773 resultados para Testbeds, Denial Of Service
Resumo:
The development of 3G (the 3rd generation telecommunication) value-added services brings higher requirements of Quality of Service (QoS). Wideband Code Division Multiple Access (WCDMA) is one of three 3G standards, and enhancement of QoS for WCDMA Core Network (CN) becomes more and more important for users and carriers. The dissertation focuses on enhancement of QoS for WCDMA CN. The purpose is to realize the DiffServ (Differentiated Services) model of QoS for WCDMA CN. Based on the parallelism characteristic of Network Processors (NPs), the NP programming model is classified as Pool of Threads (POTs) and Hyper Task Chaining (HTC). In this study, an integrated programming model that combines both of the two models was designed. This model has highly efficient and flexible features, and also solves the problems of sharing conflicts and packet ordering. We used this model as the programming model to realize DiffServ QoS for WCDMA CN. ^ The realization mechanism of the DiffServ model mainly consists of buffer management, packet scheduling and packet classification algorithms based on NPs. First, we proposed an adaptive buffer management algorithm called Packet Adaptive Fair Dropping (PAFD), which takes into consideration of both fairness and throughput, and has smooth service curves. Then, an improved packet scheduling algorithm called Priority-based Weighted Fair Queuing (PWFQ) was introduced to ensure the fairness of packet scheduling and reduce queue time of data packets. At the same time, the delay and jitter are also maintained in a small range. Thirdly, a multi-dimensional packet classification algorithm called Classification Based on Network Processors (CBNPs) was designed. It effectively reduces the memory access and storage space, and provides less time and space complexity. ^ Lastly, an integrated hardware and software system of the DiffServ model of QoS for WCDMA CN was proposed. It was implemented on the NP IXP2400. According to the corresponding experiment results, the proposed system significantly enhanced QoS for WCDMA CN. It extensively improves consistent response time, display distortion and sound image synchronization, and thus increases network efficiency and saves network resource.^
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A model is presented that captures the complex nature of the service experience in an attempt to aid in the design, development and evaluation of service delivery personnel and systems.
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The authors investigate the relationship between loyalty and perceived service quality of hotel customers and discus managerial implications to develop strategies to enhance loyalty of hotel customers. A survey was conducted among customers in the San Francisco Bay Area. Results indicate that customer loyalty is dependent on perceived service quality which is observed in terms of timelines, facilities, and ambience.
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A mystery shopper study was used to examine the influence of service times on customer satisfaction. The impact of management emphasis on service quality was also examined. In the restaurants studied, service time influenced customer satisfaction. Management attention to service time improved performance in direct relationship to the level of emphasis.
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The purpose of this study is to determine the potential impact of selected organizational factors on boundary-spanning-role employees’ perceptions of service recovery performance. This study also aims to assess the impact of service recovery performance on the intention to leave the job and extrinsic job satisfaction. This study uses a sample of frontline employees in Belek, Antalya, Turkey. The empirical findings revealed that education, team work and role ambiguity as frontline job perceptions were found to exert positive influences on the service recovery performance, but, empowerment, reward, and organizational commitment were found to have negative effects on the service recovery performance.
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This study examined the motivation of college and university faculty to implement service-learning into their traditional courses. The benefits derived by faculty, as well as those issues of maintenance, including supports and/or obstacles, were also investigated in relation to their impact on motivation. The focus was on generating theory from the emerging data. ^ Data were collected from interviews with 17 faculty teaching courses that included a component of service-learning. A maximum variation sampling of participants from six South Florida colleges and universities was utilized. Faculty participants represented a wide range of academic disciplines, faculty ranks, years of experience in teaching and using service-learning as well as gender and ethnic diversity. For data triangulation, a focus group with eight additional college faculty was conducted and documents, including course syllabi and institutional service-learning handbooks, collected during the interviews were examined. The interviews were transcribed and coded using traditional methods as well as with the assistance of the computerized assisted qualitative data analysis software, Atlas.ti. The data were organized into five major categories with themes and sub-themes emerging for each. ^ While intrinsic or personal factors along with extrinsic factors all serve to influence faculty motivation, the study's findings revealed that the primary factors influencing faculty motivation to adopt service-learning were those that were intrinsic or personal in nature. These factors included: (a) past experiences, (b) personal characteristics including the value of serving, (c) involvement with community service, (d) interactions and relationships with peers, (e) benefits to students, (f) benefits to teaching, and (g) perceived career benefits. Implications and recommendations from the study encompass suggestions for administrators in higher education institutions for supporting and encouraging faculty adoption of service-learning including a well developed infrastructure as well as incentives, particularly during the initial implementation period, rewards providing recognition for the academic nature of service-learning and support for the development of peer relationships among service-learning faculty. ^
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This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.
The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.
The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.
Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.
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Background: Concerns exist about the end of life care
that people with intellectual disabilities receive. This population
are seldom referred to palliative care services and
inadequate data sets exist about their place of death.
Aim: To scope the extent of service provision to people
with intellectual disabilities at the end of life by specialist
palliative care and intellectual disability services in one
region of the United Kingdom.
Methods: As part of a larger doctoral study a regional survey
took place of a total sample (n=66) of specialist palliative
care and intellectual disability services using a postal
questionnaire containing forty items. The questionnaire
was informed by the literature and consultation with an
expert reference group. Data were analysed using SPSS to
obtain descriptive statistics.
Results: A total response rate from services of 71.2%
(n=47) was generated. Findings showed a range of experience
among services in providing end of life care to people
with intellectual disabilities in the previous five years, but
general hospitals were reported the most common place of
death. A lack of accessible information on end of life care
for people with learning disabilities was apparent. A few
services (n=14) had a policy to support this population to
make decisions about their care or had used adapted Breaking
Bad News guidelines (n=5) to meet their additional
needs. Both services recognised the value of partnership
working in assessing and meeting the holistic needs of
people with intellectual disabilities at end of life.
Conclusions: A range of experience in caring for people
with intellectual disabilities was present across services,
but more emphasis is required on adapting communication
for this population to facilitate them to participate in their
care. These findings could have international significance
given that studies in other countries have highlighted a
need to widen access to palliative care for this group of
people.
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Abstract Reputation, influenced by ratings from past clients, is crucial for providers competing for custom. For new providers with less track record, a few negative ratings can harm their chances of growing. In the JASPR project, we aim to look at how to ensure automated reputation assessments are justified and informative. Even an honest balanced review of a service provision may still be an unreliable predictor of future performance if the circumstances differ. For example, a service may have previously relied on different sub-providers to now, or been affected by season-specific weather events. A common way to ameliorate the ratings that may not reflect future performance is by weighting by recency. We argue that better results are obtained by querying provenance records on how services are provided for the circumstances of provision, to determine the significance of past interactions. Informed by case studies in global logistics, taxi hire, and courtesy car leasing, we are going on to explore the generation of explanations for reputation assessments, which can be valuable both for clients and for providers wishing to improve their match to the market, and applying machine learning to predict aspects of service provision which may influence decisions on the appropriateness of a provider. In this talk, I will give an overview of the research conducted and planned on JASPR. Speaker Biography Dr Simon Miles Simon Miles is a Reader in Computer Science at King's College London, UK, and head of the Agents and Intelligent Systems group. He conducts research in the areas of normative systems, data provenance, and medical informatics at King's, and has published widely and manages a number of research projects in these areas. He was previously a researcher at the University of Southampton after graduating from his PhD at Warwick. He has twice been an organising committee member for the Autonomous Agents and Multi-Agent Systems conference series, and was a member of the W3C working group which published standards on interoperable provenance data in 2013.
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Providing good customer service, inexpensively, is a problem commonly faced by managers of service operations. To tackle this problem, managers must do four tasks: forecast customer demand for the service; translate these forecasts into employee requirements; develop a labor schedule that provides appropriate numbers of employees at appropriate times; and control the delivery of the service in real-time. This paper focuses upon the translation of forecasts of customer demand into employee requirements. Specifically, it presents and evaluates two methods for determining desired staffing levels. One of these methods is a traditional approach to the task, while the other, by using modified customer arrival rates, offers a better means of accounting for the multi-period impact of customer service. To calculate the modified arrival rates, the latter method reduces (increases) the actual customer arrival rate for a period to account for customers who arrived in the period (in earlier periods) but have some of their service performed in subsequent periods (in the period). In an experiment simulating 13824 service delivery environments, the new method demonstrated its superiority by serving 2.74% more customers within the specified waiting time limit while using 7.57% fewer labor hours.
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Given the nature of employment relationships today, service organizations can strengthen the organization commitment levels and reduce the turnover intentions of its professionals through providing job features important to their careers. These features include opportunities to perform challenging work, experience trusting relationships with customers/clients, and obtain extrinsic rewards. Using a sample of alumni from a hospitality business program, hypotheses that these features impact organizational commitment and turnover intentions, partially through strengthening professionals' career commitment, are developed and tested. Findings suggest that challenging work opportunities impact these attitudes both directly and indirectly. So too trusting relationships with customers and clients indirectly impact organization commitment and intent to turnover (ITO). Results also suggest that, as a whole, satisfaction with extrinsic rewards has no effect. However, an analysis of multigroup mediation results revealed that for professionals working in professional service firms, satisfaction with pay reduces both attitudes. Implications for research in organization commitment and ITO, specifically the role and impact of career-based antecedents, are discussed.
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The electrical outage in the summer of 2003 that interrupted power to thousands of hotels wrought a variety of facilities failures and service-process problems. Fortunately, strong service-recovery efforts from hotel employees mitigated the worst of the blackout’s effects. Using survey data from hotel managers who experienced the blackout, this study highlights those employee actions that most contributed to immediate service recovery; however, the study also reveals limited organizational learning or efforts to failsafe hospitality service from the eventuality of future power failures.
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The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However, as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.
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Particular strengths of the MRC Needs for Care Assessment Schedule have been used to investigate the treatment status of patients with persistent psychiatric disability in ways that other needs assessment tools are unable to. One hundred and seventy-nine such patients from three settings; a private sector psychiatric hospital, two public sector day hospitals situated in the same town, and a high security hospital, were found to have a high level of need. Although there were differences between settings, overall these needs were well met in all three. The high level of persistent disability found amongst these patients could not be attributed to failure on the part of those treating them to use the best available methods, or to failures to comply or engage with treatment on the patient's part. In some two thirds of instances persistent disability was best explained by the fact that even the most suitable available treatments have to be considered only partially effective.