snu-aidas/HVMap-dataset huggingface dataset:
Depression Scale Recognition from Audio, Visual and Text Analysis

Depression is a major mental health disorder that is rapidly affecting lives worldwide. Depression not only impacts emotional but also physical and psychological state of the person. Its symptoms include lack of interest in daily activities, feeling low, anxiety, frustration, loss of weight and even feeling of selfhatred. This report describes work done by us for Audio Visual Emotion Challenge (AVEC) 2017 during our second year BTech summer internship. With the increase in demand to detect depression automatically with the help of machine learning algorithms, we present our multimodal feature extraction and decision level fusion approach for the same. Features are extracted by processing on the provided Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector approach were applied on the visual data; statistical descriptors on gaze, pose; low level audio features and head pose and text features were also extracted. Classification is done on fused as well as independent features using Support Vector Machine (SVM) and neural networks. The results obtained were able to cross the provided baseline on validation data set by 17% on audio features and 24.5% on video features. Keywords—AVEC 2017, SVM, Depression, Neural network, RMSE, MAE, fusion, speech processing.
OpinionFinder: A System for Subjectivity Analysis

Vancouver, October 2005. OpinionFinder: A system for subjectivity analysis Theresa Wilson‡, Paul Hoffmann‡, Swapna Somasundaran†, Jason Kessler†, Janyce Wiebe†‡, Yejin Choi§, Claire Cardie§, Ellen Riloff∗, Siddharth Patwardhan∗ ‡Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260 †Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260 §Department of Computer Science, Cornell University, Ithaca, NY 14853 ∗School of Computing, University of Utah, Salt Lake City, UT 84112
THE RESTORATIVE BENEFITS OF NATURE : TOWARD AN INTEGRATIVE

Directed attention plays an important role in human information processing; its fatigue, in turn, has far­ reaching consequences. Attention Restoration Theory provides an analysis of the kinds of experiences that lead to recovery from such fatigue. Natural environments turn out to be particularly rich in the character­ istics necessary for restorative experiences. An integrative framework is proposed that places both directed attention and stress in the larger context of human-environment relationships. © 1995 Academic Press Limited
Survey on Hadoop and Introduction to YARN Amogh

Big Data, the analysis of large quantities of data to gain new insight has become a ubiquitous phrase in recent years. Day by day the data is growing at a staggering rate. One of the efficient technologies that deal with the Big Data is Hadoop, which will be discussed in this paper. Hadoop, for processing large data volume jobs uses MapReduce programming model. Hadoop makes use of different schedulers for executing the jobs in parallel. The default scheduler is FIFO (First In First Out) Scheduler. Other schedulers with priority, pre-emption and non-pre-emption options have also been developed. As the time has passed the MapReduce has reached few of its limitations. So in order to overcome the limitations of MapReduce, the next generation of MapReduce has been developed called as YARN (Yet Another Resource Negotiator). So, this paper provides a survey on Hadoop, few scheduling methods it uses and a brief introduction to YARN. Keywords—Hadoop, HDFS, MapReduce, Schedulers, YARN.
A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives

BACKGROUND
Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.


OBJECTIVE
The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases.


METHODS
From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords "flu", "influenza", "gripe", and "high fever". The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis.


RESULTS
Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal.


CONCLUSIONS
While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.
Assessing forecast model performance in an ERP environment

Purpose – The paper aims to describe and apply a commercially oriented method of forecast performance measurement (cost of forecast error – CFE) and to compare the results with commonly adopted statistical measures of forecast accuracy in an enterprise resource planning (ERP) environment. Design/methodology/approach – The study adopts a quantitative methodology to evaluate the nine forecasting models (two moving average and seven exponential smoothing) of SAP’s ERP system. Event management adjustment and fitted smoothing parameters are also assessed. SAP is the largest European software enterprise and the third largest in the world, with headquarters in Walldorf, Germany. Findings – The findings of the study support the adoption of CFE as a more relevant commercial decision-making measure than commonly applied statistical forecast measures. Practical implications – The findings of the study provide forecast model selection guidance to SAP’s 12 þ million worldwide users. However, the CFE metric can be adopted in any commercial forecasting situation. Originality/value – This study is the first published cost assessment of SAP’s forecasting models.
ATR-Vis: Visual and Interactive Information Retrieval for Parliamentary Discussions in Twitter

The worldwide adoption of Twitter turned it into one of the most popular platforms for content analysis as it serves as a gauge of the public’s feeling and opinion on a variety of topics. This is particularly true of political discussions and lawmakers’ actions and initiatives. Yet, one common but unrealistic assumption is that the data of interest for analysis is readily available in a comprehensive and accurate form. Data need to be retrieved, but due to the brevity and noisy nature of Twitter content, it is difficult to formulate user queries that match relevant posts that use different terminology without introducing a considerable volume of unwanted content. This problem is aggravated when the analysis must contemplate multiple and related topics of interest, for which comments are being concurrently posted. This article presents Active Tweet Retrieval Visualization (ATR-Vis), a user-driven visual approach for the retrieval of Twitter content applicable to this scenario. The method proposes a set of active retrieval strategies to involve an analyst in such a way that a major improvement in retrieval coverage and precision is attained with minimal user effort. ATR-Vis enables non-technical users to benefit from the aforementioned active learning strategies by providing visual aids to facilitate the requested supervision. This supports the exploration of the space of potentially relevant tweets, and affords a better understanding of the retrieval results. We evaluate our approach in scenarios in which the task is to retrieve tweets related to multiple parliamentary debates within a specific time span. We collected two Twitter datasets, one associated with debates in the Canadian House of Commons during a particular week in May 2014, and another associated with debates in the Brazilian Federal Senate during a selected week in May 2015. The two use cases illustrate the effectiveness of ATR-Vis for the retrieval of relevant tweets, while quantitative results show that our approach achieves high retrieval quality with a modest amount of supervision. Finally, we evaluated our tool with three external users who perform searching in social media as part of their professional work.
Rapid growing fibroadenoma in an adolescent.

INTRODUCTION
We report a case of rapidly growing fibroadenoma.


PATIENT
A 13-year-old girl consulted the outpatient clinic regarding a left breast mass. The mass was diagnosed as fibroadenoma by clinical examinations, and the patient was carefully monitored. The mass enlarged rapidly with each menses and showed a 50% increase in volume four months later. Lumpectomy was performed. The tumor was histologically diagnosed as fibroadenoma organized type and many glandular epithelial cells had positive immunohistochemical staining for anti-estrogen receptor antibody in the nuclei.


CONCLUSION
The estrogen sensitivity of the tumor could account for the rapid growth.
Understanding Online Consumer Stickiness in E-commerce Environment : A Relationship Formation Model

Consumers with online shopping experience often stick to some special websites. Does it mean that long-term relationships between consumers and the websites form? To solve the problem, this paper analyzed the belief and attitude factors influencing online consumer stickiness intention. Based on the relationships between belief-attitude-intention implied in TPB (Theory of Planned Behavior), according to Expectation-Confirmation theory and Commitment-trust theory, we developed a concept model on online consumer stickiness. Six research hypotheses derived from this model were empirically validated using a survey of online shoppers. SEM (Structure Equation Modeling) was as a data analysis method. The results suggest that online consumer stickiness intention is influenced by consumers’ commitment to the website (e-vendors) and their overall satisfaction to the transaction process, and commitment’s effect is stronger; in turn, both consumers’ commitment and overall satisfaction are influenced by consumers’ ongoing trust significantly; confirmation has a significant effect on overall satisfaction but has not the same effect on commitment. The findings show that the long-term relationships between consumers and special websites exactly exist during online repeat consumption.
Satellite remote sensing of particulate matter and air quality assessment over global cities

Using 1 year of aerosol optical thickness (AOT) retrievals from the MODerate resolution Imaging Spectro-radiometer (MODIS) on board NASA’s Terra and Aqua satellite along with ground measurements of PM2.5 mass concentration, we assess particulate matter air quality over different locations across the global urban areas spread over 26 locations in Sydney, Delhi, Hong Kong, New York City and Switzerland. An empirical relationship between AOT and PM2.5 mass is obtained and results show that there is an excellent correlation between the bin-averaged daily mean satellite and groundbased values with a linear correlation coefficient of 0.96. Using meteorological and other ancillary datasets, we assess the effects of wind speed, cloud cover, and mixing height (MH) on particulate matter (PM) air quality and conclude that these data are necessary to further apply satellite data for air quality research. Our study clearly demonstrates that satellitederived AOT is a good surrogate for monitoring PM air quality over the earth. However, our analysis shows that the PM2.5–AOT relationship strongly depends on aerosol concentrations, ambient relative humidity (RH), fractional cloud cover and height of the mixing layer. Highest correlation between MODIS AOT and PM2.5 mass is found under clear sky conditions with less than 40–50% RH and when atmospheric MH ranges from 100 to 200m. Future remote sensing sensors such as Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that have the capability to provide vertical distribution of aerosols will further enhance our ability to monitor and forecast air pollution. This study is among the first to examine the relationship between satellite and ground measurements over several global locations. r 2006 Elsevier Ltd. All rights reserved.
HERB: a home exploring robotic butler

We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating doors and other constrained objects using caging grasps, grasp planning and execution in clutter, and manipulation on pose and torque constraint manifolds. S.S. Srinivasa ( ) · D. Ferguson · C.J. Helfrich Intel Research Pittsburgh, 4720 Forbes Avenue, Suite 410, Pittsburgh, PA 15213, USA e-mail: siddhartha.srinivasa@intel.com C.J. Helfrich e-mail: casey.j.helfrich@intel.com D. Berenson · A. Collet · R. Diankov · G. Gallagher · G. Hollinger · J. Kuffner · M.V. Weghe The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA D. Berenson e-mail: dberenso@ri.cmu.edu A. Collet e-mail: acollet@ri.cmu.edu R. Diankov e-mail: rdiankov@ri.cmu.edu G. Gallagher e-mail: ggallagh@ri.cmu.edu G. Hollinger e-mail: gholling@ri.cmu.edu J. Kuffner e-mail: kuffner@ri.cmu.edu M.V. Weghe e-mail: vandeweg@ri.cmu.edu We also present numerous severe real-world test results from the integration of these algorithms into a single mobile manipulator.
Real-time 3D object pose estimation and tracking for natural landmark based visual servo

A real-time solution for estimating and tracking the 3D pose of a rigid object is presented for image-based visual servo with natural landmarks. The many state-of-the-art technologies that are available for recognizing the 3D pose of an object in a natural setting are not suitable for real-time servo due to their time lags. This paper demonstrates that a real-time solution of 3D pose estimation become feasible by combining a fast tracker such as KLT [7] [8] with a method of determining the 3D coordinates of tracking points on an object at the time of SIFT based tracking point initiation, assuming that a 3D geometric model with SIFT description of an object is known a-priori. Keeping track of tracking points with KLT, removing the tracking point outliers automatically, and reinitiating the tracking points using SIFT once deteriorated, the 3D pose of an object can be estimated and tracked in real-time. This method can be applied to both mono and stereo camera based 3D pose estimation and tracking. The former guarantees higher frame rates with about 1 ms of local pose estimation, while the latter assures of more precise pose results but with about 16 ms of local pose estimation. The experimental investigations have shown the effectiveness of the proposed approach with real-time performance.
DTC-SVM Scheme for Induction Motors Fedwith a Three-level Inverter

Direct Torque Control is a control technique in AC drive systems to obtain high performance torque control. The conventional DTC drive contains a pair of hysteresis comparators. DTC drives utilizing hysteresis comparators suffer from high torque ripple and variable switching frequency. The most common solution to those problems is to use the space vector depends on the reference torque and flux. In this Paper The space vector modulation technique (SVPWM) is applied to 2 level inverter control in the proposed DTC-based induction motor drive system, thereby dramatically reducing the torque ripple. Then the controller based on space vector modulation is designed to be applied in the control of Induction Motor (IM) with a three-level Inverter. This type of Inverter has several advantages over the standard two-level VSI, such as a greater number of levels in the output voltage waveforms, Lower dV/dt, less harmonic distortion in voltage and current waveforms and lower switching frequencies. This paper proposes a general SVPWM algorithm for three-level based on standard two-level SVPWM. The proposed scheme is described clearly and simulation results are reported to demonstrate its effectiveness. The entire control scheme is implemented with Matlab/Simulink. Keywords—Direct torque control, space vector Pulsewidth modulation(SVPWM), neutral point clamped(NPC), two-level inverter.
Destination bonding : Hybrid cognition using Instagram

Article history: Received August 28, 2015 Received in revised format November 28, 2015 Accepted December 1, 2015 Available online December 1, 2015 Empirical research has identified the phenomenon of destination bonding as a result of summated physical and emotional values associated with the destination. Physical values, namely natural landscape & other physical settings and emotional values, namely the enculturation processes, have a significant role to play in portraying visitors’ cognitive framework for destination preference. The physical values seemed to be the stimulator for bonding that embodies action or behavior tendencies in imagery. The emotional values were the conditions that lead to affective bonding and are reflected in attitudes for a place which were evident in text narratives. Social networking on virtual platforms offers the scope for hybrid cognitive expression using imagery and text to the visitors. Instagram has emerged as an application-window to capture these hybrid cognitions of visitors. This study focuses on assessing the relationship between hybrid cognition of visitors expressed via Instagram and their bond with the destination. Further to this, the study attempts to examine the impact of hybrid cognition of visitors on the behavioral pattern of prospective visitors to the destination. The study revealed that sharing of visual imageries and related text by the visitors is an expression of the physico-emotional bonding with the destination. It was further established that hybrid cognition strongly asserts destination bonding and has been also found to have moderating impact on the link between destination bonding and electronic-word-of-mouth. © 2016 Growing Science Ltd. All rights reserved.
The Diffusion of Innovations in Social Networks ∗

This paper determines how different network structures influence the diffusion of innovations. We develop a model of diffusion where: 1. an individual’s decision to adopt a new technology is influenced by his contacts; and 2. contacts can discuss, coordinate, and make adoption decisions together. A measure of connectedness, ‘cohesion’, determines diffusion. A cohesive community is defined as a group in which all members have a high proportion of their contacts within the group. We show a key trade-off: on one hand, a cohesive community can hinder diffusion by blocking the spread of a technology into the group; on the other hand, cohesive communities can be particularly effective at acting collectively to adopt an innovation. We find that for technologies with low externalities (that require few people to adopt before others are willing to adopt), social structures with loose ties, where people are not part of cohesive groups, enable greater diffusion. However, as externalities increase (technologies require more people to adopt before others are willing to adopt), social structures with increasingly cohesive groups enable greater diffusion. Given that societal structure is known to differ systematically along this dimension, our findings point to specialization in technological progress exhibiting these patterns. ∗Bryony Reich, Faculty of Economics, University College London. Email: b.reich@ucl.ac.uk. I would like to thank Alberto Alesina, Antonio Cabrales, Sanjeev Goyal, and Jorgen Weibull for their invaluable guidance and support. I benefited greatly from conversations with and comments of Marco Bassetto, Lars Nesheim and Imran Rasul. I am grateful to Jonathan Newton for numerous interactions at all stages of this project. For helpful comments I would also like to thank Lucie Gadenne, Terri Kneeland, and Sueyhun Kwon, as well as seminar participants at Alicante, Cambridge, INET Contagion Conference, Oxford, PET Luxembourg, and UCL. I gratefully acknowledge financial support from the UK Economic and Social Research Council (grant number ES/K001396/1).
Finding scientific topics.

A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
Influence of External Currents in Sensors Based on PCB Rogowski Coils

A current sensor based in the Rogowski coil is an innovative measuring system that gives advantages with respect to conventional measuring systems based in current transformers with magnetic core [1], [2].and [3]. Their main advantages are: the linearity, the span and the bandwidth. Different kinds of manufacturing allow to obtain an important variety of Rogowski coils with different properties. One way of manufacturing is using the same method as for producing printed circuit boards, so by this way is possible to produce coils very similar and with a high precision. The authors are working in current measurement with Rogowski coils or Hall effect sensors and in particular in the realization of good and accurate coils [4] and [5]. In this work, the influence of external currents to the coil in the measured current by the coil has been evaluated.