Activity Based Metadata for Semantic Desktop Search

Poe in Cyberspace: The “Mobile-Friendly” Sites

Existing desktop search applications, trying to keep up with the rapidly increasing storage capacities of our hard disks, offer an incomplete solution for information retrieval. In this paper we describe our Beagle++ desktop search prototype, which enhances conventional full-text search with semantics and ranking modules. This prototype extracts and stores activity-based metadata explicitly as RDF annotations. Our main contributions are extensions we integrate into the Beagle desktop search infrastructure to exploit this additional contextual information for searching and ranking the resources on the desktop. Contextual information plus ranking brings desktop search much closer to the performance of web search engines. Initially disconnected sets of resources on the desktop are connected by our contextual metadata, PageRank derived algorithms allow us to rank these resources appropriately. First experiments investigating precision and recall quality of our search prototype show encouraging improvements over standard search.

With the advent of the Internet, searching for information became a fundamental part of our daily computing. When large storage devices were integrated to our personal computers, desktop search solved the problem of locating information. With larger storage integrated into mobile devices, a solution for the problem is required in the mobile environment. Users often possess multiple devices, such as laptops, PDAs and smartphones, making information synchronization more important. The Dessy desktop search and synchronization system for mobile devices strives to answer these challenges. This paper introduces some of Dessy's features and demonstrates the Dessy prototype for mobile devices.

Semantic annotation for Web services represents an important approach that transforms Web services into Semantic Web services. This paper analyzes the distribution of implicit semantic information to enhance the understanding about Web services. After quantifying implicit semantic information in Web services, the PCA methodology is utilized for obtaining principal components of implicit semantic information. The experiment conducted on 11836 WSDL documentations shows that almost 97 percent of implicit semantic information is distributed in inputs and outputs. Thus, the evaluation of how much implicit semantic information can be described by semantic annotation is offered.

Geospatial data analytical model is developed in this paper to model the spatial suitability of malaria outbreak in Vellore, Tamil Nadu, India. In general, Disease control strategies are only the spatial information like landscape, weather and climate, but also spatially explicit information like socioeconomic variable, population density, behavior and natural habits of the people. The spatial multi-criteria decision analysis approach combines the multi-criteria decision analysis and geographic information system (GIS) to model the spatially explicit and implicit information and to make a practical decision under different scenarios and different environment. Malaria is one of the emerging diseases worldwide; the cause of malaria is weather & climate condition of the study area. The climate condition is often called as spatially implicit information, traditional decision-making models do not use the spatially implicit information it most often uses spatially explicit information such as socio-economic, natural habits of the people. There is need to develop an integrated approach that consists of spatially implicit and explicit information. The proposed approach is used to identity an effective control strategy that prevents and control of malaria. Inverse Distance Weighting (IDW) is a type of deterministic method used in this paper to assign the weight values based on the neighborhood locations. ArcGIS software is used to develop the geospatial habitat suitability model.

Traditional desktop search engines such as Google Desktop Search usually return a document ranking which still takes time to filter the desired documents. To improve the document ranking, this work adopts the query-specific clustering approach and proposes a two stage clustering scheme. Based on the returned snippets, the first stage divides the snippets into two groups. The first group contains all keywords in the query and the second group contains partial or no query keywords. The ranking of the first group will be ahead of the second group. In the second stage, the first group is further applied the Group-Average Agglomerative Clustering (GACC) to form hierarchical clusters that all have a combination similarity above a given threshold. Based on the GAAC result, non-singleton clusters are ordered from high to low by their last combination similarity. Within each cluster, the two last combining subclusters are also ordered from high to low by their last combination similarity. Having a combination similarity of 0, singleton clusters will be located behind following their initial snippet order. As test dataset, a standard Chinese news dataset CIRB030 is used which consists of 49210 documents and 42 enquiry topics. An original document ranking is obtained from Google Desktop Search. Then the snippets are tokenized and filtered to extract the representative keywords and form the snippet vectors. The snippets then go through the two stage clustering scheme to adjust their ranking. The result shows that the two stage clustering scheme can significantly improve the document ranking and the processing time is short.

One initial goal for the DRMF is to seed our digital compendium with fundamental orthogonal polynomial formulae. We had used the data from the NIST Digital Library of Mathematical Functions (DLMF) as initial seed for our DRMF project. The DLMF input LaTeX source already contains some semantic information encoded using a highly customized set of semantic LaTeX macros. Those macros could be converted to content MathML using LaTeXML. During that conversion the semantics were translated to an implicit DLMF content dictionary. This year, we have developed a semantic enrichment process whose goal is to infer semantic information from generic LaTeX sources. The generated context-free semantic information is used to build DRMF formula home pages for each individual formula. We demonstrate this process using selected chapters from the book “Hypergeometric Orthogonal Polynomials and their q-Analogues” (2010) by Koekoek, Lesky and Swarttouw (KLS) as well as an actively maintained addendum to this book by Koornwinder (KLSadd). The generic input KLS and KLSadd LaTeX sources describe the printed representation of the formulae, but does not contain explicit semantic information. See http://drmf.wmflabs.org.KeywordsOrthogonal PolynomialSemantic InformationHypergeometric Orthogonal PolynomialSeeding ProjectSubstitution FormulaThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

How emotional prosody guides your way: Evidence from eye movements

Objectives The purpose of this study is to confirm the effect of explicit information provision on elementary students' cognitive thinking in online life science teaching and learning situation with DMN Analysis. Methods For this purpose, resting-state EEG data was collected for a total of 40 people, 10 men and 10 men, and a group that mainly received explicit information through online digital textbooks for more than a year and a group that received implicit information through offline educational activities. Afterwards, Lyapunov exponent analysis was performed to confirm the intensity of learning stress, and sLORETA Current density analysis and Lagged coherence connectivity analysis were performed to compare brain activity and connectivity. Results As a result of the study, it was observed that provision of explicit information tended to lower students' sensitivity and lower psychological stress compared to provision of implicit information. In addition, lower current density was confirmed in the right frontal lobe and limbic lobe in the resting state when explicit information was provided compared to implicit information, and lower connection strength was observed in all connection nodes. Conclusions This means that although providing explicit information lowers the learner's stress intensity compared to providing implicit information, it is inefficient in brain utilization because it only increases the simple sensory experience and stimulation intensity. The significance of this study is that it neuroscientifically proved that learning is not given unilaterally by someone, but is a process that learners must create themselves.

Network representation aims to learn low-dimensional vector representations of network nodes while preserving the inherent properties of the network. For all its popularity, majority of the existing methods focus on exploitation of diverse information, including network topology and semantic information on nodes of network, and ignore their implicit semantics. For example, we all know the saying that birds of a feather flock together. More concretely, semantic information of one node can be influenced by its neighbors' semantic information. Furthermore, even two nodes are not directly connected, they may have similar implicit semantic information (i.e., high-order semantic proximity). Thus, they should be close in the represented vector space. To this end, we propose a Deep Semantic Network Representation approach (DSNR) in the self-translation framework from sequence to sequence. To excavate the implicit semantic information of nodes and capture the high-order semantic proximity, three key components make our approach effective, i.e., aggregation of nodes neighbors' semantic information and enhancement to the semantic feature representations of nodes by a deep autoencoder, integration of nodes semantic information in node identity sequence to generate node semantic sequence, and translation from node semantic sequence to node identity sequence to capture the high-order semantic proximity in an attention-enhanced seq2seq framework. Extensive experiments based on three real-world datasets have verified the effectiveness of our proposed approach11Code is available at https://github.com/DASE4/DSNR.

With the rise of the internet, a lot of web services (WSs) are offered. However, most of these WSs lack explicit and sufficient semantic information. As a result, relevant WSs are not delivered to consumers during service discovery and WS composition and selection are so challenging. A possible solution to this is to provide the existing syntactic WSs with explicit information of their meaning through an annotation. Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. In this paper, we first conduct a literature review and then we present an approach for semantically annotating WSs based on linked open data (LOD) knowledge. The proposal uses similarity measure algorithms for matching the appropriate LOD concepts to the corresponding WS' parameters. A performance evaluation on real-world WSs was performed to validate the proposed approach.

Since the development of the original specifications in the '90s the PDF document format has become the de-facto standard for the distribution and archival of documents in electronic form because of its ability to preserve the original layout of the documents, independently of the hardware, operating system and application software used to visualize them.Unfortunately the PDF format does not contain explicit structural and semantic information, making it very difficult to extract structured information from them, in particular data presented in tabular form. The automatic extraction of tabular data is a difficult and challenging task because tables can have extremely different formats and layouts, and involves several complex steps, from the proper recognition and conversion of printed text into machine-encoded characters, to the identification of logically coherent table constructs (headers, columns, rows, spanning elements), and to the breaking down of the data constructs into elemental objects.Several tools have been developed to support the extraction process. In this work we survey the most interesting tools for the automatic detection and extraction of tabular data, analyzing their respective advantages and limitations. A particular emphasis is given on programmable open source tools because of their flexibility and long-term availability, together with the possibility to easily tweak them to meet the peculiar needs of the problem at hand.As a practical application, we also present a workflow based on a set of R and AWK scripts that can automatically extract daily temperature and precipitation data from the official PDF documents made available each year by Regione Puglia, in Italy. The lessons learned from the development of this workflow and the possibility to generalize the approach to different kinds of PDF documents are also discussed.

Multimodal Sentiment Analysis (MSA) aims to predict subjective human emotions by leveraging multimodal information. However, existing research inadequately utilizes explicit sentiment semantic information at the lexical level in text and overlooks noise interference from non-dominant modalities, such as irrelevant movements in visual modalities and background noise in audio modalities. To address this issue, we propose a multimodal sentiment analysis model based on knowledge enhancement and text-guided learning (MMKT). The model constructs a sentiment knowledge graph for the textual modality using the SenticNet knowledge base. This graph directly annotates word-level sentiment polarity, strengthening the model’s understanding of emotional vocabulary. Furthermore, global sentiment knowledge features are generated through graph embedding computations to enhance the multimodal fusion process. Simultaneously, a dynamic text-guided learning approach is introduced, which dynamically leverages multi-scale textual features to actively suppress redundant or conflicting information in visual and audio modalities, thereby generating purer cross-modal representations. Finally, concatenated textual features, cross-modal features, and knowledge features are utilized for sentiment prediction. Experimental results on the CMU-MOSEI and Twitter2019 dataset demonstrate the superior performance of the MMKT model.

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

This paper proposes a metadata schema relating to functionality based on Semantic Web technology for the management of the information content of engineering design documents. The schema enables us to annotate web-documents with RDF metadata, which represents devices as having specific functions. The metadata provide a clear and operational semantics for the functional terms in documents. The metadata schema is based on our own functional ontologies, which we believe to provide effective guidelines for consistent functional annotation. These ontologies have been developed over many years and deployed successfully in industry. We then demonstrate a document search system using the metadata, which enables us to retrieve web-documents using the types of function and relationships defined in the schema rather than more superficial terms. Such function-oriented management of information is especially useful in the conceptual design phase. It allows one to find previous cases of the same function in earlier designs and to find related patents. We go on to discuss the following two issues on the interoperability of the functional knowledge. The first is automatic transformation of the functional model in our modeling framework into a chart-style form commonly used for the FMEA (Failure Modes and Effects Analysis) activity using a mapping between our ontology and an FMEA ontology. The second is translation of the functional model between our functional vocabulary and other taxonomy of function, that is, Functional Basis proposed by Stone et al. Another mapping between our functional vocabulary and Functional Basis can be done via a richer, generic reference ontology of functions. This mapping would aim at clarifying ontological differences between functional taxonomies and enabling translation between them.