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The growing number and diversity of applications make malware detection and app recommendation for users more challenging. In this work, we design a framework APPDNA to automatically generate a compact representation for each app to comprehensively profile its behaviors. The versatile representation can be generated once for each app, and then be used for a wide variety of objectives, including malware detection, app categorization and app version detection, etc. We propose to conduct a function-call-graph-based app profiling scheme based on a comprehensive and deep understanding of an app's behaviors. We design a graph-encoding method to convert a large function call graph to a 64-dimensional fixed length vector to achieve robust app profiling. Our extensive evaluations on 86,332 apps demonstrate that our approach performs app profiling with high accuracy and low computation cost: it takes about 46.5 seconds for one app to extract its function call graph; 0.68 seconds to encode a function call graph; it classifies all 4,024 (benign/malware) apps in around 5.06 seconds with accuracy about 93.07%; it classifies all 570 malicious apps' family (21 families in total) in around 0.83 seconds with accuracy 82.3%; it classifies 9,730 apps' functionality into 2 categories with accuracy 88.1%.

Senior tourism is a rapidly growing market in China, and global players have begun to attract senior tourists from China using diversified ways to help boost the industry in different destinations ...

This research is a longitudinal study of 77 articles on hotel corporate social responsibility (CSR) practices published in tourism and hospitality journals indexed in the Social Sciences Citation I...

Hotel websites are vital information channels for potential customers. Hence, their readability and attractiveness play a key role in online marketing for hotels. This study attempts to investigate how hotels use language strategies to better market themselves online. This study first proposes an analytical framework on the basis of appraisal theory, which aims to reveal the hidden mechanism by hotels in building interpersonal relationships, and uses such a framework to explore online language strategies used by five-star hotels in China. The general language strategy and the corresponding preference in the use of such a strategy amongst hotels are identified. Theoretical contributions and managerial implications are generated on the basis of the findings and analysis. It is among the early studies to modify appraisal theory to analyze tourism and hospitality text data and to adopt the corpus approach to explore the hidden mechanism of language use.

This study aims to take boutique luxury hotels as its research subject to enrich the view on the self-positioned image of luxury hotels and simultaneously compare this image with the one perceived by customers. It also investigates whether a gap exists between the two images.,The best-rated boutique luxury hotel in Hong Kong is selected as the case hotel. Through the interpretation and discussion of high-frequency wordlists and semantic co-occurrence network charts, major topics in the hotel’s self-presented image and customers’ perceived image are identified accordingly.,Five dimensions (event making, exquisite food, excellent amenities and services, special function venue and promotion) used by hotels to form their boutique luxury image are recognised. Between hotels’ self-positioned image and customers’ perceived images, minor similarities such as the recognition of excellent amenities and services were confirmed but considerable differences were identified, indicating the ineffective marketing by the boutique luxury hotel despite its success in terms of ranking. Managerial suggestions on how to address the image discrepancy were proposed.,This study contributes insights into hotel image discrepancy among social media platforms in luxury hotel segments by adding the category of boutique luxury hotels. It also identifies the implications for enhancing the development of a hotel image to meet customers’ needs and expectations.,酒店的自我定位形象与顾客感知形象——以香港某精品豪华酒店为例,本研究试图以精品豪华酒店为研究对象, 丰富豪华酒店自我定位形象的视角, 同时将其与顾客感知的形象进行比较, 考察二者之间是否存在差距。,本文选取香港评分最佳的精品豪华酒店为案例酒店。通过对高频词表和语义共现网络图的解读和讨论, 识别出酒店自我呈现形象和顾客感知形象中的主要话题。,酒店利用五个维度（活动打造、精致的食物、优质的设施和服务、特殊的功能场所和促销）来塑造其精品豪华形象。在酒店的自我定位形象和顾客的感知形象之间, 发现了一些细微的相似之处, 如对优质设施和服务的认可, 但差异之处相当大, 表明尽管精品豪华酒店取得了优秀的排名, 但其营销效果不佳。本文提出了解决形象差异的管理建议。,本研究通过增加精品豪华酒店类别的案例, 有助于深入了解豪华酒店细分市场中社交媒体平台之间的酒店形象差异。本文还确定了促进酒店形象发展的影响, 以满足客户需求和期望。,Imagen auto-posicionada del hotel versus imagen percibida por los clientes: un estudio de caso de un hotel boutique de lujo en Hong Kong.,Este estudio se esfuerza por tomar a los hoteles boutique de lujo como tema de investigacion para profundizar en el conocimiento sobre la imagen de los hoteles de lujo y compar el posicionamiento presentado por el hotel con el que perciben los clientes. Tambien investiga si existe una brecha entre las dos imagenes.,Para el caso se selecciona el mejor hotel de lujo calificado en Hong Kong. A traves de la interpretacion y discusion de listas de palabras de alta frecuencia y graficos de redes de coincidencia semantica se identifican los principales temas en la imagen presentada por el hotel y la imagen percibida de los clientes.,Se reconocen cinco dimensiones (creacion de eventos, comida exquisita, excelentes comodidades y servicios, lugar de funciones especiales y promocion) utilizadas por los hoteles para formar su imagen de lujo boutique. Entre la imagen auto-posicionada de los hoteles y las imagenes percibidas por los clientes, se confirmaron pequenas similitudes, como el reconocimiento de excelentes comodidades y servicios, pero se identificaron diferencias considerables, lo que indica la comercializacion ineficaz del hotel boutique de lujo a pesar de su exito en terminos de clasificacion. Se propusieron sugerencias gerenciales sobre como abordar la discrepancia de imagen.,Este estudio aporta informacion sobre la discrepancia de imagen del hotel entre las plataformas de redes sociales en los segmentos de hoteles de lujo al agregar la categoria de hoteles boutique de lujo. Tambien identifica las implicaciones para mejorar el desarrollo de una imagen de hotel que satisfaga las necesidades y expectativas del cliente.

Private social dining is a new form of service emerging in culinary tourism. Although its popularity has been increasing worldwide, studies on private social dining, especially its influence on tourism, are limited. The present work aims to explore tourists’ experiences using private social dining as a context by constructing the dimensions for tourist experience. A qualitative research method is utilized to analyze reviews from top service providers in three popular destinations. Results confirm seven dimensions of the three types of experience existing in tourists’ private social dining experiences, which affect tourist satisfaction. These results lay the foundation for future studies.

Privacy has become one of the major concerns in cloud video surveillance. Privacy protection of the surveillance videos strive to protect users’ privacy information without hampering regular security tasks of the surveillance, meanwhile retains the system’s high accuracy and efficiency. The current state of the art in protecting the video privacy is mainly realized through Privacy Region Protection, which only protects the privacy regions while keeps the non-privacy regions visually intact so that processing in the cloud is still feasible. However, the problem of determining the privacy regions has been ignored and not properly addressed. In this paper, we propose a novel notion - concept graph , and with the aid of that, we develop our system – PatronuS to determine the privacy regions subject to satisfying both privacy and security requirements. We further propose an event distilling model and a privacy inference model to assist in determining specific privacy regions. And we evaluate PatronuS in real-world settings and demonstrate its efficiency in privacy protection without degrading system’s surveillance functionality.

The article examines 2024 papers published in Tourism Management from 2001 to 2017. Overall bibliometric and thematic analysis are conducted to reveal the current trend in tourism studies. ...

Social network data is widely shared, transferred and published for research purposes and business interests, but it has raised much concern on users’ privacy. Even though users’ identity information is always removed, attackers can still de-anonymize users with the help of auxiliary information. To protect against de-anonymization attack, various privacy protection techniques for social networks have been proposed. However, most existing approaches assume specific and restrict network structure as background knowledge and ignore semantic level prior belief of attackers, which are not always realistic in practice and do not apply to arbitrary privacy scenarios. Moreover, the privacy inference attack in the presence of semantic background knowledge is barely investigated. To address these shortcomings, in this work, we introduce knowledge graphs to explicitly express arbitrary prior belief of the attacker for any individual user. The processes of de-anonymization and privacy inference are accordingly formulated based on knowledge graphs. Our experiment on data of real social networks shows that knowledge graphs can power de-anonymization and inference attacks, and thus increase the risk of privacy disclosure. This suggests the validity of knowledge graphs as a general effective model of attackers’ background knowledge for social network attack and privacy preservation.

This study adopted a longitudinal approach to examine 77 articles on tourism education research published in the Journal of Hospitality, Leisure, Sport & Tourism Education between 2008 and 2017. Content analysis was used to analyze the profile, theory, method, and theme of these articles on tourism education. The majority of the articles were atheoretical, but a growing tendency for researchers to adopt theories from multiple disciplines to investigate educational issues in tourism is observable. Quantitative studies dominated throughout the period. The analysis of statistical techniques denotes that this research area is advancing toward sophistication. Teaching and learning methods, learning motivation, and course evaluation were identified as three major themes. Discussion is offered and suggestions posed based on these findings, in the hope of constantly improving the research quality of studies on tourism education.

Sustainability is a pivotal issue for the development of hospitality. The industry has attempted multiple ways to achieve this goal, with effective communication being one of the most prevalent ones. However, the communication effect which hospitality has obtained in terms of persuading customers to practice sustainability is imperfect. This study draws upon the communication literature and textually analyzes hospitality professionals&rsquo, perception worldwide to generate a typology for hospitality practitioners to follow while conducting communication with customers on sustainability. The findings suggest two strategies and seven tactics that are of value to help hospitality achieve better communication. Thus, this study can extend the current communication theories on how to undertake sustainable hospitality communication and elucidate its operation in the industry.

Train machine learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that collects the local gradient information instead of real data. One way to achieve a strict privacy guarantee is to apply local differential privacy into federated learning. However, previous works do not give a practical solution due to three issues. First, the noisy data is close to its original value with high probability, increasing the risk of information exposure. Second, a large variance is introduced to the estimated average, causing poor accuracy. Last, the privacy budget explodes due to the high dimensionality of weights in deep learning models. In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues. It is capable of making the data more distinct from its original value and introducing lower variance. Moreover, the proposed mechanism bypasses the curse of dimensionality by splitting and shuffling model updates. A series of empirical evaluations on three commonly used datasets, MNIST, Fashion-MNIST and CIFAR-10, demonstrate that our solution can not only achieve superior deep learning performance but also provide a strong privacy guarantee at the same time., Accepted by IJCAI-21

This research aims to identify the recent research trends of Chinese scholarship in tourism. In this study, a systematic review of articles from the Journal of Travel Research from 2011 to ...

In view of the gradual integration of information technology into modern life, travel websites have substantially affected the decision-making behaviors of tourists. The academia worldwide has inve...

Data quality assessment is vital for many information services ranging from sensor networks to smart city systems. The current data quality assessments, however, are often derived from intrinsic data characteristics, disconnected from specific application contexts, or are not applicable or efficient for large datasets. In this work, we propose a novel task-oriented data quality assessment framework, which balances between the intrinsic and contextual quality. We carefully craft the assessment metrics, quantify them, and fuse them to rank candidate datasets by quality given specific tasks. To improve the system efficiency, two fast calculation algorithms are designed to quantify the relationship between datasets and the task, and the distribution of data items. We conduct extensive evaluations on six public image datasets (with 460, 247 images in total) and four text document datasets (with 37, 372 documents in total) to evaluate the efficacy and efficiency of our design. Experimental results show that our algorithms can save about 90% computing time with little accuracy loss which validates the feasibility and effectiveness of our framework for large datasets.

Word-Fi is a handwriting input system, driven by wireless backscattering technology and machine learning methods. It could effectively mitigate the surrounding noise and extract the weak signals incurred by tiny writing gestures accurately. Leveraging our customized wireless backscattering system, Word-Fi could be noise tolerant across relatively complex environments, especially when multiple persons are presented around, which significantly differs from status quo wireless sensing systems that suffer from multi-user presentation. For weak signal extraction, Word- Fi incorporates an efficient feature selection scheme for classification and improves the classifier by fully exploiting the physical layer information. After using the word suggestion module, it could recognize writing words with fairly high accuracy (above 90 percent) across different volunteers (7-10).

Dear Editors:We are grateful for the opportunity to respond to the critique, which enables us to deeply think our manuscript and improve the quality of our future studies. Our response to the conce...

This article investigates the effect of cultural distance on pleasure visitors, mainly vacation overnight visitors, in Hong Kong. Secondary data on 10 source markets are extracted from the annual report of visitor profile 2014 published by Hong Kong Tourism Board (HKTB). The data are compared to recognize the influence cultural distance can have on visitor profiles and trip characteristics, including travel patterns, expenditure, and satisfaction levels. Deteriorating effects ascribed to cultural distance are clearly observed in the following three important aspects: repeated visit, length of stay, and expenditure; however, these effects do not extend to all aspects. Therefore, cultural distance may be used to complement physical distance in order to explain the tourist flow, and it should be further studied.

Tourists visiting gambling destinations are likely to participate, to some or to a great extent, in gambling activities. Therefore, their gambling experience contributes to the evaluation of their experience at the destination. This study investigates the effect of gambling results on the overall satisfaction and loyalty of 7431 tourists visiting Macao by estimating a set of ordered probit models. The results indicate an asymmetric effect on the overall satisfaction: the negative effect of a losing outcome outweighs the positive effect of a winning outcome. Both winning and losing gambling outcomes have a positive and similar effect on the likelihood to recommend the destination, and the likelihood to revisit is affected in a positive way only by a winning outcome. The main determinants of tourist satisfaction and loyalty are further verified and the influence of tourist profile variables is also explored. Theoretical and managerial implications are outlined based on the results.