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Wireless sensor networks (WSNs) will profoundly influence the ubiquitous computing landscape. Their utility derives not from the computational capabilities of any single sensor node, but from the emergent capabilities of many communicating sensor nodes. Consequently, the details of communication within and across single hop neighborhoods is a fundamental component of most WSN applications. But these details are often complex, and popular embedded languages for WSNs provide only low‐level communication primitives. We propose that the absence of suitable communication abstractions contributes to the difficulty of developing large‐scale WSN applications. To address this issue, we present the design and implementation of a Remote Procedure Call (RPC) abstraction for nesC and TinyOS, the emerging standard for developing WSN applications. We present the key language extensions, operating system services, and automation tools that enable the proposed abstraction. We illustrate these contributions in the context of a representative case study, and analyze the overhead introduced when using our approach. We use these results to draw conclusions regarding the suitably of our work to resource‐constrained sensor nodes.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

Current possibilities of accessing business data by regular users usually involve complicated user interfaces or require technical expertise. This results in situations when business owners are separated from their data. The aim of this research is to apply an innovative approach leveraging conversational interfaces to tackle this problem.

The authors examine the current possibilities of accessing business data by business, users with an emphasis on conversational interfaces employing a chatbot as an alternative to traditional approaches. The authors propose a new concept relying on a guided conversation, and through experiments with a real chatbot and database, the authors demonstrate the benefits of the proposed approach.

The authors found out that the key to the success of our approach is a decomposition of complex database queries and their incremental construction in conversations. This also enables natural discovery of the domain model through constantly provided feedback. Based on the experiments with a real chatbot, the authors demonstrate that defining conversation flows and maintaining the conversation context is a crucial aspect contributing to the overall accuracy, together with keeping the conversation within the defined limits in its certain parts.

The authors present a novel approach using natural language interfaces for accessing data by business users. In contrast to existing approaches, the authors emphasize incremental construction of queries, predefined conversation flows and constraining the conversations, when necessary.

The paper describes the use of a Digital Rainbow computer for organising, listing, displaying and retrospectively storing records of journals sent for binding at Sunderland Polytechnic Library. It touches briefly on the problems of the old manual system, and on the general difficulties of establishing a binding policy. The paper goes on to describe the creation of the main database files, and the command files which produce lists for the binders, integrated informative lists of all material away at a time, and lists of journals not bound. Also described is the creation of separate disks and files for retrospective storing of binding of records. The results are twofold: a more informative and efficient system of recording binding issues has been produced; and the automated system has generated the formation of a comprehensive binding policy.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

The digital medium has created new ways of contracting through web pages and smartphone apps. The websites and apps put numerous terms of contract. In some cases, the user is required to click on them, and other times, the user may or may not even notice the terms. Are the terms put by the sites and apps binding? The case explores the theme with the Uber Case, a judgement of the United States Court of Appeals for the Second Circuit, on the application of the terms in the Uber app.

One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.

Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.

The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.

TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

The user-centered approach to understanding information use and users has shaped research in library and information science (LIS). In a user-centered environment, catalogers are told to focus on users and adapt standards to meet users’ needs while following standards in order to be efficient in their jobs. This study describes three academic cataloging units as they negotiate both the demands to follow and adapt these standards to meet users’ needs. New institutional theory served as a framework for the study. The results suggest that standards and users are pressures that cataloging units negotiate in their jobs, along with demands for work efficiency and professional legitimacy. While negotiating these pressures, catalogers and cataloging units redefine their work jurisdiction and maintain legitimacy to remain relevant in a complex work environment. Understanding how catalogers negotiate the normative institutional pressures of standards and users leads to an understanding of the complex nature of work in areas that deal with issues of standards and users, shows how an area within a profession maintains legitimacy when the profession no longer values that work, and, finally, shows the limits of the user-centered focus in LIS practice.