•We design a CRS to establish the people reputation within Cultural spaces.•The system is able to classify visitor behaviours.•Our approach is suitable for both nonprofit and business oriented ...organizations.
In this paper, starting from a comprehensive mathematical model of a Collaborative Reputation Systems (CRSes), we present a research study within the Cultural Heritage domain. The main goal of this study has been the evaluation and classification of the visitors’ behaviour during a cultural event. By means of mobile technological instruments, opportunely deployed within the environment, it is possible to collect data representing the knowledge to be inferred and give a reliable rate for both visitors and exposed artworks. Discussed results, confirm the reliability and the usefulness of CRSes for deeply understand dynamics related to people visiting styles.
Social networking services (e.g., Facebook and Twitter) are playing a significant role of interacting with customers. In particular, most of businesses are trying to exploit such social networking ...services for more profit, since it has dramatically become an information carrier for customers who are disseminating latest information about products and services. Thus, this study examines how information shared by companies is distributed and what the important factors in understanding information dissemination are. More importantly, this study classifies the types of tweets posted by a company and then to investigate the effect of these types of tweets on diffusion. By using content analysis, this study defined three types, which are
i
) information provision (IF),
ii
) advertisement (AD), and
iii
) both (IFAD), with 8 specific concepts. These results indicate that the differences are significant for all three types of information content. It shows that companies can spread information more quickly by providing the IFAD type rather than the AD type.
Social Network Services (SNSs) have been regarded as an important source for identifying events in our society. Detecting and understanding social events from SNS has been investigated in many ...different contexts. Most of the studies have focused on detecting bursts based on textual context. In this paper, we propose a novel framework on collecting and analyzing social media data for
i
) discovering social bursts and
ii
) ranking these social bursts. Firstly, we detect social bursts from the photos textual annotations as well as visual features (e.g., timestamp and location); and then effectively identify social bursts by considering the spreading effect of social bursts in the spatio-temporal contexts. Secondly, we use these relationships among social bursts (e.g., spatial contexts, temporal contexts and content) for enhancing the precision of the algorithm. Finally, we rank social bursts by analyzing relationships between them (e.g., locations, timestamps, tags) at different period of time. The experiments have been conducted with two different approaches:
i
) offline approach with the collected dataset, and
i
i
) online approach with the streaming dataset in real time.
With a large amount of data (e.g., ratings and feedbacks) obtained from social media (e.g., TripAdvisor), smart tourism applications and services have been studied to understand the contexts of ...users. More particularly, in this work, we have been focusing on “cultural tourism” service by automatically identifying and ranking cultural things (e.g., historical places). The main aims of this research are (1) to identify useful cultural heritage resources from geotagged social media (more precisely, spatial folksonomy), and (2) to rank them, with respect to the user context (e.g., location). Thus, the smart cultural tourism service can deliver smart interactions between the visitors of smart tourism environments by collecting and analyzing geotagged multimedia data (e.g., photos, tags, and comments) from available social media. In order to evaluate the proposed service, the system has been implemented with the real-world datasets related to cultural heritage sites (e.g.,
Hue
,
Hoi An
,
My Son
in Vietnam, and
Gyeongbokgung
,
Changdeokgung
,
Gyeongju
in Korea).
Similarity-based algorithms, often referred to as memory-based collaborative filtering techniques, are one of the most successful methods in recommendation systems. When explicit ratings are ...available, similarity is usually defined using similarity functions, such as the Pearson correlation coefficient, cosine similarity or mean square difference. These metrics assume similarity is a symmetric criterion. Therefore, two users have equal impact on each other in recommending new items. In this paper, we introduce new weighting schemes that allow us to consider new features in finding similarities between users. These weighting schemes, first, transform symmetric similarity to asymmetric similarity by considering the number of ratings given by users on non-common items. Second, they take into account the habit effects of users are regarded on rating items by measuring the proximity of the number of repetitions for each rate on common rated items. Experiments on two datasets were implemented and compared to other similarity measures. The results show that adding weighted schemes to traditional similarity measures significantly improve the results obtained from traditional similarity measures.
Intersections become very congested when traffic volumes are high, creating inefficiency that results in user delay and frustration. There have been many approaches which focus on optimization signal ...of Traffic Light System and Vehicle Trajectory Analysis to improve traffic flow at intersection. However, to implement those approaches into reality become a challenges since real-time problem. In this study, inspired by recent advanced vehicle technologies, we propose an approach for traffic flow management at intersection. In particular, with the exploding at an enormous rate of Internet of Things (IoT), the connected object has been the most visible and familiar application. By this way, based on connected object, we design a model which communicating among objects to improve traffic flow at intersection with real time problem. Moreover, traffic congestion is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.
Summary
Smart traffic light control at intersections is 1 of the major issues in Intelligent Transportation System. In this paper, on the basis of the new emerging technologies of Internet of Things, ...we introduce a new approach for smart traffic light control at intersection. In particular, we firstly propose a connected intersection system where every objects such as vehicles, sensors, and traffic lights will be connected and sharing information to one another. By this way, the controller is able to collect effectively and mobility traffic flow at intersection in real‐time. Secondly, we propose the optimization algorithms for traffic lights by applying algorithmic game theory. Specially, 2 game models (which are Cournot Model and Stackelberg Model) are proposed to deal with difference scenarios of traffic flow. In this regard, based on the density of vehicles, controller will make real‐time decisions for the time durations of traffic lights to optimize traffic flow. To evaluate our approach, we have used Netlogo simulator, an agent‐based modeling environment for designing and implementing a simple working traffic. The simulation results shows that our approach achieves potential performance with various situations of traffic flow.
With remarkable successes of sharing economy services (e.g., UBER (
https://www.uber.com
), Airbnb (
https://www.airbnb.com
), and so on), the amount of items which are distributed through these ...services is rapidly increasing. Therefore recommender systems for the sharing economy services are required. However, the existing recommenders are hard to support the sharing economy services, since they have focused on a ‘Item-User’ model that the recommenders provide satisfiable items to consumers (users) in accordance with only the consumers’ preferences. In this regard, we suggest a novel recommendation model, ‘Owner-Borrower’ model which considers the preferences of both sides: owners and borrowers of properties (items). Also, we propose a recommendation method based on the proposed model by applying a tensor factorization method and the Gale-Shapley algorithm. The tensor factorization is used for estimating preferences of the owners and the borrowers. With the estimated preferences, the Gale-Shapley algorithm makes optimal matches between the borrowers and the owners’ properties.
This paper presents a system that analyzes the sentiment of figurative language contained in short texts collected from Social Networking Services (SNS). This case study sources information from ...tweets on Twitter and calculates the polarity of the figurative language with three different categories (i.e., sarcastic, ironic, and metaphorical tweets). As in Medhat et al. (Ain Shams Eng J 5(4):1093–1113,
2014
), Nguyen and Jung (Mob Netw Appl 20(4):475–486,
2015
), many related works have used a lexical-based approach (e.g., dictionary and corpus), and machine learning-based approach (e.g., decision tree, rule discovery, and probabilistic methods) to extract sentiment in a given text. This statistical approach makes use of two main features:
i
) Content-based, and
ii
) Emotion Pattern-based. We believe that this combination offers a general method to solve the current problem and easily extends for analyzing other types of figurative languages. The proposed algorithm is evaluated by using Cosine similarity to conduct an experiment over a Data set that contains about 5,000 tweets. The results show that the FIS Model (Figurative language Identification using Statistical-based Model) works well with figurative tweets with a highest achievement of 0.7813.
Movie summarization focuses on providing as much information as possible for shorter movie clips while still keeping the content of the original movie and presenting a faster way for the audience to ...understand the movie. In this paper, we propose a novel method to summarize a movie based on character network analysis and the appearance of protagonist and main characters in the movie. Experiments were carried out for 2 movies (
Titanic (1997)
and
Frozen (2013)
) to show that our method outperforms conventional approaches in terms of the movie summarization rate.
