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Role of artificial intelligence and robotics to foster the touchless travel during a pandemic: a review and research agenda

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INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT 2021年第11期33卷 4079-4098页

作者： Gaur, Loveleen Afaq, Anam Singh, Gurmeet Dwivedi, Yogesh Kumar Amity Univ Amity Int Business Sch Naida India Univ South Pacific Sch Business & Management Suva Fiji Swansea Univ Sch Management Swansea W Glam Wales

Purpose The hospitality industry experienced an unanticipated challenge from the COVID-19 pandemic. However, research in this area is scarce. Accordingly, this study aims to unfold a three-angled research agenda to intensify the knowledge advancement in the hospitality sector. It proposes a theoretical framework by extending the protection motivation theory (PMT) to explain the guest's intent to adopt artificial intelligence (AI) and robotics as a protective measure in reaction to COVID-19. Design/methodology/approach The research is centered on outlining the pertinent literature on hospitality management practices and the guest's transformed behavior during the current crisis. This study intends to identify a research agenda based on investigating hospitality service trends in today's changing times. Findings The study sets out a research agenda that includes three dimensions as follows: AI and robotics, cleanliness and sanitation and health care and wellness. This study's findings suggest that AI and robotics may bring out definite research directions at the connection of health crisis and hospitality management, taking into account the COVID-19 crisis. Practical implications The suggested research areas are anticipated to propel the knowledge base and help the hospitality industry retrieve the COVID-19 crisis through digital transformation. AI and robotics are at the cusp of invaluable advancement that can revive the hotels while re-establish guests' confidence in safe hotel practices. The proposed research areas are likely to impart pragmatic lessons to the hospitality industry to fight against disruptive situations. Originality/value This study stands out to be pioneer research that incorporated AI and robotics to expand the PMT and highlights how behavioral choices during emergencies can bring technological revolution.

关键词： Hospitality artificial intelligence and robotics COVID-19 pandemic Cleanliness and sanitation Health care and wellness

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Mapping the service recovery research landscape: A bibliometric-based systematic review

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PSYCHOLOGY & MARKETING 2023年第10期40卷 2060-2087页

作者： Mir, Mahmood Ashraf, Rohail Syed, Tahir Abbas Ali, Sara Nawaz, Raheel Manchester Metropolitan Univ Dept Operat Technol Events & Hospitality Managemen Manchester England Manchester Metropolitan Univ Dept Mkt Retail & Tourism Manchester England Univ Manchester Dept Management Sci & Mkt Manchester England Natl Univ Sci & Technol NUST Dept Robot & Artificial Intelligence Islamabad Pakistan Staffordshire Univ Pro Vice Chancellor Dept Digital Transformat Stoke On Trent Staffs England

With businesses under increasing pressure to provide excellent customer service, postfailure recovery strategies have become critical for long-term customer satisfaction and loyalty. The domain of service recovery has extensively been examined in academia;however, systematic studies that provide a consolidated overview remains scant. To this end, we provide a systematic review and synthesis of service recovery literature by conducting a bibliometric-based cocitation analysis of 24,741 cited references from 1020 articles from across disciplines. The study identifies 10 major research clusters that represent different research streams of service recovery and explores their intellectual foundations. In addition, the research presents a conceptual framework to serve as a parsimonious guide for both practitioners and researchers. Furthermore, the study reveals a number of gaps in the existing literature and suggests promising directions for further investigation, including but not limited to: expanding methodological horizons in service recovery research, understanding service recovery mechanisms in Metaverse and synthetic environments, globalizing service recovery research, revitalizing service recovery processes in the age of artificial intelligence and robotics, investigating service recovery as an investment, and exploring service recovery in shared economies. Notably, this study serves managers, firstly, by providing them with a parsimonious structure of service recovery field that could help identify areas of improvement in their own service recovery systems and, secondly, by highlighting areas where academic knowledge base could inform industry solutions.

关键词： artificial intelligence and robotics bibliometrics metaverse service failure service recovery sharing economy systematic review

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COMPACT REPRESENTATIONS OF UNCERTAINTY IN CLUSTERING

COMPACT REPRESENTATIONS OF UNCERTAINTY IN CLUSTERING

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作者： Craig Stuart Greenberg University of Massachusetts Amherst

学位级别：博士

Flat clustering and hierarchical clustering are two fundamental tasks, often used to discover meaningful structures in data, such as subtypes of cancer, phylogenetic relationships, taxonomies of concepts, and cascades of particle decays in particle physics. When multiple clusterings of the data are possible, it is useful to represent uncertainty in clustering through various probabilistic quantities, such as the distribution over partitions or tree structures, and the marginal probabilities of subpartitions or subtrees. Many compact representations exist for structured prediction problems, enabling the efficient computation of probability distributions, e.g., a trellis structure and corresponding Forward-Backward algorithm for Markov models that model sequences. However, no such representation has been proposed for either flat or hierarchical clustering models. In this thesis, we present our work developing data structures and algorithms for computing probability distributions over flat and hierarchical clusterings, as well as for finding maximum a posteriori (MAP) flat and hierarchical clusterings, and various marginal probabilities, as given by a wide range of energy-based clustering models. First, we describe a trellis structure that compactly represents distributions over flat or hierarchical clusterings. We also describe related data structures that represent approximate distributions. We then present algorithms that, using these structures, allow us to compute the partition function, MAP clustering, and the marginal proba- bilities of a cluster (and sub-hierarchy, in the case of hierarchical clustering) exactly. We also show how these and related algorithms can be used to approximate these values, and analyze the time and space complexity of our proposed methods. We demonstrate the utility of our approaches using various synthetic data of interest as well as in two real world applications, namely particle physics at the Large Hadron Collider at CERN and in can

关键词： artificial intelligence and robotics Discrete Mathematics and Combinatorics Theory and Algorithms

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Concentration Inequalities in the Wild: Case Studies in Blockchain & Reinforcement Learning

Concentration Inequalities in the Wild: Case Studies in Bloc...

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作者： A. Pinar Ozisik University of Massachusetts Amherst

学位级别：博士

Concentration inequalities (CIs) are a powerful tool that provide probability bounds on how a random variable deviates from its expectation. In this dissertation, first I describe a blockchain protocol that I have developed, called Graphene, which uses CIs to provide probabilistic guarantees on performance. Second, I analyze the extent to which CIs are robust when the assumptions they require are violated, using Reinforcement Learning (RL) as the domain. Graphene is a method for interactive set reconciliation among peers in blockchains and related distributed systems. Through the novel combination of a Bloom filter and an Invertible Bloom Lookup Table, Graphene uses a fraction of the network bandwidth used by deployed work for one- and two-way synchronization. It is a fast and implementation-independent algorithm that uses CIs for parameterizing an IBLT so that it is optimal in size for a given desired decode rate. I characterize performance improvements through analysis, detailed simulation, and deployment results for Bitcoin Cash, a prominent cryptocurrency. Implementations of Graphene, IBLTs, and the IBLT optimization algorithm are all open-source code. Second, I analyze the extent to which existing methods rely on accurate training data for a specific class of RL algorithms, known as Safe and Seldonian RL. Several Seldonian RL algorithms have a component called the safety test, which uses CIs to lower bound the performance of a new policy with training data collected from another policy. I introduce a new measure of security to quantify the susceptibility to corruptions in training data, and show that a couple of Seldonian RL methods are extremely sensitive to even a few data corruptions, completely breaking the probability bounds guaranteed by CIs. I then introduce a new algorithm, called Panacea, that is more robust against data corruptions, and demonstrate its usage in practice on some RL problems, including a grid-world and diabetes treatment simulation.

关键词： artificial intelligence and robotics Computer Sciences OS and Networks

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Sociolinguistically Driven Approaches for Just Natural Language Processing

Sociolinguistically Driven Approaches for Just Natural Langu...

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作者： Su Lin Blodgett University of Massachusetts Amherst

学位级别：博士

Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these language technologies do not accrue evenly to all users, and indeed they can be harmful; NLP systems reproduce stereotypes, prevent speakers of non-standard language varieties from participating fully in public discourse, and re-inscribe historical patterns of linguistic stigmatization and discrimination. How harms arise in NLP systems, and who is harmed by them, can only be understood at the intersection of work on NLP, fairness and justice in machine learning, and the relationships between language and social justice. In this thesis, we propose to address two questions at this intersection: i) How can we conceptualize harms arising from NLP systems?, and ii) How can we quantify such harms? We propose the following contributions. First, we contribute a model in order to collect the first large dataset of African American Language (AAL)-like social media text. We use the dataset to quantify the performance of two types of NLP systems, identifying disparities in model performance between Mainstream U.S. English (MUSE)- and AAL-like text. Turning to the landscape of bias in NLP more broadly, we then provide a critical survey of the emerging literature on bias in NLP and identify its limitations. Drawing on work across sociology, sociolinguistics, linguistic anthropology, social psychology, and education, we provide an account of the relationships between language and injustice, propose a taxonomy of harms arising from NLP systems grounded in those relationships, and propose a set of guiding research questions for work on bias in NLP. Finally, we adapt the measurement modeling framework from the quantitative social sciences to effectively evaluate approaches for quantifying bias in NLP systems. We conclude with a discussion of recent work on bias through the lens of style in NLP, raising a set of normative questions for future work.

关键词： artificial intelligence and robotics

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Neural Methods for Answer Passage Retrieval over Sparse Collections

Neural Methods for Answer Passage Retrieval over Sparse Coll...

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作者： Daniel Cohen University of Massachusetts Amherst

学位级别：博士

Recent advances in machine learning have allowed information retrieval (IR) techniques to advance beyond the stage of handcrafting domain specific features. Specifically, deep neural models incorporate varying levels of features to learn whether a document answers the information need of a query. However, these neural models rely on a large number of parameters to successfully learn a relation between a query and a relevant document. This reliance on a large number of parameters, combined with the current methods of optimization relying on small updates necessitates numerous samples to allow the neural model to converge on an effective relevance function. This presents a significant obstacle in the realm of IR as relevance judgements are often sparse or noisy and combined with a large class imbalance. This is especially true for short text retrieval where there is often only one relevant passage. This problem is exacerbated when training these artificial neural networks, as excessive negative sampling can result in poor performance. Thus, we propose approaching this task through multiple avenues and examining their effectiveness on a non-factoid question answering (QA) task. We first propose learning local embeddings specific to the relevance information of the collection to improve performance of an upstream neural model. In doing so, we find significantly improved results over standard pre-trained embeddings, despite only developing the embeddings on a small collection which would not be sufficient for a full language model. Leveraging this local representation, and inspired by recent work in machine translation, we introduce a hybrid embedding based model that incorporates both pre-trained embeddings while dynamically constructing local representations from character embeddings. The hybrid approach relies on pre-trained embeddings to achieve an effective retrieval model, and continually adjusts its character level abstraction to fit a local representation. We n

关键词： artificial intelligence and robotics Computer Sciences Databases and Information Systems

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Network interlinkages between artificial intelligence and green energy dynamics during the War in a Pandemic: An application of Bayesian vector heterogeneous autoregressions

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Environmental Challenges 2023年 13卷

作者： Loi, Ngo Thang Huyen, Nguyen Thi Thanh Thanh, To Trung Ha, Le Thanh Faculty of Economics National Economics University Viet Nam Faculty of Planning and Development National Economics University Viet Nam Faculty of Environmental Climate Change and Urban Studies National Economics University Viet Nam Department of Research Management National Economics University Viet Nam

We investigate time-varying network interlinkages between artificial intelligence development and green energy market dynamics during the War in a Pandemic. While regarding AI development, we use First Trust NASDAQ artificial intelligence and robotics ETF, Global X robotics and artificial intelligence and ishares robotics and artificial intelligence, and the green energy market, including green bonds, clean energy, wind energy, solar energy, natural gas, and crude oil using seven Bayesian vector heterogeneous autoregression fashions. During the short, medium, and long run, this paper differentiates dynamically between network interlinkages between these markets. We found some noteworthy results in our study. We show that network interlinkages exhibit remarkable differences over time. Interlinkages between networks are increased in the short, medium, and long term due to transient events occurring in markets during the studied period. As a result of the ongoing COVID-19 epidemic and the Russia-Ukraine conflict, the long-term ties within the system are significantly impacted. Additionally, based on net directional linkages, market directional links indicate a shift in roles (from shock receiver to shock transmitter) during the epidemic and at the beginning of the Russia-Ukraine conflict. Observations of short- and medium-term trends reveal that all three indexes for artificial intelligence development are shock receivers from outside, which is transmitted to the green energy sector. The results indicate that artificial intelligence development persists as shock receivers in terms of long-horizon measures but become shocking transmitters during the illness crisis of COVID-19 (from January 2019 to January 2020) and the Russia-Ukraine conflict (from early 2022). © 2023

关键词： artificial intelligence and robotics Bayesian vector heterogeneous autoregressions COVID-19 Energy dynamics Network interlinkages Russia-Ukraine conflict

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Geometric Representation Learning

Geometric Representation Learning

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作者： Luke Vilnis University of Massachusetts Amherst

学位级别：博士

Vector embedding models are a cornerstone of modern machine learning methods for knowledge representation and reasoning. These methods aim to turn semantic questions into geometric questions by learning representations of concepts and other domain objects in a lower-dimensional vector space. In that spirit, this work advocates for density- and region-based representation learning. Embedding domain elements as geometric objects beyond a single point enables us to naturally represent breadth and polysemy, make asymmetric comparisons, answer complex queries, and provides a strong inductive bias when labeled data is scarce. We present a model for word representation using Gaussian densities, enabling asymmetric entailment judgments between concepts, and a probabilistic model for weighted transitive relations and multivariate discrete data based on a lattice of axis-aligned hyperrectangle representations (boxes). We explore the suitability of these embedding methods in different regimes of sparsity, edge weight, correlation, and independence structure, as well as extensions of the representation and different optimization strategies. We make a theoretical investigation of the representational power of the box lattice, and propose extensions to address shortcomings in modeling difficult distributions and graphs.

关键词： Applied Statistics artificial intelligence and robotics Data Science

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Utilizing Graph Structure for Machine Learning

Utilizing Graph Structure for Machine Learning

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作者： Stefan Dernbach University of Massachusetts Amherst

学位级别：博士

The information age has led to an explosion in the size and availability of data. This data often exhibits graph-structure that is either explicitly defined, as in the web of a social network, or is implicitly defined and can be determined by measuring similarity between objects. Utilizing this graph-structure allows for the design of machine learning algorithms that reflect not only the attributes of individual objects but their relationships to every other object in the domain as well. This thesis investigates three machine learning problems and proposes novel methods that leverage the graph-structure inherent in the tasks. Quantum walk neural networks are classical neural nets that use quantum random walks for classifying and regressing on graphs. Asymmetric directed node embeddings are another neural network architecture designed to embed the nodes of a directed graph into a vector space. Filtered manifold alignment is a novel two-step approach to domain adaptation.

关键词： artificial intelligence and robotics

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Effects of COVID-19 on hotel marketing and management: a perspective article

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INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT 2020年第8期32卷 2563-2573页

作者： Jiang, Yangyang Wen, Jun Univ Nottingham Ningbo China Nottingham Univ Business Sch China Ningbo Peoples R China Edith Cowan Univ Sch Business & Law Joondalup Australia

Purpose This paper aims to discuss the effects of COVID-19 on hotel marketing and management practices and outlines a three-pronged research agenda to stimulate knowledge development in the hotel sector. Design/methodology/approach This paper is based on an overview of the relevant literature on hotel marketing and management and the hotel guest behavior. The authors also investigated hospitality service trends to propose a research agenda. Findings This paper presents a research agenda from three dimensions - artificial intelligence (AI) and robotics, hygiene and cleanliness and health and health care. First, different types of AI (mechanical, thinking and feeling) might open up distinct research streams at the intersection of health crises and hotel management, in light of the COVID-19 pandemic. Additionally, this paper recommends that researchers move beyond typical perspectives on the antecedents and outcomes of hotel hygiene and cleanliness to delve into guests' perceptions of the cleanliness of specific hotel surfaces. Furthermore, a more in-depth analysis is warranted about the evolving relationship between hotels and the health-care sector. Practical implications The recommended research areas are intended to advance the knowledge base to help hotels recover from the COVID-19 pandemic. The suggested research streams are expected to provide actionable insights to promote the development and sustainability of the hotel sector. Originality/value This paper appears to be a frontier study, critically examining possible effects of the COVID-19 pandemic on hotel marketing and management practices and how hoteliers may respond to such challenges to recover after this pandemic.

关键词： artificial intelligence and robotics Hygiene and cleanliness Health and health care Hotel management COVID-19 pandemic

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