Toward location-aware Web: extraction method,applications and evaluation

Location-based services (LBS) belong to one of the most popular types of services today. However, a recurring issue is that most of the content in LBS has to be created from scratch and needs to be explicitly tagged to locations, which makes existing Web content not directly usable for LBS. In this paper, we aim at making Web sites location-aware and feed this information to LBS. Our approach toward location-aware Web is threefold: First, we present a location extraction method: SALT. It receives Web sites as input and equips them with location tags. Compared to other approaches, SALT is capable of extracting locations with a precision up to the street level. Performance evaluations further show high applicability for practice. Second, we present three applications for SALT: Webnear.me, Local Browsing and Local Facebook. Webnear.me offers location-aware Web surfing through a mobile Web site and a smartphone app. Local Browsing adds the feature to browse by nearby tags, extracted from Web sites delivered by SALT. Local Facebook extends location tagging to social networks, allowing to run SALT on one’s own and one’s friends’ timeline. Finally, we evaluate SALT for technology acceptance of Webnear.me through a formative user study. Through real user data, collected during a 3 months pilot field deployment of Webnear.me, we assess whether SALT is a proper instance of “location of a Web site”.     

Modeling semantic information in engineering applications: a review

Due to the latest advances of information technology and the increasing complexity of engineering applications, it is becoming more and more important to model semantic information. There are many modeling methodologies to do the work of modeling semantic information instead of natural language processing. Since this field is very broad, the comparison discussed here is not an exhaustive study but rather the partial views of the coauthors from our own perspectives. In the present paper we give a review of the literature of conceptual models especially static one and then classify them into four type models namely structure-based model, object-oriented model, knowledge semantic-based model, and web semantic-based model. Based on the classification given above, a hierarchy structured criteria is given. According to the criteria we pick one or two representative conceptual models from each type to conduct the comparison. We compare the following five aspects of conceptual models: expressivity, clarity, semantics, formal foundation, and application fields. The comparative study shows that different models have different features and fit different fields of engineering applications. The present comparison study is useful for users to understand and choose right conceptual models combining with specific requirements of engineering applications.     

Challenges in location-aware computing

The availability of massive amounts of high-quality data as well as advances in computing and communications suggest the possibility of powerful new applications in science, commerce, environment, and government-but many research challenges remain. Research in geospatial information systems is inherently multidisciplinary, and research efforts are likely to be most successful if conducted by teams that combine expertise in applications as well as information technology. Advances in location-aware computing, in particular, could have important implications not just for how geospatial data are acquired but also for how and with what quality they can be delivered and how mobile and geographically distributed systems are designed.     

融合上下文信息的场景结构恢复

提出了一种融合场景上下文信息的两级分类算法,从单幅图像中恢复场景结构。室外场景的结构化特征使其3维结构可以粗略地分为3类:"地面","天空"以及"竖直物体"。首先,把图像分割成具有灰度和颜色一致性的区域;其次确定特征显著区域("确定区域")的结构,将特征不明显的区域标记为"未知区域";然后根据"未知区域"与"确定区域"的相似性及"确定区域"场景结构对"未知区域"的可能结构进行投票,将投票最多的结构类型赋予"未知区域";最后介绍场景结构恢复在构造场景3维模型方面的应用。实验结果表明,由于利用了场景结构的上下文信息,该算法场景结构恢复的正确率为92.3%,优于现有算法88.1%的恢复正确率。     

Time-optimized contextual information forwarding in mobile sensor networks

We study on the forwarding of quality contextual information in mobile sensor networks (MSNs). Mobile nodes form ad-hoc distributed processing networks that produce accessible and quality-stamped information about the surrounding environment. Due to the dynamic network topology of such networks the context quality indicators seen by the nodes vary over time. A node delays the context forwarding decision until context of better quality is attained. Moreover, nodes have limited resources, thus, they have to balance between energy conservation and quality of context. We propose a time-optimized, distributed decision making model for forwarding context in a MSN based on the theory of optimal stopping. We compare our findings with certain context forwarding schemes found in the literature and pinpoint the advantages of the proposed model.     

Towards sustainable mobility behavior: research challenges for location-aware information and communication technology

Private transport accounts for a large amount of total CO₂ emissions, thus significantly contributing to global warming. Tools that actively support people in engaging in a more sustainable life-style without restricting their mobility are urgently needed. How can location-aware information and communication technology (ICT) enable novel interactive and participatory approaches that help people in becoming more sustainable? In this survey paper, we discuss the different aspects of this challenge from a technological and cognitive engineering perspective, based on an overview of the main information processes that may influence mobility behavior. We review the state-of-the-art of research with respect to various ways of influencing mobility behavior (e.g., through providing real-time, user-specific, and location-based feedback) and suggest a corresponding research agenda. We conclude that future research has to focus on reflecting individual goals in providing personal feedback and recommendations that take into account different motivational stages. In addition, a long-term and large-scale empirical evaluation of such tools is necessary.     

Ranking kinematics for revising by contextual information

Annals of Mathematics and Artificial Intelligence - Probability kinematics is a leading paradigm in probabilistic belief change. It is based on the idea that conditional beliefs should be...     

Privileged contextual information for context-aware recommender systems

A recommender system is used in various fields to recommend items of interest to the users. Most recommender approaches focus only on the users and items to make the recommendations. However, in many applications, it is also important to incorporate contextual information into the recommendation process. Although the use of contextual information has received great focus in recent years, there is a lack of automatic methods to obtain such information for context-aware recommender systems. Some works address this problem by proposing supervised methods, which require greater human effort and whose results are not so satisfactory. In this scenario, we propose an unsupervised method to extract contextual information from web page content. Our method builds topic hierarchies from page textual content considering, besides the traditional bag-of-words, valuable information of texts as named entities and domain terms (privileged information). The topics extracted from the hierarchies are used as contextual information in context-aware recommender systems. We conducted experiments by using two data sets and two baselines: the first baseline is a recommendation system that does not use contextual information and the second baseline is a method proposed in literature to extract contextual information. The results are, in general, very good and present significant gains. In conclusion, our method has advantages and innovations:(i) it is unsupervised; (ii) it considers the context of the item (Web page), instead of the context of the user as in most of the few existing methods, which is an innovation; (iii) it uses privileged information in addition to the existing technical information from pages; and (iv) it presented good and promising empirical results. This work represents an advance in the state-of-the-art in context extraction, which means an important contribution to context-aware recommender systems, a kind of specialized and intelligent system.     

Enriching machine-mediated speech-to-speech translation using contextual information

Conventional approaches to speech-to-speech (S2S) translation typically ignore key contextual information such as prosody, emphasis, discourse state in the translation process. Capturing and exploiting such contextual information is especially important in machine-mediated S2S translation as it can serve as a complementary knowledge source that can potentially aid the end users in improved understanding and disambiguation. In this work, we present a general framework for integrating rich contextual information in S2S translation. We present novel methodologies for integrating source side context in the form of dialog act (DA) tags, and target side context using prosodic word prominence. We demonstrate the integration of the DA tags in two different statistical translation frameworks, phrase-based translation and a bag-of-words lexical choice model. In addition to producing interpretable DA annotated target language translations, we also obtain significant improvements in terms of automatic evaluation metrics such as lexical selection accuracy and BLEU score. Our experiments also indicate that finer representation of dialog information such as yes–no questions, wh-questions and open questions are the most useful in improving translation quality. For target side enrichment, we employ factored translation models to integrate the assignment and transfer of prosodic word prominence (pitch accents) during translation. The factored translation models provide significant improvement in assignment of correct pitch accents to the target words in comparison with a post-processing approach. Our framework is suitable for integrating any word or utterance level contextual information that can be reliably detected (recognized) from speech and/or text.     

An augmented reality interface to contextual information

In this paper, we report on a prototype augmented reality (AR) platform for accessing abstract information in real-world pervasive computing environments. Using this platform, objects, people, and the environment serve as contextual channels to more information. The user??s interest with respect to the environment is inferred from eye movement patterns, speech, and other implicit feedback signals, and these data are used for information filtering. The results of proactive context-sensitive information retrieval are augmented onto the view of a handheld or head-mounted display or uttered as synthetic speech. The augmented information becomes part of the user??s context, and if the user shows interest in the AR content, the system detects this and provides progressively more information. In this paper, we describe the first use of the platform to develop a pilot application, Virtual Laboratory Guide, and early evaluation results of this application.     

Inter-process communication in MVS/XA and applications for scientific and engineering information processing

This paper surveys inter-process communication in MVS/XA (multiple virtual system/extended architecture) and explores the use of cross-memory services for this purpose. The lack of high-level interfaces to system services is a major handicap in MVS/XA. Tools to provide high-level services, in particular, tools for inter-process communication, can be implemented in a simple, elegant and efficient way. The implementation and use of a communication subsystem, the extended communication facility (ECF), are also described. The ideas and mechanisms presented are useful for many scientific and engineering applications running under MVS/XA. Examples include distributed systems, applications using concurrent programming, a quasi-batch environment, communication subsystems, and mail subsystems.     

Robust image recognition by fusion of contextual information

This paper studies the fusion of contextual information in pattern recognition, with applications to biomedical image identification. In the real world there are cases where the identity of an object is ambiguous if the classification is based only on its own features. It is helpful to reduce the ambiguity by utilizing extra information, referred to as context, provided by accompanying objects. We investigate two techniques that incorporate context. The first approach, based on compound Bayesian theory, incorporates context by fusing the measurements of all objects under consideration. It is an optimal strategy in terms of achieving minimum set-by-set error probability. The second approach fuses the measurements of an object with explicitly extracted context. Its linear computational complexity makes it more tractable than the first approach, which requires exponential computation. These two techniques are applied to two medical applications: white blood cell image classification and microscopic urinalysis. It is demonstrated that superior classification performances are achieved by using context. In our particular applications, it reduces overall classification error, as well as false positive and false negative diagnosis rates.     

Use of contextual information for feature ranking anddiscretization

Deriving classification rules or decision trees from examples is an important problem. When there are too many features, discarding weak features before the derivation process is highly desirable. When there are numeric features, they need to be discretized for the rule generation. We present a new approach to these problems. Traditional techniques make use of feature merits based on either the information theoretic, or the statistical correlation between each feature and the class. We instead assign merits to features by finding each feature's “obligation” to the class discrimination in the context of other features. The merits are then used to rank the features, select a feature subset, and discretize the numeric variables. Experience with benchmark example sets demonstrates that the new approach is a powerful alternative to the traditional methods. This paper concludes by posing some new technical issues that arise from this approach     

Annotation processes for flexible management of contextual information

We propose the use of annotations as a way to flexibly enrich a domain of interest with information concerning different contexts of use for its elements. We provide a formal model of annotation in the framework of typed graphs, in which the presence of annotations is reified through nodes and edges of specific types, relating nodes from different domains. This allows the flexible activation and de-activation of annotations, as well as the addition of several annotations from different domains on the same element. We show that annotations give rise to a category, where pushouts are the basic construct for the composition of annotation-related processes. We prove some properties of annotated graphs and discuss examples drawn from several fields.     

CI-SNF: Exploiting contextual information to improve SNF based information retrieval

Similarity networks contain important topological features and patterns critical to understanding interactions among samples in a large dataset. To create a comprehensive view of the interactions within a dataset, the Similarity Network Fusion (SNF) technique has been proposed to fuse the similarity networks based on different data types into one similarity network that represents the full spectrum of underlying data. In this paper, a modified version of SNF, which is named as Contextual Information based SNF (CI-SNF), is proposed. In CI-SNF, first, modified Jaccard distance is performed on the SNF fused similarity to utilize the contextual information contained in the fused similarity network. Second, the local consistency of samples from the same category is enhanced by speculating that the samples which are located high in the Jaccard distance based ranking list of a specific query are from the same category as the query. Third, the inverted index technique is introduced to utilize the sparsity property of the locally consistent similarity network to enhance the computational efficiency. To verify the effectiveness and efficiency of CI-SNF model, it is applied in four different tasks, Cover Song Identification (CSI), image classification, cancer subtype identification, and drug taxonomy, respectively. Extensive experiments on thirteen challenging datasets demonstrate that CI-SNF scheme outperforms state-of-the-art similarity fusion algorithms including SNF in all four tasks. It is also verified that utilizing the contextual information contained in the SNF-based similarity network helps to enhance the performance of the SNF-based scheme, further.     

Measures for investigating the contextual modulation of information transmission

The aim of this paper is to show how information theoretic measures can be used to analyse and interpret the results of psychophysical experiments designed to search for conditions under which information from one source modulates the transmission of information from another source. We therefore use measures of mutual and conditional information to analyse systems with two inputs. The information transmitted by such a system can be split into three components depending on whether it is shared between the two inputs or is specific to each. We are concerned here with distinguishing systems that use one input to modulate transmission of information about the other from systems that simply add both inputs, and show how the three components provide evidence for distinguishing between additive and modulatory effects. We also report numerical simulations of the sampling biasses and variances of these measures as a function of the sample size and propose minimum sample sizes that should be used to overcome the bias.     

Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications

This paper addresses the problem of estimating continuous boundaries between acceptable and unacceptable engineering design parameters in complex engineering applications. In particular, a procedure is proposed to reduce the computational cost of finding and representing the boundary. The proposed methodology combines a low-discrepancy sequence (Sobol) and a support vector machine (SVM) in an active learning procedure able to efficiently and accurately estimate the boundary surface. The paper describes the approach and methodological choices resulting in the desired level of boundary surface refinement and the new algorithm is applied to both two highly-nonlinear test functions and a real-world train stability design problem. It is expected that the new method will provide designers with a tool for the evaluation of the acceptability of designs, particularly for engineering systems whose behaviour can only be determined through complex simulations.     

ContextAug: model-domain failing test augmentation with contextual information

In the process of software development, the ability to localize faults is crucial for improving the efficiency of debugging. Generally speaking, detecting and repairing errant behavior at an early stage of the development cycle considerably reduces costs and development time. Researchers have tried to utilize various methods to locate the faulty codes. However, failing test cases usually account for a small portion of the test suite, which inevitably leads to the class-imbalance phenomenon and hampers the effectiveness of fault localization. Accordingly, in this work, we propose a new fault localization approach named ContextAug. After obtaining dynamic execution through test cases, ContextAug traces these executions to build an information model; subsequently, it constructs a failure context with propagation dependencies to intersect with new model-domain failing test samples synthesized by the minimum variability of the minority feature space. In contrast to traditional test generation directly from the input domain, ContextAug seeks a new perspective to synthesize failing test samples from the model domain, which is much easier to augment test suites. Through conducting empirical research on real large-sized programs with 13 state-of-the-art fault localization approaches, ContextAug could significantly improve fault localization effectiveness with up to 54.53%. Thus, ContextAug is verified as able to improve fault localization effectiveness.