11,459 research outputs found

    Security and Privacy Problems in Voice Assistant Applications: A Survey

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    Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.Comment: 5 figure

    Testing the nomological network for the Personal Engagement Model

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    The study of employee engagement has been a key focus of management for over three decades. The academic literature on engagement has generated multiple definitions but there are two primary models of engagement: the Personal Engagement Model of Kahn (1990), and the Work Engagement Model (WEM) of Schaufeli et al., (2002). While the former is cited by most authors as the seminal work on engagement, research has tended to focus on elements of the model and most theoretical work on engagement has predominantly used the WEM to consider the topic. The purpose of this study was to test all the elements of the nomological network of the PEM to determine whether the complete model of personal engagement is viable. This was done using data from a large, complex public sector workforce. Survey questions were designed to test each element of the PEM and administered to a sample of the workforce (n = 3,103). The scales were tested and refined using confirmatory factor analysis and then the model was tested determine the structure of the nomological network. This was validated and the generalisability of the final model was tested across different work and organisational types. The results showed that the PEM is viable but there were differences from what was originally proposed by Kahn (1990). Specifically, of the three psychological conditions deemed necessary for engagement to occur, meaningfulness, safety, and availability, only meaningfulness was found to contribute to employee engagement. The model demonstrated that employees experience meaningfulness through both the nature of the work that they do and the organisation within which they do their work. Finally, the findings were replicated across employees in different work types and different organisational types. This thesis makes five contributions to the engagement paradigm. It advances engagement theory by testing the PEM and showing that it is an adequate representation of engagement. A model for testing the causal mechanism for engagement has been articulated, demonstrating that meaningfulness in work is a primary mechanism for engagement. The research has shown the key aspects of the workplace in which employees experience meaningfulness, the nature of the work that they do and the organisation within which they do it. It has demonstrated that this is consistent across organisations and the type of work. Finally, it has developed a reliable measure of the different elements of the PEM which will support future research in this area

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Multiple criteria approach applied to digital transformation in fashion stores: the case of physical retailers in Spain

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    This research is funded by the Spanish State Research Agency, as part of the project PID2019103880RB-I00/AEI/10.13039/501100011033, and by the Andalusian Government, as part of the project P20_00673.In a very open competitive context where pure online players are consistently gaining market share, the use of digital devices is a steady trend which is penetrating physical retail stores as a tool for retailers to improve customer experience and increase engagement. This need has increased with the COVID-19 pandemic as electronic devices in physical stores reduce the contact between people providing a greater sense of health safety, hence improving the customer experience. This work develops a multiple-criteria decision-making model for retailers who want to digitize their physical stores, providing a systematic approach to manage investment priorities in the organization. Important decisions should involve all different areas of the organization: Finance, Clients, Internal Processes and Learning & Growth departments. This strategic decision can be made hierarchically to obtain consistent decisions, also the use of the Order Weighted Average operator allows for alternative scenarios to be presented and agreed among the different areas of the business. The authors develop a use case for a Spanish fashion retailer. In the most widely agreed scenario the preferred devices were more technologically complex and expensive, while in the scenarios where the head of Finance is more predominant, cheaper and simpler devices were selected.Spanish Government PID2019103880RB-I00/AEI/10.13039/501100011033Andalusian Government P20_0067

    Mine the right process – towards a method for selecting a suitable use case for process mining adoption

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    Process mining (PM) is a big data analytics technology assisting organizations in process optimization by creating insights from event log data available in existing information systems. Although research on PM utilization exists, literature on the adoption phase is scarce. Hence, organizations lack an understanding of how to determine suitable use cases. Accordingly, we followed a design science-based approach and systematically identified twenty criteria, e.g., process variants, processual weaknesses, and analytical skills, to select suitable use cases for PM adoption. The criteria were evaluated with Celonis and Munich Airport and guide PM vendors, organizations, and consultancies through the evaluation process. Hence, we contribute to the early steps of PM diffusion by assisting in determining its consequences and founding the adoption decision. Future research may consider the criteria as a research framework to investigate their effects on the adoption decision

    TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH

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    Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)

    Consolidation of Urban Freight Transport – Models and Algorithms

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    Urban freight transport is an indispensable component of economic and social life in cities. Compared to other types of transport, however, it contributes disproportionately to the negative impacts of traffic. As a result, urban freight transport is closely linked to social, environmental, and economic challenges. Managing urban freight transport and addressing these issues poses challenges not only for local city administrations but also for companies, such as logistics service providers (LSPs). Numerous policy measures and company-driven initiatives exist in the area of urban freight transport to overcome these challenges. One central approach is the consolidation of urban freight transport. This dissertation focuses on urban consolidation centers (UCCs) which are a widely studied and applied measure in urban freight transport. The fundamental idea of UCCs is to consolidate freight transport across companies in logistics facilities close to an urban area in order to increase the efficiency of vehicles delivering goods within the urban area. Although the concept has been researched and tested for several decades and it was shown that it can reduce the negative externalities of freight transport in cities, in practice many UCCs struggle with a lack of business participation and financial difficulties. This dissertation is primarily focused on the costs and savings associated with the use of UCCs from the perspective of LSPs. The cost-effectiveness of UCC use, which is also referred to as cost attractiveness, can be seen as a crucial condition for LSPs to be interested in using UCC systems. The overall objective of this dissertation is two-fold. First, it aims to develop models to provide decision support for evaluating the cost-effectiveness of using UCCs. Second, it aims to analyze the impacts of urban freight transport regulations and operational characteristics on the cost attractiveness of using UCCs from the perspective of LSPs. In this context, a distinction is made between UCCs that are jointly operated by a group of LSPs and UCCs that are operated by third parties who offer their urban transport service for a fee. The main body of this dissertation is based on three research papers. The first paper focuses on jointly-operated UCCs that are operated by a group of cooperating LSPs. It presents a simulation model to analyze the financial impacts on LSPs participating in such a scheme. In doing so, a particular focus is placed on urban freight transport regulations. A case study is used to analyze the operation of a jointly-operated UCC for scenarios involving three freight transport regulations. The second and third papers take on a different perspective on UCCs by focusing on third-party operated UCCs. In contrast to the first paper, the second and third papers present an evaluation approach in which the decision to use UCCs is integrated with the vehicle route planning of LSPs. In addition to addressing the basic version of this integrated routing problem, known as the vehicle routing problem with transshipment facilities (VRPTF), the second paper presents problem extensions that incorporate time windows, fleet size and mix decisions, and refined objective functions. To heuristically solve the basic problem and the new problem variants, an adaptive large neighborhood search (ALNS) heuristic with embedded local search heuristic and set partitioning problem (SPP) is presented. Furthermore, various factors influencing the cost attractiveness of UCCs, including time windows and usage fees, are analyzed using a real-world case study. The third paper extends the work of the second paper and incorporates daily and entrance-based city toll schemes and enables multi-trip routing. A mixed-integer linear programming (MILP) formulation of the resulting problem is proposed, as well as an ALNS solution heuristic. Moreover, a real-world case study with three European cities is used to analyze the impact of the two city toll systems in different operational contexts

    A review of game theory and multi-criteria decision-making methods with 10 application to the oil production and price

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    The oil production and price issues have been discovered a long time ago, and always be a continuous problem to the globe especially during the current global threats of the coronavirus pandemic. This paper provides a literature review that involves game theory and multi-criteria decision-making (MCDM) methods with its applications to oil production and price problems. This paper identifies and analyses the use of the game theory and MCDM methods on oil production and price to compare the situation studied, to determine the model that has been used, the trend of past literature and also the details of the basic elements for the game theory framework. Therefore, the oil production and price problem using the game theory and MCDM methods are reviewed and numerous applications studied from the past works of literature are highlighted. The trend of oil production and price which used the game theory and MCDM methods based on the year 2001 till 2021 is still lacking sources from the Web of Science and Scopus databases. The main contribution of the recent study is the employment of the game theory and MCDM methods to the oil production and price problem

    Valuing Intrinsic and Instrumental Preferences for Privacy

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    I empirically separate two components in a consumer’s privacy preference. The intrinsic component is a “taste” for privacy, a utility primitive. The instrumental component comes from the consumer’s anticipated economic loss from revealing his private information to the firm, and arises endogenously from a firm’s usage of consumer data. Combining an experiment and a structural model, I measure the revealed preferences separately for each component. Intrinsic preferences have seemingly small mean values, ranging from 0.14to0.14 to 2.37 per demographic variable. Meanwhile, they are highly heterogeneous across consumers and categories of data: The valuations of consumers at the right tail often exceed the firm’s valuation of consumer data. Consumers’ self-selection into data sharing depends on the respective magnitudes and correlation between the two preference components, and often deviate from the “low types are more willing to hide” argument. Through counterfactual analysis, I show how this more nuanced selection pattern changes a firm’s inference from consumers’ privacy decisions and its data buying strategy.https://tesarylin.github.io/uploads/JMP-Tesary.pdfFirst author draf
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