Question: Technology serving as an operant resource within service (eco)systems.

This manifesto calls for the establishment of a services science discipline to address the inadequate scientific comprehension of the modern services sector, which has significantly expanded in recent decades to become the primary driver of economic activity in developed industrial nations. We advocate for a multidisciplinary approach that breaks down academic barriers to expedite service innovation and enhance the scientific understanding of services. By fostering collaboration among various academic fields, services science can lead to more effective and efficient service systems, contributing to economic growth and societal well-being. This initiative aims to unify fragmented research efforts and provide a comprehensive framework for studying service phenomena, ultimately leading to the development of innovative service solutions and improved service delivery.

ed as devices to provide services at low-levels as network discovery services, metadata exchange services, and asynchronous publish and subscribe event in (Kranenburg et al. 2011; Sundmaeker et al. 2010); In (Peris-Lopez et al. 2006), a representational state transfer (REST) is defined to increase interoperability between Page 14 of 32 http://mc.manuscriptcentral.com/intr Internet Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 or Peer Rview loosely coupled services and distributed applications. In (Hernandez-Castro et al. 2013), the services layer introduced a service provisioning process (SPP) that can provide the interaction between applications and services. It is important to design an effective security strategy to protect services against attacks in the service layer. The security requirements in the service layer include: • Authorization, service authentication, group authentication, privacy protection, integrity, integrity, security of keys, non-repudiation, anti-replay, availability,

Here we present a review of PetaJakarta.org, a system designed to harness social media use in Jakarta for the purpose of relaying information about flood locations from citizen to citizen and from citizens and the city’s emergency management agency. The project aimed to produce an open, real-time situational overview of flood conditions and provide decision support for the management agency, as well as offering the government a data source for post-event analysis. As such, the platform was designed as a socio-technological system and developed as a civic co-management tool to enable climate adaptation and community resilience in Jakarta, a delta megacity suffering enormous infrastructural instability due to a troubled confluence of environmental factors—the city’s rapid urbanization, its unique geographic limitations, and increasing sea-levels and monsoon rainfalls resulting from climate change. The chapter concludes with a discussion of future research in open source platform and their role in infrastructure and disaster management. From now on there is an interconnection, an intertwining, even a symbiosis of technologies, exchanges, movements, which makes it so that a flood—for instance—wherever it may occur, must necessarily involve relationships with any number of technical, social, economic, political intricacies that keep us from regarding it simply as a misadventure or a misfortune whose consequences can be more or less easily circumscribed. — Jean-Luc Nancy (2015, 3–4) * E. Turpin & T. Holderness SMART Infrastructure Facility, University of Wollongong, Australia e-mail: eturpin@uow.edu.au | tomas@uow.edu.au Chapter 6 in Social Media for Government Services, Springer 2016 (preprint version)

Understanding software design practice is critical to understanding modern information systems development. New developments in empirical software engineering, information systems design science and the interdisciplinary design literature combined with recent advances in process theory and testability have created a situation ripe for innovation. Consequently, this paper utilizes these breakthroughs to formulate a process theory of software design practice: SensemakingCoevolution-Implementation Theory explains how complex software systems are created by collocated software development teams in organizations. It posits that an independent agent (design team) creates a software system by alternating between three activities: organizing their perceptions about the context, mutually refining their understandings of the context and design space, and manifesting their understanding of the design space in a technological artifact. This theory development paper defines and illustrates Sensemaking-Coevolution-Implementation Theory, grounds its concepts and relationships in existing literature, conceptually evaluates the theory and situates it in the broader context of information systems development.

The Internet of Things (IoT) has been a hot topic for the future of computing and communication. It will not only have a broad impact on our everyday life in the near future, but also create a new ecosystem involving a wide array of players such as device developers, service providers, software developers, network operators, and service users. In this paper, we present an open service framework for the Internet of Things, facilitating entrance into the IoT-related mass market, and establishing a global IoT ecosystem with the worldwide use of IoT devices and softwares. We expect that the open IoT service framework we proposed will play an important role in the widespread adoption of the Internet of Things in our everyday life, enhancing our quality of life with a large number of innovative applications and services, but also offering endless opportunities to all of the stakeholders in the world of information and communication technologies.

We begin with a retrospective reflection on the first author’s research career, which in large part is devoted to research about the implications of information technology (IT) for organizational change. Although IT has long been associated with organizational change, our historical review of the treatment of technology in organization theory demonstrates how easily the material aspects of organizations can disappear into the backwaters of theory development. This is an unfortunate result since the material characteristics of IT initiatives distinguish them from other organizational change initiatives. Our aim is to restore materiality to studies of IT impact by tracing the reasons for its disappearance and by offering options in which IT’s materiality plays a more central theoretical role. We adopt a socio-technical perspective that differs from a strict sociomaterial perspective insofar as we wish to preserve the ontological distinction between material artifacts and their social context of use. Our analysis proceeds using the concept of “affordance” as a relational concept consistent with the socio-technical perspective. We then propose extensions of organizational routines theory that incorporate material artifacts in the generative system known as routines. These contributions exemplify two of the many challenges inherent in adopting materiality as a new research focus in the study of IT’s organizational impacts.

Enterprise Architecture Management (EAM) is considered to provide the mechanism for, amongst others, governing enterprise transformations required by changes in the environment. In this paper, we focus on changes that result from the analysis of information security risks and of their impacts on the services delivered by an enterprise. We present how the concepts of an information system security risks management domain can be mapped into the ArchiMate enterprise architecture modeling language. We illustrate the application of the proposed approach through the handling of a lab case.

Customer-centric Web-based systems, such as ecommerce Web sites, or sites that support customer relationship management (CRM) activities, are themselves information systems, but their design and maintenance need to follow vastly different approaches from the traditional systems lifecycle approach. Based on marketing frameworks that are applicable to the online world, and following design science principles, we develop a model to guide the design and the continuous management of such sites. The model makes extensive use of current technologies for tracking the customers and their behaviors, and combines elements of data mining and statistical analyses. A case study based on a financial services Web site is used to provide a preliminary validation and design evaluation of our approach. The case study showed considerable measured improvement in the effectiveness of the companys Web site. In addition, it also highlighted an important benefit of the our approach: the identification of previously unknown or unexpected segments of visitors. This finding can lead to promising new business opportunities.

In Telerobotics, Automation, and Human Supervisory Control, Thomas B. Sheridan shows that progress in robotics depends not only on change in technology, but also on advances in humans’ relationships to machines. Sheridan, Professor of Engineering and Applied Psychology at M.I.T., has spent decades studying human-machine relations. His idea of “human supervisory control” (just “supervisory control” for short) has the potential to bring robotics out of the laboratory and into the difficult and messy world. Traditionally, the public and researchers alike have focused on automation which seeks to build into a machine all necessary capabilities for achieving a particular task, including sensors, actuators, servos, and algorithms. In contrast to automatic control, supervisory control puts a human back “in the loop,” usually at the highest levels of abstraction, while maintaining lower-level functions within the machine. In a supervisory control system human and machine work together. The operator may intervene and operate manually in a particular situation, or she (Sheridan refers to the generic operator as female) might turn over control to an automatic controller for a time, acting as monitor of the task at hand. Typically the “system” (including the human) will operate somewhere on the spectrum between manual and automatic, dividing the task in different degrees depending on the situation. When the operator and the machine are not collocated but are connected by some communication channel, the system is said to be telerobotic. Determining the proper balance of man and machine, manual and automatic control, or providing tools to do so, is the goal of Sheridan’s book. An important modern example of a supervisory control system (although not a telerobotic one) is a modem aircraft. The pilot can command the flight surfaces directly and fly manually, or she can program an autopilot to fly a complete route, including nearly complete takeoff and landing. Typically, the pilot performs some

1.3 The environmental docility hypothesis suggests that environmental stimuli ("press", in Murray's terms) have a greater demand quality as the competence of the individual decreases. The dynamics of ecological transactions are considered as a function of personal competence, strength of environmental press, the dual characteristics of the individual's response (affective quality and adaptiveness of behavior), adaptation level, and the optimization function. Behavioral homeostasis is maintained by the individual as both respondent and initiator in interaction with his environment. Hypotheses are suggested to account for striving vs. relaxation and for changes in the individual's level of personal competence. Four transaction-al types discussed are environmental engineering, rehabilitation and therapy, individual growth, and active change of the environment. Recent work in the psychology of stimulation (1) has led to theoretical advances in the area of social ecology. We propose an elaboration in this area that is middle-range, in the sense of attempting to account for a limited aspect of human behavior. This contribution to the theory of man-environment relationships deals with the aspects of human responses that can be viewed in evaluative terms, that is, behavior that can be rated on the continuum of adaptiveness, and inner states that can be rated on the continuum of positive to negative. This is, perhaps, a limited view of the human response repertory, but it stems from the traditional concern of the psychologist with mental health and mental illness. Similarly, our view of environment for this purpose is limited to the "demand quality" of the environment, an abstraction that represents only one of many ways of dimensionalizing the environment. We shall use our knowledge from the area of gerontology to provide content for the theoretical structure, but suggest that the constructs are more generally applicable to any area involving the understanding of mental or social pathology [see Lawton and Nahemow, (2) for a more complete discussion]. One way to begin is to look at the old ecological equation B = f (P, E) to acknowledge its veracity and familiarity, but linger on a few of its implications: 1. All behavior is transactional, that is, not explainable solely on the basis of knowledge about either the person behaving or the environment in which it occurs.

The Resource-Event-Agent (REA) model for enterprise economic phenomena was first published in The Accounting Review in 1982. Since that time its concepts and use have been extended far beyond its original accountability infrastructure to a framework for enterprise information architectures. The granularity of the model has been extended both up (to enterprise value chains) and down (to workflow or task specification) in the aggregation plane, and additional conceptual type-images and commitment-images have been proposed as well. The REA model actually fits the notion of domain ontology well, a notion that is becoming increasingly important in the era of e-commerce and virtual companies. However, its present and future components have never been analyzed formally from an ontological perspective. This paper intends to do just that, relying primarily on the conceptual terminology of John Sowa. The economic primitives of the original REA model (economic resources, economic events, economic agents, duality relationships, stock-flow relationships, control relationships and responsibility relationships) plus some newer components (commitments, types, custody, reciprocal, etc.) will be analyzed individually and collectively as a specific domain ontology. Such a review can be used to guide further conceptual development of REA extensions.


Question: Technology serving as an operant resource within service (eco)systems.

In the service industry, maintaining high-quality standards has become increasingly challenging, with the American Customer Satisfaction Index for services dropping to 69.4% in 2000, a significant decline from 1994. This paper examines the factors contributing to this decline, focusing on the inherent characteristics of services—such as inseparability, intangibility, and labor intensity—and how they interact with management practices to inadvertently lower service levels. We propose that these interactions create a downward spiral, where entire service industries become trapped in a cycle of diminishing service standards. To investigate this hypothesis, we construct a formal model that encompasses the critical components of service delivery. Using a combination of econometric analysis, interviews, observational studies, and archival data, we calibrate this model specifically for a consumer-lending service center within a major British bank. Our findings reveal that temporary mismatches between service capacity and demand, when coupled with specific managerial decision-making rules regarding effort allocation, capacity management, overtime policies, and quality goals, can lead to a permanent degradation of service standards and a consequent loss of revenue. We further explore potential strategies to enhance service performance and discuss the implications for organizational design within the service sector, emphasizing the need for a systemic approach to service quality management. (Keywords: Organizational Learning, Service Management Performance, Service Operations, Service Quality, Simulation, System Dynamics)

The importance of service delivery technology and online service adoption and usage in the banking industry has received an increased discussion in the literature in recent years. Owing to the fact that Strong online banking services are important drivers for bank performance and customer service delivery; several studies have been carried out on online banking service adoption or acceptance where services are already deployed and on the factors that influence customers' adoption and use or intention to use those services. However, despite the increasing discussion in the literatures, no attempt has been made to look at consumers' preference in terms of future online banking service adoption. This study used conjoint analysis and stated preference methods with discrete choice model to analyze the technology adoption pattern regarding consumers' preference for potential future online banking services in the Nigerian banking industry. The result revealed that to increase efficiency and strengthen competitiveness, banks need to promote smart and practical branded services especially self-services at the same time promote a universal adoption of e-banking system services that add entertainment or extra convenience to customers such as ease of usage including digital wallet, real-time interaction (video banking), ATMs integrated with smart phones, website customization, biometric services, and digital currency. These services can contribute to an increasing adoption of online services. & 2015 Elsevier Ltd. All rights reserved.

Developers aim at providing value through their systems and products. However, value is not financial only, but depends on usage and users’ perceptions of value. In this paper, we clarify the concept of value from the users’ perspective and the role of user involvement in providing value. First, theories and approaches of psychology, marketing and human-computer interaction are reviewed. Secondly, the concept of ‘user values’ is suggested to clarify the concept of value from the user’s point of view and a category framework of user values is presented to make them more concrete and easier to identify. Thirdly, the activities and methods for adopting user values in development work are discussed. The analysis of the literature shows that value has been considered in multiple ways in development. However, users’ perspectives have received less attention. As a conclusion, we draw future research directions for value-centered design and propose that user involvement is essential in identifying user values, interpreting the practical meaning of the values and implementing them in development work.

Web service technology can be used for integrating heterogeneous and autonomous applications into cross-organizational systems. A key problem is to support a high quality of service-oriented systems despite vulnerabilities caused by the use of external web services. One important aspect that has received little attention so far is risk management for such systems. This paper discusses risks peculiar for servicebased systems, their impact and ways of mitigation. In the context of service-oriented design, risks can be reduced by selection of appropriate business partners, web service discovery, service composition and Quality of Service (QoS) management. Advisors. Vincenzo D’Andrea.

Traditional retailers are facing strong competition from e-commerce. One way to meet this challenge is to follow the marketing movement of focusing on customer experiences. This transformation is based on the notion of engaging customers and one way to drive this engagement is through gamification to support value creation. In this study, we have identified variables affecting intentions to use gamified services and in what ways. For this purpose, we developed an app that generated different levels of gamification by varying the number of game elements. The data from a survey distributed during a field experiment indicates that an increasing level of gamification and technology experience have direct positive associations with intrinsic motivation. Furthermore, intrinsic motivation has a positive direct association with satisfaction, although this is partly mediated by mood. Finally, satisfaction has a positive direct relation with intention to use.

Using IT in ways that can trigger major organizational changes creates high-risk, potentially high-reward, situations that I call technochange (for technology-driven organizational change). Technochange differs from typical IT projects and from typical organizational change programs and therefore requires a different approach. One major risk in technochange—that people will not use information technology and related work practices—is not thoroughly addressed by the discipline of IT project management, which focuses on project cost, project schedule, and solution functionality. Organizational change management approaches are also generally not effective on their own, because they take as a given the IT ‘‘solutions’’ developed by a technical team. Consequently, the potential for the IT ‘‘solution’’ to be misaligned with important organizational characteristics, such as culture or incentives, is great. Merely combining IT project management and organizational change management approaches does not produce the best results, for two reasons. First, the additive approach does not effectively address the many failure-threatening problems that can arise over the lengthy sequential process of the typical technochange lifecycle. Second, the additive approach is not structured to produce the characteristics of a good technochange solution: a complete intervention consisting of IT and complementary organizational changes, an implementable solution with minimal misfits with the existing organization, and an organization primed to appropriate the potential bene¢ts of the technochange solution. With hard work and care, the combined IT project management plus organizational change approach can be made to work. However, an iterative, incremental approach to implementing technochange can be a better strategy in many situations. The essential characteristic of the technochange prototyping approach is that each phase involves both new IT functionality and related organizational changes, such as redesigned business processes, new performance metrics, and training. Journal of Information Technology (2004) 19, 4–20. doi:10.1057/palgrave.jit.2000002

This research tests and develops the Technology Acceptance Model (TAM), introduced by Davis (1986), which attempts to explain end users’ attitudes to computing technologies. It introduces several new variables, including compatibility, user characteristics, system rating and the enduser computing satisfaction (EUCS) construct, a surrogate measure for IT success and acceptance. A questionnaire with over seventy items was completed by a large number of users and LISREL, a technique for modelling a system of structural equations, was used to analyse the responses. The output shows the model as a whole ® ts the data very well and indicates signi® cant relationships between variables in the model. These results con® rm that TAM is a valuable tool for predicting attitudes, satisfaction, and usage from beliefs and external variables. They also show that relative advantage of the system contributed most to attitudes and satisfaction. Compatibility (of the system to the task performed) contributed most to usage and was the most important antecedent of the belief variables, including relative advantage.

Received: 30 December 2006 Revised: 12 July 2007 2nd Revision: 16 December 2007 3rd Revision: 29 April 2008 Accepted: 15 May 2008 Abstract Since DeLone and McLean (D&M) developed their model of IS success, there has been much research on the topic of success as well as extensions and tests of their model. Using the technique of a qualitative literature review, this research reviews 180 papers found in the academic literature for the period 1992–2007 dealing with some aspect of IS success. Using the six dimensions of the D&M model – system quality, information quality, service quality, use, user satisfaction, and net benefits – 90 empirical studies were examined and the results summarized. Measures for the six success constructs are described and 15 pairwise associations between the success constructs are analyzed. This work builds on the prior research related to IS success by summarizing the measures applied to the evaluation of IS success and by examining the relationships that comprise the D&M IS success model in both individual and organizational contexts. European Journal of Information Systems (2008) 17, 236–263. doi:10.1057/ejis.2008.15

This paper reports on the findings of a survey and case study research into the understanding and application of business process management (BPM) in European companies. The process perspective is increasingly being seen as a mechanism for achieving competitive advantage through performance improvement and in response to market pressures, customer expectations for better and more reliable service and increasing competition. We reveal the level of importance European companies attach to BPM, what it means to them and what they have done in practice. The paper draws on a postal survey conducted with quality directors and business process managers in organisations which are members of the European Foundation for Quality Management (EFQM) and case studies in a number of organisations regarded as being leaders in the adoption of BPM. The study has helped to highlight some interesting approaches and reveal features which are important for BPM to be successful. Introduction One of the difficulties with business process management (BPM) is that of terminology. The term process can be found in many disciplines which contribute to our understanding of organisations in the management literature. An operational view is seen in quality improvement (Deming, 1986), total quality management (Oakland, 1989) and the concept of just-in-time (Harrison, 1992). Systems thinking (Jenkins, 1971; Checkland, 1981), cybernetics (Beer, 1966) and systems dynamics (Senge, 1990) give a richer meaning to the term. Organisational theorists have also talked in terms of social and organisational processes (Burrell and Morgan, 1979; Monge, 1990). A useful review of these antecedents has been provided by Peppard and Preece (1995). The domain in which the current study is centred develops out of recent approaches which seek to improve organisational effectiveness through the attachment of managerial thinking to Total Quality or Business Excellence models. These have been essentially practitioner driven and not grounded in academic theory. Examples include the European Foundation for Quality Management model (EFQM) (Hakes, 1995) and the Malcom Baldrige National Quality Award model (MBNQA) (George, 1992). While these models espouse multi-factorial and multi-constituency models of organisational effectiveness they remain essentially goal-based (Cameron, 1986). They have also developed from a strong operational framework and often have absorbed the approaches of We would like to thank Bob Dart, Simon Machin and Tony Grant from Royal Mail. We also gratefully appreciate the assistance of the EFQM, Rank Xerox, British Telecom, TNT and Nortel during the various stages of our research. D ow nl oa de d by S E L C U K U N IV E R SI T Y A t 0 2: 52 0 8 Fe br ua ry 2 01 5 (P T ) Lessons from European business 11 business process re-engineering (Hammer, 1990). They have also been influenced by strategic thinking in the strong process orientation of the value chain analysis (Porter, 1985) and they accommodate the resources based view of the firm (Grant, 1991). Use of the models may lead to questioning the design of an organisation at a strategic level by reassessing the value of functions in favour of processes which transcend functionality (Ghoshal and Bartlett, 1995; Galbraith, 1995). However, neither the EFQM nor the MBNQA provide direct guidance on how to deploy BPM. Approaches often incorporate attempts to identify business processes and to classify them as being operational, supporting or direction setting. This activity is often facilitated by consultants using a range of methods but commonly including aspects of process mapping at least at the top level of the organisation. There is evidence that adopting the process paradigm is favoured at least by senior managers (Garvin, 1995), although it is by no means clear that this is a widely held opinion throughout organisations. While it is possible to point to aspects of good practice in BPM, at least at an operational level (Armistead, 1996), we do not know how organisations apply the notion in practice and what they have found to be the key components of a BPM approach. Method The aim of the research has been to develop a better understanding of BPM and how it can be applied as a route to achieving organisational effectiveness. We have been especially interested to find out how companies have used a business process perspective as a way of managing their whole organisation, rather than just the application of process improvement techniques. In particular we have sought to explore the following questions: . How important is BPM for European managers? . Is there a common understanding of BPM among European organisations? . How are European organisations implementing BPM in practice? In addressing these questions we hope to shed light on how organisations conceptualise BPM and draw on their experiences in order to enlighten others both in terms of strategy formulation and deployment. This paper discusses the findings of the research and proposes lessons which can be learnt. During our research we have built up a rich databank of case study material. The case studies have been compiled using an open-ended interview format where senior executives (usually the quality director or business process manager) have been invited to elaborate on their organisation's approach to BPM. The interviews were recorded and transcribed and a cognitive map was developed to identify concepts. In some cases data from the interviews were supplemented by material used for internal self assessments against the EFQM model. Organisations were typically chosen because they were known to have adopted BPM approaches. This paper refers specifically to case studies with Rank Xerox, Nortel, British Telecom and TNT, all of whom have been winners in some form (either directly of through subsidiaries) of European Quality Awards. D ow nl oa de d by S E L C U K U N IV E R SI T Y A t 0 2: 52 0 8 Fe br ua ry 2 01 5 (P T )

DevOps is a trend towards a tighter integration between development (Dev) and operations (Ops) teams. The need for such an integration is driven by the requirement to continuously adapt enterprise applications (EAs) to changes in the business environment. As of today, DevOps concepts have been primarily introduced to ensure a constant flow of features and bug fixes into new releases from a functional perspective. In order to integrate a non-functional perspective into these DevOps concepts this report focuses on tools, activities, and processes to ensure one of the most important quality attributes of a software system, namely performance. Performance describes system properties concerning its timeliness and use of resources. Common metrics are response time, throughput, and resource utilization. Performance goals for EAs are typically defined by setting upper and/or lower bounds for these metrics and specific business transactions. In order to ensure that such performance goals can be met, several activities are required during development and operation of these systems as well as during the transition from Dev to Ops. Activities during development are typically summarized by the term Software Performance Engineering (SPE), whereas activities during operations are called Application Performance Management (APM). SPE and APM were historically tackled independently from each other, but the newly emerging DevOps concepts require and enable a tighter integration between both activity streams. This report presents existing solutions to support this integration as well as open research challenges in this area. The report starts by defining EAs and summarizes their characteristics that make performance evaluations for these systems particularly challenging. It continues by describing our understanding of DevOps and explaining the roots of this trend to set the context for the remaining parts of the report. Afterwards, performance management activities that are common in both life cycle phases are explained, until the particularities of SPE and APM are discussed in separate sections. Finally, the report concludes by outlining activities and challenges to support the rapid iteration between Dev and Ops.

Background: The notion of smart city has grown popular over the past few years. It embraces several dimensions depending on the meaning of the word “smart” and benefits from innovative applications of new kinds of information and communications technology to support communal sharing. Methods: By relying on prior literature, this paper proposes a conceptual framework with three dimensions: (1) human, (2) technology, and (3) organization, and explores a set of fundamental factors that make a city smart from a sharing economy perspective. Results: Using this triangle framework, we discuss what emerging blockchain technology may contribute to these factors and how its elements can help smart cities develop sharing services. Conclusions: This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework. We hope it can stimulate interest in theory and practice to foster discussions in this area.


Question: Technology serving as an operant resource within service ecosystems.

Transitioning to a Service-Dominant Logic in Marketing

Marketing's foundational framework was derived from economics, with a prevailing paradigm centered on the exchange of "goods," typically referring to manufactured products. This paradigm emphasized tangible assets, embedded value, and transactional dynamics. However, over recent decades, innovative viewpoints have surfaced, introducing a revised logic that places greater emphasis on intangible resources, the co-creation of value, and enduring relationships. We posit that these new perspectives are coalescing to form a service-dominant logic for marketing, where service delivery becomes the cornerstone of economic exchange, superseding goods. This paper delves into the evolution of this logic and the associated paradigm shift for marketing scholars, practitioners, and educators alike.

In this new service-dominant logic, the focus shifts from the mere exchange of goods to the provision of services, which are increasingly recognized as the primary source of value in economic activities. This transformation necessitates a reevaluation of marketing strategies, theories, and educational curricula. For marketing scholars, it implies a need to explore service-oriented theories and frameworks that can better explain and predict market behaviors in a service-dominant economy. Practitioners must adapt their approaches to emphasize service delivery and customer engagement, fostering long-term relationships rather than one-off transactions. Educators, in turn, must update their teaching methods and content to reflect the service-dominant logic, preparing future marketers for a world where service is king.

This transition represents a fundamental shift in the way marketing operates, requiring a comprehensive overhaul of existing models, practices, and educational approaches. As the service-dominant logic becomes more prevalent, it will reshape the entire marketing landscape, from the strategies employed by companies to the skills required of marketing professionals. Embracing this new logic is crucial for staying relevant in an increasingly service-oriented economy.

Enterprise Architecture Management (EAM) is considered to provide the mechanism for, amongst others, governing enterprise transformations required by changes in the environment. In this paper, we focus on changes that result from the analysis of information security risks and of their impacts on the services delivered by an enterprise. We present how the concepts of an information system security risks management domain can be mapped into the ArchiMate enterprise architecture modeling language. We illustrate the application of the proposed approach through the handling of a lab case.

The life cycle approach is widely used in the analysis of sustainability. Its application to supply chains is necessary since the product flows, from processing of raw materials to the final customer, are considered. The role of the organizational aspects, expressed in terms of relationships between the supply chain agents, is little considered in the life cycle analysis approach. The aim of this paper is to extend the scope of the food chain life cycle analysis by adding the organizational dimension to the environmental, economic and social ones. Within this context, Collaboration and Sustainable Relationships concepts have been explored based on a literature survey. A theoretical framework, describing their role in assessing the organizational dimension in the life cycle analysis of the food supply chains, is defined. A hypothesis on their joint influence on the supply chains performances is formulated.

The potential of the artificial intelligence, data mining and the so-called Big Data to build solutions based on the understanding of human behaviours is being extended with the capabilities of the Future Internet and the Internet of Things (IoT). The described potential of data and networks have been analyzed by the complex network theory, offering a specialized branch, the defined as Human Dynamics. The goal with the Internet of Things in the social area is to describe in real-time the human behaviours and activities. These goals are starting to be feasible through the quantity of data provided by the personal devices such as smart phone, and the smart environments such as Smart cities that makes more intelligent the environment and offer a smart space that sense our movements, actions, and the evolution of the ecosystem. This work analyses the ecosystem defined through the triangle formed by Big Data, Smart Cities and Personal/Wearable Computing to determinate human behaviours and human dynamics. For this purpose, the life-cycle of human dynamics determinations in Smart Cities have been analysed in order to determinate the current status of the technology, challenges, and opportunities.

Customer Relationship Management (CRM) is increasingly found at the top of corporate agendas. Online companies in particular are embracing CRM as a major element of corporate strategy, because online technological applications permit a precise segmentation, profiling and targeting of customers, and the competitive pressures of the digital markets require a customer-centric corporate culture. The implementation of CRM systems in online organisation determines a complex restructuring of all organisational elements and processes. The strategic planning process will have to adapt to new customer-centric procedures. The present paper analyses the implementation process of a CRM system in online retail businesses and develops a model of the strategic planning function in a customer-centric context.

Existing models for describing a process such as a business process or a software development process tend to focus on the what or the how of the process For example a health insurance claim process would typically be described in terms of a number of steps for assessing and approving a claim In trying to improve or redesign a process however one also needs to have an understanding of the why for example why do physicians submit treatment plans to insurance companies before giving treatment and why do claims managers seek medical opinions when assessing treatment plans An understanding of the motivations and interests of process participants is often crucial to the successful redesign of processes This thesis proposes a modelling framework i pronounced i star consisting of two mod elling components The Strategic Dependency SD model describes a process in terms of in tentional dependency relationships among agents Agents depend on each other for goals to be achieved tasks to be performed and resources to be furnished Agents are intentional in that they have desires and wants and strategic in that they are concerned about opportunities and vulnerabilities The Strategic Rationale SR model describes the issues and concerns that agents have about existing processes and proposed alternatives and how they might be addressed in terms of a network of means ends relationships An agent s routines for carrying out a process can be analyzed for their ability workability viability and believability Means ends rules are used to suggest methods for addressing issues related issues to be raised and assumptions to be challenged The models are represented in the conceptual modelling language Telos The modelling concepts are axiomatically characterized The utility of the framework is illustrated in each of four application areas requirements engineering business process reengineering organizational impacts analysis and software pro cess modelling Advantages of i over existing modelling techniques in each of these areas are described

Many products have little or no value in isolation, but generate value when combined with others. Examples include: nuts and bolts, which together provide fastening services; home audio or video components and programming, which together provide entertainment services; automobiles, repair parts and service, which together provide transportation services; facsimile machines and their associated communications protocols, which together provide fax services; automatic teller machines and ATM cards, which together provide transaction services; camera bodies and lenses, which together provide photographic services. These are all examples of products that are strongly complementary, although they need not be consumed in fixed proportions. We describe them as forming systems, which refers to collections of two or more components together with an interface that allows the components to work together. This paper and the others in this symposium explore the economics of such systems. Market competition between systems, as opposed to market competition between individual products, highlights at least three important issues: expectations, coordination, and compatibility. A recent wave of research has focused on the behavior and performance of the variety of private and public institutions that arise in systems markets to influence expectations, facilitate coordination, and achieve compatibility. In many cases, the components purchased for a single system are spread over time, which means that rational buyers must form expectations about

With the increase of energy consumption associated with IT infrastructures, energy management is becoming a priority in the design and operation of complex service-based systems. At the same time, service providers need to comply with Service Level Agreement (SLA) contracts which determine the revenues and penalties on the basis of the achieved performance level. This paper focuses on the resource allocation problem in multitier virtualized systems with the goal of maximizing the SLAs revenue while minimizing energy costs. The main novelty of our approach is to address-in a unifying framework-service centers resource management by exploiting as actuation mechanisms allocation of virtual machines (VMs) to servers, load balancing, capacity allocation, server power state tuning, and dynamic voltage/frequency scaling. Resource management is modeled as an NP-hard mixed integer nonlinear programming problem, and solved by a local search procedure. To validate its effectiveness, the proposed model is compared to top-performing state-of-the-art techniques. The evaluation is based on simulation and on real experiments performed in a prototype environment. Synthetic as well as realistic workloads and a number of different scenarios of interest are considered. Results show that we are able to yield significant revenue gains for the provider when compared to alternative methods (up to 45 percent). Moreover, solutions are robust to service time and workload variations.

John Boyd recognized in the 1960's the importance of situation awareness for military operations and introduced the notion of the OODA loop (Observe, Orient, Decide, and Act). Today we realize that many applications have to deal with situation awareness: Customer Relationship Management, Human Capital Management, Supply Chain Management, patient care, power grid management, and cloud services management, as well as any IoT (Internet of Things) related application; the list seems to be endless. Situation awareness requires applications to support the management of data, knowledge, processes, and other services such as social networking in an integrated way. These applications additionally require high personalization as well as rapid and continuous evolution. They must provide a wide variety of operational and functional requirements, including real time processing.
 Handcrafting these applications is an almost impossible task requiring exhaustive resources for development and maintenance. Due to the resources and time involved in their development, these applications typically fall way short of the desired functionality, operational characteristics, and ease and speed of evolution. We - the authors - have developed a model enabling the development and maintenance of situation-aware applications in a declarative and therefore economical manner; we call this model KIDS - Knowledge Intensive Data-processing System.

The field of information systems is premised on the centrality of information technology in everyday socio-economic life. Yet, drawing on a review of the full set of articles published in Information Systems Research (ISR) over the past ten years, we argue that the field has not deeply engaged its core subject matter—the information technology (IT) artifact. Instead, we find that IS researchers tend to give central theoretical significance to the context (within which some usually unspecified technology is seen to operate), the discrete processing capabilities of the artifact (as separable from its context or use), or the dependent variable (that which is posited to be affected or changed as technology is developed, implemented, and used). The IT artifact itself tends to disappear from view, be taken for granted, or is presumed to be unproblematic once it is built and installed. After discussing the implications of our findings, we propose a research direction for the IS field that begins to take technology as seriously as its effects, context, and capabilities. In particular, we propose that IS researchers begin to theorize specifically about IT artifacts, and then incorporate these theories explicitly into their studies. We believe that such a research direction is critical if IS research is to make a significant contribution to the understanding of a world increasingly suffused with ubiquitous, interdependent, and emergent information technologies. (Information Systems Research; Information Technology; IT Research; IT Theory; Technological Artifacts; Technology Change)

The paper presents our exploration into an organization ontology for the TOVE enterprise model. Its primary focus has been in linking structure and behavior through the concept of empowerment. Empowerment is the right of an organization agent to perform status changing actions. This linkage is critical to the unification of enterprise models and their executability.


Question: Categorization of sensor inaccuracies for the statistical modeling of environmental awareness in autonomous driving systems.

This paper recounts the development and success of Stanley, the autonomous vehicle that triumphed in the DARPA Grand Challenge, a prestigious competition for self-driving cars. Under the guidance of Sebastian Thrun and his team from Stanford University, Stanley was meticulously designed and engineered to navigate through complex and unpredictable terrains. The team's innovative approach combined advanced sensing technologies with sophisticated algorithms to enable Stanley to perceive its environment, make decisions, and execute maneuvers autonomously. Key to Stanley's success was its robust sensor suite, which included cameras, LIDAR, and GPS systems, all integrated with a powerful onboard computer for real-time data processing. The vehicle's control system was designed to handle various driving scenarios, from off-road navigation to urban environments, showcasing the team's expertise in robotics and artificial intelligence. Stanley's victory in the DARPA Grand Challenge not only demonstrated the feasibility of autonomous driving but also highlighted the potential of these technologies in transforming the future of transportation.

**Question**: The Role of Technology in Enhancing Collaborative Learning Environments

**Documents**: The Impact of Technology on Communication and Collaboration in the Workplace
The advent of technology has significantly altered the landscape of communication and collaboration in the workplace. From emails and instant messaging to video conferencing and cloud-based collaboration tools, technology has made it easier for teams to connect, share information, and work together, regardless of their physical location. These tools not only enhance productivity but also foster a sense of community and inclusivity among team members. For instance, virtual meeting platforms allow for face-to-face interactions, reducing the need for physical travel and saving time and resources. Cloud-based document management systems enable real-time collaboration, allowing multiple users to edit and share files simultaneously, streamlining the workflow. Furthermore, social media platforms have also been integrated into the workplace, facilitating informal communication and knowledge sharing. However, while technology has its advantages, it also poses challenges, such as information overload, privacy concerns, and the digital divide, which can hinder collaboration and communication. Organizations must therefore strike a balance in leveraging technology to enhance collaboration while addressing these potential drawbacks.

**Similar Documents**: The Transformation of Collaboration and Communication through Technological Advancements
In recent years, technological advancements have revolutionized the way we communicate and collaborate, particularly in professional settings. The proliferation of digital tools and platforms has enabled teams to overcome geographical barriers, fostering a more connected and efficient work environment. Email, instant messaging, and video conferencing have become ubiquitous, allowing for seamless communication and the sharing of ideas. Cloud-based collaboration tools have taken this a step further, enabling

What machine learning (ML) technique is used for system intelligence implementation in ADAS (advanced driving assistance system)? This paper tries to answer this question. This paper analyzes ADAS and ML independently and then relate which ML technique is applicable to what ADAS component and why. The paper gives a good grasp of the current state-of-the-art. Sample works in supervised, unsupervised, deep and reinforcement learnings are presented; their strengths and rooms for improvements are also discussed. This forms part of the basics in understanding autonomous vehicle. This work is a contribution to the ongoing research in ML aimed at reducing road traffic accidents and fatalities, and the invocation of safe driving.

This thesis investigates the use of wearable sensors to recognize human activity. The activity of the user is one example of context information – others include the user’s location or the state of his environment – which can help computer applications to adapt to the user depending on the situation. In this thesis we use wearable sensors – mainly accelerometers – to record, model and recognize human activities. Using wearable sensors allows continuous recording of activities across different locations and independent from external infrastructure. There are many possible applications for activity recognition with wearable sensors, for instance in the areas of healthcare, elderly care, personal fitness, entertainment, or performing arts. In this thesis we focus on two particular research challenges in activity recognition, namely the need for less supervision, and the recognition of high-level activities. We make several contributions towards addressing these challenges. Our first contribution is an analysis of features for activity recognition. Using a data set of activities such as walking, standing, sitting, or hopping, we analyze the performance of commonly used features and window lengths over which the features are computed. Our results indicate that different features perform well for different activities, and that in order to achieve best recognition performance, features and window lengths should be chosen specific for each activity. In order to reduce the need for labeled training data, we propose an unsupervised algorithm which can discover structure in unlabeled recordings of activities. The approach identifies correlated subsets in feature space, and represents these subsets with low-dimensional models. We show that the discovered subsets often correspond to distinct activities, and that the resulting models can be used for recognition of activities in unknown data. In a separate study, we show that the approach can be effectively deployed in a semi-supervised learning framework. More specifically, we combine the approach with a discriminant classifier, and show that this scheme allows high recognition rates even when using only a small amount of labeled training data. Recognition of higher-level activities such as shopping, doing housework, or commuting is challenging, as these activities are composed of changing sub-activities and vary strongly across individuals. We present one study in which we recorded 10h of three different high-level activities, investigating to which extent methods for low-level activities can be scaled to the recognition of high-level activities. Our results indicate that for settings as ours, traditional supervised approaches in combination with data from wearable accelerometers can achieve recognition rates of more than 90%. While unsupervised techniques are desirable for short-term activities, they become crucial for long-term activities, for which annotation is often impractical or impossible. To this end we propose an unsupervised approach based on topic models that allows to discover high-level structure in human activity data. The discovered activity patterns correlate with daily routines such as commuting, office work, or lunch routine, and they can be used to recognize such routines in unknown data.

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.

Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.

This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.

Detection of non-motorized road users, such as cyclists and pedestrians, is a challenging problem in collision warning/collision avoidance (CW/CA) systems as direct information (e.g. location, speed, and class) cannot be obtained from such users. In this paper, we propose a fusion of LIDAR data and a deep learning-based computer vision algorithm, to substantially improve the detection of regions of interest (ROIs) and subsequent identification of road users. Experimental results on the KITTI object detection benchmark quantify the effectiveness of incorporating LIDAR data with region-based deep convolutional networks. Thus our work provides another step towards the goal of designing safe and smart transportation systems of the future.

This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of objects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arisingfrom diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached, to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those/rom an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accurately enough for some task and, i f not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees of freedom and provides a practical means of estimating the relationships (position and orientation) among objects, as well as estimating the uncertainty associated with the relationships.

While the accuracy of feature measurements heavily depends on changing environmental conditions, studying the consequences of this fact in pattern recognition tasks has received relatively little attention to date. In this paper, we explicitly take feature measurement uncertainty into account and show how multimodal classification and learning rules should be adjusted to compensate for its effects. Our approach is particularly fruitful in multimodal fusion scenarios, such as audiovisual speech recognition, where multiple streams of complementary time-evolving features are integrated. For such applications, provided that the measurement noise uncertainty for each feature stream can be estimated, the proposed framework leads to highly adaptive multimodal fusion rules which are easy and efficient to implement. Our technique is widely applicable and can be transparently integrated with either synchronous or asynchronous multimodal sequence integration architectures. We further show that multimodal fusion methods relying on stream weights can naturally emerge from our scheme under certain assumptions; this connection provides valuable insights into the adaptivity properties of our multimodal uncertainty compensation approach. We show how these ideas can be practically applied for audiovisual speech recognition. In this context, we propose improved techniques for person-independent visual feature extraction and uncertainty estimation with active appearance models, and also discuss how enhanced audio features along with their uncertainty estimates can be effectively computed. We demonstrate the efficacy of our approach in audiovisual speech recognition experiments on the CUAVE database using either synchronous or asynchronous multimodal integration models.

This paper presents vehicle black box using camera image and 24GHz Frequency Modulation Continuous Wave (FMCW) radar. Currently, almost all of vehicle black boxes are recording driving data using a single camera. They have some problems on low image quality and narrow viewing angle. In addition, it is difficult to record a clean image when the weather is in bad condition due to heavy rain, snow and night. These problems may have some troubles to investigate car accident accurately. In this paper, we propose a novel black box fusing data from radar and cameras. We estimate the distance and velocity of obstacle using the Doppler Effect and FMCW radar. In particular, we compare various Direction of Arrival (DOA) algorithms for estimation of obstacle angle. The experimental results indicate that the Multiple Signal Classification (MUSIC) is more accurate compared to other methods. Moreover, we formulate the vehicle tracking image through the camera image processing and radar information. This image can be used to widen viewing angle and it may be helpful to investigate a car accident. The experimental study results indicate that the new black box overcome the disadvantages of the current black boxes and may be useful for safe driving and accurate accident investigation.

Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the object-free representation of arbitrarily shaped obstacles and free-space areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation.


Question: Categorization of sensor faults for the statistical modeling of environmental awareness in autonomous driving systems.

This paper offers a comprehensive overview of the evolution, core objectives, and future prospects of driver assistance systems, tracing their development over the past three decades. The universal human aspiration for mobility, characterized by safety, efficiency, and minimal ecological and economic impact, has driven the technological innovation in this field. However, the manifestation of this aspiration varies across different societies, influenced by cultural norms and levels of industrialization. The paper outlines a potential trajectory for the advancement of driver assistance systems, starting from their inception with proprioceptive sensors, exemplified by ABS and ESC, to the more sophisticated systems enabled by exteroceptive sensors like radar, video, and lidar. Although the ultimate goal of fully automated and cooperative traffic management remains a future vision, significant strides have been made through the implementation of systems designed to mitigate or prevent collisions in specific driving scenarios. Ongoing research efforts are focused on enhancing automated driving in urban environments and fostering cooperative driving, which encompasses vehicle-to-vehicle communication, collaborative operations among different vehicles, and the harmonious interaction between human drivers and vehicular artificial intelligence. These incremental advancements are considered pivotal during the transitional phase until the advent of fully autonomous driving capable of handling all conceivable traffic conditions. The future of driver assistance systems will be shaped by concurrent technological advancements, societal shifts, and market dynamics. The paper concludes by highlighting the current areas of research that are expected to propel the field forward.

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

We study the use of kinematic and dynamic vehicle models for model-based control design used in autonomous driving. In particular, we analyze the statistics of the forecast error of these two models by using experimental data. In addition, we study the effect of discretization on forecast error. We use the results of the first part to motivate the design of a controller for an autonomous vehicle using model predictive control (MPC) and a simple kinematic bicycle model. The proposed approach is less computationally expensive than existing methods which use vehicle tire models. Moreover it can be implemented at low vehicle speeds where tire models become singular. Experimental results show the effectiveness of the proposed approach at various speeds on windy roads.

This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.

Intelligent fault diagnosis of machines for early recognition of faults saves industry from heavy losses occurring due to machine breakdowns. This paper proposes a process with a generic data mining model that can be used for developing acoustic signal-based fault diagnosis systems for reciprocating air compressors. The process includes details of data acquisition, sensitive position analysis for deciding suitable sensor locations, signal pre-processing, feature extraction, feature selection, and a classification approach. This process was validated by developing a real time fault diagnosis system on a reciprocating type air compressor having 8 designated states, including one healthy state, and 7 faulty states. The system was able to accurately detect all the faults by analyzing acoustic recordings taken from just a single position. Additionally, thorough analysis has been presented where performance of the system is compared while varying feature selection techniques, the number of selected features, and multiclass decomposition algorithms meant for binary classifiers.

Predicting the reliability of a system in its actual life-cycle conditions and estimating its time to failure is helpful in decision making to mitigate system risks. There are three approaches to prognostics: the physics-of-failure approach, the data-driven approach, and the fusion approach. A key requirement in all these approaches is identification of the appropriate parameter(s) to monitor to gather data that can be used to assess impending failure. This paper presents a physics-of-failure approach, which uses failure modes, mechanisms and effects analysis (FMMEA) to enhance prognostics planning and implementation. This paper also presents the fusion approach to prognostics and the applicability of FMMEA to this approach. An example case of generating FMMEA information and using that to identify appropriate parameters to monitor is presented.

In sensing applications where multiple sensors observe the same scene, fusing sensor outputs can provide improved results. However, if some of the sensors are providing lower quality outputs, e.g. when one or more sensors has a poor signal-tonoise ratio (SNR) and therefore provides very noisy data, the fused results can be degraded. In this work, a multi-sensor conflict measure is proposed which estimates multi-sensor conflict by representing each sensor output as interval-valued information and examines the sensor output overlaps on all possible n-tuple sensor combinations. The conflict is based on the sizes of the intervals and how many sensors output values lie in these intervals. In this work, conflict is defined in terms of how little the output from multiple sensors overlap. That is, high degrees of overlap mean low sensor conflict, while low degrees of overlap mean high conflict. This work is a preliminary step towards a robust conflict and sensor fusion framework. In addition, a sensor fusion algorithm is proposed based on a weighted sum of sensor outputs, where the weights for each sensor diminish as the conflict measure increases. The proposed methods can be utilized to (1) assess a measure of multi-sensor conflict, and (2) improve sensor output fusion by lessening weighting for sensors with high conflict. Using this measure, a simulated example is given to explain the mechanics of calculating the conflict measure, and stereo camera 3D outputs are analyzed and fused. In the stereo camera case, the sensor output is corrupted by additive impulse noise, DC offset, and Gaussian noise. Impulse noise is common in sensors due to intermittent interference, a DC offset a sensor bias or registration error, and Gaussian noise represents a sensor output with low SNR. The results show that sensor output fusion based on the conflict measure shows improved accuracy over a simple averaging fusion strategy.

Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.

Active safety systems are based upon the accurate and fast estimation of the value of important dynamical variables such as forces, load transfer, actual tire-road friction (kinetic friction) muk, and maximum tire-road friction available (potential friction) mup. Measuring these parameters directly from tires offers the potential for improving significantly the performance of active safety systems. We present a distributed architecture for a data-acquisition system that is based on a number of complex intelligent sensors inside the tire that form a wireless sensor network with coordination nodes placed on the body of the car. The design of this system has been extremely challenging due to the very limited available energy combined with strict application requirements for data rate, delay, size, weight, and reliability in a highly dynamical environment. Moreover, it required expertise in multiple engineering disciplines, including control-system design, signal processing, integrated-circuit design, communications, real-time software design, antenna design, energy scavenging, and system assembly.

Autonomous driving poses unique challenges for vehicle environment perception in complex driving environments. Due to the uncertain nature of the vehicle environment and imperfection of any perception framework, multiple stages of estimation might be necessary to achieve the desired performance. However, it is highly possible that the estimation of one stage might result in output estimates with significant auto/cross-correlation, which would pass to another stage. In such situations, a decorrelation procedure is required. We present an object tracking approach taking into consideration the auto-correlation (of the velocity components) introduced by an upfront dynamic occupancy gridmap system. More specifically, we use a linear state-space system to approximately “reconstruct” the nonlinear estimation/mapping procedure for the purpose of auto-correlation quantification. We focus on demonstrating an estimation improvement of the proposed decorrelation tracker over a direct Kalman filter (i.e., KF that ignores the auto-correlation). For this we recorded several scenarios of different target motion behaviors. It is shown that the decorrelation tracker indeed introduces noticable estimation improvement in particularly velocity space if the target moves in a relatively smooth manner.


Question: Categorization of sensor inaccuracies for the statistical emulation of environmental awareness in autonomous driving systems.

Introducing USARSim: An Advanced Simulator for Robotic Research and Education

This article introduces USARSim, an open-source, high-fidelity robot simulator designed for both research and educational purposes. USARSim stands out from other simulators through several unique features, particularly its role as the simulation engine for the virtual robots competition within the Robocup initiative. The simulator's architecture is detailed, highlighting its suitability for a variety of applications, including education, research, and competitions. The paper also provides illustrative examples of its usage, offering a thorough guide for individuals interested in exploring robot simulations in these domains. USARSim's versatility and high-quality simulation capabilities make it a valuable tool for anyone looking to delve into robotic simulations for educational or research purposes.

The emergence of Mobile Mapping Systems (MMS) has set a marked paradigm in the photogrammetric and mapping community that has not only facilitated comprehensive 3D mapping of different environments but has also paved way for new aspects of applied research in this direction. Out of the many essential blocks that make these MMS a viable tool for mapping, the positioning and orientation module is considered to be a crucial yet an expensive component. The integration of such a module with mapping sensors has allowed for the extensive implementation of such systems to provide high-quality maps. However, while such systems do not lack in system robustness and performance in general, the deployment of these systems is restricted to applications and environments where a consistent availability of GNSS signals is assured. Extending these MMS to GNSS-denied areas, such as indoor environments, is therefore quite challenging and necessitates the development of an alternative module that can act as a viable substitute to GNSS/INS for system operation without having to resort to an exhaustive modification of the same to function in GNSS-denied locations. In this research, such a case has been considered for the implementation of an indoor MMS using an Unmanned Ground Vehicle (UGV) and a 3D laser scanner for the task of generating high density maps of GNSS-denied indoor areas. To mitigate the absence of GNSS data, this paper proposes a Pseudo-GNSS/INS module integrated framework which utilizes probabilistic Simultaneous Localization and Mapping (SLAM) techniques to estimate the platform pose and heading from 3D laser scanner data. This proposed framework has been implemented based on three major notions: (i) using geometric methods for sparse point cloud extraction to carry out real-time SLAM, (ii) generating position data and geo-referencing signals from these real-time SLAM pose estimates, and (iii) carrying out the entire operation through use of a single 3D mapping sensor. The final geo-referenced point cloud can then be generated through post-processing by the Iterative Closest Projected Point (ICPP) registration technique which also diminishes the effect of sensor measurement noise. The implementation, performance and results of the proposed MMS framework for an indoor mapping system have been presented in this paper that demonstrate the ability of this Pseudo-GNSS/INS framework to operate flexibly in GNSS-denied areas.

This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method reduces sensitivity to the position and orientation of the sensor on the body, which is inherent in traditional methods, by transforming the observed signals to a “virtual” sensor orientation. By means of this computationally low-cost transform, the inputs to the classification algorithm are made invariant to sensor orientation, despite the signals being recorded from arbitrary sensor placements. Classification results show that improved performance, in terms of both precision and recall, is achieved with the transformed signals, relative to classification using raw sensor signals, and the algorithm performs competitively compared to the state-of-the-art. Activity recognition using data from a sensor with completely unknown orientation is shown to perform very well over a long term recording in a real-life setting.

Algorithms for mobile robots, such as navigation, mapping, and SLAM, require proper modelling of environment. Our paper presents an automatic tool that allows creating a realistic 3D-landscape in Gazebo simulation, which is based on the real sensor-based experimental results. The tool provides an occupancy grid map automatic filtering and import to Gazebo framework as a heightmap and enables to configure the settings for created simulation environment. In addition, the tool is capable to create a 3D Gazebo map from any arbitrary image.

In this paper, we present the concept of a collision prevention assistant, a system that we believe can significantly contribute to road safety. We propose a new situation assessment algorithm which is tailored to the action of braking and that further accounts for the nonlinearities that arise when vehicles cut out or come to a standstill. The effect of sensor uncertainty on the performance of the proposed algorithm is modelled using a Markov chain and analyzed by means of a Monte Carlo simulation on a typical traffic situation.

Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues,including: • reaching a static target (Baturone, et al., 2004, Boada, et al., 2005, El Hajjaji & Bentalba, 2003, Maeda et al., 1991, Chen & Ozguner, 2005), • tracking moving targets (Ollero, et al., 2001), • maintaining stable vehicular velocity (Holzmann et al., 1998, Nobe & Wang, 2001), • following a lane or a wall (Rosa & Garcia-Alegre, 1990, Hessburg & Tomizuka, 1994, Peng & Tomizuka, 1993) and, • avoiding collision with static and dynamic obstacles (Baturone, et al., 2004, Murphy, 2001, Godjevac, et al., 2001, Seraji, 2005, Lee & Wang, 1994, Wheeler & Shoureshi, 1994, Ye & Wang, 2001). Several researchers combined fuzzy logic controllers with various learning techniques, such as: • supervised learning method (Godjevac, 2001), • evolutionary method (Hoffman, 2001, Kim, et al., 2001), • neural network (Pasquier, et al., 2001, Cang, et al., 2003), • reinforcement learning (Dai, et al., 2005) and, • optimization methods (Hong, 1997, Sanchez, et al., 1999).

Driving through dynamically changing traffic scenarios is a highly challenging task for autonomous vehicles, especially on urban roadways. Prediction of surrounding vehicles’ driving behaviors plays a crucial role in autonomous vehicles. Most traditional driving behavior prediction models work only for a specific traffic scenario and cannot be adapted to different scenarios. In addition, priori driving knowledge was never considered sufficiently. This study proposes a novel scenario-adaptive approach to solve these problems. A novel ontology model was developed to model traffic scenarios. Continuous features of driving behavior were learned by Hidden Markov Models (HMMs). Then, a knowledge base was constructed to specify the model adaptation strategies and store priori probabilities based on the scenario’s characteristics. Finally, the target vehicle’s future behavior was predicted considering both a posteriori probabilities and a priori probabilities. The proposed approach was sufficiently evaluated with a real autonomous vehicle. The application scope of traditional models can be extended to a variety of scenarios, while the prediction performance can be improved by the consideration of priori knowledge. For lane-changing behaviors, the prediction time horizon can be extended by up to 56% (0.76 s) on average. Meanwhile, long-term prediction precision can be enhanced by over 26%.

Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.

User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.

The vision of autonomous driving is piecewise becoming reality. Still the problem of executing the driving task in a safe and comfortable way in all possible environments, for instance highway, city or rural road scenarios is a challenging task. In this paper we present a novel approach to planning trajectories for autonomous vehicles. Hereby we focus on the problem of planning a trajectory, given a specific behavior option, e.g. merging into a specific gap at a highway entrance or a roundabout. Therefore we explicitly take arbitrary road geometry and prediction information of other traffic participants into account. We extend former contributions in this field by providing a flexible problem description and a trajectory planner without specialization to distinct classes of maneuvers beforehand. Using a carefully chosen representation of the dynamic free space, the method is capable of considering multiple lanes including the predicted dynamics of other traffic participants, while being real-time capable at the same time. The combination of those properties in one general planning method represents the novelty of the proposed method. We demonstrate the capability of our algorithm to plan safe trajectories in simulation and in real traffic in real-time.

With near-ubiquitous availability of smartphones equipped with a wide variety of sensors, research in building contextaware services has been growing. However, despite a large number of services proposed and developed as research prototypes, the number of truly context-aware applications available in smartphone applications market is quite limited. A major barrier for the large-scale proliferation of context aware applications is poor accuracy. This paper addresses one of the key reasons for this poor accuracy, which is the impact of smartphone positions. Users carry their smartphones in different positions such as holding in their hand, keeping inside their pants or jacket pocket, keeping in their purse, and so on. This paper addresses the issue of poor application accuracy due to varying smartphone positions. It first shows that smartphone positions significantly affect the values of the sensor data being collected by a context aware application, and this in turn has a significant impact on the accuracy of the application. Next, it describes the design and prototype development of a smartphone position discovery service that accurately detects a smartphone position. This service is based on the sensor data collected from carefully chosen sensors and runs orthogonal to any other context aware application or service. Finally, the paper demonstrates that the accuracy of an existing context aware service or application is significantly enhanced when run in conjunction with the proposed smartphone position discovery service.