Adaptive Clustering of Search Results

Travel behaviour market segmentations have become a popular method of identifying different types of car users, bicyclists or public transport users. However, while previous studies have looked at different types of users within single modes, such as the car, little research has explored the existence of traveller types transcending modes. The study presented here is an extension of an earlier segmentation study that distinguished travellers based on their individual preferences, yet did so independent of their current mode choice. The data came from a travel survey at a middle-sized UK university and were analysed using a combination of hierarchical and iterative partitioning methods. Crucially, however, the current study uses a different theoretical framework to previous segmentation research—goal framing theory—which may more adequately explain the findings than models used in the past such as the theory of planned behaviour. The findings supported earlier work, suggesting the presence of seemingly stable traveller types that cut across modes and can be distinguished based on gain, hedonic and normative goals. This has important implications for policies aimed at encouraging mode change which may have been too preoccupied with changing people’s attitudes rather than paying attention to people’s underlying travel preferences.

How to facilitate efficient video manipulation and access in a web-based environment is becoming a popular trend for video applications. In this paper, we present a web-oriented video management and application processing system, based on our previous work on multimedia database and content-based retrieval. In particular, we extend the VideoMAP architecture with specific web-oriented mechanisms, which include: (1) Concurrency control facilities for the editing of video data among different types of users, such as Video Administrator, Video Producer, Video Editor, and Video Query Client; different users are assigned various priority levels for different operations on the database. (2) Versatile video retrieval mechanism which employs a hybrid approach by integrating a query-based (database) mechanism with content- based retrieval (CBR) functions; its specific language (CAROL/ST with CBR) supports spatio-temporal semantics of video objects, and also offers an improved mechanism to describe visual content of videos by content-based analysis method. (3) Query profiling database which records the `histories' of various clients' query activities; such profiles can be used to provide the default query template when a similar query is encountered by the same kind of users. An experimental prototype system is being developed based on the existing VideoMAP prototype system, using Java and VC++ on the PC platform.

Nowadays the safety of computer networks becomes more important than other characteristics. The services which are used by a huge number of users entice hacker. Especially, online payment services are often the main target of malicious software. In the article, the computer virus SpyEye spread via Internet resources is considered. The Petri-object model of propagation process is created with the use of Petri-object simulation software written in Java. The model reproduces the events of damaging website, infecting user’s resource, sending information about vulnerabilities and sending personal data. It was suggested that users are connected when they use the same website. The investigation of the spreading time for different kinds of users’ connections topology is presented. It was considered not only well-known star and chain topologies but also their combinations and the topology with random connections. Simulation results confirm the virus spreading time dependence on user’s connections topology.

Telehealth has played a critical role in supporting healthcare delivery during the COVID-19 pandemic. Mitigation practices of physical distancing and sheltering-in-place continue to impose a significant burden on face-to-face healthcare visits. As a result, telehealth adoption and utilization have increased significantly after the U.S. Department of Health and Human Services waived telehealth payment requirements. These changes have been rapid and affect both individuals and organizations, ranging from healthcare and technology to news and policy. We turn to Twitter, as a broad-reaching communication platform, to elucidate and characterize Telehealth communication patterns. While different aspects of tweeting about telehealth have been used to answer specific questions, we sought to understand the collective telehealth tweeting behavior as a system of different types of users tweeting with various intent and sentiment. Such an approach combines the typically independent analysis of structure, using social network analysis, and purpose or function of communication, with topic analysis and sentiment analysis. In this paper, we apply a systems-based approach-called hetero-functional graph theory-to comprehensively describe the structure and function of the Twitter Telehealth communication system. We modeled 13 different types of telehealth users and 15 combinations of tweet intent and sentiment, which together describe 195 system capabilities. We applied machine learning to classify individual user tweeting behavior for different classes of telehealth stakeholders. We identified several patterns of longitudinal tweeting behaviors that varied by system form and function. By applying an interdependent methodology to understand both form and function, we provide deeper insights into tweeting behaviors between different classes of stakeholders. In doing so, we achieve a greater understanding of the various needs of different stakeholders, which can be leveraged as a valuable resource in informing more equitable policies as telehealth becomes a more permanent and scaled healthcare delivery service.

Machine learning algorithms are widely employed in plenty of classification or regression problems. While in real business world, it is confronted with huge and disorder data pattern. To recognize different kinds of users on the internet accurately and fast becomes a challenge. In a Wechat online bargain activity, the staff found that some strange users are highly like robots or malicious users. Thus we tried a two-level stacking model to detect them. This design got a good result of 0.98 accuracy after the training phase and an accuracy of 0.90 in a new term of the testing set. Moreover, this model is adaptable to linear and nonlinear datasets because of its diverse stacking of first-level classifiers. Therefore, this paper indicates a potential of the stacking classification model in big data times.

This paper presents ASTIS, a framework for the design and generation of adaptive spatio-temporal information systems for the historical study of natural hazard risks. ASTIS is based on a modular architecture in which every module can be personalized by the designer in order to meet the needs of different kinds of users. Personalizations are performed through a model-driven approach, each module is generated from specific models, conceived by the designer. A data management module allows personalizing the content of the application via data viewpoint mechanisms. A presentation module allows designing personalized interactive visualizations. Finally, an adaptation module is in charge of performing the appropriate personalizations at runtime, in order to adapt both the content and the presentation of the information to the user characteristics.

Considering the characteristics of different types of users in hybrid carsharing systems, in which sharing autonomous vehicles (SAVs) and conventional sharing cars (CSCs) coexist, a tailored pricing strategy (TPS) is proposed to maximize the operator’s profit and minimize all users’ costs. The fleet sizes and sizes of SAVs’ stations are also determined simultaneously. A bi-objective nonlinear programming model is established, and a genetic algorithm is applied to solve it. Based on the operational data in Lanzhou, China, carsharing users are clustered into three types. They are loyal users, losing users, and potential users, respectively. Results show the application of the TPS can help the operator increase profit and attract more users. The loyal users are assigned the highest price, while they still contribute the most to the operator’s profit with the highest number of carsharing trips. The losing users and potential users are comparable in terms of the number of trips, while the latter still makes more profit.

An information-pattern-based approach to novelty detection

The availability of the Internet at the click of a mouse brings with it a host of new problems. Although the World Wide Web was first started by physicists at CERN to enable collation and exchange of data, today, it is used for a wide range of applications. The requirements on bandwidth for each of the applications is also varied. An Internet Service Provider must ensure satisfaction across the entire spectrum of users. To ensure this, analysis of Internet usage becomes essential. Further, an administrator can keep a record of user's Internet activity and prevent unethical activities, since the Internet is also an excellent resource for providing anonymity. This analysis can also help in resource provisioning and monitoring. In this work, a web-based tool is first proposed to analyse the Internet activity. Next, data is collected from a proxy server at a campus-wide network. Traffic patterns of different types of users are studied. Finally, the paper concludes with strategies for monitoring and control of traffic.

Demographic attributes are important resources for market analysis, which are widely used to characterize different types of users. However, such signals are only available for a small fraction of users due to the difficulty in manual collection process by retailers. Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, manually defined features require professional knowledge and often suffer from under specification. Meanwhile, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. In this paper, we propose a novel Multi-task Representation Learning (MTRL) model to predict users’ demographic attributes. Comparing with the previous methods, our model conveys the following merits: (1) By using a multi-task approach to learn the tasks, our model leverages the large amounts of cross-task data, which is helpful to the task with limited data; (2) MTRL uses a supervised way to learn the shared semantic representation across multiple tasks, thus it can obtain a more general and robust representation by considering the constraints among tasks. Experiments are conducted on a real-world retail dataset where three attributes (gender, marital status, and education background) are predicted. The empirical results show that our MTRL model can improve the performance significantly compared with the state-of-the-art baselines.

Content in collaborative systems, such as Online Social Networks, is often co-owned by multiple users with different privacy expectations, leading to possible multiuser privacy conflicts. In order to address these conflicts, we argue that users should be supported by trustworthy agents that adhere to the following criteria: (i) concealment of privacy preferences, such that only necessary information is shared with others; (ii) equity of treatment, such that different kinds of users are supported equally; (iii) collaboration of users, such that a group of users can support each other in agreement and (iv) explainability of actions, such that users know why certain information about them was shared to reach a decision.

The following paper describes the implementation of three different controllers for a wheelchair. It was built for improving people's life that could have disabilities associated with functions regarding muscle strength, power of muscle of all limbs, tone of muscle of all limbs and endurance of all muscles of the body. Using three programmed methods that allow the user to have full control over the wheelchair. By acquiring voltage signals generated in eye's movements and using Neuro-Fuzzy Networks for differentiating each one, the wheelchair is able of being controlled by eye's movements. Figuring out about different kinds of user's needs and environments, the wheelchair includes voice control in spaces where noise levels are less than 40dB. The voice commands are detected using the Speech Recognition software which is a default program installed on Microsoft Windows operative system. The eye and voice controls were designed using LabVIEW and the intelligent control toolkit (ICTL). There were developed virtual simulators on C# code that enable the patient to have a previous training for having a better wheelchair control and performance. The system was tested on a person with cerebral palsy.

The nature of explanation dialogues is examined and an expert system that provides acceptable and appropriate answers and explanations to the questions posed by different kinds of users is designed. An empirical study and analysis of natural dialogues between experts, novices and partial experts is described. From this analysis a theory of explanation dialogues (extended schema-based theory, or EST) is developed. In EST questions are interpreted by combining information from different, semantically related question types that together best capture the essence and meaning of the question. This theory is applied to the design of an architecture and computational model for interpreting questions and generating explanations. The expert system, EXPLAIN, understands the nature of the question and is able to take account of the previous dialogue. The system can tailor its responses to an individual user's characteristics, including level of expertise and depth of knowledge in the domain. >

The long-tail phenomenon tells us that there are many items in the tail. However, not all tail items are the same. Each item acquires different kinds of users. Some items are loved by the general public, while some items are consumed by eccentric fans. In this paper, we propose a novel metric, item eccentricity, to incorporate this difference between consumers of the items. Eccentric items are defined as items that are consumed by eccentric users. We used this metric to analyze two real-world datasets of music and movies and observed the characteristics of items in terms of eccentricity. The results showed that our defined eccentricity of an item does not change much over time, and classified eccentric and noneccentric items present significantly distinct characteristics. The proposed metric effectively separates the eccentric and noneccentric items mixed in the tail, which could not be done with the previous measures, which only consider the popularity of items.

Today it is not easy to imagine what would be the future of user experience (UX) design in the world of Artificial Intelligence (AI). Referring to the sector of autonomous vehicles the paper aims to explore the changes that will be brought by artificial intelligence to the innovative sector of the self-driving car. According to these transformation car interiors and passengers experiences will become very different from the actual one. The aim of the research is, starting from different kind of passengers needs (Human Centered Design approach) individuating essential factors to design autonomous cars interiors and in particular to design innovative interfaces for a better communication with the passengers and a high level of living experience for different kind of users.