Technical Target Setting in QFD for Web Service Systems using an Artificial Neural Network

There are at least two challenges with quality management of service-oriented architecture based web service systems: 1) how to link its technical capabilities with customer\u27s needs explicitly to satisfy customers\u27 functional and nonfunctional requirements; and 2) how to determine targets of web service design attributes. Currently, the first issue is not addressed and the second one is dealt with subjectively. Quality Function Deployment (QFD), a quality management system, has found its success in improving quality of complex products although it has not been used for developing web service systems. In this paper, we analyze requirements for web services and their design attributes, and apply the QFD for developing web service systems by linking quality of service requirements to web service design attributes. A new method for technical target setting in QFD, based on an artificial neural network, is also presented. Compared with the conventional methods for technical target setting in QFD, such as benchmarking and the linear regression method, which fail to incorporate nonlinear relationships between design attributes and quality of service requirements, it sets up technical targets consistent with relationships between quality of web service requirements and design attributes, no matter whether they are linear or nonlinear

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Studies on Some Aspects of Service Quality Evaluation with Specific Relevance to Indian Service Industries

Quality has been treated as a major competing weapon by both the manufacturing industries and service providers to gain market share, improve productivity and profitability and sustain in business from long term perspective. Therefore, organizations throughout the world dealing with products or services or both are contemplating to implement Total Quality Management (TQM) principles for enhancing system effectiveness. Literature on TQM suggests that twenty critical factors ease the process of TQM implementation in any organization. However, few critical factors viz., leadership, customer satisfaction, training, and employee’s participation are emphasized more frequently in the literature compared to other factors. It is also observed that standardization of the best set of principles of TQM and their implementation sequence is difficult because diverse set of TQM principles are being adopted by organizations. Exhaustive investigation on implementation aspects of TQM results in an in..

5G & SLAs: Automated proposition and management of agreements towards QoS enforcement

Efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become mandatory to guarantee the required service quality in software- defined and 5G networks. To create an operational Network Service, it is highly envisaged to associate it with their network-related parameters that reflect the corresponding quality levels. These are included in policies but while SLAs target usually business users, there is a challenge for mechanisms that bridge this abstraction gap. In this paper, a generic black box approach is used to map high-level requirements expressed by users in SLAs to low-level network parameters included in policies, enabling Quality of Service (QoS) enforcement by triggering the required policies and manage the infrastructure accordingly. In addition, a mechanism for determining the importance of different QoS parameters is presented, mainly used for “relevant” QoS metrics recommendation in the SLA template

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

A misleading answer generation system for exam questions

University professors are responsible for teaching and grading their students in each semester. Normally, in order to evaluate the students progress, professors create exams that are composed of questions regarding the subjects taught in the teaching period. Each year, professors need to develop new questions for their exams since students are free to discuss and register the correct answers to the various questions on prior exams. Professors want to be able to grade students based on their knowledge and not on their memorization skills. Each year, as discovered by our research, professors spend over roughtly 2:30 hours each year for a single course only on multiple answer questions sections. This solution will have at its core a misleading answer generator that would reduce the time and effort when creating a Fill Gap Type Questions through the merger of highly biased lexical model towards a specific subject with a generalist model. To help the most amount of professors with this task a web-server was implemented that served as an access to a exam creator interface with the misleading answer generator feature. To implement the misleading answer generator feature, several accessory programs had to be created as well as manually edditing textbooks pertaining to the question base topic. To evaluate the effectiveness of our implementation, several evaluation methods were proposed composed of objective measurements of the misleading answers generator, as well as subjective methods of evaluation by expert input. The development of the misleading answer suggestion function required us to build a lexical model composed from a highly biased corpus in a specific curricular subject. A highly biased model is probable to give good in-context misleading answers but their variance would most likely be limited. To counteract this the model was merged with a generalist model, in hopes of improving its overall performance. With the development of the custom lexical model and the server the professor can receive misleading answers suggestions to a newly formed question reducing the time spent on creating new exams questions each year to assess students’ knowledge

Automatic Maritime Traffic Anomalous Behaviors Detection

Maritime traffic plays a very important role in the world economy, with over 90% of global trading done through naval transportation. The high amount of vessel traffic, mainly due to cargo transportation, leads to several new risks, threats, and concerns, such as increased criminal activity in the sea. The OVERSEE project is proprietary software developed by Crit ical Software and used by Marinha Portuguesa, Irish Coast Guard, and Papua New Guinea’s Coast Guard. The OVERSEE project displays vessel information in real-time through AIS messages, which are mandatory for most cargo vessels to report consistently. Anomaly de tection and behavior monitoring tools are computer-based systems that analyse real-time data to detect anomalous behaviors. This project aims to develop a solution capable of detecting anomalous behaviors committed by vessels using AIS messages, which will be re ported in real-time automatically via e-mail and the extant OVERSEE graphical interface. The solution is developed with the use of Long Short-Term Memory Recurrent Neural Net works, and a deeper analysis is provided to compare the obtained results with the ideal results. The network training and testing are done with real data, with cross-classification techniques to improve the trustworthiness of the algorithm, hence providing more accurate results.O tráfego marítimo desempenha um papel muito importante na economia mundial, com mais de 90% do comércio global feito por meio do transporte naval. O grande volume de tráfego de embarcações, principalmente devido ao transporte de cargas, leva a vários novos riscos, ameaças e preocupações, como o aumento da criminalidade no mar. O projeto OVERSEE é um software proprietário desenvolvido pela Critical Software e usado pela Marinha Portuguesa, Guarda Costeira Irlandesa e Guarda Costeira da Papua Nova Guiné. O projeto OVERSEE exibe informações da embarcação em tempo real por meio de mensagens AIS, cuja maioria das embarcações de carga são obrigadas a relatar num período de tempo regular. As ferramentas de detecção de anomalias e monitoramento de comportamento são sistemas baseados em computador que analisam dados em tempo real para detetar comportamentos anómalos. Este projeto visa desenvolver uma solução capaz de detetar comportamentos anómalos cometidos por embarcações por meio de mensagens AIS, que serão reportados em tempo real automaticamente via e-mail e interface gráfica existente do OVERSEE. A solução está desenvolvida com o uso de Redes Neurais Recorrentes1 de Memória-Curta de Longo Prazo2 . Uma análise mais profunda é fornecida para comparar os resultados obtidos com os resultados ideais. O treinamento e teste da rede são feitos com dados reais, com técnicas de classificação cruzada para melhorar a confiabilidade do algoritmo, fornecendo resultados mais precisos

Analyzing service quality via QFD and SERVQUAL: Applications in accommodation services and distance learning

Master'sMASTER OF ENGINEERIN