Information

983 KiB

Open AccessArticle

ODQ: A Fluid Office Document Query Language

by Xuhong Liu, Ning Li, Yunmei Shi and Xia Hou

Information 2015, 6(2), 275-286; https://doi.org/10.3390/info6020275 - 11 Jun 2015

Viewed by 4993

Fluid office documents, as semi-structured data often represented by Extensible Markup Language (XML) are important parts of Big Data. These office documents have different formats, and their matching Application Programming Interfaces (APIs) depend on developing platform and versions, which causes difficulty in custom [...] Read more.

Fluid office documents, as semi-structured data often represented by Extensible Markup Language (XML) are important parts of Big Data. These office documents have different formats, and their matching Application Programming Interfaces (APIs) depend on developing platform and versions, which causes difficulty in custom development and information retrieval from them. To solve this problem, we have been developing an office document query (ODQ) language which provides a uniform method to retrieve content from documents with different formats and versions. ODQ builds common document model ontology to conceal the format details of documents and provides a uniform operation interface to handle office documents with different formats. The results show that ODQ has advantages in format independence, and can facilitate users in developing documents processing systems with good interoperability. Full article

(This article belongs to the Special Issue Selected Papers from Conference on Document Information Processing (CDIP) 2014)

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741 KiB

Open AccessArticle

Efficiency and Privacy Enhancement for a Track and Trace System of RFID-Based Supply Chains

by Xunjun Chen, Yuelong Zhu, Jiguo Li, Yamin Wen and Zheng Gong

Information 2015, 6(2), 258-274; https://doi.org/10.3390/info6020258 - 11 Jun 2015

Cited by 6 | Viewed by 5685

Abstract

One of the major applications of Radio Frequency Identification (RFID) technology is in supply chain management as it promises to provide real-time visibility based on the function of track and trace. However, such an RFID-based track and trace system raises new security and [...] Read more.

One of the major applications of Radio Frequency Identification (RFID) technology is in supply chain management as it promises to provide real-time visibility based on the function of track and trace. However, such an RFID-based track and trace system raises new security and privacy challenges due to the restricted resource of tags. In this paper, we refine three privacy related models (i.e., the privacy, path unlinkability, and tag unlinkability) of RFID-based track and trace systems, and clarify the relations among these privacy models. Specifically, we have proven that privacy is equivalent to path unlinkability and tag unlinkability implies privacy. Our results simplify the privacy concept and protocol design for RFID-based track and trace systems. Furthermore, we propose an efficient track and trace scheme, Tracker+, which allows for authentic and private identification of RFID-tagged objects in supply chains. In the Tracker+, no computational ability is required for tags, but only a few bytes of storage (such as EPC Class 1 Gen 2 tags) are needed to store the tag state. Indeed, Tracker+ reduces the memory requirements for each tag by one group element compared to the Tracker presented in other literature. Moreover, Tracker+ provides privacy against supply chain inside attacks. Full article

(This article belongs to the Special Issue Cybersecurity and Cryptography)

763 KiB

Open AccessArticle

An Approach to an Intersection Traffic Delay Study Based on Shift-Share Analysis

by Jianfeng Xi, Wei Li, Shengli Wang and Chuanjiu Wang

Information 2015, 6(2), 246-257; https://doi.org/10.3390/info6020246 - 8 Jun 2015

Cited by 5 | Viewed by 6406

Abstract

Intersection traffic delay research has traditionally placed greater emphasis on the study of through and left-turning vehicles than right-turning ones, which often renders existing methods or models inapplicable to intersections with heavy pedestrian and non-motorized traffic. In the meantime, there is also a [...] Read more.

Intersection traffic delay research has traditionally placed greater emphasis on the study of through and left-turning vehicles than right-turning ones, which often renders existing methods or models inapplicable to intersections with heavy pedestrian and non-motorized traffic. In the meantime, there is also a need for understanding the relations between different types of delay and how they each contribute to the total delay of the entire intersection. In order to address these issues, this paper first examines models that focus on through and left-turn traffic delays, taking into account the presence of heavy mixed traffic flows that are prevalent in developing countries, then establishes a model for calculating right-turn traffic delay and, last, proposes an approach to analyzing how much each of the three types of traffic delay contributes to the total delay of the intersection, based on the application of shift-share analysis (SSA), which has been applied extensively in the field of economics. Full article

(This article belongs to the Special Issue Swarm Information Acquisition and Swarm Intelligence in Engineering)

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1524 KiB

Open AccessArticle

CIMS: A Context-Based Intelligent Multimedia System for Ubiquitous Cloud Computing

by Abhilash Sreeramaneni, Hyungjin Im, Won Min Kang, Chan Koh and Jong Hyuk Park

Information 2015, 6(2), 228-245; https://doi.org/10.3390/info6020228 - 4 Jun 2015

Cited by 2 | Viewed by 6093

Abstract

Mobile users spend a tremendous amount of time surfing multimedia contents over the Internet to pursue their interests. A resource-constrained smart device demands more intensive computing tasks and lessens the battery life. To address the resource limitations (i.e., memory, lower maintenance [...] Read more.

Mobile users spend a tremendous amount of time surfing multimedia contents over the Internet to pursue their interests. A resource-constrained smart device demands more intensive computing tasks and lessens the battery life. To address the resource limitations (i.e., memory, lower maintenance cost, easier access, computing tasks) in mobile devices, mobile cloud computing is needed. Several approaches have been proposed to confront the challenges of mobile cloud computing, but difficulties still remain. However, in the coming years, context collecting, processing, and interchanging the results on a heavy network will cause vast computations and reduce the battery life in mobiles. In this paper, we propose a “context-based intelligent multimedia system” (CIMS) for ubiquitous cloud computing. The main goal of this research is to lessen the computing percentage, storage complexities, and battery life for mobile users by using pervasive cloud computing. Moreover, to reduce the computing and storage concerns in mobiles, the cloud server collects several groups of user profiles with similarities by executing K-means clustering on users’ data (context and multimedia contents). The distribution process conveys real-time notifications to smartphone users, according to what is stated in his/her profile. We considered a mobile cloud offloading system, which decides the offloading actions to/from cloud servers. Context-aware decision-making (CAD) customizes the mobile device performance with different specifications such as short response time and lesser energy consumption. The analysis says that our CIMS takes advantage of cost-effective features to produce high-quality information for mobile (or smart device) users in real time. Moreover, our CIMS lessens the computation and storage complexities for mobile users, as well as cloud servers. Simulation analysis suggests that our approach is more efficient than existing domains. Full article

(This article belongs to the Special Issue Advances in Ubiquitous Computing and Information Science)

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838 KiB

Open AccessArticle

Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

by Fang Zong, Yu Bai, Xiao Wang, Yixin Yuan and Yanan He

Information 2015, 6(2), 212-227; https://doi.org/10.3390/info6020212 - 4 Jun 2015

Cited by 15 | Viewed by 6514

Abstract

Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing [...] Read more.

Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing the parameters in the model. The travel modes of walking, bicycle, subway, bus, and car are recognized in this model. The results indicate that the developed model shows a high level of accuracy for mode identification. The estimation results also present GA’s contribution to the optimization of the model. The findings can be used to identify travel mode based on GPS survey data, which will significantly enhance the efficiency and accuracy of travel survey and data processing. By providing crucial trip information, the results also contribute to the modeling and analyzing of travel behavior and are readily applicable to a wide range of transportation practices. Full article

(This article belongs to the Special Issue Swarm Information Acquisition and Swarm Intelligence in Engineering)

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180 KiB

Open AccessReview

The Role of Malware in Reported Cyber Espionage: A Review of the Impact and Mechanism

by Gaute Wangen

Information 2015, 6(2), 183-211; https://doi.org/10.3390/info6020183 - 18 May 2015

Cited by 33 | Viewed by 17506

Abstract

The recent emergence of the targeted use of malware in cyber espionage versus industry requires a systematic review for better understanding of its impact and mechanism. This paper proposes a basic taxonomy to document major cyber espionage incidents, describing and comparing their impacts [...] Read more.

The recent emergence of the targeted use of malware in cyber espionage versus industry requires a systematic review for better understanding of its impact and mechanism. This paper proposes a basic taxonomy to document major cyber espionage incidents, describing and comparing their impacts (geographic or political targets, origins and motivations) and their mechanisms (dropper, propagation, types of operating systems and infection rates). This taxonomy provides information on recent cyber espionage attacks that can aid in defense against cyber espionage by providing both scholars and experts a solid foundation of knowledge about the topic. The classification also provides a systematic way to document known and future attacks to facilitate research activities. Geopolitical and international relations researchers can focus on the impacts, and malware and security experts can focus on the mechanisms. We identify several dominant patterns (e.g., the prevalent use of remote access Trojan and social engineering). This article concludes that the research and professional community should collaborate to build an open data set to facilitate the geopolitical and/or technical analysis and synthesis of the role of malware in cyber espionage. Full article

(This article belongs to the Special Issue Cybersecurity and Cryptography)

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631 KiB

Open AccessArticle

Analysis of Scholarly Communication Activities in Buddhism and Buddhist Studies

by Edoardo Magnone

Information 2015, 6(2), 162-182; https://doi.org/10.3390/info6020162 - 4 May 2015

Cited by 1 | Viewed by 5639

Abstract

There is little knowledge regarding the exchange of academic information on religious contexts. The objective of this informational study was to perform an overall analysis of all Buddhism-related communications collected in the Web of Science (WoS) from 1993 to 2011. The studied informational [...] Read more.

There is little knowledge regarding the exchange of academic information on religious contexts. The objective of this informational study was to perform an overall analysis of all Buddhism-related communications collected in the Web of Science (WoS) from 1993 to 2011. The studied informational parameters include the growth in number of the scholarly communications, as well as the language-, document-, subject category-, source-, country-, and organization-wise distribution of the communications. A total of 5407 scholarly communications in this field of study were published in the selected time range. The most preferred WoS subject category was Asian Studies with 1773 communications (22.81%), followed by Religion with 1425 communications (18.33%) and Philosophy with 680 communications (8.75%). The journal with the highest mean number of citations is Numen: International Review for the History of Religions—with 2.09 citations in average per communication. The United States was the top productive country with 2159 communications (50%), where Harvard University topped the list of organization with 85 communications (12%). Full article

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318 KiB

Open AccessArticle

Graph Regularized Within-Class Sparsity Preserving Projection for Face Recognition

by Songjiang Lou, Xiaoming Zhao, Wenping Guo and Ying Chen

Information 2015, 6(2), 152-161; https://doi.org/10.3390/info6020152 - 24 Apr 2015

Cited by 1 | Viewed by 5425

Abstract

As a dominant method for face recognition, the subspace learning algorithm shows desirable performance. Manifold learning can deal with the nonlinearity hidden in the data, and can project high dimensional data onto low dimensional data while preserving manifold structure. Sparse representation shows its [...] Read more.

As a dominant method for face recognition, the subspace learning algorithm shows desirable performance. Manifold learning can deal with the nonlinearity hidden in the data, and can project high dimensional data onto low dimensional data while preserving manifold structure. Sparse representation shows its robustness for noises and is very practical for face recognition. In order to extract the facial features from face images effectively and robustly, in this paper, a method called graph regularized within-class sparsity preserving analysis (GRWSPA) is proposed, which can preserve the within-class sparse reconstructive relationship and enhances separatability for different classes. Specifically, for each sample, we use the samples in the same class (except itself) to represent it, and keep the reconstructive weight unchanged during projection. To preserve the manifold geometry structure of the original space, one adjacency graph is constructed to characterize the interclass separability and is incorporated into its criteria equation as a constraint in a supervised manner. As a result, the features extracted are sparse and discriminative and helpful for classification. Experiments are conducted on the two open face databases, the ORL and YALE face databases, and the results show that the proposed method can effectively and correctly find the key facial features from face images and can achieve better recognition rate compared with other existing ones. Full article

(This article belongs to the Special Issue Intelligent Data Analysis)

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1177 KiB

Open AccessArticle

Analysis and Visualization for Hot Spot Based Route Recommendation Using Short-Dated Taxi GPS Traces

by Ying Shen, Ligang Zhao and Jing Fan

Information 2015, 6(2), 134-151; https://doi.org/10.3390/info6020134 - 21 Apr 2015

Cited by 46 | Viewed by 8116

Abstract

Taxi GPS traces, which contain a great deal of valuable information as regards to human mobility and city traffic, can be extracted to improve the quality of our lives. Since the method of visualized analysis is believed to be an effective way to [...] Read more.

Taxi GPS traces, which contain a great deal of valuable information as regards to human mobility and city traffic, can be extracted to improve the quality of our lives. Since the method of visualized analysis is believed to be an effective way to present information vividly, we develop our analysis and visualization method based on a city’s short-dated taxi GPS traces, which can provide recommendation to help cruising taxi drivers to find potential passengers with optimal routes. With our approach, hot spots for loading and unloading passenger(s) are extracted using an improved DBSCAN algorithm after data preprocessing including cleaning and filtering. Then, this paper describes the start-end point-based similar trajectory method to get coarse-level trajectories clusters, together with the density-based ε distance trajectory clustering algorithm to identify recommended potential routes. A weighted tree is defined including such factors as driving time, velocity, distance and endpoint attractiveness for optimal route evaluation from vacant to occupied hot spots. An example is presented to show the effectiveness of our visualization method. Full article

(This article belongs to the Special Issue Intelligent Data Analysis)

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880 KiB

Open AccessArticle

An MVC-based Intelligent Document Model Using UIML

by Yunmei Shi, Xuhong Liu, Ning Li and Xia Hou

Information 2015, 6(2), 122-133; https://doi.org/10.3390/info6020122 - 27 Mar 2015

Viewed by 5139

Abstract

Aiming at the common problems of intelligent document platform-dependency, this paper proposes an MVC-based (Model View Controller-based) intelligent document model using UIML (User Interface Markup Language). The model is made on the basis of the previous work of our team, and the difference [...] Read more.

Aiming at the common problems of intelligent document platform-dependency, this paper proposes an MVC-based (Model View Controller-based) intelligent document model using UIML (User Interface Markup Language). The model is made on the basis of the previous work of our team, and the difference is that the new model separates user interface and interaction descriptions from the view component to make the intelligent document model much more independent of platform and programming language. To verify the intelligent document model, we implemented a prototype, which can support intelligent operations. The test result shows that our approach is correct. The model not only follows MVC framework, but also provides good flexibility and independence. Full article

(This article belongs to the Special Issue Selected Papers from Conference on Document Information Processing (CDIP) 2014)

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3479 KiB

Open AccessArticle

Evaluate the Interoperability of Document Format: Based on Translation Practice of OOXML and UOF

by Yaohu Lin, Xuelian Lin, Ning Li and Yongmin Mu

Information 2015, 6(2), 111-121; https://doi.org/10.3390/info6020111 - 27 Mar 2015

Cited by 2 | Viewed by 4909

Abstract

Taking both OOXML and UOF standards as examples, we empirically evaluate the interoperability of office document formats from the view of translation practice. With the aim of covering the complete feature set of OOXML and UOF, a novel UOF-Open XML Translator is developed [...] Read more.

Taking both OOXML and UOF standards as examples, we empirically evaluate the interoperability of office document formats from the view of translation practice. With the aim of covering the complete feature set of OOXML and UOF, a novel UOF-Open XML Translator is developed in this study. Thorough experiments demonstrate that our translator implements bidirectional conversion of 80.4% features perfectly and 9.9% features with acceptable discrepancy. Regarding the remaining 9.7% features, more efforts would be taken in future work. Full article

(This article belongs to the Special Issue Selected Papers from Conference on Document Information Processing (CDIP) 2014)

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Information, Volume 6, Issue 2 (June 2015) – 11 articles , Pages 111-286

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