Performance Modeling of Publish/Subscribe Middleware Components

Towards a behavioural approach to city centre parking

In many communication systems, the stronger of two or more overlapping packets might capture the receiver and thus be received without error. The effect of capture on collision-resolution algorithms in a slotted ALOHA type broadcasting network is investigated here. Extensions to the algorithms are suggested for both the situations in which the receiver can or cannot distinguish between success slots and capture slots. In particular, we present a class of retransmission schemes for packets that have been transmitted during capture slots but have not been received correctly. The performance analysis is confined to a simplified model in which the nodes of the network are divided into two groups and only packets sent by the nodes of one of them might be captured. For this simplified model and for each extended algorithm, explicit recursive equations are given, from which the average conditional collision-resolution interval length, as well as the maximal throughput, can be determined. As expected, we show that in the presence of capture, the performance of the network is improved and the maximal attainable throughput is increased. Extensions of the simplified model such as dividing the nodes into K groups instead of two, or considering the situation that capture depends on relative distances and transmission powers, are also discussed. For the latter situation we give simulation results.

In online communities, polarization refers to the phenomenon in which individuals become more divided and extreme in their opinions due to their exposure to specific content. In this paper, we present a network-based framework for evaluating polarization levels in Online Social Networks (OSNs). Starting from a dataset of comments, our framework creates a network of user interactions and leverages the Louvain algorithm, the Rao’s Quadratic Entropy, and ego networks to assess the polarization level of communities and the most influential users. To test our framework, we leveraged a dataset of tweets about climate change. After performing Extraction, Transformation and Loading activities on the dataset, we evaluated its labels, identified communities, and analyzed their polarization level and that of the most influential users. We also analyzed the ego networks of believers and deniers and the aggressiveness of the corresponding tweets. Our analysis revealed the existence of polarized communities and homophily among the most influential users. It also showed that the type of communication used to disseminate information influences the polarization level of both communities and individual users. These results demonstrate our framework’s ability to support the polarization analysis in OSNs.

Introduction: In this conceptual paper, we reflect on the emergence of citizens as active participants to care integration within Western, highly professionalized care systems. We explore how this development challenges established ways of conceptualizing and practicing integrated care. Ranging from professionals’ alignment with lay caregivers to citizens taking up formal ownership of services, citizen participation is often presented as a new frontier for care integration. Still, scholarly insight into the organizational implication of integration with and by citizens (instead of ‘merely’ for citizens) remains limited. Theory/Methods: First, we select and review key publications in the field of integrated care, analyzing how citizens and communities are positioned within the process of integration. Second, we draw on literature on citizen involvement to analyze the tension between the logics by which professional services are organized and the rationale(s) for involving citizens. We draw on our own empirical studies and examples from the literature to illustrate the areas in which such tensions surface. Findings: First, we discuss three reasons why citizens’ role in integrating services deserves more attention within the field of integrated care. These include (1) citizens’ life-world perspective, enabling them to contribute to the normative frameworks that guide service integration by prioritizing which issues ‘matter’ at the local level or for particular user groups; (2) citizens’ ability to identify whether the efforts of multiple organizations and professionals give rise to barriers to service access and quality at the individual user level; and (3) citizens’ prominent role as ‘lay’ providers of care, who are at risk of being ignored or marginalized when integration is approached as a predominantly professional or managerial endeavor. Second, we explore how these three incentives for involving citizens can be at odds with professional or managerial principles that often dominate how services are organized and integrated. We discuss five key areas of competing principles or perspectives—each potentially legitimate when considered in isolation—that become increasingly salient and require careful consideration when citizens assume a more prominent role in processes of integration: Competing ‘democratic’ and ‘instrumental’ rationales for involvement Competing claims of who represents ‘the community’ Different ways of valuing and utilizing professional knowledge and lay knowledge Trying to be ‘in control’ of work processes while flexibly aligning these with and to citizens Disparate perspectives on professional care organizations’ raison d’etre Discussion/Conclusion/Lessons Learned: We discuss how these five areas of tension force us to reconsider established assumptions that often guide efforts at care integration. While highlighting the potential value of citizen involvement, we also debunk the myth of citizens as the panacea for all challenges to service integration; particularly remaining critical of opportunistic, tokenistic or instrumental attempts at fostering their involvement. Limitations/Future Research: Our paper outlines a broad agenda to recalibrate the position of citizens and communities in integrated care. Future research needs to elaborate each of these five areas in more depth, developing our insights as well as practical tools for supporting constructive citizen involvement.

PurposeThe proliferation of online brand communities has shifted control over brands from firms to consumers. Demonstrating how marketers can stimulate consumers to use these opportunities and engage with the brand in such communities, the purpose of this paper is to address the effectiveness of normative and utilitarian appeals commonly employed in practice for enhancing engagement intensity and brand equity in turn.Design/methodology/approachThis paper presents two studies at an individual user level. The first study builds on matched data on marketing actions, user behavior, and user perceptions from a Facebook brand community. The second study uses an experiment with members of a firm-hosted online brand community. The authors employ seemingly unrelated regressions while controlling for self-selection.FindingsMarketer-generated appeals have a positive effect on brand equity that is mediated by engagement intensity. However, the strength of these effects depends highly on community, user, and relationship characteristics.Practical implicationsGenerally speaking, marketer-generated appeals are effective tools for marketers to build brand equity through enhanced user engagement. However, their effectiveness can be improved when managers use a targeted approach. To offer precise managerial guidance, this paper shows how entertainment value, content consumption asymmetry (e.g. whether a user prefers user-generated content over marketer-generated content), and membership duration increase or lower the impact of appeals in building the brand through engagement intensity.Originality/valueThe authors provide evidence that appeals designed to drive user engagement in online brand communities are effective tools for boosting brand equity.

We present in this paper a novel distributed solution to a security-aware job scheduling problem in cloud computing infrastructures. We assume that the assignment of the available resources is governed exclusively by the specialized brokers assigned to individual users submitting their jobs to the system. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and total completion time. Our approach is based on the Pareto dominance relationship and implemented at an individual user level. To select the best scheduling strategies from the resulting Pareto frontiers and construct a global scheduling solution, we developed a decision-making mechanism based on the game-theoretic model of Spatial Prisoner’s Dilemma, realized by selfish agents operating in the two-dimensional cellular automata space. Their behavior is conditioned by the objectives of the various entities involved in the scheduling process and driven towards a Nash equilibrium solution by the employed social welfare criteria. The performance of the scheduler applied is verified by a number of numerical experiments. The related results show the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources involved in the scheduling process.

Photon counting (PhC) is an effective detection technology for underwater optical wireless communication (OWC) systems. The presence of signal-dependent Poisson shot noise and asynchronous multi-user interference (MUI) complicates the processing of received data signals, hindering the effective signal detection of PhC OWC systems. This paper proposes a novel iterative signal detection method in grant-free, multi-user, underwater PhC OWC systems with signal-dependent Poisson shot noise. We first introduce a new synchronization algorithm with a unique frame structure design. The algorithm performs active user identification and transmission delay estimation. Specifically, the estimation is performed first on a user group basis and then at the individual user level with reduced complexity and latency.We also develop a nonlinear iterative multi-user detection (MUD) algorithm that utilizes a detection window for each user to identify interfering symbols and estimate MUI on a slot-by-slot basis, followed by maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a-posteriori</i> probability detection of user signals. Simulations demonstrate that our scheme achieves bit error rates comparable to scenarios with transmission delays known and signal detection perfectly synchronized.

Distributed network behaviour is increasingly attracting huge attention both in academics and industrial initiatives, and most recently machine learning has been used in every possible field, leveraging its advantages.Typically, a distributed networking allows for the execution of distributed applications which results in complex behaviours among connected systems. This complexity in itself can create grey areas and vulnerabilities in the securities of these networks, therefore predicting the behaviour of these systems both at the macro and micro level has become essential. In the past most researchers have predicted network behaviour and network attack patterns by using aggregated data, but in this paper we focus on the application of machine learning at the individual user level such that the prediction of the individual network user behaviour pattern at the micro level becomes a substasive tool in creating a realistic agent based simulation of the whole distributed network, which in turn can serve as a test bed for predicting what-if scenarios such as network attacks on the target system or exposing vulnerabilities within the target system. The simulation result was validated by comparing the simulated interaction within the simulated network to the data logged in the server logs within the real life network system. This produced a correlation above 0.8, indicating a realistic model.

Economic burden of diverticular disease: An observational analysis based on real world data from an Italian region

In this paper, we investigate a problem of personalized predicting what images are likely to appear on the Web, given a query word and a database of historical images for multiple users. Inspired by recently emerging interests on personalized image search in information retrieval research, the proposed method can infer users' implicit search intent better and provide more engaging search results according to trends of Web user photos. Firstly, we collect a user historical dataset including 40 users and a panorama recommendation test dataset including 240 pictures, both of which are thoroughly divided into 5 categories, including sky, stone, plant, water, buildings. Second, we develop a predictive framework based on the latent SVM model to retrieve the most relevant images from the dataset at an individual user level, which models the relations between scene-level features and the global-level features that influence it in a globally optimal way. The experimental results on the dataset have validated the effectiveness of the proposed approaches in images recommendation.

Commentary on ‘Physical Activity Monitoring in Patients with Peripheral Arterial Occlusive Disease: Validation of an Activity Monitor’

Efficient parallel processing on low-cost clusters with GAMMA active ports

We consider a delay-tolerant network (DTN) whose mobile nodes are assigned to collect packets from data sources and deliver them to a sink (i.e., a gateway). Each mobile node operates by using energy transferred wirelessly from the gateway. For such a network, two main issues are studied. First, when a mobile node is at the data source, this node must decide on whether to accept the packet received from the data source or not. In contrast, whenever a mobile node is at the gateway, it has to decide on whether to transmit the packets collected from the data sources or to request wireless energy transfer. Second, multiple mobile nodes can cooperate and form coalitions to help one another in the delivery of packets from their associated data sources. However, this cooperation may not be always beneficial due to the limited buffer and energy resources. Moreover, some mobile nodes may secretly decide to deviate from a given coalition, thus taking advantage of the other innocent mobile nodes. To address these two issues, this paper introduces a performance analysis and optimization framework, which is based on a joint optimization and game-theoretic framework. The optimization model is used to obtain the packet delivery policy of each individual mobile node. Then, a novel game-theoretic model, namely a repeated coalition formation game , is developed to analyze the cooperation strategies of multiple mobile nodes.

IoT devices include RFID tags, microprocessors, sensors, readers, and actuators. Their main characteristics are their limited resources and computing capabilities, which pose critical challenges to the reliability and security of their applications. Encryption is necessary for security when using these limited-resource devices, but conventional cryptographic algorithms are too heavyweight and resource-demanding to run on IoT infrastructures. This paper presents a lightweight version of AES (called LAES), which provides competitive results in terms of randomness levels and processing time, operating on GF(24). Detailed mathematical operations and proofs are presented concerning LAES rounds design fundamentals. The proposed LAES algorithm is evaluated based on its randomness, performance, and power consumption; it is then compared to other cryptographic algorithm variants, namely Present, Clefia, and AES. The design of the randomness and performance analysis is based on six measures developed with the help of the NIST test statistical suite of cryptographic applications. The performance and power consumption of LAES on a low-power, 8-bit microcontroller unit were evaluated using an Arduino Uno board. LAES was found to have competitive randomness levels, processing times, and power consumption compared to Present, Clefia, and AES.

This study examined the internal control systems and operating performance of SMEs in Ondo State. Specifically, the study analyzed the impact of internal control system components on the profitability of SMEs in Nigeria and a total of 120 SMEs were sampled for the study. Data used in the study were collected with a questionnaire. Collected and collated data were analyzed using logistic regression estimation. The result showed that the probability that an SMEs will have high level of operating performance in terms of higher actual annual profit increases insignificantly by 0.78% and 1.95% respectively, when there is a unit measure of improvement in the control environment and control activities and that the probability declines insignificantly by 0.051%, 0.33% and 4.53% respectively, with a unit measure of improvement in risk assessment, information and communication and monitoring activities. The study established that the components of internal control system has no significant impact on the operating performance of SMEs in Ondo State, though the control environment and control activities have a positive impact on the probability of an enterprise recording a high operating performance. Hence, SMEs should maintain and leverage the positive impact of the control environment and control activities to boost their operating performance, However, issues surrounding risk assessment, information and communication as well as monitoring activities should be addressed systematically based on the reality of the operational structure of each enterprise.