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As is known, cable plays a significant role in electric power transmission of the ship. From the safety perspective, this paper aims to predict the ship cable life more accurately and analyze its reliability under small sample conditions. To achieve this goal, based on the non-equidistant grey model (NEGM (1, 1)), an optimized equal-dimension dynamic NEGM (1, 1) is proposed in this paper. In the improved model, the raw data smoothness and the background value are optimized to apply to grey model parameters. Equal-dimension dynamic prediction algorithm (EDDPA) is adopted to predict the lifetime of ship CXF type cable. Compared with the aging model from literature and NEGM (1, 1), the prediction effectiveness and reliability of the proposed model are more considerable. Interestingly, with the increase of recurrence times, the numerical results show this model has higher prediction performance. Finally, the reliability and confidence interval of the life evaluation of ship CXF cable are given to provide more valuable information for the cable management. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in open government data (OGD) around the world. The governments of various countries have encouraged firms to adopt OGD to develop innovative products and services to bring about social and economic benefits. This paper examines the factors influencing the adoption of OGD among firms in Taiwan to fill a gap that prior studies do not adequately address. Based on the sociotechnical perspective, we developed a research model that integrates the following factors that are central to adoption decisions of firms: top management support, government support, competitive pressure, perceived benefits, and perceived barriers. We examined this model through survey data from firms with experience in OGD adoption in Taiwan. The results show a significant positive relationship among top management support, competitive pressure, and the adoption of OGD by firms. This paper serves as a first attempt to contribute to advancing knowledge on OGD adoption at the firm level via empirically testing a research model that integrates factors within social and technical systems. This paper has significant implications for practitioners of enterprise information management and creates a valuable reference for other countries in the early stages of OGD initiatives. [ABSTRACT FROM AUTHOR]

RDF is being increasingly considered in a broad range of information management scenarios. Governments, large corporations, startups, and other organizations around the world are using RDF as a data model to represent and share knowledge. However, there is still a long evolutionary track with multiple challenges for RDF reaching the scale of the most recent Big Data intensive applications (e.g., Smart Cities, Sensor Networks, eHealth, Internet of Things). In this survey, we review the usage of NoSQL databases to the storage of large RDF graphs by rehearsing the latest surveys and expanding their findings by updating proposals and bringing light to aspects such as model mapping between RDF and NoSQL, triple indexing and partitioning, graph fragmentation and data caching. Moreover, we explain how the surveyed works extended the RDF capabilities so the datasets can benefit of the characteristics of scalability, schemaless data, and better overall performance of NoSQL databases. The survey summarizes the current state of art, discusses open problems, and proposes a Reference Architecture (RA). For the best of our knowledge, this is the first survey where the focus is solely on papers that use one or more NoSQL systems for the RDF persistence. [ABSTRACT FROM AUTHOR]

This paper investigates the stability problem of wide-area damping controllers with intermittent information transmission. Due to the interruption in communication links between remote measurements and damping controller or from the damping controller to the damping actuators, the closed-loop system might become unstable. The instability is strongly related to the duration of interruption of information transmission. To estimate instability, this paper formulates the problem as continuous/discrete-time switched system and the stability conditions are derived using time-scale theory. This method allows us to handle continuous and discrete dynamics as two pieces of the same framework, such that the system will switch between a continuous-time subsystem (when the communication occurs without any interruption) and a discrete-time subsystem (when the communication fails). The contribution is to estimate the maximum allowable value of the time of interruption of information transmission that does not violate the exponential stability of the closed-loop system. The findings are useful in specifying the minimum requirements for communication infrastructure and the time to activate remedial action schemes. Simulations are performed based on both linear and nonlinear systems to validate the theoretical development. [ABSTRACT FROM AUTHOR]

The article offers information about the journal including the submission of manuscripts and multimedia material, copyright and the publication of information through an electronic form.

Unmanned aerial vehicles (UAVs) control information delivery is a critical communication with stringent requirements in terms of reliability and latency. In this context, link adaptation plays an essential role in the fulfillment of the required performance in terms of decode error probability and delay. Link adaptation is usually based on channel quality indicator (CQI) feedback information from the user equipment that should represent the current state of the channel. However, measurement, scheduling and processing delays introduce a CQI aging effect, that is a mismatch between the current channel state and its CQI representation. Using outdated CQI values may lead to the selection of a wrong modulation and coding scheme, with a detrimental effect on performance. This is particularly relevant in ultra reliable and low latency communications (URLLC), where the control of the reliability can be negatively impacted, and it is more evident when the channel is fast varying as the case of UAVs. This paper analyzes the effects of CQI aging on URLLCs, considering transmissions under the finite blocklength regime, that characterizes such communications type. A deep learning approach is investigated to predict the next CQI from the knowledge of past reports, and performance in terms of decode error probability and throughput is given. The results show the benefit of CQI proposed prediction mechanism also in comparison with previously proposed methods. [ABSTRACT FROM AUTHOR]

State estimation plays a key role in the transition from the passive to the active operation of distribution systems, as it allows to monitor the networks and provides the necessary information to perform control actions. However, designing state estimators for distribution systems is challenging, due to the characteristics of the networks, such as limited measurement availability. Furthermore, the features of the distribution system present significant local variations, e.g., voltage level and number and type of customers, which makes it hard to design a “one-size-fits-all” state estimator. This paper introduces a unifying framework that allows to easily implement and compare diverse unbalanced static state estimation models. This is achieved by formulating state estimation as a general constrained optimization problem. The advantages of this approach are described and supported by numerical illustration on a large set of real distribution feeders. The framework is also implemented in software and made available open-source. [ABSTRACT FROM AUTHOR]

With the ever-increasing demands for local area services of popular content sharing, device-to-device (D2D) communication is conceived to be a key component of the next-generation cellular networks. D2D communications consume low energy and provide high capacity via proximity range transmissions, where multihop transmissions are needed for real-time content sharing. Aiming to solve the challenging problem for multihop D2D communication networks, existing studies only consider communication domain information to create long-range links (LLs) to reduce multihop transmission delay. In contrast, in this paper, we propose a novel social-community-aware LLs establishment strategy, which exploits the interplay between social network features and communication domain constraints. We first formulate the LLs establishment strategy as a cost-minimization problem and then propose an efficient greedy algorithm to solve the formulated problem, which is suitable for scenarios of both single-sink and distributed content sharing. To decrease the computational complexity, we model the optimization problem as a coalition graph game and propose a dynamic algorithm based on local best response and a near-optimal algorithm based on switching operation, which both run in the distributed approach. Numerical results demonstrate that our proposed solution decreases the average path length significantly with low computational complexity compared with other state-of-the-art schemes. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Although there has been a pickup in the growth of business-to-consumer electronic commerce (EC) recently, the overall growth appears to have failed to live up to the various forecasts. Notwithstanding the concerns of security and privacy, this paper posits that for complex products and services offered on the web, existing EC interfaces (ECIs) lack the necessary "richness' to overcome the virtual nature that consumers face compared with a physical store. We empirically validate a conceptual framework that exists in the literature to address these issues of ECI design by drawing on insights from contingency research in information system (IS) design arid media choice behavior. This paper finds that a fit or congruence between the product type and the ECI richness (in product information representation, and product-related interaction and communication between the consumer and. vendor) leads to superior outcomes. Specifically, buying a complex product (e.g., digital camera) on the Web requires a greater ECI richness in the form of experiencing higher social presence and/or higher product presence. On the other hand, a lean ECI is sufficient for a simple product (e.g., diskette). The role of tolerance for ambiguity (TA), which is an individual difference variable, in this relationship is also examined. A significant three- way fit of TA with ECI richness and product types on consumer behavior is found. A number of implications and future research extensions are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven cooperative method for load frequency control (LFC) of the multi-area power system based on multi-agent deep reinforcement learning (MA-DRL) in continuous action domain. The proposed method can nonlinearly and adaptively derive the optimal coordinated control strategies for multiple LFC controllers through centralized learning and decentralized implementation. The centralized learning is achieved by MA-DRL based on a global action-value function to quantify overall LFC performance of the power system. To solve the MA-DRL problem, multi-agent deep deterministic policy gradient (DDPG) is derived to adjust control agents’ parameters considering the nonlinear generator behaviors. For implementation, each individual controller only needs local information in its control area to deliver optimal control signals. Numerical simulations on a three-area power system and the fully-modeled New-England 39-bus system demonstrate that the proposed method can effectively minimize control errors against stochastic frequency variations caused by load and renewable power fluctuations. [ABSTRACT FROM AUTHOR]

Video captioning aims to generate a natural language sentence to describe the main content of a video. Since there are multiple objects in videos, taking full exploration of the spatial and temporal relationships among them is crucial for this task. The previous methods wrap the detected objects as input sequences, and leverage vanilla self-attention or graph neural network to reason about visual relations. This cannot make full use of the spatial and temporal nature of a video, and suffers from the problems of redundant connections, over-smoothing, and relation ambiguity. In order to address the above problems, in this paper we construct a long short-term graph (LSTG) that simultaneously captures short-term spatial semantic relations and long-term transformation dependencies. Further, to perform relational reasoning over the LSTG, we design a global gated graph reasoning module (G3RM), which introduces a global gating based on global context to control information propagation between objects and alleviate relation ambiguity. Finally, by introducing G3RM into Transformer instead of self-attention, we propose the long short-term relation transformer (LSRT) to fully mine objects’ relations for caption generation. Experiments on MSVD and MSR-VTT datasets show that the LSRT achieves superior performance compared with state-of-the-art methods. The visualization results indicate that our method alleviates problem of over-smoothing and strengthens the ability of relational reasoning. [ABSTRACT FROM AUTHOR]

Advertisement: The IEEE Member Digital Library brings you access to IEEE journals, magazines and conference papers published today or in the last five years. Full-text access to the most essential information in technology today with one convenient subscription. Subscribe: www.ieee.org/ieeemdl. [ABSTRACT FROM AUTHOR]

In this paper, spectrum-sharing technology is integrated into cellular systems to improve spectrum efficiency. Macrocell users are primary users (PUs), whereas those within local cells, e.g., femtocell users, or desiring cost-effective services, e.g., roamers, are identified as secondary users (SUs). The SUs share the spectrum resources of the PUs in an underlay way; thus, the transmit power of a secondary is strictly limited by the primary's tolerable interference power. Given such constraints, a cooperative relaying transmission between an SU and the macrocell base station (BS) is necessary. To guarantee the success of dual-hop relaying and avoid multihop relaying, a new cooperative paradigm is proposed, where an idle PU (instead of a secondary, as assumed in general) in the vicinity of a target SU is chosen to serve as a relaying node, due to the fact that any PU can always transmit to the macrocell BS directly. Moreover, a two-way relaying strategy is applied at the chosen relaying node to further improve spectral efficiency. Our results demonstrate that the proposed system is particularly suitable for delay-tolerant wireless services with asymmetric downlink/uplink traffic, such as e-mail checking, web browsing, social networking, and data streaming, which are the most popular applications for SUs in spectrum-sharing cellular networks. [ABSTRACT FROM PUBLISHER]

Coordination of the Connected and Automated Vehicles (CAVs) is a demanding task in aspects of effective incorporation of road information with the control of vehicle motion states. Formulating a platoon composed of multiple CAVs initially located in multiple lanes requires integrated consideration of both steering incurred by the curved road and merging actions. Meanwhile, additional requirement of intervehicle safety distance keeping as well as bounded actuations is also critical for practical implementation. Considering the above problems, a comprehensive framework containing a distributed controller is proposed in this paper to systematically address the combined problems. A bi-level curvilinear vehicle coordination scheme is proposed and proven to be capable of guiding CAVs to desired platoon formation with theoretical analysis. Besides, both longitudinal and lateral acceleration limitations have been taken into consideration. The limited longitudinal acceleration is included in the design of nonlinear saturated controller of which the convergence has been proven. Adaptive merging is realized with a bounded lateral controller. The condition of limiting the initial states to guarantee safe intervehicle distance maintenance is formulated. Hardware-in-the-Loop (HiL) test demonstrations in multiple road scenarios with different curvatures validate the performance of the proposed controller over existing baseline methods. [ABSTRACT FROM AUTHOR]

Large-scale activity monitoring is a core component of systems aiming to improve our ability to manage fitness, deliver care, and diagnose conditions. While much research has been devoted to the accurate classification of motion, the challenges arising from scaling to large communities have received little attention. This paper introduces the problem of scaling, and addresses two of the most important issues: enabling robust large-scale ground-truth acquisition and building a common database for systems comparison. This paper presents a voice powered mobile acquisition system with efficient annotation tools and an extendable online searchable activity database with 331 datasets totaling over 700 h with 8 sensing modalities and 15 activities. [ABSTRACT FROM PUBLISHER]

In this paper, we study the effects of subjective biases on strategic information transmission (SIT) within a Stackelberg game setting, where a human transmitter (leader) communicates an encoded source message to a human receiver (follower) so that the receiver decodes back a desired version of the original source signal. We model human decisions using Rieger–Wang’s prospect theory, which is an extension of traditional prospect theory to continuous decision spaces. Having found a closed-form expression for the receiver’s best response strategy under any general setting, we consider two settings: Gaussian SIT games and exponential SIT games. While the Gaussian SIT games result in strategies that are independent of subjective biases of both the transmitter and the receiver, we show that the equilibrium strategies in exponential SIT games depend on the subjective biases of both the transmitter and the receiver. Numerical results are presented to illustrate results in both Gaussian and exponential settings. [ABSTRACT FROM AUTHOR]

This paper obtains the first degrees of freedom (DoFs) bound that is provably sensitive tonetworkcoherence time, i.e., coherence time in an interference network, where all channels experience the same coherence patterns. This is accomplished by a novel adaptation of the aligned image sets bound and settles various open problems noted previously by Naderi and Avestimehr and by Gouet al.For example, a necessary and sufficient condition is obtained for the optimality of 1/2 DoF per user in a partially connected interference network, where the channel state information at the receivers (CSIRs) is perfect, the channel state information at the transmitters (CSITs) is instantaneous but limited to finite precision, and the network coherence time is $T_{c}=1$. The surprising insight that emerges is that even with perfect CSIR and instantaneous finite precision CSIT, the network coherence time matters, i.e., it has a DoF impact. [ABSTRACT FROM AUTHOR]

An important problem in the area of homeland security is to identify abnormal or suspicious entities in large data sets. Although there are methods from data mining and social network analysis focusing on finding patterns or central nodes from networks or numerical data sets, there has been little work aimed at discovering abnormal instances in large complex semantic graphs, whose nodes are richly connected with many different types of links. In this paper, we describe a novel unsupervised framework to identify such instances. Besides discovering abnormal instances, we believe that to complete the process, a system has to also provide users with understandable explanations for its findings. Therefore, in the second part of the paper, we describe an explanation mechanism to automatically generate human-understandable explanations for the discovered results. To evaluate our discovery and explanation systems, we perform experiments on several different semantic graphs. The results show that our discovery system outperforms state-of-the-art unsupervised network algorithms used to analyze the 9/11 terrorist network and other graph-based outlier detection algorithms by a significant margin. Additionally, the human study we conducted demonstrates that our explanation system, which provides natural language explanations for the system's findings, allowed human subjects to perform complex data analysis in a much more efficient and accurate manner. [ABSTRACT FROM AUTHOR]

Flexibility of flexible manufacturing systems (FMSs) has been considered as an effective tool to compete in the present manufacturing environment. Enormous research efforts have been made to harness the benefits of flexibility through superior control strategies. While modeling flexibility and control strategies, researchers have mostly assumed an information system that can provide real-time control. Literature qualitatively reports that the real-time control can be highly capital intensive and difficult to achieve. This paper focuses on FMS operating under review-period (RP)-based control and presents a combined study of routing flexibility (RF), control strategies, and information system under Taguchi's method using simulation. RP-based control for FMS has been compared with real-time control. This paper contributes an approach for the decision maker to study the performance of an FMS operating under RP control and to identify the periodicity (time interval) of RP that will not deteriorate its performance in comparison to real-time control. It also helps the decision maker to reach a tradeoff between RP-based control and real-time control. The results show that RP-based control can be effectively implemented on an FMS having lower RF level. RP-based control can outperform real-time control with a superior control strategy and smaller RP size. The results under Taguchi's method suggest that the RF and control strategy should have maximum relative percentage contributions in FMS performance, whereas contribution of the RP (information system) should be minimum. Increasing the relative percentage contribution of the information system may deteriorate the performance of FMS. The information system is needed as a catalyst to facilitate the the contributions of other factors in improving the FMS performance and not its own contribution. [ABSTRACT FROM AUTHOR]

In next-generation wireless networks, energy efficiency optimization needs to take individual link fairness into account. In this paper, we investigate a max-min energy efficiency-optimal problem (MEP) to ensure fairness among links in terms of energy efficiency in OFDMA systems. In particular, we maximize the energy efficiency of the worst-case link subject to the rate requirements, transmit power, and subcarrier assignment constraints. Due to the nonsmooth and mixed combinatorial features of the formulation, we focus on low-complexity suboptimal algorithms design. Using a generalized fractional programming theory and the Lagrangian dual decomposition, we first propose an iterative algorithm to solve the problem. We then devise algorithms to separate the subcarrier assignment and power allocation to further reduce the computational cost. Our simulation results verify the convergence performance and the fairness achieved among links, and particularly reveal a new tradeoff between the network energy efficiency and fairness by comparing the MEP with the existing algorithms. [ABSTRACT FROM AUTHOR]

Spreadsheets are among the most commonly used applications for data management and analysis. They combine data processing with very diverse supplementary features: statistics, visualization, reporting, linear programming solvers, Web queries periodically downloading data from external sources, etc. However, the spreadsheet paradigm of computation still lacks sufficient analysis. In this article, we demonstrate that a spreadsheet can implement all data transformations definable in SQL, merely by utilizing spreadsheet formulas. We provide a query compiler, which translates any given SQL query into a worksheet of the same semantics, including NULL values. Thereby, database operations become available to the users who do not want to migrate to a database. They can define their queries using a high-level language and then get their execution plans in a plain vanilla spreadsheet. The functions available in spreadsheets impose limitations on the algorithms one can implement. In this paper, we offer $O(n\,\log ^2\,n)$ sorting spreadsheet, using a non-constant number of rows, and, surprisingly, Depth-First-Search and Breadth-First-Search on graphs. [ABSTRACT FROM PUBLISHER]

Enterprise design decisions include choosing among operational strategies, allocating scarce resources, and coordinating actions toward common goals, among others. In this paper, we explore how the concepts and applications of the efficiency performance measurement paradigm can be used to inform enterprise design. Up to date, efficiency performance measurement has been mainly used to describe how well an enterprise has performed, i.e., answering “what happened” via an ex post evaluation. However, informing enterprises on “what might happen” when making changes, i.e., an ex ante evaluation, has received less attention. To address this gap, we synthetize findings pertaining to enterprise design from the organizational design, enterprise systems engineering, and efficiency performance measurement literatures. The synthesis allows us to identify the integrative role that the efficiency performance measurement paradigm plays in informing enterprise transformation decisions. We illustrate this role by providing a case study of an enterprise dealing with design decisions. Our research provides exploratory evidence, for managerial stakeholders, about the ability of the efficiency performance measurement paradigm to answer how well an enterprise could perform if certain design decisions are made. [ABSTRACT FROM AUTHOR]

Business to Business (B2B) marketing aims at meeting the needs of other businesses instead of individual consumers, and thus entails management of more complex business needs than consumer marketing. The buying processes of the business customers involve series of different marketing campaigns providing multifaceted information about the products or services. While most existing studies focus on individual consumers, little has been done to guide business customers due to the dynamic and complex nature of these business buying processes. To this end, in this paper, we focus on providing a unified view of social and temporal modeling for B2B marketing campaign recommendation. Along this line, we first exploit the temporal behavior patterns in the B2B buying processes and develop a marketing campaign recommender system. Specifically, we start with constructing a temporal graph as the knowledge representation of the buying process of each business customer. Temporal graph can effectively extract and integrate the campaign order preferences of individual business customers. It is also worth noting that our system is backward compatible since the participating frequency used in conventional static recommender systems is naturally embedded in our temporal graph. The campaign recommender is then built in a low-rank graph reconstruction framework based on probabilistic graphical models. Our framework can identify the common graph patterns and predict missing edges in the temporal graphs. In addition, since business customers very often have different decision makers from the same company, we also incorporate social factors, such as community relationships of the business customers, for further improving overall performances of the missing edge prediction and recommendation. Finally, we have performed extensive empirical studies on real-world B2B marketing data sets and the results show that the proposed method can effectively improve the quality of the campaign recommendations for challenging B2B marketing tasks. [ABSTRACT FROM PUBLISHER]

Existing graph classification usually relies on an exhaustive enumeration of substructure patterns, where the number of substructures expands exponentially w.r.t. with the size of the graph set. Recently, the Weisfeiler-Lehman (WL) graph kernel has achieved the best performance in terms of both accuracy and efficiency among state-of-the-art methods. However, it is still time-consuming, especially for large-scale graph classification tasks. In this paper, we present a -Ary Tree based Hashing (KATH) algorithm, which is able to obtain competitive accuracy with a very fast runtime. The main idea of KATH is to construct a traversal table to quickly approximate the subtree patterns in WL using $K$-ary trees. Based on the traversal table, KATH employs a recursive indexing process that performs only $r$ times of matrix indexing to generate all $(r-1)$ -depth $K$ -ary trees, where the leaf node labels of a tree can uniquely specify the pattern. After that, the MinHash scheme is used to fingerprint the acquired subtree patterns for a graph. Our experimental results on both real world and synthetic data sets show that KATH runs significantly faster than state-of-the-art methods while achieving competitive or better accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we study a novel variant of obstructed nearest neighbor queries, namely, range-based obstructed nearest neighbor (RONN) search. As a natural generalization of continuous obstructed nearest-neighbor (CONN), an RONN query retrieves a set of obstructed nearest neighbors corresponding to every point in a specified range. We propose a new index, namely binary obstructed tree (called OB-tree), for indexing complex objects in the obstructed space. The novelty of OB-tree lies in the idea of dividing the obstructed space into non-obstructed subspaces, aiming to efficiently retrieve highly qualified candidates for RONN processing. We develop an algorithm for construction of the OB-tree and propose a space division scheme, called optimal obstacle balance (OOB2) scheme, to address the tree balance problem. Accordingly, we propose an efficient algorithm, called RONN by OB-tree Acceleration (RONN-OBA), which exploits the OB-tree and a binary traversal order of data objects to accelerate query processing of RONN. In addition, we extend our work in several aspects regarding the shape of obstacles, and range-based $k$ NN queries in obstructed space. At last, we conduct a comprehensive performance evaluation using both real and synthetic datasets to validate our ideas and the proposed algorithms. The experimental result shows that the RONN-OBA algorithm outperforms the two R-tree based algorithms and RONN-OA significantly. [ABSTRACT FROM AUTHOR]

In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which allows dynamically modeling time-changing concepts, making predictions in a local fashion. Instead of learning a single model on a fixed or adaptive sliding window of historical data or ensemble learning a set of weighted base classifiers, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a proposed P-Tree, which are obtained based on the error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drifts in data streams, PCA and statistical analysis based heuristic approaches have been introduced. To further learn the associations among distributed data streams, the extended P-Tree structure and KNN-style strategy are introduced. We demonstrate that our new data stream classification approach has several attractive benefits: (a) SyncStream is capable of dynamically modeling the evolving concepts from even a small set of prototypes. (b) Owing to synchronization-based constrained clustering and P-Tree, SyncStream supports efficient and effective data representation and maintenance. (c) SyncStream is also tolerant of inappropriate or noisy examples via error-driven representativeness learning. (d) SyncStream allows learning relationship among distributed data streams at the instance level. The experimental results indicate its efficiency and effectiveness. [ABSTRACT FROM PUBLISHER]

Product lifecycle management (PLM) is a strategic and holistic approach for the management of information, processes, and resources supporting the entire product lifecycle from concept to disposal. Several systems are available to manage data and information during the lifecycle. A PLM system does not have to be considered as a unique solution, but as a set of software supporting different perspectives and activities related to the lifecycle management. A full comprehensive implementation of PLM systems is rare. Since PLM reflects the peculiarities of processes and data structures, implementation differences are evidently observable among companies. The design of methods and tools supporting an assessment of PLM implementation inside a company can enable a correct definition of PLM strategies and goals. Based on these premises, the paper aims to specify a methodology for carrying out a gap analysis of PLM with the scope of discovering existing technological and methodological gaps, and planning actions for improvement. The proposed methodology implements a visual and lean reference model, and an assessment questionnaire for data collection. Lesson learned and feedback from three industrial applications are also described and discussed. [ABSTRACT FROM AUTHOR]

With the soaring development of large scale online social networks, online information sharing is becoming ubiquitous everyday. Various information is propagating through online social networks including both the positive and negative. In this paper, we focus on the negative information problems such as the online rumors. Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on a realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method. [ABSTRACT FROM PUBLISHER]

The utilization bounds for partitioned multiprocessor scheduling are a function of task allocation algorithms and the schedulability conditions selected for uniprocessor scheduling algorithms. In this paper, we use rate-monotonic scheduling on each processor and present the lower and upper limits of the utilization bounds for all reasonable task allocation heuristics. Unlike previous work, the hyperbolic bound due to Bini , instead of the Liu & Layland bound, is adopted to do the schedulability test on uniprocessors. We also derive the utilization bounds with respect to the worst fit allocation algorithm and reasonable allocation decreasing heuristics, and the two bounds are found to coincide with the worst and best achievable multiprocessor utilization bounds, respectively. Analytical and experimental results show that the proposed utilization bound performs better than the existing bound under quite a lot of parameter settings, and combining these two bounds together can significantly (up to 3 times) increase the number of schedulable task sets with little extra overhead. [ABSTRACT FROM AUTHOR]

This paper discusses the bias problem when estimating the population size of big data such as online social networks (OSN) using uniform random sampling and simple random walk. Unlike the traditional estimation problem where the sample size is not very small relative to the data size, in big data, a small sample relative to the data size is already very large and costly to obtain. We point out that when small samples are used, there is a bias that is no longer negligible. This paper shows analytically that the relative bias can be approximated by the reciprocal of the number of collisions; thereby, a bias correction estimator is introduced. The result is further supported by both simulation studies and the real Twitter network that contains 41.7 million nodes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) relay networks are convinced to be a significant complement to terrestrial infrastructures to provide robust network capacity. However, most of the existing works either considered enhanced mobile broadband (eMBB) payload communication or ultra-reliable and low latency communications (URLLC) control information communication. In this paper, we investigate resource allocation for the eMBB payload and URLLC control information communication multiplexing in a multi-UAV relay network. We firstly propose a multi-UAV relay model comprehensively considering path loss, small-scale channel fading and different quality of service requirements of eMBB and URLLC communications. Then we formulate the multiplexing problem as a joint user association, bandwidth and transmit power optimization problem to improve total transmission data rate and reduce power consumption. The solution of this problem is challenging due to different capacity characteristics of eMBB and URLLC communications, the coupling of continuous variables and integer variables, and the non-convexity. To mitigate these challenges, we equivalently decompose the original optimization problem into a URLLC problem and an eMBB problem. For the URLLC problem, we derive closed-form expressions of the optimal bandwidth and transmit power. For the eMBB problem, we develop an iterative solution framework of alternatively optimizing user association, bandwidth and transmit power. [ABSTRACT FROM AUTHOR]

In this paper, a distributed optimal steady-state regulation problem is formulated and investigated for heterogeneous linear multiagent systems subject to external disturbances. We aim to steer this high-order multiagent network to a prescribed steady-state determined as the optimal solution of a resource allocation problem in a distributed way. To solve this problem, we employ an embedded control design and convert the formulated problem to two simpler subproblems. Then, both state-feedback and output feedback controls are presented under mild assumptions to solve this problem with disturbance rejection. Moreover, we extend these results to the case with only real-time gradient information by high-gain control techniques. Finally, the numerical simulations verify their effectiveness. [ABSTRACT FROM AUTHOR]

Recent wireless LAN technologies and service management technologies have produced widely diverse network services with no configuration task or effort for end-users' consumer electronics (CE) devices. This environment needs a mechanism that enables end-users to access services seamlessly across separated networks. To provide such accessibility, information for services in multiple networks should be available in each terminal that is currently connected to a wireless LAN. This paper presents a proposal of a new mechanism to collect and manage service information belonging to different WLANs autonomously for end-users. Specifically, an autonomous network switch and event-driven service information sharing scheme are proposed. The functionalities which are embedded on end-user CE devices require no central control device on a WLAN or the internet and require no modification of WLAN access points. The effectiveness of the proposal is demonstrated using simulation experiments. Future work is described. [ABSTRACT FROM PUBLISHER]

This paper presents a class of integer codes that can correct any burst of length \le l within a b-bit byte. Their main advantages lie in linear complexity of encoding and decoding procedures, as well as in the fact that a look-up table-based error control procedure requires relatively small memory resources. [ABSTRACT FROM AUTHOR]

Abstract-A transaction database usually consists of a set of time-stamped transactions. Mining frequent patterns in transaction databases has been studied extensively in data mining research. However, most of the existing frequent pattern mining algorithms (such as Apriori and FP-growth) do not consider the time stamps associated with the transactions. In this paper, we extend the existing frequent pattern mining framework to take into account the time stamp of each transaction and discover patterns whose frequency dramatically changes over time. We define a new type of patterns, called transitional patterns, to capture the dynamic behavior of frequent patterns in a transaction database. Transitional patterns include both positive and negative transitional patterns. Their frequencies increase/decrease dramatically at some time points of a transaction database. We introduce the concept of significant milestones for a transitional pattern, which are time points at which the frequency of the pattern changes most significantly. Moreover, we develop an algorithm to mine from a transaction database the set of transitional patterns along with their significant milestones. Our experimental studies on real-world databases illustrate that mining positive and negative transitional patterns is highly promising as a practical and useful approach for discovering novel and interesting knowledge from large databases. [ABSTRACT FROM AUTHOR]

Software quality modeling for high-assurance systems, such as safety-critical systems, is adversely affected by the skewed distribution of fault-prone program modules. This sparsity of defect occurrence within the software system impedes training and performance of software quality estimation models. Data sampling approaches presented in data mining and machine learning literature can be used to address the imbalance problem. We present a novel genetic algorithm-based data sampling method, named Evolutionary Sampling, as a solution to improving software quality modeling for high-assurance systems. The proposed solution is compared with multiple existing data sampling techniques, including random undersampling, one-sided selection, Wilson's editing, random oversampling, cluster-based oversampling, Synthetic Minority Oversampling Technique (SMOTE), and Borderline-SMOTE. This paper involves case studies of two real-world software systems and builds C4.5- and RIPPER-based software quality models both before and after applying a given data sampling technique. It is empirically shown that Evolutionary Sampling improves performance of software quality models for high-assurance systems and is significantly better than most existing data sampling techniques. [ABSTRACT FROM AUTHOR]

In attempts to exploit a diverse set of resources in grids efficiently, numerous assays in resource management, particularly scheduling, have been made. The primary objective of these efforts is the minimization of application completion time; however, they tend to achieve this objective at the expense of redundant resource usage. This paper investigates the problem of scheduling workflow applications on grids and presents a novel scheduling algorithm for the solution of this problem. Our algorithm performs the scheduling by accounting for both completion time and resource usage-dual objectives. Since the performance of grid resources changes dynamically and the accurate estimation of their performance is very difficult, our algorithm incorporates rescheduling to deal with unforeseen performance fluctuations effectively. The paper provides a comparative evaluation study conducted by using an extensive set of experiments. The study demonstrates that the proposed algorithm delivers promising performance in three respects: completion time, resource utilization, and robustness to resource-performance fluctuations. [ABSTRACT FROM AUTHOR]

In the complex software systems, software agents always need to negotiate with other agents within their physical and social contexts when they execute tasks. Obviously, the capacity of a software agent to execute tasks is determined by not only itself but also its contextual agents; thus, the number of tasks allocated on an agent should be directly proportional to its self-owned resources as well as its contextual agents' resources. This paper presents a novel task allocation model based on the contextual resource negotiation. In the presented task allocation model, while a task comes to the software system, it is first assigned to a principal agent that has high contextual enrichment factor for the required resources; then, the principal agent will negotiate with its contextual agents to execute the assigned task. However, while multiple tasks come to the software system, it is necessary to make load balancing to avoid overconvergence of tasks at certain agents that are rich of contextual resources. Thus, this paper also presents a novel load balancing method: if there are overlarge number of tasks queued for a certain agent, the capacities of both the agent itself and its contextual agents to accept new tasks will be reduced. Therefore, in this paper, the task allocation and load balancing are implemented according to the contextual resource distribution of agents, which can be well suited for the characteristics of complex software systems; and the presented model can reduce more communication costs between allocated agents than the previous methods based on self-owned resource distribution of agents. [ABSTRACT FROM AUTHOR]

Enterprise ontologies (EOs), introduced in the mid 1990s, were expected to have a significant impact on enterprise computing, especially integration. However, despite a great deal of academic research on EOs, the actual use of EOs in real-life integration and planning projects is almost nonexisting. This paper describes an approach to build and use EOs for information system (IS) planning and integration projects with particular focus on real-life eBusiness applications. The approach is based on firsthand practical insights gained through construction and use of an IS planning and integration environment that needs to capture business processes, enterprise applications, integration technologies, and computer-communication platforms. The planning model with the aforementioned information is based on an EO and is populated by a set of intelligent advisors while they guide the users through various stages of the planning process. This ontology has been used to support over 40 real-life business scenarios in the telecom, manufacturing, financial services, retail, healthcare, and insurance industries. The practical contribution of this paper is that it connects ontologies to the practice of IS planning and integration, links ontologies to decisions such as enterprise application selection, and provides tools for automatically creating and maintaining ontology repositories. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of multiuser resource management in multihop cognitive radio networks for delay-sensitive applications. Since tolerable delay does not allow propagating global information back and forth throughout the multihop network to a centralized decision maker, the source nodes and relays need to adapt their actions (transmission frequency channel and route selections) in a distributed manner, based on local network information. We propose a distributed resource-management algorithm that allows network nodes to exchange information and that explicitly considers the delays and cost of exchanging the network information over multihop cognitive radio networks. In this paper, the term "cognitive" refers to both the capability of the network nodes to achieve large spectral efficiencies by dynamically exploiting available frequency channels and their ability to learn the "environment" (the actions of interfering nodes) based on the designed information exchange. Note that the node competition is due to the mutual interference of neighboring nodes using the same frequency channel. Based on this, we adopt a multiagent-learning approach, i.e., adaptive fictitious play, which uses the available interference information. We also discuss the tradeoff between the cost of the required information exchange and the learning efficiency. The results show that our distributed resource-management approach improves the peak signal-to-noise ratio (PSNR) of multiple video streams by more than 3 dB, as opposed to the state-of-the-art dynamic frequency channel/route selection approaches without learning capability, when the network resources are limited. [ABSTRACT FROM AUTHOR]

In this paper we introduce and evaluate a novel machine learning based approach to reduce the complexity of Intra macroblock (MB) coding. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264/AVC video have a correlation with the intensities of adjacent MBs and sub-MBs. This paper also discusses and analyzes different approaches of using machine learning in Intra prediction. We discuss, amongst other features, slices, Intra prediction scheme for H.264 and data mining. We use data mining algorithms to develop decision trees for H.264 coding mode decisions. The proposed approach reduces the H.264/AVC MB mode computation process into a decision tree lookup with very low complexity. The proposed algorithm is implemented in reference software by modifying the source code and is compared with the JM reference software for H.264/AVC. [ABSTRACT FROM AUTHOR]

This paper proposes a traffic monitoring architecture based on a high-speed communication network whose nodes are equipped with fuzzy processors and cellular neural network (CNN) embedded systems. It implements a real-time mobility information system where visual human perceptions sent by people working on the territory and video-sequences of traffic taken from webcams are jointly processed to evaluate the fundamental traffic parameters for every street of a metropolitan area. This paper presents the whole methodology for data collection and analysis and compares the accuracy and the processing time of the proposed soft computing techniques with other existing algorithms. Moreover, this paper discusses when and why it is recommended to fuse the visual perceptions of the traffic with the automated measurements taken from the webcams to compute the maximum traveling time that is likely needed to reach any destination in the traffic network. [ABSTRACT FROM AUTHOR]