computing scenarios. A central question then is: Is it possible to replace or complement scarce explicit feedback with implicit feedback inferred from various sensors not specifically designed for the task? In this paper, we present preliminary results on inferring the relevance of images based on implicit feedback about users' attention, measured using an eye tracking device. It is shown that, in reasonably controlled setups at least, already fairly simple features and classifiers are capable of detecting the relevance based on eye movements alone, without using any explicit feedback. For text retrieval it is relatively easy to define a good set of keywords to be used as query terms. For most other information retrieval (IR) tasks, such as image searches, it is far more difficult to construct efficient queries. Existing solutions typically apply either of two predominant strategies: text-based queries on metadata, or iterative retrieval based on low-level features computed from the content. For the metadata strategy, all the readily available textual IR tools can be used, but the results depend heavily on the quality of the metadata. Constructing good metadata is laborious, time-consuming, expensive and, furthermore, the metadata cannot cover all aspects of the images.. The content-based approach [3, 10] sidesteps the need for metadata, but faces the problem that constructing useful and reliable features is difficult. Even with a good feature representation, it is not straightforward to formulate a query in terms of the features. In an image retrieval task the features may be complex descriptors of local image characteristics, such as color or texture, and a user cannot be expected to specify those manually. The most practical solution is to use pictorial examples for querying, as is done in the QBIC system [5] and in most state-of-the-art methods.. Regardless of the type of query, the modern content-based image retrieval (CBIR) systems, such as PicSOM [9] , are typically highly interactive. The user is asked to refine the search by providing explicit feedback on the results, for instance by clicking on the relevant images. The images can then be analyzed and compared with images in a dataset for retrieval. Methods for image comparison have been progressing steadily, both in accuracy and in robustness, as reported in yearly benchmarks such as the PASCAL VOC [4] classification task. In classification, though, the set of classes is known a priori, and there is usually a significant number of training samples from each class.. For CBIR tasks, explicit feedback is accurate, but laborious for the user and limited in complexity. A user may at most be willing to click a few relevant images. In addition to explicit feedback, it is also possible to collect implicit feedback from sources not directly controlled by the user [8] .. Various sensors can be used for monitoring the user and the context of the search, and the implicit feedback from them could be used to either complement the explicit feedback to improve search quality, or even to replace it completely. Indeed this is the approach we take in this paper. It is important to note that sensors are typically noisy and they cannot be focused to measure solely relevance. One particular challenge of this approach is to infer the correct relevance feedback from amongst the noise and nuisance signals. In this paper, we made preliminary experiments on inferring relevance of images from the eye movement trajectory of the users. It was shown that at least in a simplified test setup it is possible to detect the relevant images reasonably accurately, even with a simple classifier that uses a set of relatively simple eye movement features. This classifier is able to detect the relevance even when not using any training data from the particular user in question, which suggests that the gaze patterns of different users are similar enough for tasks of this kind.. The visual information contained in the image dataset was evaluated with the image categorization method of [12] , showing that it is possible to obtain accurate search results if enough feedback is provided, even if the querry is not straightforwardly described by image features. A natural continuation of this work is to integrate the implicit feedback obtained from eye tracking with a CBIR system based on [12] .. Relevance of texts has earlier been estimated based on eye movements. In the most closely related works [13] , wordspecific features were computed from the gaze trajectory, averaged over the text, and classified with a simple classifier. The features were derived from reading studies, and include total lengths of fixations, lengths of transitions between words etc. We used similar features, modified to be suitable for images. Features more optimal for images need to be developed in future studies, although even the current set performed very well. Another direction forward is to model the sequence of browsing interleaved with inspection of the images, instead of simply averaging all features over the images. For the texts the browsing was modeled with a Markov Chain, in which each state is a Hidden Markov Model which models the reading pattern. Table 4 : Results of the relevance determination tasks. The first two columns (Page relevance) are for the experiment on detecting whether the page contains a relevant image. The reported values are area under curve (AUC) scores, and the baseline of random guessing would be 0.5. The last two columns (Image relevance) are for predicting which of the 4 images was relevant, and the reported values are classification accuracies (percentage of correct choices). Random guessing would achieve a score of 25%. In both experiment types, user-specific refers to a model trained with all other pages of the particular user, whereas global refers to a model trained with all other users except the one being tested.

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


IMAGE RECOGNITION USING SIMPLIFIED FUZZY ARTMAP AUGMENTED WITH A MOMENT BASED FEATURE EXTRACTOR The capability of Kasuba's Simplified Fuzzy ARTMAP (SFAM) to behave as a Pattern Recognizer/Classifier of images both noisy and noise free has been investigated in this paper. This calls for augmenting the original Neuro-Fuzzy model with a modified moment-based RST invariant feature extractor.The potential of the SFAM based Pattern Recognizer to recognize patternsmonochrome and color, noisy and noise free -has been studied on two experimental problems. The first experiment which concerns monochrome images, pertains to recognition of satellite images, a problem discussed by Wang et al. The second experiment, which concerns color images, deals with the recognition of some sample test colored patterns. The results of the computer simulation have also been presented. Pattern Recognition (PR) which is a science that deals with the description or classification (recognition) of measurements has turned out to be an important component of a dominant technology such as Machine Intelligence. Various approaches to pattern recognition include statistical (or decision theoretic), syntactic (or structural) and neural approaches. 1 Of the three major approaches, neural technology is emerging quickly as a powerful means to solve PR problems.. Fuzzy Logic (FL), which has turned out to be an excellent computational methodology has significantly contributed to the solution of PR problems. [2] [3] [4] [5] [6] The fusion of Neural Networks and Fuzzy Systems, 7 termed Neuro-Fuzzy Systems have also been applied for the solution of various PR applications.. ARTMAP is a class of Neural Network architectures that perform incremental supervised learning of recognition categories and multidimensional maps in response to input vectors presented in arbitrary order. 8 The first ARTMAP 9 system classified input patterns represented as binary values. Carpenter et al. 4 refined the system to a general one by incorporating Fuzzy ART dynamics and termed it Fuzzy ARTMAP. Fuzzy ARTMAP responds to both analog and fuzzy patterns. Kasuba 6 propounded the Simplified Fuzzy ARTMAP (SFAM) system which is a vast simplification of Fuzzy ARTMAP. The network is a step ahead of Fuzzy ARTMAP in reducing the computational overhead and architectural redundancy of Fuzzy ARTMAP.. However, Kasuba's SFAM despite its simplicity, does not display the characteristic of tolerance to pattern perturbations or noise while processing images. In this paper, the pattern recognition capability of SFAM has been discussed. The architecture augmented with a moment-based feature extractor exhibits an excellent capability to recognize patterns by working on the RST invariant feature vectors of the patterns rather than the patterns themselves.. The augmented architecture can handle both symmetric and asymmetric patterns. In the case of asymmetric patterns the RST invariant functions φ 1 − φ 7 turn out to be the same for all perturbations of a given pattern. Hence SFAM has no difficulty in recognizing perturbed patterns since it only calls for associating the same feature vectors with the top-down weight vectors which results in the invocation of the same category node. This results in the correct identification of the perturbed pattern.. However, in the case of symmetric patterns it is essential that only distinct portions of the images be trained. This is so since in the case of doubly symmetric or in general, multisymmetric images, their RST invariant feature vectors φ 2 − φ 7 acquire values which are very close to 0, and φ 1 tends to 1. This consequently results in feature vectors which are almost similar leading to a misclassification of patterns. Hence in the case of multisymmetric patterns, it is sufficient to consider

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


A second order PVM flux limiter method. Application to magnetohydrodynamics and shallow stratified flows In this work we propose a second order flux limiter finite volume method, named PVM-2U-FL, that only uses information of the two external waves of the hyperbolic system. This method could be seen as a natural extension of the well known WAF method introduced by Prof. Toro in [21] . We prove that independently of the number of unknowns of the 1D system, it recovers the second order accuracy at regular zones, while in presence of discontinuities, the scheme degenerates to PVM-2U method, which can be seen as an improvement of the HLL method (see [4] , [8] ). Another interesting property of the method is that it does not need any spectral decomposition of the Jacobian or Roe matrix associated to the flux function. Therefore, it can be easily applied to systems with a large number of unknowns or in situations where no analytical expression of the eigenvalues or eigenvectors are known. In this work, we apply the proposed method to Magnetohydrodynamics and to stratified multilayer flows. Comparison with the twowaves WAF and HLL-MUSCL methods are also presented. The numerical results show that PVM-2U-FL is the most efficient and accurate among them. The goal of this article is to design a robust, simple and fast second order flux limiter numerical scheme for solving one dimensional hyperbolic systems. An interesting technique to obtain second order accurate and robust schemes is to use a non-linear combination of first and second order methods in terms of flux limiters functions. An example of this type scheme can be defined by a suitable combination of Roe method (which is only first order) near discontinuities, and the Lax-Wendroff method (which is second order in space and time) in regular areas. Note that the previous scheme requires the explicit knowledge of the eigenstructure of the system, which is not straight forward for some hyperbolic systems, making this scheme computationally expensive in those cases.. It is also well known that the use of incomplete Riemann solvers as Rusanov, Lax-Friedrichs, HLL, among others (see [11] , [25] , [5] , [8] , [29] ) allows one to reduce the computing time required by a Roe solver (see, for instance, [9] ). Although Roe scheme gives, in general, a better resolution of the discontinuities than incomplete Riemann solvers, when combined with high order methods may be indistinguishable.. In [4] Castro and Fernández-Nieto introduce a family of incomplete simple Riemann solvers named as PVM (Polynomial Viscosity Matrix), for conservative and nonconservative hyperbolic systems, defined in terms of viscosity matrices computed by a suitable polynomial evaluation of a Roe linearization, that overcome the difficulty of the computation of the spectral decomposition of Roe matrices. PVM schemes can be seen as the natural extension of the one proposed in [8] for balance laws, and, more generally, for nonconservative systems.. An interesting numerical scheme that uses flux limiters functions is the WAF (Weighted Average Flux) method, introduced by Toro in [21] . It is a one-step Godunov-type method to solve hyperbolic conservation laws that achieves second order accuracy by averaging the solution of the Riemann problem with piecewise constant initial data. As it is well known, due to Godunov's theorem, linear schemes with high order accuracy generate spurious oscillations near discontinuities. To avoid this problem, WAF method uses flux limiter functions. The resulting scheme is a non-linear TVD (Total Variation Diminishing) scheme with second order accuracy. WAF scheme has been extensively used to approximate hyperbolic systems, see for example [22] , [23] , [10] , [24] , [28] . It has been also used as the base of higher order numerical solvers (see [27] ).. Nevertheless, WAF method needs the explicit knowledge of the structure of the approximated Riemann problem to achieve second order accuracy. For example, if we only consider the information of the two external waves and we use the HLL intermediate flux we obtain a WAF method -that we will name in what follows HLL-WAF method-that has second order accuracy for 1D 2x2 hyperbolic conservative systems.. The main objective of this paper is to obtain a new flux limiter scheme that only uses the information of the two external waves, like the HLL-WAF scheme, and that achieves second order accuracy for 1D N × N hyperbolic systems with N ≥ 2. The resulting scheme can be seen as a natural extension of the original HLL-WAF scheme and it is defined in terms of a non-linear combination of a suitable PVM scheme, that is first order, with the second order Lax-Wendroff scheme.. The paper is organized as follows: in Section 2, first we summarize how WAF and, in particular, HLL-WAF methods are derived. Next, HLL-WAF method is rewritten as a nonlinear combination of two PVM schemes. In section 3, the new flux limiter scheme is defined and finally, some numerical tests for the 1D ideal magnetohydrodynamics and the multilayer shallow-water systems are presented. Comparison with HLL-WAF and the second order HLL methods with MUSCL (see [12] , [13] , [32] ) state reconstruction are also provided. In this paper we present a computationally fast and efficient second order flux limiter finite volume method. It can be seen as a generalization of the HLL-WAF method, in the sense that it uses flux limiter functions to combine an incomplete Rieman solver with another one that allows to recover the second order accuracy in smooth regions. Moreover, both methods: HLL-WAF and PVM-2U-FL, only uses information of the two external waves. But: (i) the HLL-WAF method only recovers second order accuracy for 1D systems with two unknowns, while PVM-2U-FL recovers second order for arbitrary 1D systems; (ii) HLL-WAF degenerates to HLL method near discontinuities, while the PVM-2U-FL method degenerates to the PVM-2U method. Let us remark that the PVM-2U can be seen as a generalization of the HLL method.. Application to conservative and nonconservative systems are provided. In both cases, the numerical tests show that PVM-2U-FL is the most efficient among the compared methods and HLL-WAF and HLL-MUSCL provide similar results for MHD, but HLL-MUSCL provides better results than HLL-WAF for the multilayer shallow water system. Moreover, PVM-2U-FL is also the most accurate among them for a fixed mesh, being HLL-MUSCL more accurate than HLL-WAF for a given mesh. The extension to higher dimensions is not straightforward and it will be considered in future works.

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Converting a swap-based system to do paging in an architecture lacking page-referenced bits Abstract-This paper discusses the modifications made to the UNIX operating system for the VAX-11/780 to convert it from a swap-based segmented system to a paging-based virtual memory system. Of particular interest is that the host machine architecture does not include page-referenced bits. We discuss considerations in the design of pagereplacement and load-control policies for such an architecture, and outline current work in modeling the policies employed by the system. We describe our experience with the chosen algorithms based on benchmark-driven studies and production system use.In the fall of 1978 the Cemputer Science Division of the University, of California at Berkeley purchased a VAX-11/78~, and arranged to run an early version of UNIX for the VAX provided by Bell Laboratories under a cooperative research agreement.The VAX was purchased because it is a 32-bit machine with a large address space, and we had hopes of running UNIXD which was being used on other smaller machines. Permission to copy without fee all or part of this material is granted provided that the copies arc not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. @ 1981 ACM 0-89791-06g-1-12/81-0078 $00.75Except for the machine-dependent sections of code, UNIX for the VAX was quite similar to that for the PDP-II which has a 16-bit address space and no paging hardware.It made no use of the memorymanagement hardware available on the VAX aside from simulating the PDP-II segment registers with VAX page table entries.The main-memory management schemes employed by this first version of the system were identical to their PDP-II counterparts--processes were allocated contiguous blocks of real memory on a first-fit basis and were swapped in their entirety. A subsequent version of the system was capable of loading processes into noncontiguous real memory locations, called scatter ~, and was able to swap only portions of a process, called partial swapping, as deemed necessary by the memory contention.This would become the basis for the paging system development discussed in this paper.The user-friendliness and portability of the UNIX environment were perceived to be large advantages by our installation.However, application progrems outgrew our resources with the original swap-based system very quickly. The initial configuration of the machine had only i/2 megabyte of real memory. Although, in the long run more memory would be available, it seemed natural to incorporate paging into UNIX and thereby support larger applications and make better use of our limited main memory. This would also provide a vehicle for researching storage hierarchy performance evaluation and memory-management techniques.

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Privacy in electronic commerce and the economics of immediate gratification Dichotomies between privacy attitudes and behavior have been noted in the literature but not yet fully explained. We apply lessons from the research on behavioral economics to understand the individual decision making process with respect to privacy in electronic commerce. We show that it is unrealistic to expect individual rationality in this context. Models of self-control problems and immediate gratification offer more realistic descriptions of the decision process and are more consistent with currently available data. In particular, we show why individuals who may genuinely want to protect their privacy might not do so because of psychological distortions well documented in the behavioral literature; we show that these distortions may affect not only 'naïve' individuals but also 'sophisticated' ones; and we prove that this may occur also when individuals perceive the risks from not protecting their privacy as significant. The technology d may or may not be privacy enhancing. Since the payoffs in Equation 1 can be either positive or negative, Equation 1 embodies the duality implicit in privacy issues: there are both costs and benefits gained from revealing or from protecting personal information, and the costs and benefits from completing a transaction, vE (t), might be distinct from the costs and benefits from keeping the associated information private, vE (a). For instance, revealing one's identity to an on-line bookstore may earn a discount. Viceversa, it may also cost a larger bill, because of price discrimination. Protecting one's financial privacy by not divulging credit card information on-line may protect against future losses and hassles related to identity theft. But it may make one's on-line shopping experience more cumbersome, and therefore more expensive.. The functional parameters δ and γ embody the variable weights and attitudes an individual may have towards keeping her information private (for example, her privacy sensitivity, or her belief that privacy is a right whose respect should be enforced by the government) and completing certain transactions. Note that vE and p could refer to sets of payoffs and the associated probabilities of occurrence. The payoffs are themselves only expected because, regardless of the probability that the transaction is completed or the information remains private, they may depend on other sets of events and their associated probabilities. vE() and p d (), in other words, can be read as multi-variate parameters inside which are hidden several other variables, expectations, and functions because of the complexity of the privacy network described above.. Over time, the probability of keeping certain information private, for instance, will not only depend on the chosen technology d but also on the efforts by other parties to appropriate that information. These efforts may be function, among other things, of the expected value of that information to those parties. The probability of keeping information private will also depend on the environment in which the transaction is taking place. Similarly, the expected benefit from keeping information private will also be a collection over time of probability distributions dependent on several parameters. Imagine that the probability of keeping your financial transactions private is very high when you use a bank in Bermuda: still, the expected value from keeping your financial information confidential will depend on a number of other factors.. A rational agent would, in theory, choose the technology d that maximizes her expected payoff in Equation 1. Maybe she would choose to complete the transaction under the protection of a privacy enhancing technology. Maybe she would complete the transaction without protection. Maybe she would not complete the transaction at all (d = 0). For example, the agent may consider the costs and benefits of sending an email through an anonymous MIX-net system [8] and compare those to the costs and benefits of sending that email through a conventional, non-anonymous channel. The magnitudes of the parameters in Equation 1 will change with the chosen technology. MIX-net systems may decrease the expected losses from privacy intrusions. Nonanonymous email systems may promise comparably higher reliability and (possibly) reduced costs of operations. Applying models of self-control bias and immediate gratification to the study of privacy decision making may offer a new perspective on the ongoing privacy debate. We have shown that a model of rational privacy behavior is unrealistic, while models based on psychological distortions offer a more accurate depiction of the decision process. We have shown why individuals who genuinely would like to protect their privacy may not do so because of psychological distortions well documented in the behavioral economics literature. We have highlighted that these distortions may affect not only naïve individuals but also sophisticated ones. Surprisingly, we have also found that these inconsistencies may occur when individuals perceive the risks from not protecting their privacy as significant.. Additional uncertainties, risk aversion, and varying attitudes towards losses and gains may be confounding elements in our analysis. Empirical validation is necessary to calibrate the effects of different factors.. An empirical analysis may start with the comparison of available data on the adoption rate of privacy technologies that offer immediate refuge from minor but pressing privacy concerns (for example, 'do not call' marketing lists), with data on the adoption of privacy technologies that offer less obviously perceivable protection from more dangerous but also less visible privacy risks (for example, identity theft insurances). However, only an experimental approach over different periods of time in a controlled environment may allow us to disentangle the influence of several factors. Surveys alone cannot suffice, since we have shown why survey-time attitudes will rarely match decision-time actions. An experimental verification is part of our ongoing research agenda.. The psychological distortions we have discussed may be considered in the ongoing debate on how to deal with the privacy problem: industry self-regulation, users' self protection (through technology or other strategies), or government's intervention. The conclusions we have reached suggest that individuals may not be trusted to make decisions in their best interests when it comes to privacy. This does not mean that privacy technologies are ineffective. On the contrary, our results, by aiming at offering a more realistic model of user-behavior, could be of help to technologists in their design of privacy enhancing tools. However, our results also imply that technology alone or awareness alone may not address the heart of the privacy problem. Improved technologies (with lower costs of adoption and protection) and more information about risks and opportunities certainly can help. However, more fundamental human behavioral mechanisms must also be addressed. Self-regulation, even in presence of complete information and awareness, may not be trusted to work for the same reasons. A combination of technology, awareness, and regulative policies -calibrated to generate and enforce liabilities and incentives for the appropriate parties -may be needed for privacy-related welfare increase (as in other areas of an economy: see on a related analysis [25] ).. Observing that people do not want to pay for privacy or do not care about privacy, therefore, is only a half truth. People may not be able to act as economically rational agents when it comes to personal privacy. And the question whether "do consumers care?" is a different question from "does privacy matter?" Whether from an economic standpoint privacy ought to be protected or not, is still an open question. It is a question that involves defining specific contexts in which the concept of privacy is being invoked. But the value of privacy eventually goes beyond the realms of economic reasoning and cost benefit analysis, and ends up relating to one's views on society and freedom. Still, even from a purely economic perspective, anecdotal evidence suggest that the costs of privacy (from spam to identity theft, lost sales, intrusions, and the like [30, 12, 17, 33, 26] ) are high and increasing.

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