Visualization of Web Spaces: State of the Art and Future Directions

The World Wide Web is a dominant global communication medium and knowledge repository. It is used by a great number of people with a variety of computer skills hence its usability is critical. As with many large information collections, the challenge with web usability is understanding the structure of a collection of information objects (web pages) to find relevant ones for satisfying a specific information need. Web sites are organized in a hyperlinked structure that somewhat addresses this challenge. However, this “connectedness” also causes the now well-known “lost in cyberspace” phenomenon where one may get confused within the complex organization of a web site. Meanwhile, information exploration on the web is not limited to browsing a web site. The problem of finding relevant information applies to a collection of pages that come from various web sites as in the case of the results of a “less than perfectly constructed” search query.<div><br>Information visualization has been proposed as a way to cope with these problems by taking advantage of people’s innate perceptual skills to support their cognitive skills. Many paradigms have been proposed for the visual presentation of web spaces (i.e. structured or unstructured collection of web pages). This study surveys these paradigms to provide a map of where the research in this field is, and what directions future research and practice can take. For this, we introduce a classification scheme to help in the systematic understanding of web visualization and for providing a framework for the development of<br>future visualizations.</div>

Visualization of Web Spaces: State of the Art and Future Directions

The World Wide Web is a dominant global communication medium and knowledge repository. It is used by a great number of people with a variety of computer skills hence its usability is critical. As with many large information collections, the challenge with web usability is understanding the structure of a collection of information objects (web pages) to find relevant ones for satisfying a specific information need. Web sites are organized in a hyperlinked structure that somewhat addresses this challenge. However, this “connectedness” also causes the now well-known “lost in cyberspace” phenomenon where one may get confused within the complex organization of a web site. Meanwhile, information exploration on the web is not limited to browsing a web site. The problem of finding relevant information applies to a collection of pages that come from various web sites as in the case of the results of a “less than perfectly constructed” search query.<div><br>Information visualization has been proposed as a way to cope with these problems by taking advantage of people’s innate perceptual skills to support their cognitive skills. Many paradigms have been proposed for the visual presentation of web spaces (i.e. structured or unstructured collection of web pages). This study surveys these paradigms to provide a map of where the research in this field is, and what directions future research and practice can take. For this, we introduce a classification scheme to help in the systematic understanding of web visualization and for providing a framework for the development of<br>future visualizations.</div>

SchenQL: in-depth analysis of a query language for bibliographic metadata

Information access to bibliographic metadata needs to be uncomplicated, as users may not benefit from complex and potentially richer data that may be difficult to obtain. Sophisticated research questions including complex aggregations could be answered with complex SQL queries. However, this comes with the cost of high complexity, which requires for a high level of expertise even for trained programmers. A domain-specific query language could provide a straightforward solution to this problem. Although less generic, it can support users not familiar with query construction in the formulation of complex information needs. In this paper, we present and evaluate SchenQL, a simple and applicable query language that is accompanied by a prototypical GUI. SchenQL focuses on querying bibliographic metadata using the vocabulary of domain experts. The easy-to-learn domain-specific query language is suitable for domain experts as well as casual users while still providing the possibility to answer complex information demands. Query construction and information exploration are supported by a prototypical GUI. We present an evaluation of the complete system: different variants for executing SchenQL queries are benchmarked; interviews with domain-experts and a bipartite quantitative user study demonstrate SchenQL’s suitability and high level of users’ acceptance.

An Information-Motivated Exploration Agent to Locate Stationary Persons with Wireless Transmitters in Unknown Environments

Unmanned Aerial Vehicles (UAVs) show promise in a variety of applications and recently were explored in the area of Search and Rescue (SAR) for finding victims. In this paper we consider the problem of finding multiple unknown stationary transmitters in a discrete simulated unknown environment, where the goal is to locate all transmitters in as short a time as possible. Existing solutions in the UAV search space typically search for a single target, assume a simple environment, assume target properties are known or have other unrealistic assumptions. We simulate large, complex environments with limited a priori information about the environment and transmitter properties. We propose a Bayesian search algorithm, Information Exploration Behaviour (IEB), that maximizes predicted information gain at each search step, incorporating information from multiple sensors whilst making minimal assumptions about the scenario. This search method is inspired by the information theory concept of empowerment. Our algorithm shows significant speed-up compared to baseline algorithms, being orders of magnitude faster than a random agent and 10 times faster than a lawnmower strategy, even in complex scenarios. The IEB agent is able to make use of received transmitter signals from unknown sources and incorporate both an exploration and search strategy.

Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents

By providing a high degree of freedom to explore information, QA (question and answer) agents in museums are expected to help visitors gain knowledge on a range of exhibits. Since information exploration with a QA agent often involves a series of interactions, proper guidance is required to support users as they find out what they want to know and broaden their knowledge. In this paper, we validate topic recommendation strategies of system-initiative QA agents that suggest multiple topics in different ways to influence users’ information exploration, and to help users proceed to deeper levels in topics on the same subject, to offer them topics on various subjects, or to provide them with selections at random. To examine how different recommendations influence users’ experience, we have conducted a user study with 50 participants which has shown that providing recommendations on various subjects expands their interest on subjects, supports longer conversations, and increases willingness to use QA agents in the future.

Research Progress and Prospects of Agricultural Aero-Bionic Technology in China

Accelerating the development of agricultural aviation technology is the need of China’s modern agricultural construction. With the rise of emerging industries such as artificial intelligence, biotechnology, autonomous navigation, and the Internet of Things, agricultural aviation is further developing toward the direction of intelligence to meet the requirements of efficient and sophisticated agricultural aviation operations. Bionics is a multi-discipline and comprehensive border subject. It is produced by the mutual penetration and integration of life science and engineering science. Bionic technology has received more and more attention in recent years, and breakthroughs have been made in the fields of biomedicine and health, military, brain science and brain-like navigation, and advanced manufacturing. This study summarized the research progress of biomimetic technology in the field of agricultural aviation from three aspects of biological perception, biological behavior, and biological intelligence. On this basis, problems of related research and application of agricultural aircraft in real-time obstacle avoidance, path planning, and intelligent navigation were analyzed. Combined with the practice of the rapid development of agricultural aircraft, research and application of bionic technology suitable for agricultural aircraft were then proposed. Finally, prospects of agricultural aero-bionic technology were also discussed from multiple bionic target fusion, three-dimensional spatial information exploration, sensors, and animal brain system mechanism. This review provides a reference for the development of bionic technology in China’s agricultural aviation.

A Novel Approach in Prediction of Crop Production Using Recurrent Cuckoo Search Optimization Neural Networks

Data mining is an information exploration methodology with fascinating and understandable patterns and informative models for vast volumes of data. Agricultural productivity growth is the key to poverty alleviation. However, due to a lack of proper technical guidance in the agriculture field, crop yield differs over different years. Mining techniques were implemented in different applications, such as soil classification, rainfall prediction, and weather forecast, separately. It is proposed that an Artificial Intelligence system can combine the mined extracts of various factors such as soil, rainfall, and crop production to predict the market value to be developed. Smart analysis and a comprehensive prediction model in agriculture helps the farmer to yield the right crops at the right time. The main benefits of the proposed system are as follows: Yielding the right crop at the right time, balancing crop production, economy growth, and planning to reduce crop scarcity. Initially, the database is collected, and the input dataset is preprocessed. Feature selection is carried out followed by feature extraction techniques. The best features were then optimized using the recurrent cuckoo search optimization algorithm, then the optimized output can be given as an input for the process of classification. The classification process is conducted using the Discrete DBN-VGGNet classifier. The performance estimation is made to prove the effectiveness of the proposed scheme.

The Usage of Evidence-Based Dentistry among Postgraduate Student of Isfahan University of Medical Sciences

Introduction: Our current era is the age of information exploration and innovation that has given us a good opportunity to use evidence-based information, so this study aimed to evaluate the use of Evidence-Based Dentistry in Isfahan Dental School in 2018. Materials & Methods: The sampling method of this study was census and, the number of faculty assistants was 50. The method of collecting this information was through a questionnaire Regarding fulfillment of ethical manners in this study, participants were not required to mention their names and personal characteristics, and only if they consented participate in the study, a questionnaire was provided to them. Man withny analysis and Spear man correlation were used to evaluate the relation between EBD (Evidence Based Dentistry) according to their gender. Results: In the current evaluation, it was observed that 19 dentists (36.5%) were female and 33 (63.5%) were male. Most residents (63.5%) sometimes use evidence-based dentistry, 11 cases (21.2%) rarely and 8 cases (15.3%) using this technique always. About the frequency distribution of resources used by residents to answer questions in dealing with patients, the highest frequency (65.4%) was related to “textbooks or questions from clinical teachers” as well as “translated reference books” and the least Frequency (13.5%) was related to “Search other databases”. Conclusion: Based on the findings in this section, the use of evidence-based medicine among residents is low.

A Hybrid CNN-Based Review Helpfulness Filtering Model for Improving E-Commerce Recommendation Service

As the e-commerce market grows worldwide, personalized recommendation services have become essential to users’ personalized items or services. They can decrease the cost of user information exploration and have a positive impact on corporate sales growth. Recently, many studies have been actively conducted using reviews written by users to address traditional recommender system research problems. However, reviews can include content that is not conducive to purchasing decisions, such as advertising, false reviews, or fake reviews. Using such reviews to provide recommendation services can lower the recommendation performance as well as a trust in the company. This study proposes a novel review of the helpfulness-based recommendation methodology (RHRM) framework to support users’ purchasing decisions in personalized recommendation services. The core of our framework is a review semantics extractor and a user/item recommendation generator. The review semantics extractor learns reviews representations in a convolutional neural network and bidirectional long short-term memory hybrid neural network for review helpfulness classification. The user/item recommendation generator models the user’s preference on items based on their past interactions. Here, past interactions indicate only records in which the user-written reviews of items are helpful. Since many reviews do not have helpfulness scores, we first propose a helpfulness classification model to reflect the review helpfulness that significantly impacts users’ purchasing decisions in personalized recommendation services. The helpfulness classification model is trained about limited reviews utilizing helpfulness scores. Several experiments with the Amazon dataset show that if review helpfulness information is used in the recommender system, performance such as the accuracy of personalized recommendation service can be further improved, thereby enhancing user satisfaction and further increasing trust in the company.