A computer-vision based precision seed drill guidance assistance Abstract: This paper presents a control mechanism aiming to position seed drills relative to the previous lines, while sowing. The position was measured by a machine vision system and used in a feedback control loop. An articulated mechanism was used to ensure the lateral displacement of the drill relative to the tractor. The behaviour of the whole outfit was studied during several field tests. The standard deviation of the error, measured as the difference between the observed inter-row distance and its set value, was 23 mm and its range was less than 100 mm, which was sufficient to fulfil the requirements of the application. Sources of systematic errors were also identified as linked to the geometric considerations. Their correction requires an accurate mounting of the camera, which may be possible for a serial montage. The lateral position of a seed drill relative to a previous passage is usually controlled by the driver of the tractor. The accuracy of this task determines the feasibility and performance of subsequent works like mechanical weeding or harvesting with machines having a width not matching the seeding width (in term of line number). For example, most sugarbeet seed drills in Belgium are of 12 rows width while the harvesters are of 6 rows. Some machine manufacturers, however, have shown an interest in eight-row-harvesters to enhance the harvesting performances. To operate a harvester of one configuration on a field sown using a different configuration, the range of the movements of the drill needs to be kept to less than 150 mm around the nominal inter-row spacing (usually 450 mm for sugarbeet). Though an experienced operator may be able to achieve the required driving performance, this task requires concentration and is difficult to maintain for a long period of time. The supervision of the drill itself is also part of the driver's task and adds to the driver's load. Under these circumstances, driving assistance would be welcome.. The problem of automatically guiding a tool in a field using machine vision is not new. This literature review is limited to papers concerning prototypes used for field agricultural applications. Recent researches can be divided into either autonomous steering or guidance assistance. The techniques were used to evaluate the position of the tool in the field relative to the crop rows or to the edge of the harvested part of the field. For each research, the hardware is presented first, the image analysis techniques are described and finally the post-processing of the rough data extracted from the image (and from other sensor, if applicable), used to control the actuator are mentioned. Billingsley and Schoenfisch (1997) presented a method to steer a tractor by following crop rows such as cotton. For this kind of crop, depending on the stage of growth, the row does not always appear continuous in the images. In order to localise crop rows, the pixels were segmented between 'plant' and the surrounding by thresholding, the value of the threshold being determined by the proportion of the image occupied by plants. The row detection algorithm searched lines by regression, in 'viewports' centred on the previous detected rows. The fitted lines were defined by an offset and a slope parameters, in direct relation with the state variable of the vehicle location (the lateral position of the tractor, the heading angle and the steering angle). Hague et al. (2000) presented an experimental autonomous vehicle guided by a machine vision system and additional sensors (odometers and inertial sensors). This vehicle was able to follow cauliflowers rows and to ensure a dead reckoning. A Hough transform was used to localise the row structure and an extended Kalman filter ensured the fusion of the different sensors information and to obtain the position of the vehicle. Tillett and Hague (1999) and Tillett et al. (2002) developed a hoeing system for weed control in sugar beets. The camera was mounted on a hoe attached to the tractor three-point linkage through a mechanism allowing a lateral displacement. The camera was a mono-chrome charge-coupled device (CCD) equipped with a near infra-red bandpass Alter. Its height was of 1.14 m and inclined at 40° on the vertical. The lateral displacements were controlled by two hydraulic cylinders and the hydraulic flow was commanded by a three-position-valve. A linear variable differential transformer gave the lateral position of the hoe. In Tillett et al. (2002) , the image was divided horizontally into height bands. Each of these sub-images were merged vertically to give horizontal profiles. The position of the crop rows were found by matching a template profile. The extended Kalman filter was used to track the lateral position of the hoe relative to the rows. The measurements were made up of the position in each band and the state vector had three elements: the lateral position, the heading angle and a correction for the camera misalignment. During the computation of the recursive Alter, the error between the measured and the expected position was evaluated. If this value was too big, the data were ignored, otherwise they were incorporated in the state estimation. The trueness was below 10 mm, while the precision was within 16 mm. Authors also presented the minimum development stage required and the behaviour in presence off gaps in the culture rows. Pilarski et al. (2002) presented an automated self-propelled windrower with a control based on a camera or on a differential global positioning system (DGPS) associated with other sensors (inertial and wheel encoder). These systems were tested independently to harvest a field autonomously, after the 'opening up' by a human operator. Two cameras were mounted on the side of the cab of a self-propelled windrower. The edges of the cut and uncut areas were localised using the difference in reflectance, after a compensation of the shadows. Authors also detected the end of crop row (where there was no more edge) and obstacles (based on their different colour). The DGPS data were acquired at 5 Hz and other sensors were used to extrapolate the position. The system could work at 1.5-2.0 m/s. The vision-based error was in range of 50-300 mm, while the error of the DGPS was in a range from 40 to 60 mm. Søgaard and Olsen (2003) mounted a camera on a hand-operated vehicle and later on a weeder to evaluate the precision of an algorithm based on image analysis. The camera height was of 1.15 m and the inclination of the optical axis on the vertical of 56°. The images were also divided into band strip, which were mathematically 'enrolled'. The centre of gravity gave the position and an estimation of the relative accuracy. A weighted linear regression gave the position of the rows. The mean position returned by their algorithm (trueness) was centred with the reference trace, with no statistical differences. The standard deviation (precision) was below 5 mm in the centre of the image and about three times higher under the camera (this was 1.73 m at the rear of the image centre). The working speed was of 0.4 m/s. These results, however, concerned the image analysis, not the position of a tool.. The aim of this work was similar to row tracking, but there is a significant difference: the detection was based on images where, being of the same nature, there was poor contrast between a target (the seed lines) and its surrounding. In a previous paper (Leemans and Destain, 2006) , a background subtraction and a modified Hough transform were used to localise the seed lines. The noise was neither Gaussian nor white. In those circumstances, a non-linear recursive Alter gave better results than the Kalman Alter. This paper aims to show that the signals issued from that process are useful to control the lateral seed drill movements. The performances of a guidance assistance mechanism and control algorithm, dedicated to sugarbeet precision seed drill, was analysed during field tests. The data of the control program were recorded and the difference between the observed inter-row distance and its nominal value (450 mm) were measured on the ground.. It was found that the trueness of the system (the mean of the difference between the measurement and the set value) was strongly influenced by the mounting of the camera. When this camera was mounted properly, the trueness was below 30 mm, due mainly to the aspect of the trace left by the drill. The precision (the standard deviation of the above difference) was of 23 mm and the range of 100 mm, while the corresponding values for the tractor were of 73 and 272 mm, respectively. These values would ensure the compatibility of seed drill equipped with a guidance assistance with a non-matching width harvesting machine. During the field tests, the driver was asked to drive normally for some tests and to drive sinusoidally for the others. The value of precision and range of the former driving style were lower than those of the latter. This means that rapid direction changes should be avoided, in favour of a smoother driving style.. A detailed analysis of the results showed that the whole system acted as a low pass filter having a cut-off spatial frequency of 0.14 m -1. . Apart from the measurement noise, two other errors sources were identified. The first one was linked to the distance between the estimated position of the drill (at the bottom of the image) and the actual position and the second to the difference of curvature between the previous row and the actual trajectory. The correction of the first error as well as the increase of the cut-off frequency could be achieved by better taking into account the orientation of trace in the image. This requires accurate mounting of the camera, which was not possible in the context of these experiments, but could be achieved for an industrial application.

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Analytical Solution of Covariance Evolution for Irregular LDPC Codes A scaling law developed by Amraoui et al. is a powerful technique to estimate the block error probability of finite length low-density parity-check (LDPC) codes. Solving a system of differential equations called covariance evolution is a method to obtain the scaling parameter. However, the covariance evolution has not been analytically solved. In this paper, we present the analytical solution of the covariance evolution for irregular LDPC code ensembles. Gallager invented low-density parity-check (LDPC) codes [1] . LDPC codes are linear codes defined by sparse bipartite graphs, called Tanner graphs. Peeling algorithm (PA) [3] , [7] introduced by Luby et al. is a sequential iterative decoding algorithm for the binary erasure channel (BEC). As PA proceeds, edges and nodes are progressively removed from the original Tanner graph and the so-called residual graph is left at each iteration. The residual graph at each iteration consists of variable nodes that are still unknown and the check nodes and the edges connecting to those variable nodes. The decoding successfully halts if and only if the residual graph vanishes. It is known that PA and brief propagation (BP) decoder have the same decoding result.. The scaling law developed by Amraoui et al. [6] is a powerful technique to estimate the block and bit error probability of finite length LDPC codes. Let r i and l j be random variables representing the number of edges connecting to the check nodes of degree i and the variable nodes of degree j, respectively, in the residual graph. Then, the scaling parameter is obtained from the mean and the variance of r 1 . The means of r i and l j are determined from a system of differential equations which was derived and analytically solved by Luby et al. [3] . The covariances of r i and l j also satisfy a system of differential equations called covariance evolution which was derived by Amraoui et al. [6] . However, the analytical solution of the covariance evolution has not been known. Therefore, one had to resort to numerical computation to solve the covariance evolution.. In [5] , Amraoui et al. proposed an alternative way to determine the variance of r 1 , though only at the decoding threshold. Thereby they have given the analytic expression for the scaling parameters without using covariance evolution. They used BP decoding instead of PA. This method was applied to irregular repeat-accumulate codes in [9] , [10] and to turbo-like codes in [11] and was extended to binary memoryless symmetric channels in [8] .. Denote by ξ the total number of edges in the Tanner graph. Let µ i be the random variable which is 1 if the edge i conveys an erasure message from a variable node to a check node, and 0 otherwise, in the BP decoding. The method in [5] analyzed the random variable M := ξ i=1 µ i in the BP decoding and derived the analytical expression for the variance of M . Finally, they did make an unproved assumption that the random variable r 1 − E[r 1 ] in PA is proportional to the random variable M − E[M ] in BP and under this assumption they have given the analytical solution for the variance of r 1 .. However, no such assumption is needed if the covariance evolution is solved analytically. Moreover, we can obtain the variance of r 1 at any channel erasure probability. In this paper, we present the analytical solution of the covariance evolution for irregular LDPC code ensembles.

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A Close-to-linear Topic Detection Algorithm using Relative Entropy based Relevance Model and Inverted Indices Retrieval Although timely access to information is becoming increasingly important and gaining such access is no longer a problem, the capacity for humans to assimilate such huge amounts of information is limited. Topic Detection(TD) is then a promising research area that addresses speedy access of desired information. However, ironically, the time complexity of existing TD algorithms themselves is usually O(n 3 ) or up to the x-th power of e. Linear performance requirement of real world topic detection has not been significantly addressed. This paper reveals a new patented topic detection algorithm called RMIR that combines relevance model with information retrieval technique to improve on time efficiency. Relevance Model(RM) is a theoretical extension of statistical language modeling that was developed for the task of document retrieval. To reduce the costs of fetching RM, we reduce the number of comparisons for stories by a query-based approach that makes similar stories exist in the top-k query results. We also build our query based on inverted indices, which have the complexity close to linear. The time cost of rest of operations in the RMIR topic detection process is a constant. Hence, the total complexity of RMIR topic detection algorithm should be close to linear as shown in experimental results. In addition, RMIR also gains better detection rates and robustness by relative entropy based topic model design. Although timely access to information is becoming increasingly important in today's knowledge-based economy, gaining such access is no longer a problem because of the widespread availability of broadband in both homes and businesses. Ironically, high-speed connectivity and the explosion in the volume of digitized textual content available online has given rise to a new problem, namely, information overload. Clearly, the capacity for humans to assimilate such vast amounts of information is limited. Topic Detection (TD) has emerged as a promising research area that harnesses the power of modern computing to address this new problem.. A topic is defined as a seminal event or activity, along with all directly related events and activities [2] . Thus, we can infer that a topic consists of events and activities, both of which are defined in greater detail [1] . A Topic Detection and Tracking(TDT) event is defined as something that happens at a specific time and place, along with all the necessary preconditions and unavoidable consequences [1] . Such an event might be a car accident, a meeting, or a court hearing. A TDT activity is a connected series of events with a common focus or purpose that happens in specific places [1] . TD corresponds to a newsworthy incident such as the 2012 US presidential elections. Moreover, TDT includes several evaluation tasks, each of which explores one aspect of that organization{i.e., splitting a continuous stream of news into stories that are about a single topic ("segmentation"), gathering stories into groups that each discuss a single topic ("detection"), identifying the onset of a new topic in the news ("first story detection"), and exploiting user feedback to monitor a stream of news for additional stories on a specified topic ("tracking"). Topic Detection (TD) is an important sub-task of TDT and mainly consists of clustering news stories as topics.. We believe the performance issue of TD itself is significant to gain timely access to desired information from overloaded digitized textual contents. Topic detection is essentially text clustering. Its main characteristic is the finer granularity of clusters and such clustering is no longer a spherical shape because the topic drift may occur. In addition, clustering on a larger scale is no longer suitable for memory-based algorithms. In fact, text clustering has two common methods, i.e., partition-based approach and hierarchical based approach. The former method is simple and fast convergent, but its parameter K is difficult to be determined. Unfortunately, the topic number of text clustering is uncertain in TD. In addition, it is not suitable for topic drifting cases. Existing TD solutions have higher complexity of O(n 2 ) or O(n 3 ) usually [3, 4, 5, 6, 7, 8, 9, 10, 13, 15] . In ref. 14, we can observe its TD performance even went up to the x-th power of e. Despite the fact that existing TD solutions play important roles in their applications, they do not explicitly incorporate language model and document retrieval model into their formulations. Based on our researches [16, 17, 18, 19] , we start to view TD from an angle of information retrieval(IR) as there are lots of established high performance IR models and algorithms. Enlightened by achievements in IR field, we adopt Relevance Model(RM) for our language model and Inverted Indices for document retrieval. RM is a theoretical extension of statistical language modeling and applicable in both retrieval and TD [26] . Inverted. Indices are well known technique widely adopted in document retrieval and full text search engines [32, 33] . By treating news and stories as documents, we can use IR methods to retrieve relevant news or stories and form them into TD's clusters. This paper hence aims to combine relevance language model with inverted indices retrieval technique. Moreover, by creating relative entropy for RM, we have better topic detection rates while achieving linear TD performance. The remainder of this paper is organized as follows: In Section 2, we define key concepts and terms and introduce works that are directly related to the ideas presented in this paper. Section 3 describes our novel approach for TD via relative entropy based RM and inverted indices modeling. In Section 4, we demonstrate the superiority of our approach with comparative empirical results for both English and Chinese news. Finally, in Section 5, we present our conclusions. Although timely access to information is becoming increasingly important and gaining such access is no longer a problem, the capacity for humans to assimilate such huge amounts of information is limited. TD is then a promising research area that addresses speedy access of desired information. However, ironically, the time complexity of existing topic detection solutions themselves is usually O(n 3 ) or up to the x-th power of e.. Linear performance requirement of real world topic detection has not been seriously addressed in literatures. Based on relevance model selection, our RMIR algorithm obtains close-to-linear time complexity and good robustness for stories both in English and Chinese. Even in the dynamic cases of real time feeding of on line stories, RMIR still performs well. To reduce the costs of fetching RM, we reduce the number of comparisons for stories by a query-based approach that makes similar stories exist in the top-k query results. Since we design the query based on inverted table, its complexity is linear or better than linear [33] when quantity of stories increases. The time cost of rest of operations in the RMIR topic detection process is a constant. Hence, in theory, the total time complexity of RMIR topic detection should be close to linear. Experimental results prove that the time complexity of RMIR algorithm is consistent with our theory with the sacrifice of extra space needs for inverted indices. Meanwhile, RMIR achieves better detection rates by comparing with other historical results due to relative entropy based topic model design. RMIR has been successfully deployed in Topic Detection and Tracking System of Business Intelligence for National Incubation Center and granted a distinguished patent [47] under the patent no. of 200810063295.1.

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SNR scalability based on matching pursuits Abstract-In this paper, SNR scalable representations of video signals are studied. The investigated codecs are well suited for communications applications because they are all based on backward motion-compensated predictive coding, which provides the necessary low-delay property. In a very-low bit rate context (VLBR), the matching pursuits (MP) signal representation algorithm is used to represent the displaced frame difference (DFD) of each layer of a multilevel decomposition of the video signal. A number of conventional prediction schemes that can be generalized to any DFD representation technique are considered. They are compared with an original and MP specific DFD prediction method. Two scenari have been considered. In the first scenario, an enhancement layer is built on a base layer that has been encoded using a classical, i.e., nonscalable scheme. In that case, all methods appear to be comparable. In the second scenario, the fact that the base layer is used as a reference for an enhancement layer is taken into account to build it. In that case, the proposed MP prediction method clearly outperforms all other conventional approaches. Additional lessons can be drawn from this work. The same motion vectors can be used in both SNR layers, and the DFD prediction between layers improves coding efficiency. Moreover, the MP representation of the signal enable us to measure the predictability of the high SNR layer DFD from the low SNR layer DFD, i.e., to quantify the part of the low SNR layer information that also belongs to the high SNR layer. I N ADDITION to the new requirements for real-time transmission, video applications are quickly evolving from one-to-one communications to one-to-many and many-to-many communications. Widespread use of these applications can easily overload existing networks when the same bits of information have to be transmitted to different users at the same time. Multicast, where a single packet is addressed to all intended recipients, and where the network replicates packets only as needed, make it possible to reduce unnecessary duplication of bits. It relieves some of the network load [1] , [2] . Nevertheless, in this context, a single, fixed-rate video stream can not satisfy the conflicting requirements of a heterogeneous set of receivers. One approach for delivering multiple resolution, frame rate, or levels of quality across multiple network connections is to encode the video signal with a set of independent encoders, each producing a different output rate. This approach, called simulcast, does not exploit statistical correlations across subflows. Its compression performance Manuscript received April 7, 1999 ; revised September 28, 2000. The research of C. De Vleeschouwer was supported by the Belgian NFS. The associate editor coordinating the review of this paper and approving it for publications was Dr. Hong-Yuan Mark Liao.. The authors are with the Laboratoire de Telecommunications et Teledetection, Universite Catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium (e-mail: devlees@tele.ucl.ac.be; macq@tele.ucl.ac.be).. Publisher Item Identifier S 1520-9210(00)11072-7.. is thus suboptimal. By contrast, a layered, or scalable, coder exploits correlations across subflows to achieve better overall compression [3] - [5] . The input signal is compressed into a number of discrete layers, arranged in hierarchically to provide progressive refinement. With scalable coding algorithms, one original compressed video bitstream is generated; but different subsets of the bitstream can then be selected at the decoder end to support a multitude of display specifications such as the quality level (SNR scalability), the spatial resolution (spatial scalability), and the frame rate (temporal scalability). Developing (spatially) scalable video compression algorithms has attracted considerable attention in recent year [6] - [11] . In this paper, we focus our attention to the study of SNR scalable schemes based on the extension of the hybrid motion prediction/matching pursuits coding algorithm [12] . While not inherently suited for scalability, hybrid schemes provide the low delay property, a major feature for communication applications.. Section II discusses the problem encountered when coping with SNR scalability. In Section III, a method is proposed to predict the high SNR layer DFD from the low SNR layer reconstructed DFD. This method is specific to the use of the Matching Pursuits (MP) representation technique and has been implemented to minimize the size of either the low SNR layer bitstream, or the complete scalable bitstream. In Section IV, it is explained how the MP framework permits to quantify the relevance of performing prediction between the DFD of each layer. For the sake of comparison, other prediction schemes have been considered in Section V. They include frame-based or macroblock-based DFD prediction modes but also propose others. Results and discussions are provided in Section VI. Section VII concludes. The results have been generated using a set of four video sequences selected among the ones recommended in the MPEG-4 video coding group for the test of VLBR coding algorithm. For the sake of comparison, common coding conditions (see Table I ) have been fixed. For each sequence, PSNR quality levels (in dB) have been settled for both the low and the high SNR layers. Matching Pursuits allow respecting this constraint stringently as, for each frame, atoms can be added to the reconstructed prediction error until the required quality level has been achieved. Comparison between the considered coding schemes is based on the size of the bitstreams.

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


A formal framework for a functional language with adaptable components We propose a component programming language called FLAC, Functional Language for Adaptable Components, on top of a functional programming language which authorizes full adaptability of components while ensuring type safety. The langage is given together with a type system that offers a complete static type ckecking of any programs (including adaptations) to ensure error-free run-time adaptations. Dynamic adaptability and static type checking might seem at first sight paradoxical, but our approach allows it because, first, we use a single language for traditional services and control services (i.e., services for adaptations), and secondly, a specific merge operation takes care of adaptations. Component-based programming is like a construction game: programs are assembled out of black boxes called components. The term 'black box' is justified by the fact that only the interfaces of components are publicly available. Such an interface characterizes the signatures of services the component offers or needs. The basic construct plug assembles a component that provides a service to a component that needs it. The domain of component-based systems (CBS) is mature enough to actually endorse the benefits of such a paradigm [5] . Safety and adaptativity are the two main requirements for such systems. Safety is ensured when execution is error-free. For that purpose, most CBSs expect each required service used by a component to be satisfied by a service provided by 1 This work is supported by the Marie Curie action 29849 Websicola.. 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. another component (soundness) and any service offered by a component to be concretely defined (completeness) [4, 3] . Obviously, type checking must be done accordingly. Adaptative capabilities are required to fit the evolution of the environment. However, in most existing CBSs, adaptability is limited if type checking is in use: the type of the new component should be a subtype of the type of the old component.. The language FLAC, Functional Language for Adaptable Components, presented in this paper allows dynamic internal adaptations without the two previous drawbacks: dynamic adaptations may remove or add requested or provided services, while types are statically checked:. 1. Adaptation constructs are first-class entities: there is no metalanguage. Hence, type specification integrates adaptations declared in the code: the type of a component is not only given by the actual signatures of requested and provided services but also by those resulting from its adaptations. To the best of our knowledge it is the first proposal where the type of a component integrates adaptation services. It means that in our language, the type of a component without adaptation capabilities is not identical to the "same" component with such adaptation properties. Indeed, how a simple car toy can be considered as having the same type than a car toy which can become a robot thanks to an adaptation property? Consequently, type checking may be completely done statically.. 2. Executing an adaptation creates a new component in the memory, whereas current approaches change bytecodes in place.. Hence components have only one type for the whole run of the program. A garbage collector mechanism, concretely the one given with OCaml [2] , allows for freeing unused components.. 3. The language is strongly-typed (as well as OCaml) ensuring error-free run-time adaptations. The FLAC programming language deals with adaptable components. Its main feature concerns dynamic internal adaptations in a strongly-typed language. Among several directions we currently study, structuring components is in practice highly expected as it increases the modularity of the language. This may be done by adding named parameters to component definitions. Such named parameters not only allow assignments of (sub)components but they may be used for denoting them. It is then possible, for the programmer, to write control services such that the part of the component not involved in the evolution is automatically rebuilt. This may be implemented by abstractly manipulating the structure of components, i.e., addressing each subcomponent by its logical named path.

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 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 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 B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 class of acyclic digraphs with interval competition graphs Let D be an acyclic digraph. The competition graph of D has the same set of vertices as D and an edge between vertices u and v if and only if there is a vertex x in D such that (u, x) and (v, x) are arcs of D. In this paper, we show that the competition graphs of doubly partial orders are interval graphs. We also show that an interval graph together with enough isolated vertices is the competition graph of a doubly partial order. Finally, we introduce a new notion called the doubly partial order competition number of an interval graph and present some open questions. Given a digraph D = (V , A), the competition graph G = C(D) of D has the same vertex set and has an edge xy if for some vertex u ∈ V , the arcs (x, u) and (y, u) are in D. The notion of the competition graph is due to Cohen [2] and has arisen from ecology. A food web in an ecosystem is a digraph whose vertices are the species of the system and which has an arc from vertex u to vertex v if and only if u prey on v. Given a food web F, it is said that species u and v compete if and only if they have a common prey. Competition graphs also have applications in coding, radio transmission, and modelling of complex economic systems. (See [16, 18] for a summary of these applications and [6] for a sample paper on the modelling application. ) We say a graph G is an interval graph if it is the intersection graph of some family of intervals on the real line. Cohen [2] [3] [4] observed empirically that most competition graphs of acyclic digraphs representing food webs are interval graphs. Roberts [17] asked whether or not Cohen's observation was just an artifact of the construction and concluded that it was not by showing that if G is an arbitrary graph, then G together with as many isolated vertices as the edges of G is the competition graph of an acyclic digraph D. (Add a vertex i corresponding to each edge ={a, b} of G, and draw arcs from a and b to i .) He then asked for a characterization of acyclic digraph D whose competition graphs C(D) are interval graphs. Since then the problem has remained elusive and it has been the basic open problem in the study of competition graphs. There have been efforts at settling the problem and some progress has been made. Cohen [4] approached the problem from a statistical point of view, trying to build statistical models for the construction of D so that C(D) is (likely to be) an interval graph. Steif [21] showed that there could be no forbidden subgraph characterization of acyclic digraphs whose competition graphs are interval. Lundgren and Maybee [13] gave some results which characterize such a digraph D. But these results essentially boiled down to calculating C(D) and using one of the well-known (and efficient) characterizations of an interval graph. While this solves the problem, it is not what we want: a characterization in terms of properties of D. Since the general problem of characterizing acyclic digraphs whose competition graphs are interval seems difficult, Hefner et al. [8] attacked it by putting a constraint on both the indegrees and outdegrees of D.. The study on acyclic digraphs whose competition graphs are interval led to several new problems and applications. One of them is to characterize competition graphs of an interesting family of digraphs. There have been a number of papers about competition graphs of specific classes of digraphs. For instance, competition graphs of acyclic digraphs have been studied in [1, 19] , of arbitrary digraphs with or without loops in [1, 12] , of strongly connected digraphs in [5] , of Hamiltonian digraphs in [5, 7] , of interval digraphs in [11] , for various classes of symmetric digraphs in [14] [15] [16] , of semiorders, of acyclic digraphs satisfying property C(p), and of acyclic digraphs satisfying property C * (p) in [10] . By means of extending the results obtained in [10] , Roberts (pers. comm.) proposed a problem of characterizing competition graphs of doubly partial orders. While we tried to answer his question, we could show that the competition graphs of doubly partial orders are interval graphs. We also show that an interval graph together with enough isolated vertices is the competition graph of a doubly partial order. The authors believe that these results make a significant contribution to the problem of characterizing acyclic digraphs whose competition graphs are interval.. The vast literature of competition graphs is summarized in the survey paper by Lundgren [12] and Kim [9] . Given an interval graph G, Theorem 2 tells us that G is a component of graph G that contains G together with k isolated vertices and that is the competition graph of a doubly partial order. Given an interval graph G, let D(G) be the family of acyclic digraphs whose competition graph is G together with k isolated vertices for some k. Finding properties shared by the digraphs in D(G) will help in characterizing an acyclic digraph D whose competition graph C(D) is interval. We know from Theorem 2 that there is a doubly partial order in D(G). As the competition number of an interval graph is one, there is an acyclic digraph in D(G) with k = 1. It would be interesting to ask: what is the smallest number k such that the competition graph of a doubly partial order in D(G) is G together with k added isolated vertices? To answer this question, we define the doubly partial order competition number p(G) of an interval graph G by the smallest number p such that G together with p added isolated vertices is isomorphic to the competition graph of a doubly partial order. It is analogous to the notion of the competition number k(G) of a graph G that is the smallest number k such that G together with k added isolated vertices is the competition graph of an acyclic digraph. (Answering Roberts' first question, that of characterizing competition graphs of acyclic digraphs, leads to computing the competition number of a graph.) It is interesting to find the doubly partial order competition number of various interval graphs. It is obvious that the doubly partial order competition number of a complete graph with at least two vertices is the same as its competition number. However, the doubly partial order competition number of path xyz of length two has to be greater than one. Suppose that D is a doubly partial order whose competition number is isomorphic to xyz together with some isolated vertices. Suppose x, y, z correspond to (x 1 , x 2 ), (y 1 , y 2 ), (z 1 , z 2 ), respectively. Then either x 1 z 1 and x 2 z 2 or x 1 z 1 and z 2 z 2 , for, otherwise, a prey of one is that of the other and so x and z are adjacent in C(D), a contradiction. This implies that a common prey of (x 1 , x 2 ) and (y 1 , y 2 ) and a common prey of(y 1 , y 2 ) and (z 1 , z 2 ) which are distinct cannot be any of x, y, or z. Thus, the doubly partial order competition number of P 3 of length 2 is two as shown in Fig. 5 . It is not difficult to show that the doubly partial order competition number of a path P n is n − 1.. It is also interesting to ask: is there always an acyclic interval digraph in D(G)? (See [20] for the definition of an interval digraph.) Do these digraphs in D(G) have any properties in common? We have shown that there is a doubly partial order with k = n − 1 in D(P n ). How does this digraph compare to a digraph that we obtain by adding one isolated vertex?

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


Visual scanning as a reference framework for interactive representation design When designing a representation, the designer implicitly formulates a sequence of visual tasks required to understand and use the representation effectively. This paper aims at making the sequence of visual tasks explicit in order to help designers elicit their design choices. In particular, we present a set of concepts to systematically analyse what a user must theoretically do to decipher representations. The analysis consists of a decomposition of the activity of scanning into elementary visualization operations. We show how the analysis applies to various existing representations, and how expected benefits can be expressed in terms of elementary operations. The set of elementary operations form the basis of a shared language for representation designers. The decomposition highlights the challenges encountered by a user when deciphering a representation and helps designers to exhibit possible flaws in their design, justify their choices, and compare designs. We also show that interaction with a representation can be considered as facilitation to perform the elementary operations. Designing representation is often considered to be a craft. The design activity requires multiple iterations that mix ad hoc testing, discussions with users, controlled experiments, and personal preferences. These ways of designing are either costly (controlled experiment), error-prone (ad hoc testing), or lead to non-optimal results (personal preference). Although a number of theoretical works help to explain the strengths or weaknesses of a representation, [1] [2] [3] [4] [5] [6] [7] no systematic method exists that would help designers to assess their design in an a priori manner, i.e. before user experiments. As suggested by Munzner, 8 such a method would help not only for formative purposes, but also as a summative evaluation before actual user experiments.. When designing a representation, a designer implicitly formulates a way to understand and use the representation effectively. For example, reading a city map requires scanning it, finding noteworthy locations (metro stations, connections, etc.), devising a path to go from one point to another, and so on. 9 For a user, except for very specialized graphics and narrow tasks, figuring out a representation is like interacting using the eyes only: a user has to figure out a solution to his task at hand by scanning the picture, seeking graphics, memorizing things, etc. The succession of these small visualization operations induces a cost that deserves to be evaluated before acceptance of a final design.. We suggest that most design decisions can be explained by the willingness of the designer to reduce the cost of deciphering the representation.. However, there is no common core of concepts that allows designers to precisely express the rationale behind a design decision. This hinders the design process because it makes it hard for designers to explain to users and stakeholders why a representation is suitable for their tasks (justification) and how a new prototype is better than a previous one (comparison). Furthermore, they cannot justify their choices in a design rationale document, which makes the decisions susceptible to disappearance in future evolutions of the system. This paper presents a set of concepts for analysing how a user deciphers a representation. It relies on and extends previous works about visual scanning and design elicitation. The goal of the paper is not to show better designs for a particular problem. Rather, the goal of the paper is to present an analysis that exhibits the steps required to figure out a particular representation, and help justify design choices and compare representations. In this paper, we presented a method to analyse idealized scanning of graphical representations. The method relies on a set of elementary operations, which includes operations from previous taxonomies and new ones (entering, exiting, memorizing) together with new considerations (back steps, visual steering, and the use of ordered or quantitative arrangement). We argue that rationale for design can be expressed in terms of these elementary operations. We showed in various examples how such an analysis can be achieved and how gains and losses can be explained with elementary operations, including when considering interaction as a change of representation. The set of elementary operations forms the basis of a shared common language that helps designers justify and compare their choices.. In its current form, the method is descriptive, not predictive. We believe that we are still far from a fully predictive model of human performance in representation use, if only because performance depends on multiple external factors, as demonstrated by Lohse. 17 Instead, we take another perspective: we argue that a descriptive-only method is useful for designers, since the decomposition highlights the challenges encountered by a user when deciphering a representation. The benefit is equivalent to one of the two benefits of KLM: in addition to predicting completion times, KLM helps designers to understand what a user must do to accomplish an interaction task. Similarly to KLM, this makes our model a comparative one, as it helps designers to choose designs based on a sound analysis.. In addition to the examples presented here, we have successfully applied our analysis method presented to other representations, such as item rating by customers in online stores, widgets, and radar images. Work is certainly needed to expand the set of operations and the elements that aid or affect their realization. For example, we do not yet take into account the fact that tasks can be aided when externalizing constraints into the real world, 40 nor did we take into account representations that ease mental computation. 41 Furthermore, different acts of mental computation and memorization may exhibit very different costs. In addition, while we tackled the 'what to do' question in this paper, we did not tackle the question of 'how to do it'. Eventually, we need to propose a systematic method that will help designers find for themselves the steps and considerations to take into account when evaluating the effectiveness of a particular representation. The concept of interacting to perceive better is not new: in fact, this is a concept that is shared among psychologists of action and perception. 38 Designers adapt the representation to make it easier to answer specific preestablished problems. Users also adapt the representation to make it easier to answer a problem at hand. Hence, interacting to change views is of the same nature as designing. In both cases, the present work is helpful as an account of the visual task at play, but it is not helpful at describing the 'design manoeuvre' 26 required to get a better design. There may be new concepts remaining to be identified, in both the design space and the user space, which would form the basis of a prescriptive method for designing better (interactive) representations.. Designing an interactive representation cannot be as simple as taking into account visual scanning alone, nor can it be as simple as counting the number of KLM operators alone. The design must be analysed as a whole, and actions to switch from a representation to another should be taken into account. Interestingly, KLM, despite being focused on the users' actions, accounts for its perception and memorization activities through its operator M. One could consider our work on visual scanning as a first attempt at describing some aspects of this operator in more detail. One can use this perspective to extend our approach to representations that the user can manipulate, indifferently considering actions as part of the scanning process or scanning as part of a global interaction process. One way of proceeding would be to add an interaction operation to the visual scanning language. This would provide user interface designers with two dual languages for analysing their designs: one focused on the user's physical actions, with operator M used to capture other types of interaction, and the other focused on visual scanning, with operator I used to capture other types of interaction. At a finer level of analysis, the two languages would then appear to be simplified and practical versions of a common language that describe all interaction operations on the same foot.. Note that considering scanning as interaction is not as artificial as it may seem. On the one hand, at the physical level there is some interaction through the emitted light, and it does indeed trigger significant changes in the user. And on the other hand, the use of speech that acts to describe multimodal interaction has shown that combining different interaction modalities in the same abstract framework can provide designers with an adequate description language. Finally, proponents of enaction think that perceiving is acting: 'the content of perception is not like the content of a picture; the world is not given to consciousness all at once but is gained gradually by active inquiry and exploration'. 39 If this theory proves true, the total costs of adapting the view and scanning would be difficult to estimate with a method as simple as summing the cost of individual operations. A finer language that accounts for the concurrency between operations might prove more suitable with this regard. We based our work on previous studies that can be roughly divided into three groups. The first group concerns eye gaze, representation scanning, and models of visual perception; the second concerns visual task taxonomies; and the third concerns design formulation.

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


Bias-corrected realized variance under dependent microstructure noise The aim of this study is to develop a bias-correction method for realized variance (RV) estimation, where the equilibrium price process is contaminated with market microstructure noise, such as bid-ask bounces and price changes discreteness. Though RV constitutes the simplest estimator of daily integrated variance, it remains strongly biased and many estimators proposed in previous studies require prior knowledge about the dependence structure of microstructure noise to ensure unbiasedness and consistency. The dependence structure is unknown however and it needs to be estimated. A bias-correction method based on statistical inference from the general noise dependence structure is thus proposed. The results of Monte Carlo simulation indicate that the new approach is robust with respect to changes in the dependence of microstructure noise. JEL Classifications: C01; C13; C51 The estimation of the daily integrated variance of returns on financial assets is important for derivatives pricing and risk management purposes. While realized variance (RV) constitutes a simple but useful estimator of daily integrated variance (IV), it remains also a strongly biased estimator, where the equilibrium price process is contaminated with market microstructure noise. This microstructure noise can be induced by various market frictions such as bid-ask bounces and the discreteness of price changes, inter alia. There are three approaches to cope with noise contamination, including (i) use of returns on the appropriate interval length based on optimal sampling frequency proposed by Bandi and Russell [2] , (ii) subsampling and bias correction proposed by Zhang et al. [8] and (iii) kernel estimation following Barndorff-Nielsen, et al. [4] . McAleer and Medeiros [6] provide an extensive review of the recent literature on RV estimation. It is the time-dependent noise structure that ensures the unbiasedness and consistency of IV estimators. The estimations proposed in previous studies ultimately require prior knowledge about this noise dependency, which needs to be rather estimated. The present study addresses these estimation issues and uses the consistent cross-covariance and autocovariance estimators of microstructure noise, and the tests statistics developed by Ubukata and Oya [7] to identify the noise dependence structure. The selection procedure of time scales based on Aït-Sahalia et al. [1] 's Two Scales RV (TSRV) is also provided under the general conditions of dependent noise. An alternative bias-corrected estimator of IV can be also proposed using the autocovariance of microstructure noise. The remainder of this paper is organized as follows. The price process and market microstructure noise are presented in section 2. Section 3 discusses the realized variance and related estimators are given. Section 4 provides a brief review of the autocovariance estimator of microstructure noise proposed by Ubukata and Oya [7] followed by a discussion of of the selection two-scales TSRV estimator and alternative bias-corrected RV estimator. Section 5 presents the results of Monte Carlo simulation for the finite sample properties of the proposed selection procedure and the new bias-corrected estimator under the general noise dependence. Section 6 concludes the paper. This paper proposes a new approach to the problem of appropriate lag selection for the twoscales realized variance under dependent microstructure noise. An alternative bias-adjusted estimator based on the variance of microstructure noise is also proposed, along the lines of Ubukata and Oya [7] . The evidence from Monte Carlo simulation suggests that the proposed lag selection procedure is appropriate as the the proposed estimator is associated with relatively smaller bias and RMSE values. The proposed procedure for lag selection and the new IV estimator can thus be useful for empirical studies based on transactions price data. It should be noted that kernel-type estimators may be considered to be more efficient. But as noted by Bandi and Russell [3] , this asymptotic property is not necessarily satisfied in the presence of large samples typical of empirical studies based on transactions data. The comparative analysis is nevertheless important and therefore warranted. It falls beyond the scope of the present study, which introduces a new approach that may open interesting avenues for future research.

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Software architecture improvement through test-driven development This research involves empirical software engineering studies applied in academic and professional settings to assess the influence of test-driven development on software quality. Particular focus is given to internal software design quality. Pedagogical implications are also examined. Initial results and the study protocol and plans will be presented.

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Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering Abstract. This paper evaluates the performance of different criterion functions in the context of partitional clustering algorithms for document datasets. Our study involves a total of seven different criterion functions, three of which are introduced in this paper and four that have been proposed in the past. We present a comprehensive experimental evaluation involving 15 different datasets, as well as an analysis of the characteristics of the various criterion functions and their effect on the clusters they produce. Our experimental results show that there are a set of criterion functions that consistently outperform the rest, and that some of the newly proposed criterion functions lead to the best overall results. Our theoretical analysis shows that the relative performance of the criterion functions depends on (i) the degree to which they can correctly operate when the clusters are of different tightness, and (ii) the degree to which they can lead to reasonably balanced clusters. The topic of clustering has been extensively studied in many scientific disciplines and a variety of different algorithms have been developed (MacQueen, 1967; King, 1967; Zahn, 1971; Sneath & Sokal, 1973; Dempster, Laird, & Rubin, 1977; Jackson, 1991; Ng & Han, 1994; Berry, Dumais, & O'Brien, 1995; Cheeseman & Stutz, 1996; Ester et al., 1996; Guha, Rastogi, & Shim, 1998; Boley, 1998; Guha, Rastogi, & Shim, 1999; Karypis, Han, & Kumar, 1999a; Strehl & Ghosh, 2000; Ding et al., 2001) . Two recent surveys on the topics (Jain, Murty, & Flynn, 1999; Han, Kamber, & Tung, 2001 ) offer a comprehensive summary of the different applications and algorithms. These algorithms can be categorized along different dimensions based either on the underlying methodology of the algorithm, leading to agglomerative or partitional approaches, or on the structure of the final solution, leading to hierarchical or non-hierarchical solutions.. In recent years, various researchers have recognized that partitional clustering algorithms are well-suited for clustering large document datasets due to their relatively low computational requirements (Cutting et al., 1992; Larsen & Aone, 1999; Steinbach, Karypis, & Kumar, 2000) . A key characteristic of many partitional clustering algorithms is that they use a global criterion function whose optimization drives the entire clustering process. For some of these algorithms the criterion function is implicit (e.g., PDDP (Boley, 1998) ), whereas for other algorithms (e.g, K -means (MacQueen, 1967) , Cobweb (Fisher, 1987) , and Autoclass (Cheeseman & Stutz, 1996) ) the criterion function is explicit and can be easily stated. This latter class of algorithms can be thought of as consisting of two key components. First is the criterion function that the clustering solution optimizes, and second is the actual algorithm that achieves this optimization.. The focus of this paper is to study the suitability of different criterion functions to the problem of clustering document datasets. In particular, we evaluate a total of seven criterion functions that measure various aspects of intra-cluster similarity, inter-cluster dissimilarity, and their combinations. These criterion functions utilize different views of the underlying collection by either modeling the documents as vectors in a high dimensional space or by modeling the collection as a graph. We experimentally evaluated the performance of these criterion functions using 15 different datasets obtained from various sources. Our experiments show that different criterion functions do lead to substantially different results and that there are a set of criterion functions that produce the best clustering solutions.. Our analysis of the different criterion functions shows that their overall performance depends on the degree to which they can correctly operate when the dataset contains clusters of different tightness (i.e., they contain documents whose average pairwise similarities are different) and the degree to which they can produce balanced clusters. Moreover, our analysis also shows that the sensitivity to the difference in the cluster tightness can also explain an outcome of our study (that was also observed in earlier results reported in Steinbach, Karypis, and Kumar (2000) ), that for some clustering algorithms the solution obtained by performing a sequence of repeated bisections is better (and for some criterion functions by a considerable amount) than the solution obtained by computing the clustering directly. When the solution is computed via repeated bisections, the tightness difference between the two clusters that are discovered is in general smaller than the tightness differences between all the clusters. As a result, criterion functions that cannot handle well variation in cluster tightness tend to perform substantially better when used to compute the clustering via repeated bisections.. The rest this paper is organized as follows. Section 2 provides some information on the document representation and similarity measure used in our study. Section 3 describes the different criterion functions and the algorithms used to optimize them. Section 4 provides the detailed experimental evaluation of the various criterion functions. Section 5 analyzes the different criterion functions and explains their performance. Finally, Section 6 provides some concluding remarks. In this paper we studied seven different global criterion functions for clustering large documents datasets. Four of these functions (I 1 , I 2 , G 1 , and G 2 ) have been previously proposed for document clustering, whereas the remaining three (E 1 , H 1 , and H 2 ) were introduced by us. Our study consisted of a detailed experimental evaluation using fifteen different datasets and three different approaches to find the desired clusters, followed by a theoretical analysis of the characteristics of the various criterion functions. Our experiments showed that I 1 performs poorly whereas I 2 leads to reasonably good results that outperform the solutions produced by some recently proposed criterion functions (G 1 and G 2 ). Our three new criterion functions performed reasonably well, with the H 2 criterion function achieving the best overall results. Our analysis showed that the performance difference observed by the various criterion functions can be attributed to the extent to which the criterion functions are sensitive to clusters of different degrees of tightness, and the extent to which they can lead to reasonably balanced solutions. Moreover, our analysis was able to identify a key property of the I 1 criterion function that can be useful in clustering noisy datasets, in which many documents are segregated to a separate "garbage" cluster.. The various clustering algorithms and criterion functions described in this paper are available in the CLUTO clustering toolkit that is available online at http://www.cs.umn.edu/˜cluto.

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


On the Statistical Characterization of Flows in Internet Traffic with Application to Sampling Abstract. A new method of estimating some statistical characteristics of TCP flows in the Internet is developed in this paper. For this purpose, a new set of random variables (referred to as observables) is defined. When dealing with sampled traffic, these observables can easily be computed from sampled data. By adopting a convenient mouse/elephant dichotomy also dependent on traffic, it is shown how these variables give a reliable statistical representation of the number of packets transmitted by large flows during successive time intervals with an appropriate duration. A mathematical framework is developed to estimate the accuracy of the method. As an application, it is shown how one can estimate the number of large TCP flows when only sampled traffic is available. The algorithm proposed is tested against experimental data collected from different types of IP networks. In Internet traffic a flow is classically defined as the set of those packets with the same source and destination IP addresses together with the same source and destination port numbers and the same protocol type. It is well known that if large TCP flows carry the prevalent part of traffic (in Bytes), most of flows are small (in number of packets). A formal definition of "large" and "small" will be given later in the paper. As it will be seen, it may depend on the context; in a first step, the discussion is kept informal.. We investigate in this paper how to characterize the statistical properties of the sizes of large flows (notably their number of packets) in Internet traffic. It is commonly observed in the technical literature and in real experiments that the total size (in packets or bytes) of such flows has a heavy tailed distribution. In practice, however, this characterization holds only for very large values of the flow size. Consequently, in order to accurately estimate the tail of the size probability distribution, a large number of large flows is necessary. To increase the sample size when empirically estimating probability distribution tails, one is led to increase the length of the observation period. But the counterpart is that the distribution of the flow size can no more be described in terms of simple probability distributions, of the Pareto type for example. This is due to the fact that traffic is not stationary over long time periods, for instance because of daily variations of interactive services (video, web, etc.).. Actually, numerous approaches have been proposed in the technical literature in order to model large flows as well as their superposition properties. One can roughly classify them in two categories: signal processing models and statistical models. Using ideas from signal processing, Abry and Veitch [1] , see also Feldman et al. [14, 15] and Crovella and Bestravos [8] , describe the spectral properties of the time series associated with IP traffic by using wavelets. In this way, a characterization of long range dependence (the Hurst parameter for example) can be provided. Straight lines in the log-log plot of the power spectrum support some of the "fractal" properties of the IP traffic, even if they may simply be due to packet bursts in data flows. See Rolland et al. [23] .. Signal processing tools provide information on aggregated traffic but not on characteristics on individual TCP flows, like the number of packets or their transmission time. For statistical models, a representation with Poisson shot noise processes (and therefore some independence properties) has been used to describe the dynamics of IP traffic, see Hohn and Veitch [17] , Duffield et al. [11] , Gong et al. [16] , Barakat et al. [4] and Krunz and Makowski [18] for example. In Ben Azzouna et al. [3] , Loiseau et al. [20, 19] and Gong et al. [16] , the distribution of the size of large flows is represented by a Pareto distribution, i.e. a probability distribution whose tail decays on a polynomial scale. ,. The starting point of some of these analyses is the need for understanding the relation between the distribution of the numberŜ of sampled packets when performing packet sampling and the distribution of the flow size S. The problem can be described as follows: P(Ŝ = j) = Q(P(S = ·), j), j ≥ 1, with. The problem then consists of finding a distribution φ 0 maximizing some functional L(φ) so that the relation P(Ŝ = j) = Q(φ, j) holds. See Loiseau et al. [19] for an extensive discussion of the current literature where our algorithm is called "stochastic counting". As it will be seen in the following, we will not rely on the maximum likelihood ratio of distributions in our approach but on estimations of some averages to estimate some key parameters.. Statistical Characterization Method. We develop in this paper an alternative method of obtaining a statistical description of the size of large flows in IP traffic by means of a Pareto distribution: Statistics are collected during successive time windows of limited length (instead of one single time window for the whole trace). It must be emphasized that this characterization in terms of a Pareto distribution does not rely on the asymptotic behavior of the tail distribution but only on statistics on some range of values for the sizes of flows. The advantage of the proposed method is that with a careful procedure, a simple statistical characterization is possible and seems to be quite reliable as shown by our experiments for various sets of traffic traces. The intuitive reason for considering short time periods is that on such times scales, flows exhibit only one major statistical mode (typically a Pareto behavior). In larger time windows, different modes due to the wide variety of flows and non-stationarity in IP traffic necessarily appear. (See Feldman et al. [15] .) This approach allows us to establish a reliable statistical characterization of flows which is used to infer information from sampled traffic as it will be seen. The counterpart of that the distribution of the total size of a large flow (obtained when considering the complete traffic trace) cannot be obtained directly in this way since the trace is cut into small pieces.. An algorithm is proposed to obtain the statistical representation of large flows when all the packets of the trace are available. The constants used in our algorithms are explicitly expressed as either universal constants (independent of traffic) or constants depending on traffic : Length of the observation window, definition of TCP flows referred to as large flows, etc. The procedure invoked to estimate flow statistics should not depend on some hidden pre-processing of the trace. Our algorithms determine on-line the constants depending on the traffic. This is, in our view, one important aspect which is sometimes neglected in the technical literature Application to Sampled Traffic. The basic motivation for developing a flow characterization method is to infer flow characteristics from sampled data. This is notably the case for sampling processes such as the 1-out-of-k sampling scheme implemented by CISCO's NetFlow [7] , which greatly degrades information on flows. What we advocate in this paper is that it is still possible to infer relevant characteristics on flows from sampled data if some characteristics of the flow size can be confidently described by means of a simple Pareto distribution. By using the statistical representation described above, we propose a method of inferring the number of large flows from sampled traffic.. The proposed method relies on a new set of random variables, referred to as observables and computed in successive time intervals with fixed length. Specifically, these random variables count the number of flows sampled once, twice or more in the successive observation windows. The properties of these variables can be obtained through simple characteristics, in particular mean values of variables instead of remote quantiles of the tail distribution, which are much more difficult to accurately estimate. By developing a convenient mathematical setting (Poisson approximation methods), it is moreover possible to show that quantities related to the observables under consideration are close to Poisson random variables with an explicit bound on the error. This Poisson approximation is the key result to estimate the total number of large flows.. Organization of the paper. The organization of the paper is as follows. A statistical description of large TCP flows is presented in Section 2, this representation is tested against five exhaustive sets of traffic traces: three from the France Telecom (FT) commercial IP network carrying residential ADSL traffic and two others from Abilene network. An algorithm is developed in this section to compute the characteristics of the Pareto distributions describing flows. In Section 3, some assumptions on sampled traffic are introduced and the observables for describing traffic are defined. The mathematical properties are analyzed in light of Poisson approximation methods in Section 4. The results developed in this section are crucial to infer the statistics of an IP traffic from sampled data. Experiments with the five sets of sampled traces used in this paper are presented and discussed in Section 5. Some concluding remarks are presented in Section 6. We have developed in this paper one method of characterizing flows in IP traffic by a few parameters and another one of inferring these parameters from sampled data obtained via deterministic 1-out-of-k sampling. For this purpose, we have made some restrictive assumptions, which are in our opinion essential in order to establish an accurate characterization of flows. The basic principle we have adopted consists of describing flows in successive observation windows of limited length, which has to satisfy two contradicting requirements. On the one hand, observation windows shall not to be too large in order to preserve a description of flow statistics as simple as possible, for instance their size by means of a simple Pareto distribution.. On the other hand, a sufficiently large number of packets has to be present in each observation window in order to be able of computing flow characteristics with sufficient accuracy, in particular the tail of the distribution of the flow size. By assuming that large flows (elephants) have a size which is Pareto distributed, we have developed an algorithm to determine the optimal observation window length together with the parameters of the Pareto distribution. The location parameter B min (see Equation (1)) leads to a natural division of the total flow population into two sets: those flows with at least B min packets, referred to as elephants, and those flows with less than B min packets,called mice. This method of characterizing flows has been tested against traffic traces from the France Telecom and Abilene networks carrying completely different types of traffic.. For interpreting sampled data, we have made assumptions on the sampling process. We have specifically supposed that flows are sufficiently interleaved in order to introduce some randomness in the packet selection process (mixing condition) and that there are no dominating flows so that there is no bias with regard to the probability of sampling a flow (negligibility condition). These two assumptions allows us to establish rigorous results for the number of times an elephant is sampled, in particular for the mean values of the random variables W j , j ≥ 1.. Of course, when analyzing sampled data, the original flow statistics are not known. In particular, the length of the observation window necessary to characterize the flow size by means of a unique Pareto distribution is unknown. To overcome this problem, we have proposed an algorithm to fix the observation window length and the minimal length of elephants. Then, by choosing the index j sufficiently large so as to neglect the impact of mice, the theoretical results are used to complete the flow parameter inference. This method has been tested against the Abilene and the France Telecom traffic traces and yields satisfactory results.

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Establishing Logical Rules from Empirical Data We review a method of generating logical rules, or axioms, from empirical data. This method, using closed set properties of formal concept analysis, has been previously described and tested on rather large sets of deterministic data.The contribution of this paper is a completely new extension of this method to create implications involving numeric inequalities. Using the properties of closed sets to establish logical axioms for inference systems has great promise. The two examples of Section 4 demonstrate that we can automatically extract valid rules from rather large sets of data. The extraction of logical implications where some of the predicates are mathematical inequalities is, we believe, completely new. Unfortunately, we have not yet automated it in a software system because there are subtiles we do not fully understand. Instead, we are just beginning to explore its potential, which we believe is considerable.. Our goal is to begin generating implications of the form "if [p 1 ∧ (x > 7)] ∨ [p 2 ∧ p 3 ∧ (y < 9)] then . . .", We know, for example, that gene expression is dependent not only on the boolean presence of specific genes, but also on the stage (time) of the replication and degree of separation of gene sequences. These latter factors are best expressed as numeric inequalities. Similarly, evidence based diagnostic systems typically consider both the boolean presence of symptoms and the bounded behaviour of specific variables, .e.g. cholesterol > 180.. The approach of this paper can facilitate the creation of, or refinement of, a variety of intelligent computer systems.

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