Abstract

Earthquake induced landslides during rapid flow cause several damages to human life in different ways. In September 2011, Sikkim earthquake with 6.9 Mw is considered to be the biggest earthquake in few decades in Sikkim state that effected with several low to rapid landslides blocked several roadways, destroyed many structures and human life etc. The slope is selected from Northern part of Sikkim state which is 42 Km from Mangan Village at 270 23 1 24.56 11 N - 88 0 38 1 30.35 11 E latitude and longitudes with 5844 feet and 7102 feet elevation and altitude. Due to quite history of several earthquakes from past ten years slope is selected near Mangan village. The Main central thrust is passing from Mangan town having with 0.5H high geohazard, amplification factor of 8.5 and predominant frequency of 10Hz and 0.82g average peak ground acceleration. The slope is divided in too fine mesh using Finite element modeling. Ten nodal points on the slope profile are selected for the analysis. The analysis was done for considering three conditions i.e., saturated, partially saturated and fully saturated depending upon the climatic condition. The acceleration time history data collected from Gangtok station are used for the present analysis. The maximum displacements and velocity flow both in X and Y direction of the slope is calculated under dynamic loading using finite element approach.The results obtained are compared with actual slope profile.

Abstract

Missing data in satellite images is a known phenomenon in remote sensing. There are various reasons for these gaps in satellite images e.g. shadowed area for SAR data sets, cloud coverage for optical imagery and instrument errors such as Scan Line Corrector(SLC-off) failure. The Scan Line Corrector (SLC) of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed permanently causing around 23% of pixels not scanned in each scene and this failure has seriously limited the scientific applications of ETM+ data. Reconstructing the gap regions is an important issue in remote sensing. To address this issue, we have proposed an idea to find the homogeneous and heterogeneous regions of ETM+ image using Morphological operations and replace the unscanned pixel value by Neighborhood similar pixel threshold based Local Binary Pattern value. We have applied padding approach with additional rows and columns for unscanned mixels at borders to replace with the corresponding NLBP value. It is observed that our results are promising to fill the data gaps in both homogeneous and heterogeneous regions.

Abstract

Forest fires are the most frequently occurred phenomenon during summer seasons in the state Madhya Pradesh. Monitoring and assessment of forest fires are the crucial steps in effective forest fire management. Forest fire danger estimation helps the disaster management authorities to take necessary mitigation measures for minimizing the losses and to evacuate the local people. Fire danger rating systems predict the fire danger based on the meteorological station parameters and ground datasets. McArthur Forest Fire Danger Index (FFDI) is the most popularly used fire danger rating systems using in the country Australia. This index requires large amount of ground datasets for the computation of drought parameter. In India, it is very difficult to compute the drought parameter due to the unavailability of instruments and man power. In the present research, McArthur Fire Danger Index was modified by inducing Normalized multiband drought index (NMDI) that was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA surface reflectance product MOD09GA as a substitute for fuel availability parameter. To test the robustness of modified FFDI, the research was carried out on Madhya Pradesh state for the assessment of forest fire danger. The results obtained from

Abstract

Due to the large quantity and diversity of content being eas- ily available to users, recommender systems (RS) have be- come an integral part of nearly every online system. They allow users to resolve the information overload problem by proactively generating high-quality personalized recommen- dations. Trust metrics help leverage preferences of similar users and have led to improved predictive accuracy which is why they have become an important consideration in the design of RSs. We argue that there are additional aspects of trust as a human notion, that can be integrated with col- laborative filtering techniques to suggest to users items that they might like. In this paper, we present an approach for the top-N recommendation task that computes prediction scores for items as a user specific combination of global and local trust models to capture differences in preferences. Our experiments show that the proposed method improves upon the standard trust model and outperforms competing top- N recommendation approaches on real world data by upto 19%.

Abstract

In this paper, we present a method that focuses on posture correction for stable quadruped locomotion over uneven terrain. Stability is ensured by switching to stable postures during gait transitions, where the posture is selected based on the terrain, foothold reachability and gait sequence. For fast and efficient posture evaluation, we use value functions that approximate stability and kinematic parameters. Learning using regression methods is used to create the value functions, which eliminates the need for additional sensors and computation for posture evaluation. This approach has been verified both numerically and experimentally.

Abstract

We examine the utility of implicit behavioral cues in the form of EEG brain signals and eye movements for gender recognition (GR) and emotion recognition (ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded (no mask) as well as partially occluded (eye and mouth masked) emotive faces. Obtained experimental results reveal that (a) reliable GR and ER is achievable with EEG and eye features, (b) differential cognitive processing especially for negative emotions is observed for males and females and (c) some of these cognitive differences manifest under partial face occlusion, as typified by the eye and mouth mask conditions.

Abstract

We examine the utility of implicit user behavioral signals cap- tured using low-cost, off-the-shelf devices for anonymous gender and emotion recognition. A user study designed to examine male and female sensitivity to facial emotions confirms that females rec- ognize (especially negative) emotions quicker and more accurately than men, mirroring prior findings. Implicit viewer responses in the form of EEG brain signals and eye movements are then ex- amined for existence of (a) emotion and gender-specific patterns from event-related potentials (ERPs) and fixation distributions and (b) emotion and gender discriminability. Experiments reveal that (i) Gender and emotion-specific differences are observable from ERPs, (ii) multiple similarities exist between explicit responses gathered from users and their implicit behavioral signals, and (iii) Signifi- cantly above-chance (≈70%) gender recognition is achievable on comparing emotion-specific EEG responses– gender differences are encoded best for anger and disgust. Also, fairly modest valence (positive vs negative emotion) recognition is achieved with EEG and eye-based features.

Abstract

In the states of Andhra Pradesh and Telangana in India, the number of engineering institutions grew from under 10 to over 500 from 1978 to 2008. This resulted in a severe shortage of good quality teachers and therefore poor quality of graduating students. In India, the employability of engineering graduates is 25%. Considering the scale and wickedness of the problem, the lead author and her team worked on designing innovative and radical solutions to this problem during the last eight years. We restrict our research focus to those colleges who responded to our call for partnership in Andhra Pradesh and Telangana and to the center, EnhanceEdu, we created at IIIT (International Institute of Information Technology) at Hyderabad. We present the EnhanceEdu design story using a new pragmatic approach called Design Story Research (DeStoRe) which has its basis in Design Science Research. An overarching design story frames past, present and future work of an entity, seeing possibilities for radical innovation

Abstract

Unsupervised representation learning for tweets is an important research field which helps in solving several business applications such as sentiment analysis, hashtag prediction, paraphrase detection and microblog ranking. A good tweet representation learning model must handle the idiosyncratic nature of tweets which poses several challenges such as short length, informal words, unusual grammar and misspellings. However, there is a lack of prior work which surveys the representation learning models with a focus on tweets. In this work, we organize the models based on its objective function which aids the understanding of the literature. We also provide interesting future directions, which we believe are fruitful in advancing this field by building high-quality tweet representation learning models.

Abstract

Web is indeed prolific with useful content. However, some segments of the user population – for example, students of less rigorous colleges from rural India – may find some of the relevant material on the web complex, foreign and incomprehensible. So, while good content exists, not all are able to benefit from it. The intent of our research effort is to be able to make existing web content be more accessible to wider range of end-users who may have different sociocultural and cognitive abilities. We propose to overcome this gap at a Presentation Semantics level by developing a framework for renarration through Semantic Style Sheets. Using this framework, we facilitate the renarration of existing educational content to be used by thus far overlooked community of users.