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Information on the papers to be published in future issues of "IEEE Transactions on Engineering Management," including one on outsourcing and offshoring engineering projects, one on management software outsourcing and one on information technology.

Sourcing and leveraging knowledge from an external network is only half the battle for firms that would become more successful. In fact, the mere access and acquisition of the knowledge itself through embedded ties does not secure to perform exploration and exploitation activities, and consequently, to achieve better performance because knowledge has to be managed. Firms' knowledge management (KM) orientation may help in the process of knowledge acquisition, sharing, and transfer, consequently, improving firms' ambidexterity and competitiveness. Thus, this paper proposes that the KM plays a key role in determining the outcomes of firm's external embeddedness, i.e., the characteristics and ties of the external network, on the ambidexterity and performances of small and medium enterprises (SMEs). An empirical analysis has been developed by using structural equations modeling with data collected from CEOs in 119 Italian SMEs in the ceramic tile industry. Findings show that the KM plays a significant role in mediating the effects of the external embeddedness on the firm's ambidexterity that in turn enhances the performances of Italian SMEs in our sample. Based on our results, implications for academics and managers and future line of research are proposed. [ABSTRACT FROM AUTHOR]

Amidst a contemporary fast-changing business environment, scholars and practitioners alike increasingly recognize knowledge management (KM) and dynamic capabilities as key elements in the development of firms’ competitive advantage. Our understanding of the effect of KM on firm performance, nonetheless, is still limited, as in fact are the circumstances under which KM and dynamic capabilities affect firms’ ambidexterity, which reflects firms’ ability to conduct synchronous exploration and exploitation activities. Thus, building on KM and dynamic capability literature, and implementing a quantitative methodology, this paper aims to investigate the elusive relationship among KM orientation, dynamic capabilities, and ambidextrous entrepreneurial intensity (EI). Employing a dataset composed of 181 Italian firms operating in the ICT industry, and using structural equation modeling, the research subsequently investigates whether and how this relationship affects the overall firm performance. Results indicate that KM orientation has a positive and significant impact on ambidextrous EI and performance, especially when the firm has substantial dynamic capabilities. These findings further facilitate the identification and prescription of explicit scholarly and managerial implications. [ABSTRACT FROM AUTHOR]

Clinical Guidelines (CGs) provide general evidence-based recommendations and physicians often have to resort also to their Basic Medical Knowledge (BMK) to cope with specific patients. In this paper, we explore the interplay between CGs and BMK from the viewpoint of a-posteriori conformance analysis, intended as the adherence of a specific execution log to both the CG and the BMK. In this paper, we consider also the temporal dimension: the guideline may include temporal constraints for the execution of actions, and its adaptation to a specific patient and context may add or modify conditions and temporal constraints for actions. We propose an approach for analyzing execution traces in Answer Set Programming with respect to a guideline and BMK, pointing out discrepancies – including temporal discrepancies – with respect to the different knowledge sources, and providing explanations regarding how the applications of the CG and the BMK have interacted, especially in case strictly adhering to both is not possible. [ABSTRACT FROM PUBLISHER]

Absorptive capacity is mediated through knowledge management capacity on innovation output and performance in technology-oriented firms. While prior research work has focused on the direct effect of absorptive capacity on innovation, in this paper, our model posits that absorptive capacity is more efficient in promoting firms’ innovation provided that it is supported by systematic knowledge management practices. We tested this model that included all four components of absorptive capacity using a sample of 127 manufacturing and technology firms in Croatia. Structural equation modeling procedures were used to test hypotheses. Our findings confirm the significance of the relationship between absorptive capacity and knowledge management within firms. Furthermore, we found that firms with higher acquisition and transformation dimensions of absorptive capacity can enhance and replenish their knowledge management practices, which in return result in higher innovation output. These findings extend previous research work by explaining the sometimes-contradictory findings concerning knowledge management practices, which firms may adopt to enhance their absorptive capacity. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the influence of intellectual capital (IC) on supply chain collaboration (SCC) and business performance. Two mechanisms of SCC are considered, including interorganizational communication (IOC) and shared vision (SV). Based on knowledge-based view, this study explains how IOC and SV mediate the impact of IC on business performance. Structural equation modeling was used to test the hypothesized relationships based on the survey data of 1008 firms in the manufacturing industry. The results demonstrate that IC promotes IOC directly and influences the establishment of SV indirectly through IOC in the supply chain context. In addition, IOC mediates the relationship between IC and business performance. This study establishes the interface between internal knowledge base and external supply chain relationships by examining IC as an antecedent for SCC, and it also contributes to knowledge management literature by revealing that the organization's knowledge resources can influence its performance through collaborative supply chain relationships. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new concept of a knowledge management framework to enable a self-optimizing and self-learning for wireless system operation in real time. The framework encapsulates both environment and intelligent agent to reach optimal operation through sensing, perception, reasoning, and learning in a truly autonomous fashion. The agent derives adequate knowledge from previous actions improving the quality of future decisions. Domain experience was provided to guide the agent while exploring and exploiting the set of possible actions in the environment. Thus, it guarantees low-cost learning and achieves a near-optimal network configuration addressing the non-deterministic polynomial-time hardness problem of joint channel and location optimization in a wireless system. Extensive simulations are run to validate its fast convergence, high throughput, and resilience to dynamic interference conditions. We deploy the framework on off-the-shelf wireless devices to propose autonomous self-optimization with knowledge management. [ABSTRACT FROM AUTHOR]

Rapidly evolving embedded applications continuously demand more functionalities and better performance under tight energy and thermal budgets, and maintaining high energy efficiency has become a significant design challenge for mobile devices. Learning-based methods are adaptive to dynamic conditions and show great potential for runtime power management. However, with the ever-increasing complexity of both hardware and software, it is a challenging issue for a learning agent to explore the state-action space sufficiently and quickly find an efficient management policy. In this paper, we propose a reinforcement learning-based multi-device collaborative power management approach to address this issue. Multiple devices with different runtime conditions can acquire related knowledge during the learning process. Efficient knowledge sharing among these devices can potentially accelerate the learning process and improve the quality of the learned policies. We integrate the proposed method with dynamic voltage and frequency scaling on the multicore processors in mobile devices. Experimental results on realistic applications show that the collaborative power management can achieve up to a $7 \times$ speedup and 10% energy reduction compared with state-of-the-art learning-based approaches. [ABSTRACT FROM AUTHOR]

Medical imaging systems (MIS) constitute an important emergent subdiscipline of engineering studies. In the context of electrical and electronics engineering (EEE) education, MIS courses cover physics, instrumentation, data acquisition, image formation, modeling, and quality assessment of various modalities. Many well-structured MIS courses are available for EEE curricula, providing introduction to all modern diagnostic imaging systems. However, in these courses the laboratory component is limited to image formation and analysis. This paper proposes a wide range of experiments that incorporate various disciplines of EEE education into MIS courses. These experiments are designed to integrate knowledge that students have acquired previously from key EEE courses (such as circuit theory, differential equations, wave theory, energy conversion, control theory, and signal processing) into their new MIS knowledge. The proposed laboratory was adapted to a senior-year MIS class in the EEE Department of Dokuz Eylül University, Turkey. This paper presents the application of these new laboratory experiments, along with the assessment results. [ABSTRACT FROM PUBLISHER]

This paper presents the “Multi-Role Project” method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity as a role-playing game based on two projects: a learning project and an engineering project. The meta-principle is complemented by five principles that provide a framework to guide the working practices of student teams: distribution of responsibilities; regular interactions and solicitations within the team; anticipation and continuous improvement; positive interdependence and alternating individual/collective work; and open communication and content management. This paper presents the implementation of MRP in a course teaching software engineering, UML language, and project management. The results show that MRP helped the course's students to acquire important professional knowledge and skills, experience near-real-world professional realities, and develop their abilities to work both in teams and autonomously. [ABSTRACT FROM PUBLISHER]

This paper studies the problem of mining named entity translations by aligning comparable corpora. Current state-of-the-art approaches mine a translation pair by aligning an entity graph in one language to another based on node similarity or propagated similarity of related entities. However, they, building on the assumption of “symmetry”, quickly deteriorate on “weakly” comparable corpora with some asymmetry. In this paper, we pursue two directions for overcoming relation and entity asymmetry respectively. The first approach starts from weakly comparable corpora (for high recall) then ensures precision by selective propagation only to entities of symmetric relations. The second approach starts from parallel corpora (for high precision) then enhances recall by extending the translation matrix based on node similarity and contextual similarity. Our experimental results on English-Chinese corpora show that both approaches are effective and complementary. Our combined approach outperforms the best-performing baseline in terms of F1-score by up to 0.28. [ABSTRACT FROM AUTHOR]

A top-k query retrieves the best \(k\) tuples by assigning scores for each tuple in a target relation with respect to a user-specific scoring function. This paper studies the problem of constructing an indexing structure for supporting top-k queries over varying scoring functions and retrieval sizes. The existing research efforts can be categorized into three approaches: list-, layer-, and view-based approaches. In this paper, we mainly focus on the layer-based approach that pre-materializes tuples into consecutive multiple layers. We first propose a dual-resolution layer that consists of coarse-level and fine-level layers. Specifically, we build coarse-level layers using skylines, and divide each coarse-level layer into fine-level sublayers using convex skylines. To make our proposed dual-resolution layer scalable, we then address the following optimization directions: 1) index construction; 2) disk-based storage scheme; 3) the design of the virtual layer; and 4) index maintenance for tuple updates. Our evaluation results show that our proposed method is more scalable than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Ontology reuse offers great benefits by measuring and comparing ontologies. However, the state of art approaches for measuring ontologies neglects the problems of both the polymorphism of ontology representation and the addition of implicit semantic knowledge. One way to tackle these problems is to devise a mechanism for ontology measurement that is stable, the basic criteria for automatic measurement. In this paper, we present a graph derivation representation based approach (GDR) for stable semantic measurement, which captures structural semantics of ontologies and addresses those problems that cause unstable measurement of ontologies. This paper makes three original contributions. First, we introduce and define the concept of semantic measurement and the concept of stable measurement. We present the GDR based approach, a three-phase process to transform an ontology to its GDR. Second, we formally analyze important properties of GDRs based on which stable semantic measurement and comparison can be achieved successfully. Third but not the least, we compare our GDR based approach with existing graph based methods using a dozen real world exemplar ontologies. Our experimental comparison is conducted based on nine ontology measurement entities and distance metric, which stably compares the similarity of two ontologies in terms of their GDRs. [ABSTRACT FROM AUTHOR]

A qualitative empirical study of 29 semiconductor manufacturer and supplier firms investigates the challenges associated with implementing lean practices that require broadly based collaboration across firms. The study's primary contribution is a model of the semiconductor manufacturing ecosystem, which shows how chipmakers, suppliers of enabling technologies, subsystem suppliers, and their respective competitors interact to develop the right technologies at the right time. The study finds that the biggest challenge to industry-wide collaboration is managing knowledge flows between users and suppliers in a manner that allows all parties to collaborate without losing competitive advantage. The paper also presents insights into how inter-organizational knowledge is created synchronously in the semiconductor industry. Finally, the paper makes suggestions as to how interfirm knowledge can be managed. [ABSTRACT FROM AUTHOR]

In mining data streams the most popular tool is the Hoeffding tree algorithm. It uses the Hoeffding's bound to determine the smallest number of examples needed at a node to select a splitting attribute. In the literature the same Hoeffding's bound was used for any evaluation function (heuristic measure), e.g., information gain or Gini index. In this paper, it is shown that the Hoeffding's inequality is not appropriate to solve the underlying problem. We prove two theorems presenting the McDiarmid's bound for both the information gain, used in ID3 algorithm, and for Gini index, used in Classification and Regression Trees (CART) algorithm. The results of the paper guarantee that a decision tree learning system, applied to data streams and based on the McDiarmid's bound, has the property that its output is nearly identical to that of a conventional learner. The results of the paper have a great impact on the state of the art of mining data streams and various developed so far methods and algorithms should be reconsidered. [ABSTRACT FROM PUBLISHER]

This paper presents a self-tuning framework for the diagnosis of routine alarms in steam turbine generators utilizing a combination of inductive machine learning and knowledge-based heuristics. The techniques provide a novel basis for initializing and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine-specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm, and the applicability of systems using self-tuning techniques. The approaches discussed throughout are presented to provide useful diagnosis tools for the reliability and maintenance analysis of steam turbine generators. [ABSTRACT FROM PUBLISHER]

Managing and improving organizational capabilities is a significant and complex issue for many companies. To support management and enable improvement, performance assessments are commonly used. One way of assessing organizational capabilities is by means of maturity grids. While maturity grids may share a common structure, their content differs and very often they are developed anew. This paper presents both a reference point and guidance for developing maturity grids. This is achieved by reviewing 24 existing maturity grids and by suggesting a roadmap for their development. The review places particular emphasis on embedded assumptions about organizational change in the formulation of the maturity ratings. The suggested roadmap encompasses four phases: planning, development, evaluation, and maintenance. Each phase discusses a number of decision points for development, such as the selection of process areas, maturity levels, and the delivery mechanism. An example demonstrating the roadmap's utility in industrial practice is provided. The roadmap can also be used to evaluate existing approaches. In concluding the paper, implications for management practice and research are presented. [ABSTRACT FROM PUBLISHER]

This paper focuses on the role of user modeling and semantically enhanced representations for personalization. This paper presents a generic Ontology-based User Modeling framework (OntobUMf), its components, and its associated user modeling processes. This framework models the behavior of the users and classifies its users according to their behavior. The user ontology is the backbone of OntobUMf and has been designed according to the Information Management System Learning Information Package (IMS LIP). The user ontology includes a Behavior concept that extends IMS LIP specification and defines characteristics of the users interacting with the system. Concrete examples of how OntobUMf is used in the context of a Knowledge Management (KM) System are provided. This paper discusses some of the implications of ontology-based user modeling for semantically enhanced KM and, in particular, for personal KM. The results of this research may contribute to the development of other frameworks for modeling user behavior, other semantically enhanced user modeling frameworks, or other semantically enhanced information systems. [ABSTRACT FROM AUTHOR]

Class imbalance is a problem that is common to many application domains. When examples of one class in a training data set vastly outnumber examples of the other class(es), traditional data mining algorithms tend to create suboptimal classification models. Several techniques have been used to alleviate the problem of class imbalance, including data sampling and boosting. In this paper, we present a new hybrid sampling/boosting algorithm, called RUSBoost, for learning from skewed training data. This algorithm provides a simpler and faster alternative to SMOTEBoost, which is another algorithm that combines boosting and data sampling. This paper evaluates the performances of RUSBoost and SMOTEBoost, as well as their individual components (random undersampling, synthetic minority oversampling technique, and AdaBoost). We conduct experiments using 15 data sets from various application domains, four base learners, and four evaluation metrics. RUSBoost and SMOTEBoost both outperform the other procedures, and RUSBoost performs comparably to (and often better than) SMOTEBoost while being a simpler and faster technique. Given these experimental results, we highly recommend RUSBoost as an attractive alternative for improving the classification performance of learners built using imbalanced data. [ABSTRACT FROM AUTHOR]

On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and personalized products and services to them. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying distance-based clustering algorithms in the space of these statistics. In this paper, we present a direct grouping-based approach to computing customer segments that groups customers not based on computed statistics, but in terms of optimally combining transactional data of several customers to build a data mining model of customer behavior for each group. Then, building customer segments becomes a combinatorial optimization problem of finding the best partitioning of the customer base into disjoint groups. This paper shows that finding an optimal customer partition is NP-hard, proposes several suboptimal direct grouping segmentation methods, and empirically compares them among themselves, traditional statistics-based hierarchical and affinity propagation-based segmentation, and one-to-one methods across multiple experimental conditions. It is shown that the best direct grouping method significantly dominates the statistics-based and one-to-one approaches across most of the experimental conditions, while still being computationally tractable, It is also shown that the distribution of the sizes of customer segments generated by the best direct grouping method follows a power law distribution and that microsegmentation provides the best approach to personalization. [ABSTRACT FROM AUTHOR]

Blended learning has become an increasingly popular form of e-learning, and is particularly suitable to the process of transitioning towards e-learning from traditional forms of learning and teaching. This paper describes the use of the blended e-learning model, which is based on a mixture of collaborative learning, problem-based learning (PBL) and independent learning, in a course "Teaching Methods in Information Science," given at the University of Rijeka, Rijeka, Croatia. This model is realized as a combination of a face-to-face environment and online learning, using a proprietary learning management system (LMS) named adaptive hypermedia courseware (AHyCo). AHyCo is based on adaptive hypermedia and in addition to supporting learning and testing, introduces completely new constructivist and cognitivist elements to education. By supporting collaborative and project-oriented activities AHyCo promotes students' motivation for learning and establishes learning as an active and interactive process. This paper describes both the technology for, and the methodological approach to, course design and development which is aimed at supporting the evolution from traditional teaching to active learning, and raising interest in the topics of e-learning and Web courseware development among IT students. A survey conducted in the end of the course showed that students were satisfied with the pedagogical approach, and their academic achievements were also better than expected. Particularly important is that the dropout rate was greatly diminished, which could be related to students' satisfaction with the support they received from the instructor and the system. [ABSTRACT FROM AUTHOR]

Maximal biclique (also known as complete bipartite) subgraphs can model many applications in Web mining, business, and bioinformatics. Enumerating maximal biclique subgraphs from a graph is a computationally challenging problem, as the size of the output can become exponentially large with respect to the vertex number when the graph grows. In this paper, we efficiently enumerate them through the use of closed patterns of the adjacency matrix of the graph. For an undirected graph G without self-loops, we prove that 1) the number of closed patterns in the adjacency matrix of G is even, 2) the number of the closed patterns is precisely double the number of maximal biclique subgraphs of G, and 3) for every maximal biclique subgraph, there always exists a unique pair of closed patterns that matches the two vertex sets of the subgraph. Therefore, the problem of enumerating maximal bicliques can be solved by using efficient algorithms for mining closed patterns, which are algorithms extensively studied in the data mining field. However, this direct use of existing algorithms causes a duplicated enumeration. To achieve high efficiency, we propose an O(mn) time delay algorithm for a nonduplicated enumeration, in particular, for enumerating those maximal bicliques with a large size, where m and n are the number of edges and vertices of the graph, respectively. We evaluate the high efficiency of our algorithm by comparing it to state- of-the-art algorithms on three categories of graphs: randomly generated graphs, benchmarks, and a real-life protein interaction network. In this paper, we also prove that if self-loops are allowed in a graph, then the number of closed patterns in the adjacency matrix is not necessarily even, but the maximal bicliques are exactly the same as those of the graph after removing all the self-loops. [ABSTRACT FROM AUTHOR]

Technology mediated learning (TML) is gaining interest from both academic researchers and communication professionals as training with internet technology and web-based distance learning become increasingly popular. This paper investigates social norms, individual-level cultural orientation (collectivism), and affective commitment (internalization and identification) and studies their influences on the system users' (or learners') attitude toward sharing knowledge by email in the TML environment. An empirical test of the proposed model was conducted in the pilot test (71 = 155) and the main test (7 = 411). Theoretical and practical implications of these findings for TML, knowledge management, and e-collaboration are discussed in the paper. [ABSTRACT FROM AUTHOR]

Data mining techniques have been widely used in various applications. However, the misuse of these techniques may lead to the disclosure of sensitive information. Researchers have recently made efforts at hiding sensitive association rules. Nevertheless, undesired side effects, e.g., nonsensitive rules falsely hidden and spurious rules falsely generated, may be produced in the rule hiding process. In this paper, we present a novel approach that strategically modifies a few transactions in the transaction database to decrease the supports or confidences of sensitive rules without producing the side effects. Since the correlation among rules can make it impossible to achieve this goal, in this paper, we propose heuristic methods for increasing the number of hidden sensitive rules and reducing the number of modified entries. The experimental results show the effectiveness of our approach, i.e., undesired side effects are avoided in the rule hiding process. The results also report that in most cases, all the sensitive rules are hidden without spurious rules falsely generated. Moreover, the good scalability of our approach in terms of database size and the influence of the correlation among rules on rule hiding are observed. [ABSTRACT FROM AUTHOR]

When symbolic AI approaches are applied to handle continuous valued attributes, there is a requirement to transform the continuous attribute values to symbolic data. In this paper, a novel distribution-index-based discretizer is proposed for such a transformation. Based on definitions of dichotomic entropy and a compound distributional index, a simple criterion is applied to discretize continuous attributes adaptively. The dichotomic entropy indicates the homogeneity degree of the decision value distribution, and is applied to determine the best splitting point. The compound distributional index combines both the homogeneity degrees of attribute value distributions and the decision value distribution, and is applied to determine which interval should be split further; thus, a potentially improved solution of the discretization problem can be found efficiently. Based on multiple reducts in rough set theory, a multiknowledge approach can attain high decision accuracy for information systems with a large number of attributes and missing values. In this paper, our discretizer is combined with the multiknowledge approach to further improve decision accuracy for information systems with continuous attributes. Experimental results on benchmark data sets show that the new discretizer can improve not only the multiknowledge approach, but also the naïve Bayes classifier and the C5.0 tree. [ABSTRACT FROM AUTHOR]

Multiattribute decision-making problems with imprecise data refer to a situation in which at least one of the parameters such as attribute weights and value scores is not specified in precise numerical values. Often the imprecision of preference information, on one hand, may give a decision maker chances that are enhanced freedom of choice and comforts of specification and, on the other hand, may cause decision analysts difficulties in establishing dominance relations among alternatives. The model, imprecisely specified multiattribute utility theory (ISMAUT), developed by Sage and White in 1984, is a generalization of the standard multiattribute decision-analysis paradigm in that they extend the types of preference specifications and provide a novel approach to resolve the complication of a problem caused by imprecision on both attribute weights and value scores. This paper is intended to extend the ISMAUT in several aspects. For the first part, we present the properties of decision rules and their relationships in the presence of imprecise weight and value information in a systematical way though many research efforts, differing by respective problem domains considered, have been devoted to deal with them. Further, methods for resolving a nonlinearity inherent in the formulation while cutting into the number of linear programs to be solved are also presented. For the second part, a method for determining multiattribute weights is presented when paired comparison judgments on alternatives are articulated. The attribute weights are to be estimated in the direction of minimizing the amount of violations and thus to be as consistent as possible with a decision maker's a priori ordered pairs of alternatives. The derived multiattribute weights can be utilized for prioritizing the other alternatives that are not included in a set of a priori ordered pairs of alternatives. For the third part, the paper deals with a prescriptive group decision-making method by aggregating group members' imprecise preference judgments. The imprecise additive group value function can be decomposed into the individual decision maker's imprecise decision-making problems, which are finally aggregated to identify a group's preferred alternative. The group decision rules, analogous to the rules dealt in a single decision-making context, are presented as well. [ABSTRACT FROM AUTHOR]

Secure knowledge management for eBusiness processes that span multiple organizations requires intraorganizational and interorganizational perspectives on security and access control issues. There is paucity in research on information assurance of distributed interorganizational eBusiness processes from a business process perspective. This paper presents a frame- work for secure semantic eBusiness processes integrating three streams of research, namely: 1) eBusiness processes; 2) information assurance; and 3) semantic technology. This paper presents the conceptualization and analysis of a secure semantic eBusiness process framework and architecture, and provides a holistic view of a secure interorganizational semantic eBusiness process. This paper fills a gap in the existing literature by extending role-based access control models for eBusiness processes that are done by using ontological analysis and semantic Web technologies to develop a framework for computationally feasible secure eBusiness process knowledge representations. An integrated secure eBusiness process approach is needed to provide a unifying conceptual framework to understand the issues surrounding access control over distributed information and knowledge resources. [ABSTRACT FROM AUTHOR]

Technological advances in the collection, storage, and analysis of data have increased the ease with which businesses can make profitable use of information about individuals. Some of this information is private, and individuals are simultaneously becoming more aware of the value of the information and how the loss of control over this information impacts their personal privacy. As a partial solution to these concerns, this paper presents a mechanism that serves two purposes. The first enables the use of private, numerical data in the answering of queries while simultaneously providing a security feature that protects the data owners from a loss of privacy that could result from an unauthorized access. The second develops a compensation model for the use of the data that allows individuals to dynamically redefine their security requirements. The compensation model is built on the information-security mechanism to create the foundation of a market for private information. This paper illustrates how compensation models like the one presented here could be used in a self-regulating market for private information. Additionally, the compensation component of an intermediated market for private information is developed and extensively analyzed. Finally, this paper provides insights and draws several important conclusions on markets for private information. [ABSTRACT FROM AUTHOR]

This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores Independent Component Analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacy- preserving data mining applications. [ABSTRACT FROM AUTHOR]

Traditionally, building a classifier requires two sets of examples: positive examples and negative examples. This paper studies the problem of building a text classifier using positive examples (P) and unlabeled examples (U). The unlabeled examples are mixed with both positive and negative examples. Since no negative example is given explicitly, the task of building a reliable text classifier becomes far more challenging. Simply treating all of the unlabeled examples as negative examples and building a classifier thereafter is undoubtedly a poor approach to tackling this problem. Generally speaking, most of the studies solved this problem by a two-step heuristic: First, extract negative examples (N) from U. Second, build a classifier based on P and N. Surprisingly, most studies did not try to extract positive examples from U. Intuitively, enlarging P by P' (positive examples extracted from U) and building a classifier thereafter should enhance the effectiveness of the classifier. Throughout our study, we find that extracting P' is very difficult. A document in U that possesses the features exhibited in P does not necessarily mean that it is a positive example, and vice versa. The very large size of and very high diversity in U also contribute to the difficulties of extracting P'. In this paper, we propose a labeling heuristic called PNLH to tackle this problem. PNLH aims at extracting high quality positive examples and negative examples from U and can be used on top of any existing classifiers. Extensive experiments based on several benchmarks are conducted. The results indicated that PNLH is highly feasible, especially in the situation where ∣P∣ is extremely small. [ABSTRACT FROM AUTHOR]

Modeling user preference is one of the challenging issues in intelligent information systems. Extensive research has been performed to automatically analyze user preference and to utilize it. One problem still remains: The representation of preference, usually given by measure of vector similarity or probability, does not always correspond to common sense of preference. This problem gets worse in the case of negative preference. To overcome this problem, this paper presents a preference model using mutual information in a statistical framework. This paper also presents a method that combines information of joint features and alleviates problems arising from sparse data. Experimental results, compared with the previous recommendation models, show that the proposed model has the highest accuracy in recommendation tests. [ABSTRACT FROM AUTHOR]

This paper addresses the three-dimensional (3-fl) tracking of pose and animation of the human face in monocular image sequences using active appearance models. The major problem of the classical appearance-based adaptation is the high computational time resulting from the inclusion of a synthesis step in the iterative optimization. Whenever the dimension of the face space is large, a real-time performance cannot be achieved. In this paper, we aim at designing a fast and stable active appearance model search for 3-fl face tracking. The main contribution is a search algorithm whose CPU-time is not dependent on the dimension of the face space. Using this algorithm, we show that both the CPU-time and the likelihood of a nonaccurate tracking are reduced. Experiments evaluating the effectiveness of the proposed algorithm are reported, as well as method comparison and tracking synthetic and real Image sequences. [ABSTRACT FROM AUTHOR]

Experience management refers to the capture, structuring, analysis, synthesis, and reuse of an organization's experience in the form of documents, plans, templates, processes, data, etc. The problem of managing experience effectively is not unique to software development, but the field of software engineering has had a high-level approach to this problem for some time. The Experience Factory is an organizational infrastructure whose goal is to produce, store, and reuse experiences gained in a software development organization. This paper describes The Q-Labs Experience Management System (Q-Labs EMS), which is based on the Experience Factory concept and was developed for use in a multinational software engineering consultancy. A critical aspect of the Q-Labs EMS project is its emphasis on empirical evaluation as a major driver of its development and evolution. The initial prototype requirements were grounded in the organizational needs and vision of Q-Labs, as were the goals and evaluation criteria later used to evaluate the prototype. However, the Q-Labs EMS architecture, data model, and user interface were designed to evolve, based on evolving user needs. This paper describes this approach, including the evaluation that was conducted of the initial prototype and its implications for the further development of systems to support software experience management. [ABSTRACT FROM AUTHOR]

In recent years, many universities have opened courses to increase students' knowledge in the field of nanotechnology. These have been shown to increase students' knowledge of nanotechnology, but beyond this, advanced and applied nanotechnology courses should also focus on learning motivation and scientific enquiry abilities to equip students to develop the deeper knowledge and skills required for scientific application. This paper addresses this challenge. Due to the abstract nature of many nanotechnology concepts and in order to move from abstract knowledge to hands-on learning, an inquiry-based learning approach was adopted. Among the diverse inquiry-based learning models proposed, the 5E mobile inquiry-based approach, including the steps of engagement, exploration, explanation, elaboration, and evaluation, was considered most effective to enhance learners' understanding of nanotechnology. To evaluate the effectiveness of this proposed approach, a pretest–posttest quasi-experimental design was adopted with a total of 32 university students. Two sections of Nanotechnology Engineering, a general education course, were randomly assigned as either the comparison group (18 students; receiving lecture-based instruction, and using mobile devices) or the experimental group (14 students; receiving 5E inquiry learning, and using mobile devices). Mobile devices were adopted to enhance learners' experience, provide immediate access to information online, and provide enhanced hands-on learning. The empirical results demonstrate that the experimental condition, 5E mobile inquiry learning, had a positive impact on participants' learning motivation and scientific inquiry abilities. [ABSTRACT FROM PUBLISHER]

Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative to generalization and suppression to generate $k$ -anonymous data sets, where the identity of each subject is hidden within a group of $k$ subjects. Unlike generalization, microaggregation perturbs the data and this additional masking freedom allows improving data utility in several ways, such as increasing data granularity, reducing the impact of outliers, and avoiding discretization of numerical data. $k$ -Anonymity, on the other side, does not protect against attribute disclosure, which occurs if the variability of the confidential values in a group of $k$ subjects is too small. To address this issue, several refinements of $k$ -anonymity have been proposed, among which $t$ -closeness stands out as providing one of the strictest privacy guarantees. Existing algorithms to generate $t$ -close data sets are based on generalization and suppression (they are extensions of $k$ -anonymization algorithms based on the same principles). This paper proposes and shows how to use microaggregation to generate $k$ -anonymous $t$ -close data sets. The advantages of microaggregation are analyzed, and then several microaggregation algorithms for $k$ -anonymous $t$ -closeness are presented and empirically evaluated. [ABSTRACT FROM PUBLISHER]

Recommender systems can suffer from data sparsity and cold start issues. However, social networks, which enable users to build relationships and create different types of items, present an unprecedented opportunity to alleviate these issues. In this paper, we represent a social network as a star-structured hybrid graph centered on a social domain, which connects with other item domains. With this innovative representation, useful knowledge from an auxiliary domain can be transferred through the social domain to a target domain. Various factors of item transferability, including popularity and behavioral consistency, are determined. We propose a novel Hybrid Random Walk (HRW) method, which incorporates such factors, to select transferable items in auxiliary domains, bridge cross-domain knowledge with the social domain, and accurately predict user-item links in a target domain. Extensive experiments on a real social dataset demonstrate that HRW significantly outperforms existing approaches. [ABSTRACT FROM PUBLISHER]

Mining of sensor data for useful knowledge extraction is a very challenging task. Existing works generate sensor association rules using occurrence frequency of patterns to extract the knowledge. These techniques often generate huge number of rules, most of which are non-informative or fail to reflect true correlation among sensor data. In this paper, we propose a new type of behavioral pattern called associated sensor patterns which capture association-like co-occurrences as well as temporal correlations which are linked with such co-occurrences. To capture such patterns a compact tree structure, called associated sensor pattern tree (ASP-tree) and a mining algorithm (ASP) are proposed which use pattern growth-based approach to generate all associated patterns with only one scan over dataset. Moreover, when data stream flows through, old information may lose significance for the current time. To capture significance of recent data, ASP-tree is further enhanced to SWASP-tree by adopting sliding observation window and updating the tree structure accordingly. Finally, window size is made dynamically adaptive to ensure efficient resource usage. Different characteristics of the proposed techniques and their computational complexity are presented. Experimental results show that our approach is very efficient in discovering associated sensor patterns and outperforms existing techniques. [ABSTRACT FROM AUTHOR]

Runtime power management using dynamic voltage and frequency scaling (DVFS) has been extensively studied for video processing applications. But there is only a little work on game power management although gaming applications are now widely run on battery-operated portable devices like mobile phones. Taking a cue from video power management, where PID controllers have been successfully used, they were recently applied to game workload prediction and DVFS. However, the use of hand-tuned PID controller gains on relatively short game plays left open questions on the robustness of the controller and the sensitivity of prediction quality on the choice of the gain values. In this paper, we try to systematically answer these questions. We first show that from the space of PID controller gain values, only a small subset leads to good game quality and power savings. Further, the choice of this set highly depends on the scene and the game application. For most gain values the controller becomes unstable, which can lead to large oscillations in the processor’s frequency setting and thereby poor results. We then study a number of time series models, such as a Least Mean Squares (LMS) Linear Predictor and its generalizations in the form of Autoregressive Moving Average (ARMA) models. These models learn most of the relevant model parameters iteratively as the game progresses, thereby dramatically reducing the complexity of manual parameter estimation. This makes them deployable in real setups, where all game plays and even game applications are not a priori known. We have evaluated each of these models (PID, LMS, and ARMA) for a variety of games—ranging from Quake II to more recent closed-source games such as Crysis, Need for Speed—Shift and World in Conflict—with very encouraging results. To the best of our knowledge, this is the first work that systematically explores (a) the feasibility of manually tuning PID controller parameters for power management, (b) time series models for workload prediction for gaming applications, and (c) power management for closed-source games. [ABSTRACT FROM AUTHOR]

Originating from the strategic management theory, resource-based view of firm believes that firms rely on bundles of resources which enable them to gain competitive advantage. Patent, as an integral part of intellectual capital, is an important resource for organizations building capabilities. With the heterogeneous in nature and imperfectly mobile, patent and its management becomes increasingly crucial for organizations facing globalization, information transparency, and intensified competition in developing a sustainable competitive advantage. Drawing upon the literature on strategic alliance, resource-based view, and patent management, we study how sharing patents can create sharing value added and synergy value added that enable participants in the patent alliance to obtain better financial payoff. Specifically, in this paper, we use the evolutionary stable strategy and replicator dynamic from evolutionary game theory to model the synergy effects in patent alliance. Our evolutionary dynamic modeling shows that an evolutionary stable state converges when organizations share patents, and supports that both patent sharing value added and synergy value added from patent alliance would be financially beneficial to organizations. [ABSTRACT FROM PUBLISHER]

This paper describes a knowledge-based strategy for recommending educational resources—worked problems, exercises, quiz questions, and lecture notes—to learners in the first two courses in the introductory sequence of a computer science major (CS1 and CS2). The goal of the recommendation strategy is to provide support for personalized access to the resources that exist in open educational repositories. The strategy uses: 1) a description of the resources based on metadata standards enriched by ontology-based semantic indexing, and 2) contextual information about the user (her knowledge of that particular field of learning). The results of an experimental analysis of the strategy's performance are presented. These demonstrate that the proposed strategy offers a high level of personalization and can be adapted to the user. An application of the strategy to a repository of computer science open educational resources was well received by both educators and students and had promising effects on the student performance and dropout rates. [ABSTRACT FROM AUTHOR]

Recently, the management of transportation systems has become increasingly important in many real applications such as location-based services, supply chain management, traffic control, and so on. These applications usually involve queries over spatial road networks with dynamically changing and complicated traffic conditions. In this paper, we model such a network by a probabilistic time-dependent graph (PT-Graph), whose edges are associated with uncertain delay functions. We propose a useful query in the PT-Graph, namely a trip planner query (TPQ), which retrieves trip plans that traverse a set of query points in PT-Graph, having the minimum traveling time with high confidence. To tackle the efficiency issue, we present the pruning methods time interval pruning and probabilistic pruning to effectively rule out false alarms of trip plans. Furthermore, we design a pre-computation technique based on the cost model and construct an index structure over the pre-computed data to enable the pruning via the index. We integrate our proposed pruning methods into an efficient query procedure to answer TPQs. Through extensive experiments, we demonstrate the efficiency and effectiveness of our TPQ query answering approach. [ABSTRACT FROM PUBLISHER]

The task of assigning geographic coordinates to textual resources plays an increasingly central role in geographic information retrieval. The ability to select those terms from a given collection that are most indicative of geographic location is of key importance in successfully addressing this task. However, this process of selecting spatially relevant terms is at present not well understood, and the majority of current systems are based on standard term selection techniques, such as $(\chi^2)$ or information gain, and thus fail to exploit the spatial nature of the domain. In this paper, we propose two classes of term selection techniques based on standard geostatistical methods. First, to implement the idea of spatial smoothing of term occurrences, we investigate the use of kernel density estimation (KDE) to model each term as a two-dimensional probability distribution over the surface of the Earth. The second class of term selection methods we consider is based on Ripley's K statistic, which measures the deviation of a point set from spatial homogeneity. We provide experimental results which compare these classes of methods against existing baseline techniques on the tasks of assigning coordinates to Flickr photos and to Wikipedia articles, revealing marked improvements in cases where only a relatively small number of terms can be selected. [ABSTRACT FROM AUTHOR]

The development of the smart grid calls for enhanced power system application interoperability and knowledge management. The IEC Common Information Model (CIM) supports semantic interoperability but multiple identities attributed to common power system resources present challenges to unambiguous metadata model merging within a repository. This paper describes an original methodology for the building of a novel metadata model repository that concentrates our knowledge of enterprise power system resources. We leverage the value of model namespaces and resource description framework (RDF) technology in providing contexts for multiple identities referring to common power system resources. This novel approach aims to develop a more realistic understanding of network reality than repositories depending on a single CIM XML namespace and contributes to engineering an “enterprise ontology” supporting interoperability and business intelligence. We demonstrate this novel approach with reference to National Grid use cases for network operation and planning model management roles. [ABSTRACT FROM PUBLISHER]

The community-based generation of content has been tremendously successful in the World-Wide Web—people help each other by providing information that could be useful to others. We are trying to transfer this approach to robotics in order to help robots acquire the vast amounts of knowledge needed to competently perform everyday tasks. RoboEarth is intended to be a web community by robots for robots to autonomously share descriptions of tasks they have learned, object models they have created, and environments they have explored. In this paper, we report on the formal language we developed for encoding this information and present our approaches to solve the inference problems related to finding information, to determining if information is usable by a robot, and to grounding it on the robot platform. [ABSTRACT FROM AUTHOR]

Despite successive attempts to protect critical infrastructures against incidents and malicious threats by using traditional situational awareness solutions, the complex and critical nature of these infrastructures makes this adaptation difficult. For this reason, experts are reconsidering the topic of Wide-Area Situational Awareness (WASA) to provide monitoring of performance at all times from anywhere while ensuring dynamic prevention and response services. Given the novelty of this new research field, a WASA methodological framework together with a set of requirements for awareness construction are presented in this paper in order to help in the development and commissioning of future WASA defense solutions. [ABSTRACT FROM AUTHOR]

The current trend for building an ontology-based data management system (DMS) is to capitalize on efforts made to design a preexisting well-established DMS (a reference system). The method amounts to extracting from the reference DMS a piece of schema relevant to the new application needs—a module—, possibly personalizing it with extra constraints w.r.t. the application under construction, and then managing a data set using the resulting schema. In this paper, we extend the existing definitions of modules and we introduce novel properties of robustness that provide means for checking easily that a robust module-based DMS evolves safely w.r.t. both the schema and the data of the reference DMS. We carry out our investigations in the setting of description logics which underlie modern ontology languages, like RDFS, OWL, and OWL2 from W3C. Notably, we focus on the DL\-lite\cal A dialect of the DL-lite family, which encompasses the foundations of the QL profile of OWL2 (i.e., DL\-lite\cal R): the W3C recommendation for efficiently managing large data sets. [ABSTRACT FROM AUTHOR]

The pressure to accelerate the transfer of information between tasks in order to reduce the duration of projects may lead to rework due to the uncertainty of data assertiveness. This paper assesses the impact of rework with regard to both a project's costs and its duration. We have introduced a model that estimates the project development time by taking into account the premises of the task overlapping, the moment of information transfer, the risks of rework, and the consequences of the premature transfer of information. The model was used in the planning of a project for the construction of a fuel distribution terminal in a large Brazilian oil company. In this case, it was possible to demonstrate that the uncertainty derived from the premature transfer of information may increase a project's duration and should be analyzed in the light of cost variations due to rework. The model determines the best moment in which to transmit information and assesses the impact of premature information transfer. More importantly, it demonstrates that the alternative to the early transfer of information does not necessarily result in a shorter project duration or in larger costs. [ABSTRACT FROM PUBLISHER]

Many studies have demonstrated that academic research plays an important role in the development of emerging technologies. Publishing academic research, research by scientists that is shared with the broader research community via journal publications and conference presentations, plays an important role in the development of emerging technologies. While much is known about the relationship between academic research and invention capability (e.g., patent-generation capability), the link between academic research and commercial products demands further investigation. This paper presents a longitudinal study of the link between firms’ academic research activity and commercial exploration and exploitation of emerging technology knowledge. According to our findings, firms with an active program of publishing academic research are more likely to commercially explore (via pioneer products) and exploit (via greater product scope) their emerging technology investment. [ABSTRACT FROM PUBLISHER]

The relationship between innovation and product diversification in firms has been studied and debated for decades. Early articles proposed a positive relationship, while subsequent research supported a negative influence on innovation from product diversification based on observable reductions in research and development expenditures. Such findings also suggest a negative influence on absorptive capacity from increasing product diversification. This paper uses an absorptive capacity perspective to revisit the relationship. Together with related literature on knowledge creation and transfer processes, a positive association between related product diversification by firms and the quantity of created technological knowledge is suggested. Evidence to support such a relationship is provided using patent data from technology-based firms in a sample of 1997 firm years between 1990 and 2006. Some evidence of a negative association between knowledge creation and very high levels of unrelated diversification is indicated, qualifying and supporting the “M-form” hypothesis. The findings more closely align understandings of the relationship between product diversification and innovation with the relationship between product diversification and firm performance. [ABSTRACT FROM PUBLISHER]

Organizations continue to struggle with low returns on knowledge management (KM) investments. This paper's goal is to prescribe a KM strategy that maximizes organizational knowledge reuse efficiency (KRE). Knowledge reuse is defined as the totality of all knowledge transfers, from all producers to all consumers in the same organization, over all locations. Organizational inefficiencies result from individual knowledge producers and consumers having different priorities and agendas during the knowledge exchange. Furthermore, these producers' and consumers' priorities overlap with, but are not congruent with, the organization's priorities to maximize knowledge reuse efficiency. By combining a marketing perspective with a marketing consumer stages process model of knowledge reuse, we develop a contingency model which prescribes the strategy which maximizes KRE. The organizational characteristics on which the model is contingent include organization size, the number of knowledge producers, consumers, these producer and consumer costs and utilities during the knowledge transfer, and the organization's KM infrastructure costs. The prescribed approach specifies the degree to which a personalization and codification strategy should be combined to optimize KRE, contrary to some suggestions in the literature. A simulation supports that the model's prescribed strategy is not overly sensitive to its contingency variables. [ABSTRACT FROM PUBLISHER]