In the recent past, domain specific solutions for detailed semantic analysis have got acceptable by natural language processing community and use of applications involving natural language based user interface. Different approaches that has been previously used is focusing on quality of text and improving the text contents by adding semantic information with text then the existing approaches used for semantic analysis can provide better results. In this, an approach was presented to address the problem of non- availability of semantic information required for better semantic analysis. This problem is solved by using semantic technology to annotate text of software requirements expressed in a natural language with their domain specific semantics and investigate the effect of semantic analysis with attached semantics. The presented approach uses a semantic framework specifically designed for interpretation and detailed semantic analysis of natural language software requirement specifications. The used framework is based on semantic technology involves knowledge extracted from existing software requirement documents and knowledge extracted from existing applications. The presented approach shows that by adapting and combing existing ontologies to support knowledge management, developing system and performing experiments on requirement of real world software systems. In this approach start with software requirement specification, after this clean the irrelevant requirements, convert the cleaned requirements into graph that represents inter related different elements. Represent the requirement graph into sparse matrix, after these all steps; we generate ontology with the help of OntoGen tool.
In these days, we have lots of LiDAR (Light Detection And Ranging) data, for applications of high-resolution maps, geodesy, geomatics, archaeology, geography, geology, geomorphology, seismology, forestry, atmospheric physics, airborne laser swath mapping, laser altimetry, and others. One of current research and industrial issues is efficient ways of storing the LiDAR data itself, and also elegant ways of extracting geometric primitives from 3D point clouds of LiDAR data. In this paper, we analyze the characteristics of LiDAR data and tis storage schemes. Based on them, we present our own way of combining the existing general purpose solutions to achieve our special purpose implementation. We achieved reasonable solutions in a very short period and with very limited budgets.
Sentiment Analysis, is the field which has been looked into a great depth recently. There has been a lot of work in identification of polarities. There are many techniques developed by researchers to classify the opinionated data. During our research we have found out that there is no technique which could give 100% accuracy. There are many challenges to Sentiment Analysis like semantic ambiguity statements, comparison sentences, domain specific adaptation, sarcastic statements etc. We have found a solution to one of the limitations, thereby contributing to the elevation of accuracy level. The basic problem that we have identified is polarity switching of a word based on the domain in which it is being used (Domain Specific Adaptation). To solve this problem we have developed an algorithm which could correctly classify the statement based on the domain in which the word is being used. The proposed algorithm is HARN algorithm. It is an unsupervised learning method which uses basic structure of the sentences, domain dictionaries and pre-defined polarities to classify the given sentence. The below report discusses the present existing approaches to sentiment classification, HARN algorithm and its implementation details.
Software applications are designed with a concrete purpose in mind, specified by business owners basing on the individual requirements. The user-system interaction is specified in the analysis phase. This phase specifies inputs, outputs, interaction etc. These elements could be specified separately based on the target platform. The designers know that the mobile clients could have a different application flow than desktop clients. However, this is not a rule and designers often times do not think about the user context nor do they consider the application context. This implies that outputs, inputs and interactions do not change automatically during the software life cycle. In this paper we present techniques that are able to determine whether the user, which is in a particular context, should be required to spend his/her time to fill in fields that are not needed for accomplishing a specific business task. Moreover, these techniques are able to determine whether the fields should display or are not necessary, as well as how the system might interact with the user. Finally, we present a computational architecture that is able to make these types of determinations.
Wireless body area network (WBAN) plays an important part in mobile healthcare. WBAN can be imagined as a small wireless local area network around our body. In WBAN, there exist three roles: sensors, gateway, and healthcare center. However, the communication distance between sensors and gateway is only 1-2 meters. If the gateway is lost or leaves the range of WBAN consisting of the sensors, the sensed data will not be aggregated and forwarded. Furthermore, the original gateway holds the long-term key shared with the healthcare center, but the user's devices which may serve as the backup gateway do not hold the long-term key. In order to deal with the problems, we propose a key reconstruction protocol for WBAN. In the proposed protocol, the original gateway enables the backup gateway to reconstruct a temporary token, and the backup gateway will use the temporary token to establish a secure channel with the healthcare center without using the long-term key of the original gateway.
Uniform resource locator (URL) filtering is a fundamental technology for intrusion detection, HTTP proxies, content distribution networks, content-centric networks, and many other application areas. Some applications adopt URL filtering to protect user privacy from malicious or insecure websites. AdBlock Plus is an example of a URL-filtering application, which filters sites that intend to steal sensitive information. Unfortunately, AdBlock Plus is implemented inefficiently, resulting in a slow application that consumes much memory. Although it provides a domain-specific language (DSL) to represent URLs, it internally uses regular expressions and does not take advantage of the benefits of the DSL. In addition, the number of filter rules become large, which makes matters worse. In this paper, we propose the fast uniform resource identifier-specific filter, which is a domain-specific pseudo-machine for the DSL, to improve the performance of AdBlock Plus. Compared with a conventional implementation that internally adopts regular expressions, our proof-of-concept implementation is fast and small memory footprint.
This paper is concerned with speech enhancement using Phase-Error based Filters (PEF) and Excitation Source (ES) information in car environments. For this purpose, we firstly use ES information to determine the time-delay from speech signals obtained by two microphones for sound source localization. Then, the phase-error based filters are performed by prior knowledge regarding the time delay obtained by ES information and the phases of the signals recorded by the microphones. The experimental results showed the effectiveness of the presented method for speech enhancement.
This paper presents the effects of the noise on the performance of the classifier in detecting people using Infrared (IR) camera, and then improve its performance by using denoising wavelet transformer techniques. The local binary pattern (LBP) detector is used in detecting person in IR images. The LBP features are extracted to train the classifier using a support vector machine (SVM). Experimentally, we find the classifier performs very poorly with a noisy image. Three wavelet functions (Harr, db2, and db4) are used in denoising process with different levels in an effort to find an efficient denoising method. Three type of noise models are added to the original test data set and three metrics namely- True positive Rate (TPR), False Negative per Frame (FNPF) and False Positive Per Frame (FPPF) are used to evaluate the performance of the classifier in detection process. The results show that denoising using db2 and db4 wavelet transforms with level 4 improve the classification results by removing successfully the three types of noise preserving the texture features of the original image which are used by the LBP detector.
Piping work is consist of design, making and installation. Pipe line is consist of spool pipes which are made in fabrication shop. And these spool pipes installation in shipyard. Design of spool pipe is not based 3D CAD model, using 2D Drawing(ISO Drawing). Therefore, not consider working area, that wake decreasing working efficiency and delay working time. In this paper, suggest make spool pipe design method using analysis working area about 3D CAD model and genetic algorithm.
In this paper, we propose power-aware data structure using loop transformation in preprocessing of motion recognition systems based on EPS (Electromagnetic Potential Sensor). First, we estimate power consumption of smart devices or tablets in gesture recognition preprocessing. We analyze input signals from sensors, filter, store, and measure the power efficiency of each of different data structures using loop transformation. We search data structure for optimal transformation. Proposed method based on EPS for gesture recognition in smart devices demonstrates to reduce power consumption significantly, about 20%.
We conducted this study to confirm an effects of displaying augmented-reality information on the drivers' driving behavior under the low visibility conditions. An experiment was conducted for collecting the response time as a driving behavior. And we used a questionnaire to collect the drivers' psychological characteristics. The 35 male drivers participated in this study. As a result, the Problem Evading had a positive correlation to the response time under the control condition. In contrast, under the head-up display system usage condition which provides augmented-reality information, the Anti-Personal Anxiety was negatively correlated with the response time. This means that it may affect to the object detection behavior of the drivers with specific psychological characteristics and may partially induce a relaxation of tension or stress when they enable to use the system. Therefore, it might contribute the psychological driving safety of the drivers with the Problem Evading or Anti-Personal Anxiety.
In this paper, we designed and implemented a security management application based on user-centric evaluation, which is named HOT SAUCE. HOT SAUCE collects statistics of user's individual evaluation and comments about the security of applications, and it helps user to manage the security of installed applications. It also helps user to decide whether or not to install a new application by giving the security rating and comments on the application evaluated by other users. It is expected that HOT SAUCE provides Android users with enhanced security management.
In the maritime field, e-navigation, the new maritime service environment paradigm, is a topic of wide discussion. According to the e-navigation concept, the various marine and marine-related domains' data both on- and off-shore are used as resources for maritime services, the purpose of which are to facilitate communication at sea and to ensure safe navigation. To those ends, e-navigation services exchange, share, and utilize data resources created and distributed according to the data product specifications based on the e-navigation Common Maritime Data Structure (CMDS). Currently, maritime equipment and systems transfer data among themselves over ships' networks. In e-navigation, those equipment and systems' data will be utilized as useful data resources. But before that data can be used, it is necessary to create and distribute it according to the above-noted CMDS-based data product specifications. When a data product specification is developed for a specific domain, a common vocabulary for that domain is needed for consistent specification of the data elements that represent the data contents. In this paper, we present a common vocabulary set for consistent representation of the data elements of maritime equipment and systems shipboard. To build the common vocabulary set, we analyzed the digital interface standards for maritime navigation and radio- communication equipment and systems. By analysis of approved sentences for data transmission of maritime equipment and systems, we extracted candidate terms and built the common vocabulary according to the e- navigation CMDS base model.
Classification of Electrocardiogram (ECG) plays an important role in clinical diagnosis of cardiac diseases. In this paper, we introduce an ECG beat classification system using convolutional neural networks (CNNs). The proposed model integrates two main parts, feature extraction and classification, of ECG pattern recognition system. This model automatically learns a suitable feature representation from raw ECG data and thus negates the need of hand-crafted features. By using a small and patient-specific training data, the proposed classification system efficiently classified ECG beats into five different classes recommended by Association for Advancement of Medical Instrumentation (AAMI). ECG signal from 44 recordings of the MIT-BIH database are used to evaluate the classification performance and the results demonstrate that the proposed approach achieves a significant classification accuracy and superior computational efficiency than most of the state-of-the-art methods for ECG signal classification.
In-memory databases are gaining attention as a solution to efficiently support SQL queries on large volume of data, as main memories are becoming cheaper and grow in size. However, their query performance is not well improved on modern hardware with faster CPUs, registers and caches due to the limitation of the classical iterator style query processing model. We propose a unified SQL query optimization system using JIT compilation of OLTP, OLAP, and Stored Procedure workloads for enhanced performance on modern hardware.
In this era of information technology, network traffic classification is a very important and hot topic from the perspective of network security and management due to substantial use of dynamic applications. Numerous research models have been proposed in network traffic classification to classify different types of applications and achieve significant accuracy results. However, no work has been done to classify WeChat messages flow traffic. WeChat is a free instant messaging application. Hence, it is very important to classify WeChat text messages traffic. In this paper, we classify WeChat messages flows traffic using two different data sets, which are first captured using Wireshark tool from two different locations network environments, Harbin Institute of Technology Lab and Jinyuan Hotel and then 50 features are extracted from captured traffic. After that four machine learning algorithms SVM, C4.5, Bayes Net and nalve Byes are applied to classify the WeChat text messages traffic. Experimental results show that all classifiers give very high accuracy results using two different data sets. Using Jinyuan data set SVM and C4.5 decision tree algorithm give 100% accuracy result as compared to Bayes Net and Naive Bayes algorithm and using Harbin Institute of Technology Lab data set all classifiers give 99.7% high accuracy results.
This paper proposes a novel motion retrieval system featured with an example-based query interface. The system makes use of a user-created query sequence to search interesting motion in a huge motion database. The target movement of this study is motions of Korean POP (K-POP) dances which are much more complex and dynamic than any other human motions. To cope with those challenging dance motions, we present discriminative pose descriptors based on the overall configuration of a pose and dynamics of interesting joints. As a method to measure motion similarity, subsequence Dynamic Time Warping algorithm is explored that supports partial matches. The proposed intuitive query interface is efficient and especially necessary for the retrieval of K-POP dance motions that are difficult to describe and annotate.
Significant interest in internet of things drives both research and industry production these days. A lot of important questions has been solved but some remain opened. One of the essential unresolved issue is the identity management of single devices. This paper proposes solution for management of devices for internet of things. The solution is based on central identity store. Each device has an associated account in the store with corresponding roles. For any communication the device retrieves OAuth 2.0 token and uses it to certify itself in every network connection. The proposed framework creates trusted environment and enables rapid response for any security events.
Sports paradigm shift gave an opportunity to transform into transform into production-oriented industry activities creating high value-added. Sports industrial cluster, organic union of qualitative industries producing and distributing of various sports derivative products, is necessary system in these sports paradigm shift. This study is intended to suggest the construction and direction for future of sports industrial cluster.
Nowadays, businesses have to be competitive and to make effective decisions in order to achieve their goals. Strategic Information Systems Planning (SISP) and Decision Support System (DSS) contribute to this effort. This paper aims to propose the steps of a framework which combines both the SISP concept and DSS, based on previous models. These models present the steps of a DSS. This proposed framework is developed in order to acquire and store significant information for its strategic decisions and implement them more effectively. The paper contributes to the DSS literature in the two ways. Firstly, it suggests a framework which is believed to maintain the empirical research of SISP in DSS field. Secondly, it outstretches the scope of SISP on DSS research area, beginning with the need for more research to be implemented regarding the DSS in SISP through strategic management. By presenting this framework, a number of implications and proposals for further investigation are accrued.
