Query: Limiting the Spread of Fake News on Social Media Platforms by Evaluating Users' Trustworthiness

Relevant passages:
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation <=SEP=> Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, CURB, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.

The wisdom of the few: a collaborative filtering approach based on expert opinions from the web <=SEP=> Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.

A Survey of Collaborative Filtering Techniques <=SEP=> As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

Features for Measuring Credibility on Facebook Information <=SEP=> Abstract—Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system.

The dynamics of viral marketing <=SEP=> We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.

Neural Kinematic Networks for Unsupervised Motion Retargetting <=SEP=> We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data1 to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator.

A Supervised Learning Framework for Arbitrary Lagrangian-Eulerian Simulations <=SEP=> The Arbitrary Lagrangian-Eulerian (ALE) method is used in a variety of engineering and scientific applications for enabling multi-physics simulations. Unfortunately, the ALE method can suffer from simulation failures that require users to adjust parameters iteratively in order to complete a simulation. In this paper, we present a supervised learning framework for predicting conditions leading to simulation failures. To our knowledge, this is the first time machine learning has been applied to ALE simulations. We propose a novel learning representation for mapping the ALE domain onto a supervised learning formulation. We analyze the predictability of these failures and evaluate our framework using well-known test problems.

A Compact Multipath Mitigating Ground Plane for Multiband GNSS Antennas <=SEP=> This paper presents the design of a novel multipath mitigating ground plane for global navigation satellite system (GNSS) antennas. First, the concept of a compact low multipath cross-plate reflector ground plane (CPRGP) is presented. In comparison with the choke ring and electromagnetic band gap (EBG) ground planes, the proposed CPRGP has compact size, low mass, wide operational bandwidth, and simple configuration. The proposed CPRGP is then integrated with a circularly polarized dual-band GNSS antenna in order to assess the multipath mitigating performance over two frequency bands. Measurement results of the proposed CPRGP with GNSS antenna achieves a front-to-back ratio (FBR) over 25 dB at L1 (1.575 GHz) and L2 (1.227 GHz) bands and maximum backward cross-polarization levels below -23 dB at both bands. Antenna phase center variation remains less than 2 mm across both L1 and L2 bands. Furthermore, the performance comparison of the proposed CPRGP with the commercially available pinwheel antenna and the shallow corrugated ground plane is presented, showing the advantages of CPRGP for high precision GNSS applications.

DESH: Database evaluation system with hibernate ORM framework <=SEP=> Relational databases have been the predominant choice for back-ends in enterprise applications for several decades. JDBC - a Java API - that is used for developing such applications and persisting data on the back-end requires enormous time and effort. JDBC makes the application logic to become tightly coupled with the database and consequently is inadequate for building enterprise applications that need to adopt to dynamic requirements. Hence, ORM frameworks such as Hibernate, became prominent. However, even with ORM, the relational back-end often faces a drawback of lack of scalability and flexibility. In this context, NoSQL databases are increasingly gaining popularity. Existing research works have either benchmarked Hibernate with an RDBMS or with one of the NoSQL databases. However, it has not been attempted in the literature to test both an RDBMS and a NoSQL database for their performance within the context of a single application developed using the features of Hibernate. This kind of a study will provide an insight that using Hibernate ORM solution would help us to develop database-independent applications and how much performance gain can be achieved when an application is ported from an RDBMS back-end to a NoSQL database backend. The objective of this work is to develop a business application using Hibernate framework that can individually communicate with an RDBMS as well as a specific NoSQL database and to evaluate the performance of both these databases.

Emotion regulation through listening to music in everyday situations. <=SEP=> Music is a stimulus capable of triggering an array of basic and complex emotions. We investigated whether and how individuals employ music to induce specific emotional states in everyday situations for the purpose of emotion regulation. Furthermore, we wanted to examine whether specific emotion-regulation styles influence music selection in specific situations. Participants indicated how likely it would be that they would want to listen to various pieces of music (which are known to elicit specific emotions) in various emotional situations. Data analyses by means of non-metric multidimensional scaling revealed a clear preference for pieces of music that were emotionally congruent with an emotional situation. In addition, we found that specific emotion-regulation styles might influence the selection of pieces of music characterised by specific emotions. Our findings demonstrate emotion-congruent music selection and highlight the important role of specific emotion-regulation styles in the selection of music in everyday situations.

An efficient message access quality model in vehicular communication networks <=SEP=> In vehicular ad hoc network (VANET), vehicles equipped with computing, sensing, and communication capabilities can exchange information within a geographical area to distribute emergency messages and achieve safety system. Then how to enforce fine grained control of these messages and ensure the receiving messages coming from the claimed source in such a highly dynamic environments remains a key challenge that affects the quality of service. In this paper, we propose a hierarchical access control with authentication scheme for transmitted messages with security assurance over VANET. By extending ciphertext-policy attribute-based encryption (CP-ABE) with a hierarchical structure of multiple authorities, the scheme not only achieves scalability due to its hierarchical structure, but also inherits fine-grained access control on the transmitted messages. Also by exploiting attribute-based signature (ABS), the scheme can authorize the vehicles that can most appropriately deal with the message efficiently. The results of efficiency analysis and comparison with the related works show that the proposed scheme is efficient and scalable in dealing with access control and message authentication for data dissemination in VANET. & 2014 Elsevier B.V. All rights reserved.

Asymmetrical duty cycle control with phase limit of LLC resonant inverter for an induction furnace <=SEP=> This paper proposes a power control of LLC resonant inverter for an induction furnace using asymmetrical duty cycle control (ADC) with phase limit to guarantee zero voltage switching (ZVS) operation and protect switching devices from spike current during the operation. The output power can be simply controlled through duty cycle of gate signals of a full-bridge inverter. With the phase limit control, non-ZVS operation and spike current caused by a change of duty cycle with fixed frequency and load Curie's temperature can be eliminated. Theoretical and simulation analyses of the proposed method have been investigated. The experimental results of heating a 300 g of Tin from room temperature until it is melted at 232 °C provided.

Moving beyond the Turing Test with the Allen AI Science Challenge <=SEP=> Answering questions correctly from standardized eighth-grade science tests is itself a test of machine intelligence.

Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. <=SEP=> Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting "alert fatigue" and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of "big data" in health care provide the potential not only to perform "in silico" research but also to provide "real time" diagnostic and (potentially) therapeutic recommendations based on empirical data. "On demand" access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.

Arabic language sentiment analysis on health services <=SEP=> The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naïve Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.

Neck lift my way: an update. <=SEP=> BACKGROUND
The author updates prior descriptions of an approach to the surgical neck lift that aims for a maximum degree of control over the size, shape, and position of every anatomical feature of the neck that is negatively affecting its appearance.


METHODS
A 38-year clinical experience guided the development of the operative tactics that define the strategy. Data collected from a records review of 522 consecutive neck lifts performed during the 10-year period 2004 through 2013 further inform the report. The approach has eight features: (1) nearly routine use of open submental access to all tissue layers of the central neck, including a regimen that curbed the problems that may attend an extensive tissue dissection; (2) management of lax neck skin by lateral excision using a specific postauricular incision, or by using the nonexcisional method of redistribution; (3) open lipectomy for precise removal of excess subcutaneous neck and jawline fat; (4) individualized modifications to subplatysmal fat, perihyoid fascia, and anterior digastric muscles; (5) treatment of large, ptotic, or malpositioned submandibular salivary glands by partial excision using a transcutaneous traction suture; (6) the current version of the corset platysmaplasty, which is used to treat static paramedian platysma muscle bands, and to avoid contour imperfections following subplatysmal maneuvers; (7) an approach that facilitates an isolated neck lift; and (8) durable results.


RESULTS
Case examples demonstrate outcomes.


CONCLUSION
Although the updated approach remains relatively complex and invasive, the author believes that the ends justify the means.

Synthetic Biology for Space Exploration : Promises and Societal Implications <=SEP=> 

Emulation of Software Defined Networks Using Mininet in Different Simulation Environments <=SEP=> In this paper an evaluation of an SDN emulation tool called Mininet is conducted. Tests were conducted to study Mininnet limitations related to the simulation environment, resource capabilities. To evaluate the later, the scalability of Mininet in term of creating many topologies is tested with varying number of nodes and two different environment scenarios. Results show that the simulation environment has a remarkable effect on the required time for building a topology, for instance, the powerful resources scenario needed only 0.19 sec, whereas, 5.611 sec were needed when the resources were less. However, the required time were increased in both scenarios when the number of nodes was increased into 242.842 and 3718.117 sec for the powerful and less capabilities resources respectively.

Identifying Women's Experiences With and Strategies for Mitigating Negative Effects of Online Harassment <=SEP=> The popularity, availability, and ubiquity of information and communication technologies create new opportunities for online harassment. The present study evaluates factors associated with young adult women's online harassment experiences through a multi-factor measure accounting for the frequency and severity of negative events. Findings from a survey of 659 undergraduate and graduate students highlight the relationship between harassment, well-being, and engagement in strategies to manage one's online identity. We further identify differences in harassment experiences across three popular social media platforms: Facebook, Twitter, and Instagram. We conclude by discussing this study's contribution to feminist theory and describing five potential design interventions derived from our data that may minimize these negative experiences, mitigate the psychological harm they cause, and provide women with more proactive ways to regain agency when using communication technologies.

Silhouette Smoothing for Real-Time Rendering of Mesh Surfaces <=SEP=> Coarse piecewise linear approximation of surfaces causes the undesirable polygonal appearance of silhouettes. We present an efficient method for smoothing the silhouettes of coarse triangle meshes using efficient 3D curve reconstruction and simple local remeshing. It does not assume the availability of a fine mesh and generates only a moderate amount of additional data at runtime. Furthermore, polygonal feature edges are also smoothed in a unified framework. Our method is based on a novel interpolation scheme over silhouette triangles, and this ensures that smooth silhouettes are faithfully reconstructed and always change continuously with respect to the continuous movement of the viewpoint or objects. We speed up computation with GPU assistance to achieve real-time rendering of coarse meshes with the smoothed silhouettes. Experiments show that this method outperforms previous methods for silhouette smoothing.

Robustness Analysis of Artificial Neural Networks and Support Vector Machine in Making Prediction <=SEP=> This This study aims to investigate the robustness of prediction model by comparing artificial neural networks (ANNs), and support vector machine (SVMs) model. The study employs ten years monthly data of six types of macroeconomic variables as independent variables and the average rate of return of one-month time deposit of Indonesian Islamic banks (RR) as dependent variable. Finally, the performance is evaluated through graph analysis, statistical parameters and accuracy rate measurement. This research found that ANNs outperforms SVMs empirically resulted from the training process and overall data prediction. This is indicating that ANNs model is better in the context of capturing all data pattern and explaining the volatility of RR.

A systematic review of implementation strategies for assessment, prevention, and management of ICU delirium and their effect on clinical outcomes <=SEP=> INTRODUCTION
Despite recommendations from professional societies and patient safety organizations, the majority of ICU patients worldwide are not routinely monitored for delirium, thus preventing timely prevention and management. The purpose of this systematic review is to summarize what types of implementation strategies have been tested to improve ICU clinicians' ability to effectively assess, prevent and treat delirium and to evaluate the effect of these strategies on clinical outcomes.


METHOD
We searched PubMed, Embase, PsychINFO, Cochrane and CINAHL (January 2000 and April 2014) for studies on implementation strategies that included delirium-oriented interventions in adult ICU patients. Studies were suitable for inclusion if implementation strategies' efficacy, in terms of a clinical outcome, or process outcome was described.


RESULTS
We included 21 studies, all including process measures, while 9 reported both process measures and clinical outcomes. Some individual strategies such as "audit and feedback" and "tailored interventions" may be important to establish clinical outcome improvements, but otherwise robust data on effectiveness of specific implementation strategies were scarce. Successful implementation interventions were frequently reported to change process measures, such as improvements in adherence to delirium screening with up to 92%, but relating process measures to outcome changes was generally not possible. In meta-analyses, reduced mortality and ICU length of stay reduction were statistically more likely with implementation programs that employed more (six or more) rather than less implementation strategies and when a framework was used that either integrated current evidence on pain, agitation and delirium management (PAD) or when a strategy of early awakening, breathing, delirium screening and early exercise (ABCDE bundle) was employed. Using implementation strategies aimed at organizational change, next to behavioral change, was also associated with reduced mortality.


CONCLUSION
Our findings may indicate that multi-component implementation programs with a higher number of strategies targeting ICU delirium assessment, prevention and treatment and integrated within PAD or ABCDE bundle have the potential to improve clinical outcomes. However, prospective confirmation of these findings is needed to inform the most effective implementation practice with regard to integrated delirium management and such research should clearly delineate effective practice change from improvements in clinical outcomes.

Natural Language Processing using NLTK and WordNet <=SEP=> Natural Language Processing is a theoretically motivated range of computational techniques for analysing and representing naturally occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks or applications [1]. To perform natural language processing a variety of tools and platform have been developed, in our case we will discuss about NLTK for Python.The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python programming language[2]. It provides easy-to-use interfaces to many corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. In this paper we discuss different approaches for natural language processing using NLTK.

Biofeedback-based behavioral treatment for chronic tinnitus: results of a randomized controlled trial. <=SEP=> Many tinnitus sufferers believe that their tinnitus has an organic basis and thus seek medical rather than psychological treatments. Tinnitus has been found to be associated with negative appraisal, dysfunctional attention shift, and heightened psychophysiological arousal, so cognitive-behavioral interventions and biofeedback are commonly suggested as treatments. This study developed and investigated the efficacy of a biofeedback-based cognitive-behavioral treatment for tinnitus. In total, 130 tinnitus patients were randomly assigned to an intervention or a wait-list control group. Treatment consisted of 12 sessions of a biofeedback-based behavioral intervention over a 3-month period. Patients in the wait-list group participated in the treatment after the intervention group had completed the treatment. Results showed clear improvements regarding tinnitus annoyance, diary ratings of loudness, and feelings of controllability. Furthermore, changes in coping cognitions as well as changes in depressive symptoms were found. Improvements were maintained over a 6-month follow-up period in which medium-to-large effect sizes were observed. The treatment developed and investigated in this study is well accepted and leads to clear and stable improvements. Through demonstration of psychophysiological interrelationships, the treatment enables patients to change their somatic illness perceptions to a more psychosomatic point of view.

Lung nodule detection in CT images using deep convolutional neural networks <=SEP=> Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improvements in screening technologies, and an increased demand for their use, radiologists are required to analyze an ever increasing amount of image data, which can affect the quality of their diagnoses. Computer-Aided Detection (CADe) systems are designed to assist radiologists in this endeavor. Here, we present a CADe system for the detection of lung nodules in thoracic CT images. Our system is based on (1) the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which contains 1018 thoracic CT scans with nodules of different shape and size, and (2) a deep Convolutional Neural Network (CNN), which is trained, using the back-propagation algorithm, to extract valuable volumetric features from the input data and detect lung nodules in sub-volumes of CT images. Considering only those test nodules that have been annotated by four radiologists, our CADe system achieves a sensitivity (true positive rate) of 78.9% with 20 false positives (FPs) per scan, or a sensitivity of 71.2% with 10 FPs per scan. This is achieved without using any segmentation or additional FP reduction procedures, both of which are commonly used in other CADe systems. Furthermore, our CADe system is validated on a larger number of lung nodules compared to other studies, which increases the variation in their appearance, and therefore, makes their detection by a CADe system more challenging.

MAPGEN: Mixed-Initiative Planning and Scheduling for the Mars Exploration Rover Mission <=SEP=> 8 1094-7167/04/$20.00 © 2004 IEEE IEEE INTELLIGENT SYSTEMS to elucidate the planet’s past climate, water activity, and habitability. Science is MER’s primary driver, so making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this criticality, the MER project team selected MAPGEN (Mixed Initiative Activity Plan Generator) as an activity-planning tool. MAPGEN combines two existing systems, each with a strong heritage: the APGEN activity-planning tool1 from the Jet Propulsion Laboratory and the Europa planning and scheduling system2 from NASA Ames Research Center. This article discusses the issues arising from combining these tools in this mission’s context.

Face recognition using HOG-EBGM <=SEP=> 

Robot control design using virtual path from android smartphone <=SEP=> Progress robot development very varied almost all fields seize the attention of the world's scientists and researchers. For some cases, robots are generally used to help humans in relieving job. The robot can replace human mobilization for move so that the efficiency of wasted time will be less. Many things need to be note what kind of robot control reliable to replace human work in mobilization. Virtual world as in android smartphone greatly benefit humans to creating control tools easy to use anywhere and could represent a desire humans. By making control robot using virtual paths on the Android smartphone which have an accuracy of more than 70% to a scale of 1:50 with the actual track in an environment that is not wide enough, the advantage can be used to control the robot move easily and efficiently compared to the usual manual control robot that will take more time to control The robot moves its place as desired.

Scalable Modified Kneser-Ney Language Model Estimation <=SEP=> We present an efficient algorithm to estimate large modified Kneser-Ney models including interpolation. Streaming and sorting enables the algorithm to scale to much larger models by using a fixed amount of RAM and variable amount of disk. Using one machine with 140 GB RAM for 2.8 days, we built an unpruned model on 126 billion tokens. Machine translation experiments with this model show improvement of 0.8 BLEU point over constrained systems for the 2013 Workshop on Machine Translation task in three language pairs. Our algorithm is also faster for small models: we estimated a model on 302 million tokens using 7.7% of the RAM and 14.0% of the wall time taken by SRILM. The code is open source as part of KenLM.

3D Human Pose Estimation in RGBD Images for Robotic Task Learning <=SEP=> We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.