Polarization and opinion analysis in an online argumentation system for collaborative decision support

Argumentation is an important process in a collaborative decision making environment. Argumentation from a large number of stakeholders often produces a large argumentation tree. It is challenging to comprehend such an argumentation tree without intelligent analysis tools. Also, limited decision support is provided for its analysis by the existing argumentation systems. In an argumentation process, stakeholders tend to polarize on their opinions, and form polarization groups. Each group is usually led by a group leader. Polarization groups often overlap and a stakeholder is a member of multiple polarization groups. Identifying polarization groups and quantifying a stakeholder\u27s degree of membership in multiple polarization groups helps the decision maker understand both the social dynamics and the post-decision effects on each group. Frameworks are developed in this dissertation to identify both polarization groups and quantify a stakeholder\u27s degree of membership in multiple polarization groups. These tasks are performed by quantifying opinions of stakeholders using argumentation reduction fuzzy inference system and further clustering opinions based on K-means and Fuzzy c-means algorithms. Assessing the collective opinion of the group on individual arguments is also important. This helps stakeholders understand individual arguments from the collective perspective of the group. A framework is developed to derive the collective assessment score of individual arguments in a tree using the argumentation reduction inference system. Further, these arguments are clustered using argument strength and collective assessment score to identify clusters of arguments with collective support and collective attack. Identifying outlier opinions in an argumentation tree helps in understanding opinions that are further away from the mean group opinion in the opinion space. Outlier opinions may exist from two perspectives in argumentation: individual viewpoint and collective viewpoint of the group. A framework is developed in this dissertation to address this challenge from both perspectives. Evaluation of the methods is also presented and it shows that the proposed methods are effective in identifying polarization groups and outlier opinions. The information produced by these methods help decision makers and stakeholders in making more informed decisions --Abstract, pages iii-iv

GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics

Large scale graph processing is a major research area for Big Data exploration. Vertex centric programming models like Pregel are gaining traction due to their simple abstraction that allows for scalable execution on distributed systems naturally. However, there are limitations to this approach which cause vertex centric algorithms to under-perform due to poor compute to communication overhead ratio and slow convergence of iterative superstep. In this paper we introduce GoFFish a scalable sub-graph centric framework co-designed with a distributed persistent graph storage for large scale graph analytics on commodity clusters. We introduce a sub-graph centric programming abstraction that combines the scalability of a vertex centric approach with the flexibility of shared memory sub-graph computation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation.Comment: Under review by a conference, 201

Depression Detection Using Stacked Autoencoder from Facial Features and NLP

Depression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. This research work aims to develop an objective and convenient method to assist depression detection using facial features as well as textual features. Most of the people conceal their depression from everyone. So, an automated system is required that will pick out them who are dealing with depression. In this research, different research work focused for detecting depression are discussed and a hybrid approach is developed for detecting depression using facial as well as textual features. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Natural Language Processing, Stacked Deep Auto Encoder with Random forest (RF) classifier and fuzzy logic based on multi-feature depression detection system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. The result analysis shows that the developed technique significantly advantages over existing methods

Experimental Study of Hydroformed Al6061T4 Elliptical Tube Samples under Different Internal Pressures

In order to achieve crack free elliptical shape under controlled conditions, an experimental set-up was designed and fabricated. This setup consists of three hydraulic cylinders, an intensifier, a hydraulic power pack, storage tanks, forming die, and all parts are controlled by a Programmable Logic Controller (PLC) system. The elliptical samples can be achieved through proper control of internal pressure and axial force with proper sealing. Experimental work has been carried out with different magnitudes of internal pressure and constrained conditions of axial force. Initially die of elliptical shape has been designed and modeled in Abaqus to successfully achieve the particular shape of the Al6061T4 tube under different internal pressure. The fabricated tube hydroforming machine set-up is highly effective for forming 0.5 mm-2 mm thick Al6061T4 alloy tube samples. The Experimental test has been carried out at 12.7 mm outer diameter, 175 mm length and 0.5 mm thick Al6061T4 samples. Bulge height parameters measured at different points of regular distance gap on the axial direction of the tube length and corner radius found at different pressures range of the samples are plotted under different internal pressures. Samples having an 18.7 mm major elliptical bulge were achieved during the experiment. The experimental data was validated by simulation results

Improving Volumetric Accuracy of AM Part Using Adaptive Slicing of Octree Based Structure

In Additive Manufacturing (AM) processes, the layer-by-layer fabrication of complex geometries may lead to stair casing and thus error resulting in volumetric inaccuracies in the model. Using thinner slices reduces the staircase error and improves part accuracy but there is a tradeoff between number of layers and the build time for manufacturing part. This paper presents a octree based structure to improve the accuracy as well as reduces the build time. In the current work, firstly converting STL file into a modified boundary octree data structure (MBODS) and then calculating the non-uniform slice thicknesses (adaptive slicing) from the octree representation. This slice thickness at any height is computed from the AM machine parameters and the smallest octree size at that available height. After the computation of the variable slice thicknesses has been completed, the part is virtually manufactured and the part errors are calculated. The virtually manufactured part and physical models are inspected to evaluate the volumetric errors. This algorithm uses an octree approach to improve the volumetric accuracy. And build time for the two different case studies are also done.Mechanical Engineerin

Vocational Education and Training Reform in India: Business Needs in India and Lessons to be Learned from Germany. Working paper

India is among the countries with the lowest proportion of trained youth in the world. Even worse, vocational education in secondary schools has received very limited funding since the mid-1980s;nit has remained non-aspirational, of poor quality and involves little industry collaboration. The Vocational Education and Training (VET) system in Germany, in contrast, shows a much higher proportion of youth participation, more intense involvement of the private sector and is anchored in the law