Analysis of Multipoint Relays Selection in the OLSR Routing Protocol with and without QoS Support

Mobile ad hoc networks have very attractive intrinsic qualities. However they will be adopted only if they are able to support applications with QoS requirements. They must provide a route providing the QoS requested by a flow. The OLSR routing protocol can be extended for that purpose. OLSR relies on multipoint relay (MPR) selection that has an important effect on the routing protocol's performances. Indeed, the overhead generated by the OLSR protocol and more particularly the flooding efficiency depend on MPR selection. Moreover, MPRs are used as intermediate nodes in the routes. The analysis of MPR selection presented in this report gives quantitative results and also takes into consideration QoS support. Simulations on large and dense networks show that our analysis is highly accurate

Evaluation of the Bandwidth Needed at the MAC 802.11b Level

Mobile ad-hoc networks (MANETs) have many advantages, self-adaptivity for instance, that make them attractive for multiple applications. Most of these networks use the MAC 802.11b protocol for the medium access control. As all nodes use the same transmission frequency, interferences can occur compromising the quality of service (QoS) provided to flows. In this report, we show how to evaluate the bandwidth required at the MAC level for a flow whose characteristics are known at the application level. The bandwidth evaluation on a node accounts for the interferences due to the flow itself and the other flows. This evaluation has been validated by confrontation with results obtained by NS-2 simulations. We then propose an efficient admission control based on this evaluation. An example illustrates the proposed solution and highlights the accuracy of the evaluation

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in IJCVR on September 201

A new stability results for the backward heat equation

In this paper, we regularize the nonlinear inverse time heat problem in the unbounded region by Fourier method. Some new convergence rates are obtained. Meanwhile, some quite sharp error estimates between the approximate solution and exact solution are provided. Especially, the optimal convergence of the approximate solution at t = 0 is also proved. This work extends to many earlier results in (f2,f3, hao1,Quan,tau1, tau2, Trong3,x1).Comment: 13 page

The Capability of E-reviews in Online Shopping. Integration of the PLS- SEM and ANN Method

Purpose: The aim of this study is to investigate the impact of e-review on iGen's propensity to purchase online. Especially, it can be better understood by dissecting the relationship among 3 variables of e-review (review valence, quantity of e-review and quality of e-review), e-satisfaction, and intention to buy.   Theoretical framework: This study classifies e-reviews according to their valence, quantity, and quality based on the study of Khammash (2008).   Design/methodology/approach: The PLS-SEM method was used to analyze data collected from online surveys administered to a sample of 222 iGen in Ho Chi Minh City to assess the hypotheses behind the study. Additionally, the Artificial Neural Network technique was used to separate SEM predictors that were relatively important.   Findings:  There are three results from the investigation: It has been found that (1) e-satisfaction is positively affected by valence, (2) e-satisfaction is generally increased with the high quality of e-review, but the quantity of e-review does not necessarily affect customers' e-satisfaction, and (3) e-satisfaction given in the context of an e-commerce platform has a strong effect on customers' online shopping intention. This study sheds new light on iGen's online buying habits and offers valuable management implications for iGen, online merchants, and e-commerce sites.   Research, Practical & Social implications:  E-reviews have become a significant factor in determining consumers' online purchase decisions. They also assist iGen in understanding how a qualified e-review—one that is clear, understandable, helpful, and has enough justification to support the opinions—will be advantageous for other consumers who wish to shop online.   Originality/value:  Provides the theory of e-review and its role in the online business environment. In addition, understand more about the behavior of igen, an age with a huge amount of spending on an online shopping platform

Evaluation of the Energy Consumption in MANET

In ad hoc mobile wireless networks, energy consumption is an important issue as most mobile hosts operate on limited battery resource. Existing models for evaluating the energy consumption behavior of a mobile ad hoc network have shown that the various components of energy related costs include transmission power as well as power of reception. In this paper, we extend the model for calculating the energy spent at a node due to a flow in the network. We include the transmission and reception costs if the node belongs to a flow, and reception costs if it is near a flow. The model gives the energy costs of nodes in ideal conditions where interferences and collisions are absent, and hence can be extended to evaluate the effect of interference between flows on energy consumption in more realistic conditions. The collisions due to the flows are also measured, which are used to evaluate the effect of such interference in the energy consumption. We then show how the extra energy spent due to collisions can be calculated by predicting the collisions in the nodes of the network. This prediction is shown to be capable of accurate calculation of the extra energy consumption

Combining content and social features in a deep learning approach to Vietnamese email prioritization

The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work

Isolation and characterization of full-length genes encoding the anti-human CD45 antibody from the hybridoma cell line 16E8-F2

Though the hybridoma technology has been widely applied in the production of monoclonal antibodies, it has existed some disadvantages including low yield and genetic instability. Therefore, an alternative approach should be taken into account. Recently, recombinant monoclonal antibody technology has emerged as the best choice to cure hybridoma related drawbacks. However, recombinant antibodies require known genes for their generation. The purpose of this study is to collect the full-length genes encoding the anti-human CD45 antibody derived from the hybridoma cell line 16E8-F2. In this research, we designed specific primer pairs to amplify the light and heavy chain genes of the antibody through the PCR method. Afterward, the genes were separately cloned into a cloning vector called pJET1.2/blunt. The generated recombinant pJET1.2 vectors will serve as the main material source to manufacture the recombinant monoclonal antibody recognizing human CD45 protein tomorrow