Search results

This study aims to analyze Kickstarter data along with social media data from a data mining perspective. Kickstarter is a crowdfunding financing plataform and is a form of fundraising and is increasingly being adopted as a source for achieving the viability of projects. Despite its importance and adoption growth, the success rate of crowdfunding campaigns was 47% in 2017, and it has decreased over the years. A way of increasing the chances of success of campaigns would be to predict, by using machine learning techniques, if a campaign would be successful. By applying classification models, it is possible to estimate if whether or not a campaign will achieve success, and by applying regression models, the authors can forecast the amount of money to be funded.

The authors propose a solution in two phases, namely, launching and campaigning. As a result, models better suited for each point in time of a campaign life cycle.

The authors produced a static predictor capable of classifying the campaigns with an accuracy of 71%. The regression method for phase one achieved a 6.45 of root mean squared error. The dynamic classifier was able to achieve 85% of accuracy before 10% of campaign duration, the equivalent of 3 days, given a campaign with 30 days of length. At this same period time, it was able to achieve a forecasting performance of 2.5 of root mean squared error.

The authors carry out this research presenting the results with a set of real data from a crowdfunding platform. The results are discussed according to the existing literature. This provides a comprehensive review, detailing important research instructions for advancing this field of literature.

To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM.

SURF are scale- and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation.

Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system.

The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems.

The purpose of this paper is to introduce an interpreting schema and semantic description framework for a collection of images of Xilankapu, a traditional Chinese form of embroidered fabric and brocade artwork.

First, the authors interpret the artwork of Xilankapu through Gillian Rose’s “four site” theory by presenting how the brocades were made, how the patterns of Xilankapu are classified and the geometrical abstraction of visual images. To further describe the images of this type of brocade, this paper presents semantic descriptions that include objective–non-objective relations and a multi-layered semantic framework. Furthermore, the authors developed corresponding methods for scanning, storage and indexing images for retrieval.

As exploratory research on describing, preserving and indexing images of Xilankapu in the context of the preservation of cultural heritage, the authors collected 1,000+ images of traditional Xilankapu, classifying and storing some of the images in a database. They developed an index schema that combines concept- and content-based approaches according to the proposed semantic description framework. They found that the framework can describe, store and preserve semantic and non-semantic information of the same image. They relate the findings of this paper to future research directions for the digital preservation of traditional cultural heritages.

The framework has been designed especially for brocade, and it needs to be extended to other types of cultural image.

The semantic description framework can describe connotative semantic information on Xilankapu. It can also assist the later information retrieval work in organizing implicit information about culturally related visual materials.

With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions.

The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used.

The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%.

This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

This study aims to focus on whether and furthermore how aesthetics-based mystery affects consumers’ responses toward relevant products.

Three experimental studies are reported. In Studies 1–2, smartphone ad flyers varying in mystery and non-mystery styles were adopted. A total of 187 undergraduate participants were recruited in Study 1 and 245 undergraduate participants in Study 2. In Study 3, a total of 193 participants who work in a range of businesses were recruited and wristwatch ad flyers were adopted.

Findings demonstrate that consumers are more willing to pay for products promoted via mystery appeal (versus non-mystery). Such positive impacts occur through consumers’ high-end perceptions of the products. Concrete, rather than abstract, verbal description of quality product features facilitate the impact of mystery appeal on consumer purchase decisions.

The findings advance an extant understanding of mystery appeal in advertising. It is among the first few to demonstrate that high-end product perceptions carry over the positive influence of mystery on consumers. This research is enlightening by suggesting an incongruity effect between pictorial stimuli and verbal information in the advertisement. This study’s scope is limited to visual mystery-evoking stimuli and Chinese participants.

When marketers/advertisers promoting products/brands with high prices, aesthetics-based mystery appeal should be considered as an effective option. This appeal is implicated as effective across gender. Moreover, visual mystery-evoking stimuli, combined with a concrete (not abstract) verbal description of product features should be optimal in promoting products.

The findings contribute to the limited empirical research on the influence processes of aesthetics-based mystery appeal. Different from the intuition, it is suggested that incongruity between visual and verbal stimuli in mystery ads that enhances the positive effect of mystery appeal.

The purpose of this paper is to examine the ability of high-functioning autistic (HFA) children to programme robotic behaviour and sought to elucidate how they describe and construct a robot’s behaviour using iconic programming software.

The robotic learning environment is based on the iPad, an iconic programming app (KinderBot), and EV3. Two case studies, of A. and N., both HFA children of average age 10.5, are the focus of this research.

The research revealed how the participants succeeded in programming the behaviour of an “other” at different programming complexity levels (from simple action to combinations of states of two binary sensors and rule with subroutine). A transformation from procedural to declarative description was also found.

This research on the ability of HFA children to programme robotic behaviour yielded results that can be implemented in K-12 education. Furthermore, learning to programme robots and understand how robotic technologies work may help HFA children to better understand other technology in their environment.

In this research, the authors present an innovative approach that for the first time enables HFA children to “design” the behaviour of smart artefacts to use their sensors to adapt in accordance with the environment. For most HFA children, this would be the first opportunity to “design” the behaviour of the other, as opposed to oneself, since in most of their experience they have been largely controlled by another person.

Aims to present the authors' efforts towards the development of a digital library environment supporting research at the Medical School of Athens University, Greece.

The digital library facilitates access to medical material produced by laboratories for both research and educational purposes. As the material produced varies (regarding its type and structure) and the search requirements imposed by potential users differ, each laboratory develops its own collection. All collections must be bilingual, supporting both Greek and English. Extended requirements were imposed regarding the services offered by the digital library environment, due to the following reasons: end‐users actively participate in the cataloguing workflow; cataloguers should be able to create and manage multiple collections in a simplified manner; and different search requirements must be supported for different user groups. To formulate and then deal with these requirements, the authors introduced the term “dynamic collection management” denoting automated collection definition and unified collection management within an integrated digital library environment. Digital library components providing the desired functionality and the interaction between them are described. System performance, especially during collection search, and bilingual support are also explored.

Finds that Athens Medical School Digital Library facilitates access to medical material to researchers and students for both research and educational purposes.

The paper provides useful information on a digital library environment which supports research.

To provide an integrated perspective to similarities and differences between approaches to automated classification in different research communities (machine learning, information retrieval and library science), and point to problems with the approaches and automated classification as such.

A range of works dealing with automated classification of full‐text web documents are discussed. Explorations of individual approaches are given in the following sections: special features (description, differences, evaluation), application and characteristics of web pages.

Provides major similarities and differences between the three approaches: document pre‐processing and utilization of web‐specific document characteristics is common to all the approaches; major differences are in applied algorithms, employment or not of the vector space model and of controlled vocabularies. Problems of automated classification are recognized.

The paper does not attempt to provide an exhaustive bibliography of related resources.

As an integrated overview of approaches from different research communities with application examples, it is very useful for students in library and information science and computer science, as well as for practitioners. Researchers from one community have the information on how similar tasks are conducted in different communities.

To the author's knowledge, no review paper on automated text classification attempted to discuss more than one community's approach from an integrated perspective.

The purpose of this paper is to describe the composite service and the context properties related to the users in the business process execution language (BPEL) file.

The authors’ approach allows expressing requirements by taking into account potential users’ context in addition to the functional one.

In this paper, the authors introduce a new context-aware approach that provides a dynamic adaptation of service compositions.

This paper introduces a user-aware approach for describing and publishing context-aware composite Web service.