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Kumar, M., Bhandari, R., Rupani, A., Ansari, J. H..  2018.  Trust-Based Performance Evaluation of Routing Protocol Design with Security and QoS over MANET. 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE). :139-142.

Nowadays, The incorporation of different function of the network, as well as routing, administration, and security, is basic to the effective operation of a mobile circumstantial network these days, in MANET thought researchers manages the problems of QoS and security severally. Currently, each the aspects of security and QoS influence negatively on the general performance of the network once thought-about in isolation. In fact, it will influence the exceptionally operating of QoS and security algorithms and should influence the important and essential services needed within the MANET. Our paper outlines 2 accomplishments via; the accomplishment of security and accomplishment of quality. The direction towards achieving these accomplishments is to style and implement a protocol to suite answer for policy-based network administration, and methodologies for key administration and causing of IPsec in a very MANET.

Su, J. C., Wu, C., Jiang, H., Maji, S..  2017.  Reasoning About Fine-Grained Attribute Phrases Using Reference Games. 2017 IEEE International Conference on Computer Vision (ICCV). :418–427.

We present a framework for learning to describe finegrained visual differences between instances using attribute phrases. Attribute phrases capture distinguishing aspects of an object (e.g., “propeller on the nose” or “door near the wing” for airplanes) in a compositional manner. Instances within a category can be described by a set of these phrases and collectively they span the space of semantic attributes for a category. We collect a large dataset of such phrases by asking annotators to describe several visual differences between a pair of instances within a category. We then learn to describe and ground these phrases to images in the context of a reference game between a speaker and a listener. The goal of a speaker is to describe attributes of an image that allows the listener to correctly identify it within a pair. Data collected in a pairwise manner improves the ability of the speaker to generate, and the ability of the listener to interpret visual descriptions. Moreover, due to the compositionality of attribute phrases, the trained listeners can interpret descriptions not seen during training for image retrieval, and the speakers can generate attribute-based explanations for differences between previously unseen categories. We also show that embedding an image into the semantic space of attribute phrases derived from listeners offers 20% improvement in accuracy over existing attributebased representations on the FGVC-aircraft dataset.

Devyatkin, D., Smirnov, I., Ananyeva, M., Kobozeva, M., Chepovskiy, A., Solovyev, F..  2017.  Exploring linguistic features for extremist texts detection (on the material of Russian-speaking illegal texts). 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). :188–190.

In this paper we present results of a research on automatic extremist text detection. For this purpose an experimental dataset in the Russian language was created. According to the Russian legislation we cannot make it publicly available. We compared various classification methods (multinomial naive Bayes, logistic regression, linear SVM, random forest, and gradient boosting) and evaluated the contribution of differentiating features (lexical, semantic and psycholinguistic) to classification quality. The results of experiments show that psycholinguistic and semantic features are promising for extremist text detection.

Vizer, L. M., Sears, A..  2015.  Classifying Text-Based Computer Interactions for Health Monitoring. IEEE Pervasive Computing. 14:64–71.

Detecting early trends indicating cognitive decline can allow older adults to better manage their health, but current assessments present barriers precluding the use of such continuous monitoring by consumers. To explore the effects of cognitive status on computer interaction patterns, the authors collected typed text samples from older adults with and without pre-mild cognitive impairment (PreMCI) and constructed statistical models from keystroke and linguistic features for differentiating between the two groups. Using both feature sets, they obtained a 77.1 percent correct classification rate with 70.6 percent sensitivity, 83.3 percent specificity, and a 0.808 area under curve (AUC). These results are in line with current assessments for MC–a more advanced disease–but using an unobtrusive method. This research contributes a combination of features for text and keystroke analysis and enhances understanding of how clinicians or older adults themselves might monitor for PreMCI through patterns in typed text. It has implications for embedded systems that can enable healthcare providers and consumers to proactively and continuously monitor changes in cognitive function.

Behjat-Jamal, S., Demirci, R., Rahkar-Farshi, T..  2015.  Hybrid bilateral filter. 2015 International Symposium on Computer Science and Software Engineering (CSSE). :1–6.

A variety of methods for images noise reduction has been developed so far. Most of them successfully remove noise but their edge preserving capabilities are weak. Therefore bilateral image filter is helpful to deal with this problem. Nevertheless, their performances depend on spatial and photometric parameters which are chosen by user. Conventionally, the geometric weight is calculated by means of distance of neighboring pixels and the photometric weight is calculated by means of color components of neighboring pixels. The range of weights is between zero and one. In this paper, geometric weights are estimated by fuzzy metrics and photometric weights are estimated by using fuzzy rule based system which does not require any predefined parameter. Experimental results of conventional, fuzzy bilateral filter and proposed approach have been included.

Vegh, L., Miclea, L..  2014.  Enhancing security in cyber-physical systems through cryptographic and steganographic techniques. Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on. :1-6.

Information technology is continually changing, discoveries are made every other day. Cyber-physical systems consist of both physical and computational elements and are becoming more and more popular in today's society. They are complex systems, used in complex applications. Therefore, security is a critical and challenging aspect when developing cyber-physical systems. In this paper, we present a solution for ensuring data confidentiality and security by combining some of the most common methods in the area of security - cryptography and steganography. Furthermore, we use hierarchical access to information to ensure confidentiality and also increase the overall security of the cyber-physical system.

Saini, V.K., Kumar, V..  2014.  AHP, fuzzy sets and TOPSIS based reliable route selection for MANET. Computing for Sustainable Global Development (INDIACom), 2014 International Conference on. :24-29.

Route selection is a very sensitive activity for mobile ad-hoc network (MANET) and ranking of multiple routes from source node to destination node can result in effective route selection and can provide many other benefits for better performance and security of MANET. This paper proposes an evaluation model based on analytical hierarchy process (AHP), fuzzy sets and technique for order performance by similarity to ideal solution (TOPSIS) to provide a useful solution for ranking of routes. The proposed model utilizes AHP to acquire criteria weights, fuzzy sets to describe vagueness with linguistic values and triangular fuzzy numbers, and TOPSIS to obtain the final ranking of routes. Final ranking of routes facilitates selection of best and most reliable route and provide alternative options for making a robust Mobile Ad-hoc network.

Heimerl, F., Lohmann, S., Lange, S., Ertl, T..  2014.  Word Cloud Explorer: Text Analytics Based on Word Clouds. System Sciences (HICSS), 2014 47th Hawaii International Conference on. :1833-1842.

Word clouds have emerged as a straightforward and visually appealing visualization method for text. They are used in various contexts as a means to provide an overview by distilling text down to those words that appear with highest frequency. Typically, this is done in a static way as pure text summarization. We think, however, that there is a larger potential to this simple yet powerful visualization paradigm in text analytics. In this work, we explore the usefulness of word clouds for general text analysis tasks. We developed a prototypical system called the Word Cloud Explorer that relies entirely on word clouds as a visualization method. It equips them with advanced natural language processing, sophisticated interaction techniques, and context information. We show how this approach can be effectively used to solve text analysis tasks and evaluate it in a qualitative user study.

Athanasiou, G., Fengou, M.-A., Beis, A., Lymberopoulos, D..  2014.  A novel trust evaluation method for Ubiquitous Healthcare based on cloud computational theory. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. :4503-4506.

The notion of trust is considered to be the cornerstone on patient-psychiatrist relationship. Thus, a trustfully background is fundamental requirement for provision of effective Ubiquitous Healthcare (UH) service. In this paper, the issue of Trust Evaluation of UH Providers when register UH environment is addressed. For that purpose a novel trust evaluation method is proposed, based on cloud theory, exploiting User Profile attributes. This theory mimics human thinking, regarding trust evaluation and captures fuzziness and randomness of this uncertain reasoning. Two case studies are investigated through simulation in MATLAB software, in order to verify the effectiveness of this novel method.

Fei Hao, Geyong Min, Man Lin, Changqing Luo, Yang, L.T..  2014.  MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks. Parallel and Distributed Systems, IEEE Transactions on. 25:2944-2955.

Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.