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With the rapid deployment of a number of sensors, it is crucial to efficiently manage their data streams with heterogeneous properties. To achieve various sensor applications such as discovery and mashup, a method of retrieving meaningful information from raw sensor data is required. However, it is hard to analyze and represent the sensor data since sensors generate streaming data of different patterns...
Nowadays, in such a high-tech living lifestyle, profusion of multimedia data are produced and propagated around the world. To identify meaningful semantic concepts from the large amount of data, one of the major challenges is called the data imbalance problem. Data imbalance occurs when the number of positive instances (i.e., instances which contain the target concept) is greatly less than the number...
With the fast growth rate of information availability through the World Wide Web, search engines' ranking become limited to deal with such enormous amount of information. Web search engines should be enriched with methodologies that enable it to understand the content of Web pages, then to align pages to the correct query category that highly match its content. In this paper, a proposed system is...
The solution of NP-hard problems requires the use of one or more explicit or implicit heuristics as a practical measure. Quantum computers promise to make this practical for O (2n) problems or less, but have yet to deliver a solution to a single NP-hard problem. The question addressed by this paper is whether domain transference and reuse of problem-solving knowledge can be mediated through the reuse...
UML is a semi-formal modeling language for object oriented systems. It is successfully used to assist all phases of software development process. With growing support by CASE tools, its applications become more and more widespread in real word projects. In fact, these tools allow code-generation from UML diagrams and reverse-engineering. In other hand, picalculus is a flexible formal theory with several...
The Integrated Modular Avionics (IMA) architecture and the Time-Triggered Ethernet (TTEthernet) network have emerged as the key components of a typical architecture model for recent civil aircrafts. It has recently been proposed to model both the (IMA) architecture and the TTEthernet network in the constraint-based TTCC calculus. In this paper, we show how the declarative aspects of this calculus...
In this paper, we propose a novel approach for reader-emotion categorization using word embedding learned from neural networks and an SVM classifier. The primary objective of such word embedding methods involves learning continuous distributed vector representations of words through neural networks. It can capture semantic context and syntactic cues, and subsequently be used to infer similarity measures...
Devices, objects, and sensors are getting to connect with one another in the Internet of Things (IoT). Although there are existing models for representing sensors, there is lack of methods of integrating sensor data with domain knowledge to construct complex sensors. Semantic models for complex sensor mashups are required. In this paper, we present a complex sensor model that enables us to combine...
Reuse requires high-level representation models. Ontology is commonly defined as a formal specification of knowledge conceptualization in a consensual form. OWL (Web Ontology Language) is the most used and expressive ontology description language that supports handling and reasoning. However, it lacks expressivity for some specific requirements. We propose, in this paper, to augment OWL with a new...
For the functioning of American democracy, the Lobbying Disclosure Act (LDA), for the very first time, provides data to empirically research interest groups behaviors and their influence on congressional policymaking. One of the main research challenges is to automatically find the topic(s), by short & sparse text classification, in a large corpus of unorganized, semi-structured, and poorly...
When a high-energy particle such as a proton strikes a CPU, the impact may result in the corruption of a data register on the CPU. Such a single-event upset (SEU), in which a random bit is flipped in the content of a data register, can lead to critical errors in the execution of a program. This is particularly problematic for security-or safety-critical systems where such errors may have grave consequences...
Data mining and machine learning methods have been playing an important role in searching and retrieving multimedia information from all kinds of multimedia repositories. Although some of these methods have been proven to be useful, it is still an interesting and active research area to effectively and efficiently retrieve multimedia information under difficult scenarios, i.e., detecting rare events...
Recognizing inference in text (RITE) plays an important role in the answer validation modules for a Question Answering (QA) system. The problem of class imbalance has received increased attention in the machine learning community. In recent years, several attempts have been made on the linguistic phenomena analysis, however, little is known about the effects of imbalanced datasets with linguistic...
Event-based online social networks are Internet-based services that enable users to participate in real world experiences together. Event-based social networks can be created by a community of end-users based on their own interests in specific types of event and sources of event information. We propose a method to create such event-based social networks through integration of existing online information...
Ontologies are reusable artifacts within the Software Engineering filed that are a pillar of the Semantic Web. Created in a different context, they can be adapted and applied in another context. This paper endeavors to develop a knowledge base in the form of an ontology that formalizes all aspects to do with culture and thus contribute to the preservation and dissemination of cultural knowledge. Written...
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