Acceptance of competency-based workplace e-learning systems: Effects of individual and peer learning support Current endeavors to integrate competency-based learning approaches with e-learning systems design for delivery of training to adult learners in the workplace are growing. However, academic efforts in examining learners' perceptions of, and reactions towards, this technology-delivered pedagogical innovation are limited. Drawing together perspectives from the literature on training and instructional design and technology acceptance, this research proposed a conceptual model to examine the influences of perceived individual and social learning support on employees' acceptance of competency-based e-learning systems. Structural equation modeling and multi-group structural equation modeling techniques were applied to sample data collected from work settings in mainland China. The results show the positive effects of perceived individual learning support and perceived support for promoting a norm of cooperation on employees' intention to use the competency-based e-learning system. It was also found that perceived support for enhancing social ties had a negative effect on employees' behavioral intention. The gender, age, and prior experience differences in the main effects were also investigated. E-learning is increasingly being used by organizations as an emergent approach for enhancing the skills of knowledge workers. In the training and human resource development (HRD) literature, e-learning is regarded as a training "medium" ( While e-learning is increasingly being adopted as a training approach in HRD practices (Burgess & Russell, 2003) , the issue of e-learning effectiveness has arisen both in practice and in academia (Park & Wentling, 2007) . The ultimate purpose of work-integrated learning is to drive business results and to bring about positive changes in workplace behaviors and job performance (Bersin, 2002; Park & Wentling, 2007) . However, according to evidence from practices reported in the corporate e-learning literature (e.g., Netteland, Wasson & Mørch, 2007; Majchrzak, Rice, Malhotra, King & Ba, 2000) , there is a lack of relevance of e-learning programs for employees' work activity and previous experiences. Misalignment has been found to exist between organizational environment and technology-supported learning activities such as knowledge capture and sharing in the workplace. Similarly, "lack of job analysis" and "weak focus on business and performance requirements" have been identified as key barriers to the successful implementation of workplace e-learning (Clark & Mayer, 2008; Rosenberg, 2006) . These barriers may lead to reduced employees' motivation and learning and transfer effectiveness. As a result, alignment of e-learning with job competencies and performance requirements has been recognized as a critical success factor in workplace e-learning effectiveness (Wang, 2010) , which elicits the need and lays the empirical foundation for competency-based approaches to be integrated into e-learning development.. In the vocational education and training (VET) literature, competency-based training can be referred to as "competency movement" (Burgoyne, 1993 ; Rubin, Bebeau, Leigh, Lichtenberg, Nelson, Portnoy, Smith & Kaslow, 2007), whereby learning is driven by development of specific competencies for dealing with needs and challenges (Ricciardi, 2005) . A competency is a combination of skills, abilities, and knowledge needed to perform a specific task. Competency-based training has been widely used by organizations to drive workplace learning initiatives to enable employees to respond quickly and flexibly to business needs. Such practices can be found in management education (Camuffo & Gerli, 2004) , medical education (Long, 2000; Folberg, Antonioli & Alexander, 2002) , and other professional training and education programs (Chang, 2006) . In recent years, researchers have made attempts to integrate competency-based models into e-learning programs to allow employees to develop competencies with a broad degree of self-regulation and self-control (Sicilia & Naeve, 2007) . Leyking, Chikova & Loos (2007) present an approach for aligning a competency-driven learning process with business process contexts via e-learning technology. Wang, Ran, Liao & Yang (2010) propose the use of Key Performance Indicators (KPIs) as a framework to link learning with work competencies and performance in e-learning applications. Schmidt (2008) applies the ontology-based work context and competency modeling approach to support the development of context-aware workplace e-learning. While current endeavors to integrate competency-based approaches to workplace e-learning development are increasing, research that examines employees' perceptions of, and reactions towards, this technology-delivered pedagogical innovation is minimum. Lack of consideration for learners' perceptions and attitudes towards workplace e-learning has been recognized as the key barrier to successful design and implementation of e-learning initiatives (e.g. Servage The literature on e-learning design suggests that learner control of learning and customization of learning experiences will meet learners' needs and preferences and improve learners' satisfaction and motivation towards e-learning (Clark & Mayer, 2008 ; DeRouin, Fritzsche, Salas & Martocchio, 2004) . It is also assumed that learner collaboration in distance learning environments may address learners' social needs and therefore contribute to their satisfaction and motivation towards using online learning (Salas, With respect to competency-based e-learning in the workplace, it is recognized that supporting a personalized learning process and facilitating peer communication and collaboration are two key elements in the instructional design of such type of learning system (Schmidt, 2008; Woelk & Lefrere, 2002; Chang, 2006) . However, evaluation of the effects of the two elements on learners' perceptions and reactions has been neglected. This study aims to examine instructional design factors that may influence learners' acceptance of competency-based e-learning systems with a focus on the effects of individual and peer learning support. The investigation is based on relevant training and instructional design theories, and validated models that explain information system usage behavior in work settings, such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Fierce competition, globalization, and technology innovation have forced organizations to search for new ways to improve competitive advantage. E-learning is increasingly being used by companies as an emergent approach for enhancing the skills of knowledge workers. In the meantime, the history of e-learning is still short, and it can be characterized as more technology-driven than problem-driven or learner-driven. Furthermore, it has been emphasized that the mainstream conceptualizations of e-learning developed in the institutional context are not transferable to workplace learning which is built on adult learners and in work situations (Wang, Ran, Liao & Yang, 2010).. Competency-based learning has recently been incorporated into e-learning applications in the workplace context. Most efforts have emphasized technical implementation and ignored pedagogical and behavioral issues that are necessary for design of effective competency-based learning systems. This study was conducted to examine instructional design factors that might influence adult learners' perceptions and reaction towards this technology-delivered pedagogical innovation. Support for learning is essential in the design of any e-learning environment, including the workplace. The study has focused on the support of competency-oriented, self-directed, and socially constructed online learning in the workplace. One challenge for workplace e-learning is how to link employees' personal development with organizational learning and development. This study uses the competency model as the pedagogical structure for curriculum design and learning resource management. This model helps identify individual needs and direct personalized navigation in the web-based learning environment. At the same time, it helps identify individual expertise and facilitates competency-oriented social learning and networking. The empirical results suggest that perceived usefulness of work-integrated pedagogical design in terms of improving self-directed learning processes and promoting collaboration among colleagues has positive influences on employees' behavioral intention to use the e-learning system that is developed with competency-based pedagogical innovations. Gender and prior experience differences in the effect of individual learning support on behavioral intention are also found. Findings of this research contribute to the current endeavors to integrate competency-based instruction and training for adults with advanced e-learning technologies from a behavioral perspective, and provide implications for instructional design of workplace learning technologies.. Limitations of this research are twofold. First, the sampling method was not rigorously random and the sample size was limited. The findings of this research, which resulted from the convenient sample data, have limited generalizability. At the same time, the relatively small sample size contrasted to the rigorous requirements of a structural equation modeling technique may have led to biased results. Second, results of this research are based on self-reported data collected solely via a questionnaire survey. Rigorous procedures were implemented to validate the reliability, convergent validity, and discriminiant validity of the scales; however, the reliability and validity of the data also relied on psychometric techniques and the results were subject to subjectivity.. The competency-based learning approach incorporated in e-learning systems design is still in a state of academic validation and not widely practiced. The results of this research regarding users' perceptions and acceptance of competency-based workplace e-learning systems are to be further validated by empirical studies and industrial practices. Further work will consider longitudinal research based on advancements in industrial practices. Investigations will be based on large representative sample data.. Multiple data sources such as data collected through interview and observation will be used for in-depth analysis. Figure 4 . Reasoning with learning ontology Figure 5 . A personalized learning syllabus Figure 6 . Screenshots of the prototype Figure 7 . Visualization of the ontology using Protégé and "Jambalaya tab" Tables Table 1. Concepts and their Relations Table 2 . Evaluation of the e-learning systems Table 3 . Preference on the e-learning systems a All paths for the two groups were allowed to be freely estimated. The purpose of this research was to examine employees' perceptions and acceptance of competency-based workplace e-learning systems, an issue that has not been adequately addressed in the current literature. The basic assumption was that perceived usefulness of performance oriented learning design for self-directed and collaborative learning embedded in e-learning systems would be helpful for diffusion of the e-learning systems to adult learners. Built on two bodies of literature, namely that of technology acceptance and that of training and instructional design, a conceptual model hypothesizing causal relationships between individual learning and peer learning support and intention to use was proposed and tested against a sample data collected from work settings in mainland China. Results of structural equation modeling and multi-group structural equation modeling applied on the sample data suggested that some of the research hypotheses were supported, while others lacked evidence to be accepted. The hypotheses testing results are discussed below.

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


Improving Handover Quality in 4G Mobile Systems In this paper, we propose a new handover algorithm to guarantee handover quality in 4G mobile systems. The proposed algorithm limits the handover interruption time by improving the HARQ retransmission latency of the first packet transmitted from new serving cell. Through the simulations, we proved that our algorithm meets the requirement of handover interruption time for TCP services with high rate. Fourth generation (4G) mobile systems aim at achieving the provision of data transmission at rates of 1 Gbps for stationary users and 100 Mbps for mobile users on top of all IP-based networks. The speed of stationary user is up to 60 km/h and that of mobile users is up to 250 km/h. To support high data rates in multi-path propagation environment, orthogonal frequency division multiplexing (OFDM) will be adopted as the physical layer technology in the systems. OFDM modulation meets the requirement for high data rates of multimedia services by mitigating inter-symbol interference (ISI) due to larger delay spread than symbol period. In the 4G mobile systems, high speed communications should be supported with minimal or no degradation during handover to guarantee high quality of real-time and TCPbased services. The packet transmission delay fluctuations due to handover execution should be within 30 ms to ensure real-time streams including VoIP [1] . For TCP-based services, the handover interruption time should be below 50 ms to obtain robust TCP throughput performance [2] .. The 4G mobile systems transmit user packets using link adaption and hybrid automatic repeat request (HARQ) techniques. The mapping design for link adaptation between the modulation and coding scheme (MCS) and channel quality feedback would determine the success probability of transmissions and packet delay. Besides, HARQ operation, which adopts fast retransmissions at layer 1, effectively compensates adaptation errors incurred in an instantaneous channel link. However, in the cell edge area, the user who just performed handover would still experience bad channel conditions. In this situation, the HARQ delay of the first packet from new serving cell can be prolonged. Moreover, if the packet delay at the IP layer becomes longer than the required interruption time, the service quality is seriously degraded. Therefore, an effective handover algorithm that makes the HARQ operation complete successfully within the specified number of transmissions is needed to satisfy the required handover interruption time. In this paper, we have proposed a new handover algorithm for limiting the interruption time caused by intra-system handover in 4G mobile systems. Our algorithm is based on the constant channel condition during retransmissions and Chase combining based HARQ mechanism. We will enhance our algorithm to efficiently deal with the variable channel conditions and incremental redundancy (IR) based HARQ mechanism as the further works.

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


Asymmetric Quantum Codes: New Codes from Old Abstract-In this paper we extend to asymmetric quantum error-correcting codes (AQECC) the construction methods, namely: puncturing, extending, expanding, direct sum and the (u|u + v) construction. By applying these methods, several families of asymmetric quantum codes can be constructed. Consequently, as an example of application of quantum code expansion developed here, new families of asymmetric quantum codes derived from generalized Reed-Muller (GRM) codes, quadratic residue (QR), Bose-Chaudhuri-Hocquenghem (BCH), character codes and affine-invariant codes are constructed. To make reliable the transmission or storage of quantum information against noise caused by the environment there exist many works available in the literature dealing with constructions of efficient quantum error-correcting codes (QECC) over unbiased quantum channels [3-5, 11, 16, 17, 20, 26, 30] . Recently, these constructions have been extended to asymmetric quantum channels in a natural way [1, 2, 7-10, 15, 18, 19, 21, 27, 28, 31, 32] .. Asymmetric quantum error-correcting codes (AQECC) are quantum codes defined over quantum channels where quditflip errors and phase-shift errors may have different probabilities. Steane [29] was the first author who introduced the notion of asymmetric quantum errors. As usual, the parameters [[n, k, d z /d x ]] q denote an asymmetric quantum code, where d z is the minimum distance corresponding to phase-shift errors and d x is the minimum distance corresponding to quditflip errors. The combined amplitude damping and dephasing channel (specific to binary systems; see [27] ) is an example for a quantum channel that satisfies d z > d x , i. e., the probability of occurrence of phase-shift errors is greater than the probability of occurrence of qudit-flip errors.. Let us give a brief summary of the papers available in the literature dealing with AQECC. In [7] , the authors explored the asymmetry between qubit-flip and phase-shift errors to perform an optimization when compared to QECC. In [15] the authors utilize BCH codes to correct qubit-flip errors and LDPC codes to correct more frequently phase-shift errors. In [31] the authors consider the investigation of AQECC via code conversion. In the papers [1, 18] , families of AQECC derived from BCH codes were constructed. Asymmetric stabilizer codes derived from LDPC codes were constructed in [27] , and in [28] , the same authors have constructed several families of both binary and nonbinary AQECC as well as to derive bounds such as the (quantum) Singleton and the linear programming bound to AQECC. In [2] , both AQECC (derived from cyclic codes) and subsystem codes were investigated. In [32] , the Giuliano G. La Guardia is with Department of Mathematics and Statistics, State University of Ponta Grossa, 84030-900, Ponta Grossa -PR, Brazil. Email: gguardia@uepg.br.. construction of nonadditive AQECC as well as constructions of asymptotically good AQECC derived from algebraic-geometry codes were presented. In [9] , the Calderbank-Shor-Steane (CSS) construction [5, 16, 24] was extended to include codes endowed with the Hermitian and also trace Hermitian inner product. In [8] , asymmetric quantum MDS codes derived from generalized Reed-Solomon (GRS) codes were constructed. More recently, in [19, 21] , constructions of families of AQECC by expanding GRS codes and by applying product codes, respectively, were presented.. In this paper we extend to asymmetric quantum errorcorrecting codes (AQECC) the construction methods, namely: puncturing, extending, expanding, direct sum and the (u|u+v) construction. An interesting fact pointed out by the referee is that the results presented in the first version of this paper (constructions of asymmetric quantum codes derived from classical linear codes endowed with the Euclidean as well as with the Hermitian inner product) also hold in a more general setting, i. e., constructions of asymmetric quantum codes derived from additive codes (see [5, 16] , where in [5] a general theory of quantum codes over GF (4) was developed, and in [16] a generalization to nonbinary alphabets was presented). Because of this fact, we keep the original constructions of AQECC derived from linear codes and we also add more results with respect to constructions of AQECC derived from additive codes. More specifically, concerning the techniques of extending and the (u|u + v) construction, the arguments shown in this paper to AQECC codes derived from linear codes are similar to the ones derived from additive codes. The techniques of puncturing, expanding and direct sum will be shown in two different ways (each of them), i. e., AQECC derived from linear and additive codes. We keep both styles of constructions (additive/linear) in this paper because although the first (additive) is more general, we utilize different tools to show the results for the linear case, and these tools can be applied in future works.. The paper is organized as follows. In Section II we fix the notation. In Section III we recall the concepts and definitions of AQECC and error operators. Section IV is devoted to establish the construction methods. More precisely, we show how to construct new AQECC by means of the techniques of puncturing, extending, expanding, direct sum and the (u|u+v) construction. In Section V, we utilize the quantum code expansion developed in Section IV applied to (classical) generalized Reed-Muller (GRM) codes, quadratic residue, character codes, BCH and affine-invariant codes in order to construct several new families of AQECC. Finally, in Section VI, we discuss the contributions presented in this paper.

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Steady States of Epidemic Spreading in Small-World Networks We consider a standard susceptible-infected-susceptible (SIS) model to study behaviors of steady states of epidemic spreading in small-world networks. Using analytical methods and large scale simulations, we recover the usual epidemic behavior with a critical threshold λ c below which infectious diseases die out. Whereas for the spreading rate λ far above λ c , it was found that the density of infected individuals ρ as a function of λ has the property ρ ≈ F (K)(ln λ − ln λ c ).Keywords: networks and genealogical trees, diseases, phase transitions.Complex networks have attracted an increasing interest recently. The main reason is that they play an important role in the understanding of complex behaviors in real world networks, [1, 2, 3] including the structure of language, [4, 5] In particular, social networks have two characters. First, they show "clustering", meaning that two of your friends are far more likely also to be friends of each other than two people chosen from the population at random. Second, they exhibit the "small-world effect", namely, that any two people can establish contact by going through only a short chain of intermediate acquaintances. These two properties appear contradictory because the first is a typical property of low-dimensional lattices but not of random graphs or other high-dimensional lattices, while the second is typically of random graphs, but not of low-dimensional lattices. D.J. Watts and 1

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Learning from ambiguously labeled examples Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete classification methods to the ALC setting (nearest neighbor classification, decision tree learning, and rule induction) and evaluating their performance on benchmark data sets, we show that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples.Our results indicate that the fundamental idea of the extended methods, namely to disambiguate the label information by means of the inductive bias underlying (heuristic) machine learning methods, works well in practice. One of the standard problems in (supervised) machine learning is inducing a classification function from a set of training data, i.e., a function which assigns objects to classes or categories. In fact, a large repertoire of corresponding classification methods is now available.. Usually, the training data consists of a set of labeled examples, i.e., a set of objects (instances) whose correct classification is known. Over the last years, however, several variants of the standard classification setting have been considered. For example, in multi-label classification a single object can have several labels (belong to several classes), that is, the labels (classes) are not mutually exclusive [25] . In semi-supervised learning, only a part of the objects in the training set is labeled [1] . In multiple-instance learning, a positive or negative label is assigned to a so-called bag rather than to an object directly [8] . Each bag is a collection of several instances. A bag is labeled negative if all the instances it contains are negative, otherwise it is labeled positive. Given a set of labeled bags, the task is to induce a model that will label unseen bags and instances correctly.. In this paper, we are concerned with another extension of the standard classification setting that has recently been introduced in [16, 19] , and that we shall subsequently refer to as ambiguous label classification (ALC). In this setting, an example might be labeled in a non-unique way by a subset of classes, just like in multi-label classification. In ALC, however, the existence of a (unique) correct classification is assumed, and the labels are simply considered as candidates.. In [16, 19] , the authors rely on probabilistic methods in order to learn a classifier in the ALC setting. The approach presented in this paper can be seen as an alternative strategy which is more in line with standard (heuristic) machine learning methods. Our idea is to exploit the inductive bias underlying these methods in order to disambiguate label information. This idea, as well as the relation between the two approaches, is discussed in more detail in section 3. Before, the problem of ALC is introduced in a more formal way (section 2). In section 4, three concrete methods for ALC are proposed, namely extensions of nearest neighbor classification, decision tree learning, and rule induction.. Experimental results are presented in section 5. In section 6, wie briefly outline two alternative applications of ALC. The paper concludes with a summary of the results in section 7. In order to successfully learn a classification function in the ALC setting, where examples can be labeled in an ambiguous way, we proposed several extensions of standard machine learning methods. The idea is to exploit the inductive bias underlying these (heuristic) methods in order to disambiguate the label information. In fact, our speculation was that looking at the label information with a "biased view" may remove the ambiguity of that information to some extent. This speculation gives furthermore rise to the conjecture that ALC learning methods with a strong (and of course approximately correct) bias can exploit the information provided by ambiguous examples better than methods with a weak bias. This conjecture has been supported empirically by experiments that have been carried out for three concrete learning techniques, namely ALC extensions of nearest neighbor classification, decision tree learning, and rule induction. The experiments also. showed that applying our ALC methods to the complete data will usually yield better results than learning with a standard method from the subset of exactly labeled examples, at least if the expected benefit of the ambiguous examples is not too low. In summary, our idea to "disambiguate via biased induction" can be seen as a simple yet effective alternative to the probabilistic approaches proposed in [16, 19] . Indeed, let us again emphasize that these two approaches, just like machine learning and statistical methods in general, are not competing but complementary, in the sense that they apply to different types of learning methods: The expectation-maximization approach assumes a parameterized (statistical) model and is not immediately applicable to machine learning methods as those considered in this paper. For example, it is not applicable to k-NN classification, as there are no parameters to estimate.. 10 Likewise, we are not aware of suitable statistical approaches to rule induction. In the case of decision trees, a statistical model amenable to ML estimation has indeed been proposed in [21] . But even if a classifier can be trained in both ways, heuristically and via maximum likelihood, the former will definitely be much more efficient than the latter. For example, the decision tree learned in [22] for 12 binary input variables already involved more than 1,000 parameters, and convergence was not achieved before approximately 100 training epochs.. Despite the fact that our approach has its own right to exist, independently of the statistical alternative, exploring the relation between the two approaches is of course interesting and worth further investigation. Another interesting topic of future work concerns a detailed elaboration of the alternative applications of ALC as outlined in section 6. We have worked with "contaminated" versions of standard benchmark data sets (in which each instance is assigned a unique label), which allowed us to conduct experiments in a controlled way. In order to contaminate a given data set, we have devised two different strategies:. • Random model: For each example in the training set, a biased coin is flipped in order to decide whether or not this example will be contaminated; the probability of contamination is p. In case an example x ı is contaminated, the set L xı of candidate labels is initialized with the original label λ xı , and all remaining labels λ ∈ L \ {λ xı } are added with probability q, independently of each other. Thus, the contamination procedure is parameterized by the probabilities p and q, where p corresponds to the expected fraction of ambiguous examples in a data set. Moreover, q reflects the "average benefit" of a contaminated example x ı : The smaller q is, the smaller the (average) number of candidate labels becomes and, hence, the more informative such an example will be. In fact, note that the expected cardinality of L xı , in the case of contamination, is given by 1 + (m − 1)q.. • Bayes model: The random model implicitly assumes that candidate labels occur independently of each other. In practice, this idealized assumption will rarely be valid. For example, the probability that a label is added will usually depend on the true label. In order to take this type of dependency into account, our second approach to contamination works as follows: First, a Naive Bayes classifier is trained using the original data, and a probabilistic prediction is derived for each input x ı .. Let Pr(λ | x ı ) denote the probability of label λ as predicted by the classifier. Whether or not an example is contaminated is decided by flipping a biased coin as before.. In the case of contamination, the true label λ xı is again retained. Moreover, the other m − 1 labels λ ∈ L \ {λ xı } are arranged in an increasing order according to their probability Pr(λ | x ı ). The k-th label in this order, λ (k) , is then added with probability (2 · k · q)/m. Thus, the expected cardinality of L xı is again 1 + (m − 1)q, but the probabilities of the individual labels are now biased in favor of the labels found to be likely by the Bayes classifier.. Intuitively, the Bayes model should come along with a decrease in performance for the ALC approach (while the baseline method is of course not affected). Roughly speaking, since the Bayes model contaminates the data in a more "systematic" way, one might expect that disambiguating the data becomes more difficult. To illustrate, consider the extreme example where the true class is always λ 1 , i.e., all instances do have label λ 1 . Now, if additional labels are added in a purely random way, most learning methods will still be able to recognize the default rule ⇒ λ 1 as a model. When always adding label λ 2 , however, a distinction between the correct model and the one that always predicts λ 2 becomes impossible.. The experimental results have been obtained in the following way: In a single experiment, the data is randomly divided into a training set and a test set of the same size. The training set is contaminated as outlined above. From the contaminated data, a model is induced using an ALC-extension of a classification method (kNN, decision trees, rule induction).. Moreover, using the classification method in its standard form, a model is learned from the reduced training set that consists of the non-contaminated examples. Then, the classification accuracy of the two models is determined by classifying the instances in the test set. The expected classification accuracy of a method -for the underlying data set and fixed parameters p, q -is approximated by averaging over 1,000 such experiments.. This was done for a number of benchmark data sets from the UCI repository.. For decision tree learning and rule induction, all numeric attributes have been discretized in advance using hierarchical entropy-based discretization [12] . 4 For the NN classifier, we used the simple Euclidean distance measure, and we didn't include feature selection or feature weighting (even though it is well-known that irrelevant features can badly deteriorate and, on the other hand, that feature weighting can greatly improve performance [32] ). In fact, we didn't try to optimize the performance of the three learning methods themselves, because this was not the goal of the experiments. Rather, the purpose of the study was to compare -under equal conditions -ALC learning with the baseline method.

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


Approximating the chromatic index of multigraphs It is well known that if G is a multigraph then χ (G) ≥ χ Let G be a multigraph. A k-edge-coloring of G is an assignment of k colors to the edges of G so that no two adjacent edges receive the same color. The chromatic index of G, denoted by χ (G), is the smallest k for which G admits a k-edge-coloring. Since it is N P -hard to determine χ (G) (see Holyer [9] ), a good estimate of χ (G) has been the focus of extensive research.. Let ∆(G) denote the maximum degree of G. Clearly χ (G) ≥ ∆(G). A classical theorem of Shannon [22] asserts that χ (G) ≤ 3∆(G)/2. Vizing [26] and Gupta [7] proved that χ (G) ≤ ∆(G) + µ(G), where µ(G) is the maximum multiplicity of an edge of G; and Kierstead [12] studied the graphs G for which χ (G) = ∆(G) + µ(G). This Vizing-Gupta result implies that if G is a simple graph then χ (G) ∈ {∆(G), ∆(G) + 1}.. Another lower bound for χ (G) is the fractional chromatic index defined below. Let The number χ * (G) := max{∆(G), Γ(G)} is the fractional chromatic index of G (see [3, 21] and [23] ), which is the solution to a linear program. The fractional chromatic index can be determined in polynomial time by using the ellipsoid algorithm; since the corresponding separation problem is equivalent to the weighted matching problem, see Theorem 28.6 in [21] .. In the 1970s, Goldberg [5] , Anderson [1] , and Seymour [23] independently (and in different forms) made the following conjecture.

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


A model of the electrical behaviour of myelinated sensory nerve fibres based on human data Calculation of the response of human myelinated sensory nerve fibres to spinal cord stimulation initiated the development of a fibre model based on electro-physiological and morphometric data for human sensory nerve fibres. The model encompasses a mathematical description of the kinetics of the nodal membrane, and a non-linear fibre geometry. Fine tuning of only a few, not well-established parameters was performed by fitting the shape of a propagating action potential and its diameter-dependent propagation velocity. The quantitative behaviour of this model corresponds better to experimentally determined human fibre properties than other mammalian, non-human models do. Typical characteristics, such as the shape of the action potential, the propagation velocity and the strength-duration behaviour show a good fit with experimental data. The introduced diameter-dependent parameters did not result in a noticeable diameter dependency of action potential duration and refractory period. The presented model provides an improved tool to analyse the electrical behaviour of human myelinated sensory nerve fibres. This paper presents a new fibre model to describe the electrical behaviour of human myelinated sensory nerve fibres. The model was based on the membrane kinetics of human sensory nerve fibres (SCHWARZ et al., 1995) and on the morphometry of human AI[ fibres (BEHSE, 1990) . We compared this model only with the fibre model we previously used for SCS modelling, since the mammalian fibre models available to date have already been compared extensively by other authors (FRIJNS et al., 1994; FRIJNS and TEN KATE, 1994; RATTAY and ABERHAM, 1993) . These studies showed that the choice of specific fibre model depends on the type of neural substrate to be analysed. The simulations were performed with the SCS computer model, which incorporated the complete fibre model with parameters as presented in the Appendix. Fibre diameters between 5 and 151.tm were applied to characterise the model, since the largest fibres in the human dorsal columns, which are most likely to be stimulated in SCS, are within this range (WESSELINK et al., 1998) . The calculated model characteristics used for validation are summarised in Table 2 , which also includes the corresponding data from the fibre model we previously used for SCS modelling (SWEENEY et al., 1987) . The corresponding experimentally obtained data from the literature are listed in Table 1 .

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Resource Sharing Architecture For Cooperative Heterogeneous P2P Overlays Abstract Resource requirements and availability in heterogeneous networks may frequently vary over their lifetime; thus producing equally variant overloaded and under-loaded situations. Typical architectures cannot cope with the frequent availability fluctuation of reusable, non-replicable and highly dynamic resources (such as network capacity). This paper proposes an unstructured P2P overlay for sharing resources between underutilized and overloaded networks. Its aim is to satisfy the excessive resource demands of some networks by using free resources from others given the high failure rate and unstable availability of these resources in wide networks. We describe and analyze the proposed Capacity Sharing Overlay Architecture and show, with extensive simulations, its ability to provide remote underutilized capacity to underlying networks, even in the presence of high node failure rates, helping the networks to handle more user queries. Heterogeneous distributed applications deployed on different networks may have quite variable network throughput performance requirements during their lifetime. We define the Network Capacity as the number of user queries a node can process within a time unit. The Network Capacity depends on the combined communication and computation throughput of that node. In case the application's workload overcomes the available network capacity (overloaded situation), new nodes are required to join the network to serve the demand. On the contrary, when the application produces traffic that fewer nodes could efficiently serve (underloaded situation) the network application may free some of them and increase the remaining nodes' utilization.. In this research, we try to address the problem of network workload fluctuations by changing appropriately the network size. While underloaded networks may provide underutilized nodes, overloaded ones may use them to serve their excessive traffic. Moving capacity from one network to another may improve the utilization of spare capacity and the reliability of networks even in high workload situations. Node and capacity sharing will be used interchangeably this point onwards since network capacity is closely related to the size of a network. In our architecture two steps are necessary to share capacity between networks: discovery, move of capacity. While the first step locates the appropriate nodes the second one fetches them to the requestor network.. CSO, Capacity Sharing Overlay, is a P2P management system that enables the sharing of reusable resources and specifically network capacity. Network capacity is a non-replicable, reusable, stochastically available resource and only one instance of it may exist within a network and no more than one user may use it each time [1] . This architecture facilitates the cooperation of heterogeneous networks for improving the utilization of the spare (currently not used) capacity in the whole system. The overlay consists of a set of interconnected servers each of which represents the nodes of an underlying network.. The main objective of the CSO is to provide a set of services to the interconnected networks to enable the node sharing. These services are the join/leave and query/ response processes that can be used by any server. A server participates in the overlay if it knows one or more servers that already have joined the CSO. Therefore, the join process informs the new server about the existence of neighbours that will make the former accessible by the other servers and, vice versa, resources of the other networks accessible from the new server. Any server may leave the overlay at any time without notifying any of its neighbours. The query/response services define the behaviour of each server while it is active within the overlay. Network applications are dynamic distributed computing environments that frequently exhibit phenomena of under-loading and over-loading due to the massive fluctuation in the number of user queries they produce and handle. For this reason it is beneficial to provide mechanisms that allow over-loaded networks to utilise the free capacity of under-loaded ones. In this way we can achieve significantly enhanced performance in terms of the number of the user queries that can be processed.. The main innovation of CSOA is the concept of logically moving free nodes on demand from one network to another in order to serve extra workload in terms of user queries. These ''mobile'' nodes also have the autonomy of removing themselves from the network they serve if they are under-utilised and make themselves available for other over-loaded networks. The resulting system is simple in design, deadlock-free (as the system does not reserve partial answers as each query propagates through the overlay) and fully supports heterogeneity as it makes no assumption regarding the topology and type of the underlying networks. Therefore, the system has good applicability in scenarios where instead of network capacity the underlying networks share processing power or even storage capacity.. Through extensive simulations, we have shown that CSOA manages to find the required spare capacity with high probability and as a result, there is a significant increase in the number of additional successfully processed user queries that would otherwise have been dropped. CSOA supports standard blind P2P search techniques such as flooding and k-walkers and it can easily be extended to use others. The CSOA performance gains remain significant even in the presence of nonnegligible failure rates of the CSOA servers and nodes. An extensive survey from Streamcheck Research [21] revealed that an increasing number of video streaming providers suffer from flash crowds especially in peak hours. For instance, in top news providers like CNN, MSNBC and ABC, because of traffic bursts, the ''breaking stories are often unreachable during peak viewing hours'' and the users experience content outages and longer startup time. BBC announced that on 7th July 2002 the download time of webpages was eight times longer, a quarter of requests for webpages were not served and ''email traffic reportedly doubled in response to congestion on mobile networks'' [22] . Any centralized approach of interconnecting all the underlying networks' servers would suffer from the high workload from frequent queries and advertisements of the requested and available capacity respectively. The high rates of leave/join actions of the servers and nodes would cause extra significant update overhead. In case of a failure of the central manager, no capacity sharing would be possible.. Adopting a more distributed approach using P2P Networks solves the singlepoint-of-failure and reliability problems of the centralised one. Replication of resources [2, 3] may increase the throughput performance of the overlay network since it increases the availability of the same resource [4] . Advertisement [5] or gossiping may direct the query faster to the resource provider thus reducing the latency. DHT-based P2P systems such as CAN [6] , Chord [7] , Pastry [8] and Tapestry [9] can guarantee successful discovery if the requested resource is available in the network within O(logn) messages [10] . However, replication or gossiping/advertisement as well as informed resource discovery techniques of P2P Networks are not applicable to the discovery of network capacity since it is a nonreplicable reusable with high fluctuations on its availability resource. Organizing the overlay servers in a Structured P2P would require the use of its lookup function each time a node joins the system resulting in a high maintenance cost.. Existing research on high throughput computing has produced several solutions to the issue of reusable resources discovery especially storage capacity and CPU cycles. Condor [11, 12] is one of the most mature high throughput computing technologies. It is a distributed job scheduler providing the appropriate services for submission and execution of jobs on remote idle resources even if they are under different administrative domains. A central manager receives the advertisements of the available resources and tries to submit the queued jobs to the appropriate ones, which report back to the manager the execution state of each job. The central manager along with the idle resources constitutes the condor pool. The flocking [13, 14] was introduced to statically link several condor pools and share resources between them. Manual configuration of a pool's neighbors is required thus limiting the adaptivity of the system in case of dynamic changes in resource availability. It is also assumed that the pool managers run on reliable machines [15] since their failure can prevent the execution of new jobs. These problems can be approached with a Pastry [8] selforganizing overlay of condor pools [15] . The condor pools are organized into a ring and proactively advertise their idle resources to their neighbors so that they can choose these advertised resources whenever necessary. Unfortunately, this P2P-based flock of Condors requires a substantial maintenance overhead for updating the proximity-aware routing tables since it is based on the advertisements of available resources. If the availability of the resources changes very frequently these updates need to be frequent and therefore high maintenance costs.. Finally, an important feature of Condor Flocking is that the execution machines are always managed by the same managers. Thus, every new discovery of the same/ similar remote resources by the same manager follows the same procedure. Given that the required network capacity could frequently exceed the locally available, the local manager would forward equally frequent queries seeking almost the same amount of capacity; thus resulting in a significant number of messages.. The benefits of P2P overlays [10, 16] for the discovery of reusable resources have been identified and used in P-Grid. P-Grid, identifying the update overhead posed by available resources' advertisements on DHT-based overlays, uses a tree-based distributed storage system of the requesting resources' advertisements [17] . The resource providers locate in this tree the requestors they can serve and offer themselves for use. While other structured P2P networks hash the indexing keys, thus limiting the searching capabilities, P-Grid enables complex queries. The organization of this overlay raises a number of concerns about its scalability in case of large highly dynamic networks since an update action of one advertisement could propagate to many peers.. Sensor Networks is another field that uses the benefits of P2P Networks to achieve reliable cooperation of networked sensors. Recent research on P2P-based end-to-end bandwidth allocation [18] proposes a wireless unstructured overlay of sensors. Initially a central peer posses all the bandwidth and distributes it ondemand and every query is broadcasted to all peers. This system cannot be applied to the case of network capacity sharing since it makes the assumption that the available bandwidth within the whole network is known a-priori and that the topology of the network remains the same. Finally, it is suitable for wireless environments where the cost of broadcasting is the same to unicasting.. All the systems described above are efficient in the context they were developed for but they are insufficient in the context of network capacity. Network characteristics may change extremely fast so that any advertisement and/or indexing scheme could result in frequent updates with a high cost on messages. G-ROME [19] builds an unstructured overlay on top of ROME-enabled Chord [7] ring networks and its aim is to discover underutilized nodes in ROME [20] node pools and transfer them to requestor pools. Trying to address the problem of the frequently changing capacity availability, G-ROME uses an unstructured overlay of ROME bootstrap servers that represented Chord idle resources. The proposed architecture (CSOA) goes one-step forward decoupling G-ROME from Chord rings and supporting a more heterogeneous environment of interconnected networks.

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


Dynamic threshold policy for delaying and breaking commitments in transportation auctions In this paper we consider a transportation procurement auction consisting of shippers and carriers. Shippers offer time sensitive pickup and delivery jobs and carriers bid on these jobs. We focus on revenue maximizing strategies for shippers in sequential auctions. For this purpose we propose two strategies, namely delaying and breaking commitments. The idea of delaying commitments is that a shipper will not agree with the best bid whenever it is above a certain reserve price. The idea of breaking commitments is that the shipper allows the carriers to break commitments against certain penalties. The benefits of both strategies are evaluated with simulation. In addition we provide insight in the distribution of the lowest bid, which is estimated by the shippers. The procurement of transportation is an important task for shippers (typically large manufacturers and retailers) because it greatly effects their costs and service levels. In practice, a procurement process includes carrier (trucking company) screening, carrier assignment, load tendering and performance review. During the last few years, this procurement has moved from telephone to web based services (Song and Regan 2001) . In this paper we consider an automated transportation procurement auction where shippers offer time sensitive pickup and delivery jobs and carriers bid on these jobs. Each auction is initiated by a shipper and ends with commitment between the shipper and a carrier.. Auctions are often considered as appropriate means for dynamic job allocation in distributed environments. Desirable properties include limited required information exchange and, in some cases, Pareto efficient outcomes (McAfee and Mc Millan 1987; Wellman and Walsh 2001) . However, when multiple jobs are auctioned at different points in time (sequential auctions), such an allocation would be less appropriate if we do not take into account the future consequences of an allocation. Also when jobs are complementary (e.g. transportation jobs that can be served sequentially by the same vehicle) or substitutable (e.g. transportation jobs that are available at the same time), a certain allocation may become unfavorable when new orders appear. To overcome this, several authors (e.g. Caplice and Sheffi (2003) ) suggested the use of combinatorial auctions for transportation procurement in which a carrier can bid on multiple jobs. However, combinatorial auctions involve many inherently difficult problems. As mentioned by Song and Regan (2005) , we face the bid construction problem, where bidders have to compute bids over different job combinations, and the winner determination problem, where jobs have to be allocated among a group of bidders. In addition (1) it may be unrealistic to bundle jobs which belong to different shippers, and (2) these procedures are not directly applicable in situations where jobs arrive at different points in time.. To improve the allocation of jobs, we take the sequential transportation procurement auction as given, and focus on strategies for the participants. In a previous paper (Mes et al. 2006) we focused on profit maximizing strategies for the carriers. We proposed a bid pricing strategy where the arrivals of future jobs are taken into account through the use of opportunity costs. In this paper we focus on strategies for the shipper. We propose two options, namely delaying and breaking commitments.. The idea of delaying commitments is that shippers postpone commitments for which they expect to make a better commitment in the future. So, if a shipper has plenty of time to auction a certain job, he will not agree with a relatively high bid. When the time for dispatch becomes nearer, the price he is willing to accept will raise. We denote this mechanism by dynamic threshold policy. The idea of breaking commitments is that the shipper allows a carrier to decommit from an agreement against a certain penalty. These penalties are chosen such, that whenever a carrier decommits a job, the shipper expects to find a new carrier for a lower price. We denote this mechanism by decommitment policy.. There is some strategic equivalence between the dynamic threshold policy and the decommitment policy. Suppose a carrier wins a certain job. After two days the carrier decommits from the contract and pays a certain penalty. One can imagine that this penalty equals the expected costs for the shipper for auctioning this job two days later, which in turn has some strong connections with the dynamic threshold policy. We develop a dynamic programming algorithm that can be used for both, the dynamic threshold and the decommitment policy.. The goal of this paper is threefold. First to derive a dynamic threshold policy and decommitment policy. Second to evaluate their benefits (will they increase the overall system performance).. Third to study their relation, that is, are they complementary or substitutable? In this paper, we presented a dynamic threshold policy. This policy enables shippers to postpone commitments for which they expect to make a better commitment in the future. In addition, we described a decommitment policy which allows carriers to decommit from an agreement with a shipper against a certain penalty.. Applications of these policies include both competitive transportation procurement auctions and closed environments. For the procurement auction we are mainly interested in the benefits of these policies for an individual shipper within a larger market. For the closed environments (group of trusted carriers, internal allocation mechanism) we investigate the impact of these policies on the total costs.. From our simulation experiments, we conclude that both policies reduce the total costs. Also the costs per order for an individual shipper using the dynamic threshold policy are significantly lower than those who did not use such a threshold policy. The decommitment policy, as we derived it without profit margins, will result in relatively higher costs per order for the individual shipper, compared to the external market.. Further research is required on learning, profits, and the combination with 'smart' carriers.. With learning we refer to updating procedures of the estimated data. By using the described policies, the market prices will change and possibly other players will react on that. This especially becomes an issue in networks that change over time, closed networks, and networks with multiple players with different strategies. A second aspect of further research is concerned with profits and auction types. For clarity of presentation we ignore profits in this paper and use a simple first-price auction. For practical applications we should also take into account profits, for example by using a second-price auction.. The last aspect of further research is concerned with the combination of profit maximizing strategies for both the shippers and carriers. In a previous paper (Mes et al. 2006) we focused on profit maximizing strategies for the carriers. We proposed a bid pricing strategy where the arrival of future jobs are taken into account through the use of opportunity costs. We evaluated this opportunity valuation method for the carriers by assuming a naïve strategy for the shipper. In this paper we evaluated profit maximizing strategies for the shipper by assuming naïve pricing-and scheduling strategies for the carriers. If both parties (carriers and shippers) are using intelligent strategies, it might be the case that they compete against each other and no party will be better of.. It might even be the case that this has a negative effect on the total system performance. Therefore we aim to enhance the strategies for both players by incorporating the opponents' behavior. In addition we will perform an extensive simulation experiment to study the interrelation of the different policies of both players.

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


Identifying modules via concept analysis We describe a general technique for identifying modules in legacy code. The method is based on concept analysis -a branch of lattice theory that can be used to identify similarities among a set of objects based on their attributes. We discuss how concept analysis can identify potential modules using both "positive" and "negative" information. W e present an algorithmic framework to construct a lattice of concepts from a program, where each concept represents a potential module. Many existing software systems were developed using programming languages and paradigms that do not incorporate object-oriented features and design principles. In particular, these systems often lack a modular style, making maintenance and further enhancement an arduous task. The software engineer's job would be less difficult if there were tools that could transform code that does not make explicit use of modules into functionally equivalent object-oriented code that does make use of modules (or classes). Given a tool to (partially) automate such a transformation, legacy systems could be modernized, making them easier to maintain. The modularization of programs offers the added benefit of increased opportunity for code reuse.. A major difficulty with software modularization is the accurate identification of potential modules and classes. This paper describes how a technique known as concept analysis can help automate modularization. The main contributions of this paper are:. e We show how to apply concept analysis to the modularization problem. We focus on one variant of the modularization problem -the conversion of a C program to a C++ program, where the C program's s t r u c t types are the starting point for the C++ program's classes.. e Previous work on the modularization problem has made use only of "positive" information: Modules are identified based on properties such as "function f uses variable x" or "f has an argument of type t". It is sometimes the case that a module can be identified by what values or types it does not depend upon -for example, "function f uses the fields of s t r u c t queue, but not the fields of struct stack". Concept analysis allows both positive and negative information to be incorporated into a modularization criterion. (See Section 3.2.). e We have implemented a prototype tool that uses concept analysis to propose modularizations of C programs. The implementation has been tested on several small and medium-sized examples. The largest example consists of about 28,000 lines of source code. (See Section 5.). As an example, consider the C implementation of stacks and queues shown in Figure la . Queues are represented by two stacks, one for the front and one for the back; information is shifted from the front stack to the back stack when the back stack is empty. The queue functions only make use of the stack fields indirectly -by calling the stack functions. Although the stack and queue functions are written in an interleaved order, we would like to be able to tease the two components apart and make them separate classes, one a client of the other, as in the C++ code given in Figure lb. This paper discusses a technique by which modules (in this case C++ classes) can be identified in legacy C code. The resulting information can then be supplied t o a suitable transformation tool that maps C code to C++ code, as in the aforementioned example. Although other modularization algorithms are able to identify the same decomposition [3, 211, they are unable to handle a variant of this example in which stack and queue are more tightly intertwined (see Section 3.2). In Section 3.2, we show that concept analysis is able to group the code from the latter example into separate queue and stack modules.. Section 2 introduces contexts and concept analysis, and an algorithm for building concept lattices from contexts. Section 3 discusses a process for identifying modules in C programs based on concept analysis. Section 4 defines the notion of a concept partition.. Section 5 discusses the implementation. Section 6 concerns related work. Because modularization reflects a design decision that is inherently subjective, it is unlikely that the modularization process can ever be fully automated. Given that some user interaction will be required, the concept-analysis approach offers certain advantages over other previously proposed techniques (e.g., [ll, 5, 13, 12, 2]), namely, the ability to "stay within the system" (as opposed to applying ad hoc methods) when the user judges that the modularization that the system suggests is unsatisfactory. If the proposed modularization is on too fine a scale, the user can "move up" the partition lattice. (See Section 4.). If the proposed modularization is too coarse, the user can add additional attributes to generate more concepts. (See Section 3.) Furthermore, concept analysis really provides a family of modularization algorithms: Rather than offering one fixed technique, different attributes can be chosen for different situations.. The reader is referred t o [2, pp. 27-32] for an extensive discussion of the literature on the modularization problem. In the remainder of this section, we discuss only the work that is most relevant to the approach we have taken. less powerful analysis. They also propose that the user intervene with ad hoc adjustments if the results of modularization are unsatisfactory. As explained above, the concept-analysis approach can naturally generate a variety of possible decompositions (i.e., different collections of concepts that partition the set of objects).. The concept-analysis approach is more general than that of Canfora et al. [3] , which identifies abstract data types by analyzing a graph that links functions to their argument types and return types. The same information can be captured using a context, where the objects are the functions, and the attributes are the possible argument and return types (for example, attributes aO, . . . , a3 in the attribute table in Section 3.1). By adding attributes that indicate whether fields of compound data types are used in a function, as is done in the example used in this paper, conceptanalysis becomes a more powerful tool for identifying potential modules than the technique described in [3].. The In most of the approaches mentioned above, spurious links arise from a function that accesses several global variables of different types. The work described in [ll, 5, 12, 21, 21 will all stumble on examples that exhibit spurious links. In our approach, an analogous kind of spurious link arises due to functions that access internal fields of more than one s t r u c t . An example is found in the tangled-code example discussed in Section 3.2, where the enq function uses the fields of both s t r u c t s t a c k and s t r u c t queue. The additional discriminatory power of the concept-analysis approach is due to the fact that it is able to exploit both positive and negative information. There has been a certain amount of work involving the use of cluster analysis to identify potential modules (e.g., [8, 1, 9, 21) . This work (implicitly or explicitly) involves the identification of potential modules by determining a similarity measure among pairs of functions. We are currently investigating the link between concept analysis and cluster analysis.. Concept analysis has been applied to many kinds of problems. Concept analysis was first applied to software engineering in the NORA/RECS tool, where it was used to identify conflicts in software-configuration informat ion [ 181.. Contemporaneously with our own work, Lindig and Snelting [lo] and Sahraoui et al. [15] independently explored the idea of applying concept analysis to the modularization problem. In both of these studies, the context relations used for concept analysis relate each function of the program to the global variables accessed by the function.. The results reported by Lindig and Snelting on two case studies of small to medium-sized Fortran and Cobol programs are not encouraging. In both cases, the concept lattice that resulted did not identify any useful ways to decompose the program into modules.. However, we believe that the results achieved by our approach to using concept analysis are more promising than those of Lindig and Snelting and Sahraoui et al. This is due to several factors:. The languages on which the techniques were applied -i.e., Fortran and Cobol (in the case of Lindig and Snelting) versus C. The C-to-C++ conversion problem is a variant of the modularization problem that has more structure than Fortran-to-X and Cobol-to-X conversion/modularization problems. In particular, the C program's struct types serve as a natural starting point for the C++ program's classes. Lindig and Snelting and Sahraoui et al. use context relations that relate each function of a program to the global variables accessed by the function. In our work, context relations relate each function of a program to (i) the fields of userdefined struct types that the function accesses, (ii) the types of sub-expressions that occur within the function, and (iii) the complements of (i) and (ii).. In our work, we employ negative information (e.g., "attributes of the form f does not use fields of struct t"). This allows the concepts identified to be based not only on the similarities between functions, but also on their differences.

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Study of continuous-time quantum walks on quotient graphs via quantum probability theory In the present paper, we study the continuous-time quantum walk on quotient graphs.On such graphs, there is a straightforward reduction of problem to a subspace that can be considerably smaller than the original one. Along the lines of reductions, by using the idea of calculation of the probability amplitudes for continuous-time quantum walk in terms of the spectral distribution associated with the adjacency matrix of graphs Two types of quantum walks, discrete and continuous time, were introduced as the quantum mechanical extension of the corresponding random walks and have been extensively studied over the last few years [1, 2] . In recent years, quantum walk has gained considerable interest in the quantum information and computation research areas due to its potential applications.. In particular, the study of continuous-time quantum walks (CTQW) on graphs has shown promising applications in the algorithmic and implementation aspects. As an alternate algorithmic technique to the Quantum Fourier Transform and the Amplitude Amplification techniques, Childs et al. [3] demonstrated the power of CTQW algorithm for solving a specific blackbox graph search problem. A study of quantum walks on simple graph is well known in physics(see [4] ). Recent studies of quantum walks on more general graphs were described in [5, 6, 7, 8, 9, 10, 11, 12] . Some of these works studies the problem in the important context of algorithmic problems on graphs and suggests that quantum walks is a promising algorithmic technique for designing future quantum algorithms.. One approach for investigation of CTQW on graphs is using the spectral distribution associated with the adjacency matrix of graphs [13, 14, 15, 16, 17] . Authors in Ref. [13] have introduced a new method for calculating the probability amplitudes of quantum walk based on spectral distribution. The method of spectral distribution only requires simple structural data of graph and allows us to avoid a heavy combinational argument often necessary to obtain full description of spectrum of the adjacency matrix. In view of the fact that the quotient graphs are important to generate the Crystallographic nets, it is tempting to try to investigate the CTQW on quotient graphs. On such graphs, there is a straightforward reduction of problem to a subspace that can be considerably smaller than the original one. In this paper, along the lines of similar reductions and the idea calculation of the probability amplitudes for CTQW in terms of the spectral distribution associated with the adjacency matrix of graphs [13] , we show continuous-time Quantum walk the CTQW on graph Γ reductions to CTQW on quotient graph Γ H such that its adjacency matrix is contained two Szegö-Jacobi sequences √ ω i and α i which denote the jumping rate from a vertex to its neighbor and self loops, respectively. Finally, we calculate the probability amplitude for CTQW on some quotient Cayley graphs.. The organization of the paper is as follows. In Section 2, we give a brief outline of Cayley graphs and automorphism groups. In Section 3, we investigate the action of groups and stratification, quantum decomposition for adjacency matrix of graphs, quotient graph, the method for obtaining spectral distribution µ and devoted to the method of computing the amplitude for CTQW, through spectral distribution µ of the adjacency matrix A. In the Section 4 we calculate the probability amplitude for CTQW on some quotient Cayley graphs.. The paper is ended with a brief conclusion. By using the method of calculation of the probability amplitude for CTQW on graph [13] , we have shown CTQW on graph Γ induces a CTQW on quotient graph Γ H . Then we obtained the probability amplitude of CTQW on some quotient Cayley graphs. In view of the fact that the quotient graphs are important to generate Crystallographic nets, it is possible to generalize this method for investigating CTQW on Crystallographic nets, which is under investigation.

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