Miracle-dz/UniFAR-FTUs huggingface dataset:
Number : WUCSE-2006-46 2006-0101 A view-based deformation tool

Camera manipulation is a hard problem since a graphics camera is defined by specifying 11 independent parameters. Manipulating such a high-dimensional space to accomplish specific tasks is difficult and requires a certain amount of expertise. We present an intuitive interface that allows novice users to perform camera operations in terms of the change they want see in the image. In addition to developing a natural means for camera interaction, our system also includes a novel interface for viewing and organizing previously saved views. When exploring complex 3D data-sets a single view is not sufficient. Instead, a composite view built from multiple views may be more useful. While changing a single camera is hard enough, manipulating several cameras in a single scene is still harder. In this thesis, we also present a framework for creating composite views and an interface that allows users to manipulate such views in real-time. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 WASHINGTON UNIVERSITY THE HENRY EDWIN SEVER GRADUATE SCHOOL DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING A THESIS ON A VIEW-BASED DEFORMATION TOOL-KIT by Nisha Sudarsanam , Bachelor Of Engineering, Computer Engineering Prepared under the direction of Professor Cindy M Grimm A thesis presented to the Henry Edwin Sever Graduate School of Washington University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE August 2006 Saint Louis, Missouri WASHINGTON UNIVERSITY THE HENRY EDWIN SEVER GRADUATE SCHOOL DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Application of X-ray spectroscopic analysis to human blood samples

X-ray emission techniques like Particle Induced X-ray emission (PIXE) and Energy Dispersive X-ray Fluorescence (EDXRF) spectrometry arc applied to analyse the elements in biomedical samples at the Institute ofPhysics, Bhubaneswar in collaboration with the Acharya Harihara Regional Cancer Centre, Cuttack. Alter the development of the systems, initially experiments were carried out with the ccrtiricd reference materials (CRMs) like IAEA animal blood standards to verify their applicability to biomedical samples llicn human blood samples from different patients as well as healthy persons were analysed to compare the results applying both the techniques In this paper, both the X-ray emission techniques are discussed in detail with their advantages and limitations The results obtained by both the methods prove their efficiency for analysis of biomedical samples. Keyworda X-ray fluorescence, PIXE analysis, trace elements PACS Nos. 87.64. Fb, 82 80.EJ
Celebrities And Celebgrams Of Cosmetics: The Mediating Effect Of Opinion Leadership On The Relationship Between Instagram Profile And Consumer Behavioral Intention

Instagram is increasingly becoming popular among Indonesian consumers as a source of reference, thereby leading to the emergence of online influencers such as ̳celebrities‘ and ̳celebgrams‘. The research purpose is to study if characteristics of an influencer‘s Instagram account affect the influencer‘s opinion leadership as well as its consequences on the behavioral intention of Indonesian cosmetic product consumers. The study uses a quantitative survey conducted on 250 Instagram users who follow selected celebgrams or celebrities who endorse cosmetic products. A hypothesized model is built to analyze the antecedents and consequences of the opinion leadership of the two types of influencer. As for opinion leadership antecedents, both models show that originality of the Instagram account is crucial in building up the opinion leadership, while quality and quantity of the Instagram posts are not significant factors. Perceived uniqueness of the account seems to affect the opinion leadership only for celebrity accounts and not for the celebgrams‘. Further, findings suggest that opinion leadership affect consumer behavioral intentions, namely intentions to interact, recommend, and follow advice. Finally, the account's perceived fit with personal interest is not affecting the relationship between opinion leadership and the intention to follow advice for both celebrity and celebgram. The findings suggest some important implications for cosmetic companies in utilizing opinion leaders as their promoters, and also for micro-influencers that are beauty gurus who wish to learn the important key factors to attract more Instagram followers and build their social media communities.
QUALITY RATINGS AND SYSTEM CHARACTERISTICS : PATTERNS IN THE ROUND 1 RACE TO THE TOP – EARLY LEARNING CHALLENGE STATES The Race

The Race to the Top–Early Learning Challenge (RTT-ELC) grants program, sponsored by the U.S. Department of Education (ED) and U.S. Department of Health and Human Services (HHS), aimed to increase the number of high quality early learning and development programs. RTT-ELC awarded $520 million in the first of three rounds of grants to help states develop and implement systems that rate early learning and development programs based on state-defined quality standards and help them improve on these standards. These systems are known as tiered quality rating and improvement systems (TQRIS). Programs at the lowest level meet a basic level of quality, whereas programs at the top levels meet the highest standards of quality as measured by one of these rating systems.
MANDARIN TONE IDENTIFICATION WITH F0-FLATTENING PROCESSED SINGLE-VOWELS

Fundamental frequency (F0) contour carries important information for lexical tone identification in a tonal language like Mandarin Chinese. To examine the perceptual contribution of F0 contour, F0-flattening processing has been commonly used in many studies. However, listeners may still capture residual F0 information contained in the F0-flatted stimuli for lexical tone identification. The present work assessed Mandarin tone identification with the F0-flattening processed single-vowels. The vowel stimuli were processed by the STRAIGHT algorithm to replace the original F0 contour with a flattened contour at the mean value of the F0 dynamic contour extracted. Listening experiments showed that under the F0-flattening processed condition, normalhearing listeners still achieved a more than 50.0% accuracy rate to identify Mandarin tones, and tone-2 was more misidentified as tone-1 than tone-3 and tone-4 were.
Interleukins 17 and 23 and Resistin Levels among Obese Diabetic Egyptian female Patients

Objectives: To assess serum Interleukin 17and 23 and resistin levels and to evaluate their relationship with BMI, waist circumference in obese diabetic female patients. Subject and methods: Thirty five female diabetic patients were included in the cross sectional study. Controls included 20 age-matched healthy females of the same socioeconomic status. Serum glucose, total cholesterol triglyceride, high density lipoprotein cholesterol (HDL-C), The serum IL-17, IL-23 and resistin concentrations were determined, low density lipoprotein cholesterol (LDL-C) was calculated. Body weight, body height and waist circumference (WC) were measured. Body mass index was calculated. Results: Body mass index and waist circumference were significantly higher in diabetic females. Moreover triglycerides, low density lipoproteins, interleukin 17, interleukin 23 levels and resistin were significantly higher in diabetic females. A significant positive correlation was obtained between the biochemical parameters and anthropometric measurements. Also, blood glucose level was significantly correlated with proinflammatory cytokines as well as resistin. Conclusion: Understanding the inflammatory characterization of Proinflammatory cytokines (interleukin 17, interleukin 23 levels) and resistin might be of fundamental importance for the prevention and treatment of obesity and diabetes.
The effect of organizational culture on knowledge transfer between headquarters and subsidiaries

Knowledge transfer within MNCs has a significant number of factors influencing its effectiveness and results. Scania, as a global company, faces daily need in exchanging specific know-how, knowwhat and know-who, not only throughout Sweden, but in more than 100 countries of its operations. Introducing strong organizational culture, which affects employees’ operation style, stakeholders’ behaviors and decision making processes is one of the ways to overcome cultural differences. Purpose The purpose of this study is to determine how organizational culture affects and supports knowledge transfer process between a headquarter and foreign subsidiaries. Method This is a qualitative single case study with three interviewees as primary sources of information. All interviewees occupy managerial positions in Scania’s headquarters in Sodertalje, and are actively involved in design of knowledge transfer processes. Key Findings One of the key findings was that Scania is still facing a lot of challenges in knowledge transfer, despite its investments into development of a strong organizational culture. Conclusion The research conducted indicated that organizational culture creates an important platform for holding an organization’s operations together and influencing employees’ commitment to perform. The major way to overcome cultural challenges within headquarters and subsidiaries is to build strong organizational culture which would ‘overpower’ and ‘erase the differences’ of national culture. It is also important to create shared values, which would be accepted internationally and integrate them into daily routines.
An Ontology BasedMultimedia Information Retrieval System for Community Question Answering ( CQA ) System Using Text Based Query

Information retrieval in present decade moving next level by providing multimedia data along with textual content for easy understanding of concepts.The Community Question Answering (CQA) services have become quiet popularity over the past 10 years;it helps the user to get information from a comprehensive set of well-answered questions. Text based Information retrieval system cannot exposes enough attention for understanding concept corresponding to given query. It will take more time to read, understand and also linguistic problems that prevent the user for continues reading of the document or the user may not have enough time to read the entire document.To address the limitations in the existing system, the proposed information retrieval system works on multimedia data such as text, audio, video and image for the any given query based on semantic analysis of text, image audio and video using ontology. The user query has been mapped, classified and modified according to retrieve text document, image, audio and video without affecting its content and meaning. The semantic details of query enhanced for mapping with text document ranking and the images, audio and video data retrieved by analysis of the metadata of its internal representation. The performance of our proposed work increased with multimedia contents by of users in compared with the existing text based CQA.
Structure Investigation of Deep Recurrent Neural Network for Human Activity Recognition

A deep recurrent neural network (DRNN) for human activity recognition is constructed. The DRNN processes directly time series data from acceleration sensor put on by a person. Moreover, the DRNN has deep layers with recurrent connections and long short-term memory (LTSM) structure. The recognition targets are six type activity and the DRNN is trained by using Human Activity Sensing Consortium (HASC) open data sets. In this paper, investigation results of structure parameters of the DRNN such as the number of the units and the number of layers to improve recognition rate are described.
Mobile Commerce Pattern Mining Behavior Prediction Based On Kernel SVM

: We suggest an implementation for the mining and prediction of mobile user’s actions and purchase transactions together. The implementation gives the user with data about that all shops a specific product is accessible and that shop gives the best proposal. With the help of this application we can create the Live shopping by the user without having to go to each shop and query. Mobile Commerce, also called as M-Commerce or commerce, is the aptitude to conduct commerce with the help of a mobile device. Research is performed using Mining and Prediction of Mobile Users’ Commerce Behaviors like their movements and purchase transactions. In our projected research we have envisioned to suggest a mobile commerce behavior prediction primarily the input information are taken from a mobile transaction database it comprises a store item information and item store information we have transform this information with the help of transformation have extract a data’s on the basis of a similarity bases now we have a store data and item data also it encompasses a store similarity and item similarity data’s from that we have taken a similarity data’s and stock that data’s these data’s are provided in to the Similarity Inference Model SIM for calculating the similarities amongst stores and items that are two basic mobile commerce entities measured in this paper these are given n to the prediction engine also it have a user transaction, identification and movement from these engine, we have envisage the user behavior with the help of Kernel based SVM.
Inference after Estimation of Breaks

Abstract In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector. Examples that can be cast into this frame include threshold regression models with estimated thresholds and structural break models with estimated break dates. Estimation and inference procedures that ignore the randomness of the target parameter can be severely biased and misleading when this randomness is non-negligible. This paper studies conditional and unconditional inference in such settings, accounting for the data-dependent choice of target parameters. We detail the construction of quantile-unbiased estimators and confidence sets with correct coverage, and prove their asymptotic validity under data generating process such that the target parameter remains random in the limit. We also provide a novel sample splitting approach that improves on conventional split-sample inference.
QUESTION PREDICTION BASED ON ITS CONTEXT, WORD EMBEDDINGS AND NEURAL NETWORKS

Mediktor is a clinically validated symptom-checker that gives accurate pre-diagnosis based on a personalized interactive questionnaire that the users have to answer. Learning to predict the next most fitting question to ask to a user, given some previously answered ones, is key for adding value to Mediktor's evaluator system. Current solutions for similar tasks, i.e. predicting a word given a specific context, are based on representing them in vector spaces. These vectors are called word embeddings* and these methods are proved to give outstanding results in Natural Language Processing tasks. For this particular project, the Continuous Bag of Words (CBOW) model from the Word2Vec models by Mikolov, T. et al (1) was the most suitable approach. It is demonstrated (2) that these models are able to learn excellent vector representations of words; the challenge is to make it work for datasets that have great variability and complexity as in our case. This work has been valuable for understanding the similarity between groups of questions that are asked together with every other question in the vocabulary. Being the first approach of using machine learning techniques to learn similarities among questions for this particular data, the results are satisfactory. The predictions obtained for the testing data and the visualization of the word embeddings in the multidimensional space are reasonable. In the process of graphical validation of the model, we have found that as expected, the learned word embeddings have formed clusters (groups of similar questions) in the vector space. Nevertheless, further research should be done into this subject to optimize the training aiming for better results in prediction. *Word embeddings are representations of words in a lower dimensional vector space. They allow to learn features about the words and how they interact with each other; words with similar meanings should have similar representations.
Multi-Task Discriminative Training of Hybrid DNN-TVM Model for Speaker Verification with Noisy and Far-Field Speech

The paper aims to address the task of speaker verification with single-channel, noisy and far-field speech by learning an embedding or feature representation that is invariant to different acoustic environments. We approach from two different directions. First, we adopt a newly proposed discriminative model that hybridizes Deep Neural Network (DNN) and Total Variability Model (TVM) with the goal of integrating their strengths. DNN helps learning a unique variable length representation of the feature sequence while TVM accumulates them into a fixed dimensional vector. Second, we propose a multitask training scheme with cross entropy and triplet losses in order to obtain good classification performance as well as distinctive speaker embeddings. The multi-task training is applied on both the DNN-TVM model and state-of-the-art x-vector system. The results on the development and evaluation sets of the VOiCES challenge reveal that the proposed multi-task training helps improving models that are solely based on cross entropy, and it works better with DNN-TVM architecture than x-vector for the current task. Moreover, the multi-task models tend to show complementary relationship with cross entropy models, and thus improved performance is observed after fusion.
Globally Optimal Grasp Planning using a Two-Stage Branch-And-Bound Algorithm

We present the first algorithm to compute the globally optimal gripper pose that maximizes a grasp metric. We solve this problem using a two-level branch-and-bound (BB) algorithm. Unlike previous work that only searches for grasp points, our method can take the gripper's kinematic feasibility into consideration. And compared with sampling-based grasp planning algorithms, our method can compute the globally optimal gripper pose or predict infeasibility with finite-time termination. Our main technical contribution is a novel mixed-integer conic programming (MICP) formulation for the inverse kinematics of the gripper which uses a small number of binary variables and tightened constraints. Our experiments show that globally optimal gripper poses for various target objects can be computed within 3hr of computation on a desktop machine and the computed grasp quality is better than those generated using sampling-based planners.
Product Reviews-Triggered Dynamic Pricing : Theory and Evidence Forthcoming : Information Systems Research

Prior works offer compelling evidence that on the demand side of the market, user-generated online product reviews play a very important role in informing consumers’ purchase decisions. On the supply side, however, the interplay between online product reviews and firm strategies is less understood. We build an analytical model that differentiates products based on consumers’ preference for tastes (horizontal differentiation) or quality (vertical differentiation) and show that a firm is able to not only manipulate its pricing to influence online product reviews (thus influencing sales), but also adjust pricing dynamically in response to online word-of-mouth. Our model derives rich and testable results on possible price trajectories. To offer empirical support for the analytical predictions, we conduct a panel data study of prices and reviews. We adopt a difference-in-differences framework to address endogeneity challenges.
Introduction to Indoor GeoNet

This paper introduces “Indoor GeoNet”, a weakly supervised depth and camera pose estimation model targeted for indoor scenes. Our paper is built upon previous works in the field of unsupervised depth and relative camera pose estimation from temporal consecutive video frames through novel view synthesis using deep learning models. However, such works are exclusively trained/tested on a few available outdoor scene datasets and we have shown they are hardly transferable to new scenes, especially to indoor environments, in which estimation requires higher precision and dealing with probable occlusions. In Indoor GeoNet, we used a hybrid learning framework introduced in a recent work called GeoNet, and took advantage of the availability of indoor RGBD datasets collected by human or robot navigators, and added partial (i.e. weak) supervision in depth training into the model. Experimental results showed that our model effectively generalizes to new scenes from different buildings. It demonstrated significant depth and pose estimation error reduction when compared to the original GeoNet, while showing 3 times more reconstruction accuracy in synthesizing novel views in indoor environments.
A Secured Routing Protocol for MANET

Recently, the privacy and security in MANETs is a major concern because of its frequent utilization which includes emergency operation, survival search, battlefield communications and sensor dust. Though, owing to mobility and consistent topology alterations in MANETs, designing routing protocols for those networks is a challenging task especially in the large-scale network. Moreover, inappropriate data usage initiates data leakage which may result in faulty outcomes. Hence, secured routing approach which comprises a trust and energy efficient routing framework to secure routing with an improved approach of bat algorithm and Firefly for optimization that is based on the integration of Inter-cluster and Intra-cluster Multihop Secured Routing Protocol (BF-ICMHSRP) in MANET is proposed in this study. The proposed BF-ICMHSRP algorithm simulation experiments will be conducted using NS2. The simulation produces two output files, a trace file used for data processing and a NAM file used to visualize the simulation. BF-ICMHSRP algorithm performance will be compared with traditional bio-inspired technique. The performance of the proposed approach is evaluated using different metric named as, average End-to-End Delay, Average throughput, Routing overhead Ratio, Packet delivery Ratio, Average Energy Consumption, Received Signal Strength (RSS) and Link Available Time (LAT).
One-Shot Malware Outbreak Detection using Spatio-Temporal Isomorphic Dynamic Features

Fingerprinting the malware by its behavioural signature has been an attractive approach for malware detection due to the homogeneity of dynamic execution patterns across different variants of similar families. Although previous researches show reasonably good performance in dynamic detection using machine learning techniques on a large corpus of training set, decisions must be undertaken based upon a scarce number of observable samples in many practical defence scenarios. This paper demonstrates the effectiveness of generative adversarial autoencoder for dynamic malware detection under outbreak situations where in most cases a single sample is available for training the machine learning algorithm to detect similar samples that are in the wild.
Efficient Monte Carlo Methods in Global Illumination Series Solving the Global Illumination Problem on the GPU

This paper discusses a combined algorithm to solve the global illumination problem on the graphics hardware (GPU) with the aim of real-time rendering. The first approach solves the Fredholm equation by stochastic iteration. The second approach is not a complete solver, but is able to accurately compute a single bounce transfer even for non-diffuse scenes. Thus, it is a candidate to improve the results of the first method.