Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic-Spatial Reasoning for Unique Role Recognition in Dynamic Environment

Autonomous reasoning is a crucial problem for intelligent system when dealing with unspecified targets in dynamic environment. In this paper, a probabilistic reasoning method is proposed that associates semantic labels with spatial observations to recognize the person who is distinctive in attachments and movements in the scene. Roles are categorized as a binary representation of the unique target and others. Two types of observation models, namely, Object Existence Model (OEM) and Human Action Model (HAM), have been established by analyzing the corresponding semantic-interaction and spatio-temporal features. Then, OEM and HAM results of each person are compared with the overall distribution, respectively. Finally, the role can be comprehensively inferred through the fusion of two observation models. Experiment results show that the proposed method is feasible in a variety of scenarios.
Axial-Flux PM BLDC Machines with Concentrated Windings: Double-Layer, Single-Layer, and Single-Layer with Unequal Tooth

This paper investigates axial-flux permanentmagnet brushless-dc (PM BLDC) machines with concentrated windings and different layer numbers. In order to analyze the influence of layer numbers and unequal tooth on machine performance, three axial-flux PM BLDC machines are considered, and compared with respect to phase back-EMF, winding inductance, cogging torque, and electromagnetic torque. Three-dimensional (3-D) finite-element analysis is used to study the machines precisely. Keywords— axial-flux permanent-magnet, unequal tooth, single-layer, BLDC, 3-D finite-element analysis
A Novel Cross-layer Design Scheme for Distributed Cooperative Communication Networks

This paper proposes a novel cross-layer design scheme,named selective-node-based retransmission scheme(SN-RT). It efficiently combines cooperative diversity at the physical layer and automatic repeat request(ARQ) at the data link layer.At the stage of retransmission,the source together with an optimal relay,which is selected based on the instantaneous signal-to-noise ratio of relay-to-destination link,transmit signals to the destination node by following Alamouti-based space-time coding.The explicit expressions of throughput with MPSK modulation over Rayleigh fading channels are derived.The simulation results show that compared to the fixed-node-based retransmission scheme(FN-RT),SN-RT scheme can notably improve system throughput.
GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery

Designing effective REDD+ policies, assessing their GHG impact, and linking them with the corresponding payments, is a resource intensive and complex task. GainForest leverages video prediction with remote sensing to monitor and forecast forest change at high resolution. Furthermore, by viewing payment allocation as a feature selection problem, GainForest can efﬁciently design payment schemes based on the Shapley value.
SIMULATION OF FAILURE IN SINGLE-LAP-JOINTS ASSEMBLIES OF CARBON FIBRE TAPES

Failure of single-lap joints of carbon fibre tapes manufactured by thermoforming was characterized, with special regard of the effect of forming pressure and overlap geometry on the ultimate tensile strength. Introducing forming pressure dependent strength parameters, the impact of the structural alterations induced by the forming stage on the assemblies strength is modelled.
Evaluation of a New Approach for Estimating the Number of U . S . Farms

USDA’s National Agricultural Statistics Service (NASS) employs the June Area Survey (JAS) to produce annual estimates of U.S. farm numbers. The JAS is an area-frame-based survey conducted every year during the first two weeks of June. NASS also publishes an independent estimate of the number of farms from the quinquennial Census of Agriculture. Studies conducted by NASS have shown that farm number estimates from the JAS can be biased, mainly due to misclassification of agricultural tracts during the pre-screening and data collection processes. To adjust for the bias, NASS has developed a capture-recapture model that uses NASS’s list frame as the second sample, where estimation is performed based on records in the JAS with matches in the list frame. In the current paper, we describe an alternative capture-recapture approach that uses all available data from the JAS and the Census of Agriculture to correct for biases due to misclassification and to produce more stable farm number estimates.
MODEL BASED TESTING MOBILE APPLICATIONS : A CASE STUDY OF MOODLE MOBILE APPLICATION

The tools for the development of mobile applications are in rapid development, with different devices and mobile operating systems that are being introduced in the market periodically, this poses a need to have an easy and effective approach of testing mobile applications in a cost effective manner, one of such approaches is testing these applications based on the way they are expected to behave with different range of inputs and scenarios which is termed Model Based Testing (MBT). The current thesis describes the process of performing MBT on the Moodle educational software mobile application. Moodle Mobile app is the official front end to Moodle and is available for Android and IOS. This thesis has presented a behavioral approach in testing mobile applications using MBT with which an application can be tested with multiple test cases on the fly or offline. This was achieved by developing an adapter between the application behavior models and a real device using JSON protocol exposed by Appium. The first part of this thesis introduces the key concepts of MBT . The second section is centered on the state of the art and the related work. The third section discusses model programs developed in this work using NModel which is the framework which is used for modelling. The fourth section focuses on tools setup and entire test architecture. Section 5 detailed the adapter implementation and the end to end process of achieving the MBT using NModel. This thesis is written in English and is 54 pages long, including 5 chapters, 14 figures and 2 tables.
Research on Score Domain Speaking Rate Normalization for Speaker Recognition

Speaker recognition suffers from serious performance degradation due to the spectrum distortion caused by different speaking rates. This paper proposes a score normalization approach to alleviate the impact of the speaking rate variety. In the global normalization algorithm, all speech utterances in different speaking rates are used as a cohort set, based on which the score distribution of imposters is computed for each enrolled speaker; in the local normalization algorithm, the utterances in the cohort set are split into several subsets according to the speaking rates, and the score distribution is estimated on each subset. During the test, the score of a test utterance against the claimed speaker is normalized based on the estimated score distribution of imposters of the claimed speaker. In order to focus on the speaking rate, a speech database named CSLT-SPRateDGT2016 was recorded, where utterances in fast, slow and normal speaking rates were collected intentionally. All the experiments were conducted with this database, based on the well-known GMM-UBM framework. The experimental results show that the global and local score normalization methods proposed in this papers provides 17.77% and 4.58% relative EER reduction. Furthermore, in order to solve the data scarcity problem, a data augmentation approach is prosed, by which utterances in different speaking rates are produced artificially by modifying the speaking rates of the original recordings. Experiments based on the augmented database show clearly performance gains, leading to 28.84% and 33.33% relative EER reduction, with the global and local normalization methods respectively.
Small Area Estimation for the National Household Survey of Drug Abuse

1. I n t r o d u c t i o n In this paper, we summarize our planned approach for producing small area estimates of drug and alcohol use for selected U.S. States and metropolitan statistical areas (MSAs). The small area statistics of primary interest are population prevalence rates of illicit drug and alcohol dependency as ascertained from responses to National Household Survey of Drug Abuse (NHSDA) questionnaire items. We plan to produce separate rates for 32 demographic subpopulations defined by the three way crossclassification of sex with four age intervals { [ 12, 17]; [18,25]; [26,34]; and [35 plus]} and four race/ethnicity groups [Hispanics, Whites, Blacks, and Other Races] where the three non-hispanic categories will exclude hispanics. While we expect most of our small area statistics for the other races category to be suppressed due to excessive mean squared error estimates, the inclusion of this other race category will permit us to report statistics for Hispanics, Whites and Blacks employing the same race/ethnicity definitions typically used in government data sources.
A SURVEY ON SENTIMENT ANALYSIS OF MOVIE REVIEWS

With the help of technology, the internet becomes a valuable place for exchanging ideas, online learning, reviews for a product or service or movies. It makes hard to record and understand the user emotion because reviews over the internet are available for millions for a product or services. Sentiment analysis is an emerging area for research to collect the subjective information in source material by applying Natural Language processing, Computational Linguistics and text analytics and categorised the polarity of the opinion or sentiment. In simple words we say that sentiment analysis is important for decision making process. This paper provides an overall survey about sentiment analysis or opinion mining related to movie reviews.
Rural Child Poverty across Immigrant Generations in New Destination States

The U.S. population has become increasingly diverse along racial, ethnic, and nativity lines over recent decades. At the same time, immigrant populations and subsequent generations have become increasingly dispersed geographically across the country. Many of these new places of settlement are rural communities and small cities in nonmetropolitan areas. Influxes of immigrant populations and their children often have significant impacts in these destinations, and may also have implications for outcomes among immigrants and later generations. We contribute to research on this topic by analyzing patterns of child poverty across immigrant generations and destination types. The overall goals of this paper are to describe patterns of child poverty across immigrant generations in new rural destinations; identify correlates of these differences; and compare these patterns with what is observed in urban new destinations and established immigrant destinations. Toward these overall goals, we first estimate rates of child poverty for the first, second (stratified by number of foreign-born parents), and third+ immigrant generations in nonmetropolitan areas of new destination states, metropolitan areas of new destination states, and both metropolitan and nonmetropolitan areas in traditional gateway states. We then estimate the overall associations between immigrant generation and child poverty, and destination type and child poverty, using linear probability models. We estimate a series of nested regression models to evaluate the extent to which these associations can be explained by socio-demographic characteristics. Finally, we evaluate whether the net association between immigrant generation and child poverty varies systematically according to residential context, and identify the social and demographic characteristics that explain inter-generational differences in child poverty within destination types. Our results highlight salient differences in child poverty rates across immigrant generations, but the pattern and magnitude of these differences vary by destination types in sometimes-unexpected ways. Inter-generational differences in racial and ethnic composition and parental work, education and marital status explain non-trivial shares of the overall disadvantage experienced by the first generation and second-generation children, particularly those with two foreign-born parents. However, these composition effects vary by destination type. Overall, our results paint a complex picture in which children’s poverty risk is contingent upon the interaction of their immigrant generation and place of residence. 1 111A Armsby Building; The Pennsylvania State University; University Park, PA 16802; Email: bct11@psu.edu; Phone: 814-865-2561;
Bank Loans and the Business Cycle: The Case of the Czech Republic

This article aims to evaluate the impact of loans provided within the Czech banking sector on the growth of the Czech economy. The article is based on research of current scientific findings in respect to bank loans and economic development. The paper is based on data taken from the Czech Statistical Office on the development of the gross domestic product and data from the Czech National Bank on the development of loans from the period 20042015. Links between selected variables are tested using Granger causality tests. The results calculated confirm the hypothesis of the impact of the loans on economic growth, with a six-month delay. The results thus correspond to the standard economic findings and results of most previous studies. Keywords—Bank, business cycle, economic growth, loans.
Environmental Regulation and Intra-Industry Trade: An Empirical Analysis

ABSTRACT This article examines the impact of environmental stringency on the pattern of intra-industry trade (IIT) flows of four pollution-intensive industries, that is, pulp and waste paper, lime, cement, and construction materials, iron ore and concentrates, and dyeing and tanning extracts, by utilizing a balanced panel dataset comprising of 36 countries over the period 1990 to 2019. The environmental stringency variable has been estimated from the energy intensity of the trading countries. The results show that the environmental stringency coefficients are highly statistically significant, implying that environmental regulation emerges as an essential indicator of the IIT flows between two trading countries.
Breast Density Scoring with Multiscale Denoising Autoencoders

Breast density scoring is an important and difficult problem that has motivated various handcrafted feature representations. In this paper, we propose to automatically generate rich feature sets from labeled or unlabeled data. Our method (MS-DAE) builds upon denoising autoencoders (DAE), an unsupervised feature learning method, and is tailored to perform contextual image segmentation. We use our method to score breast density on 85 mammograms from a placebo-controlled trial on hormone replacement therapy. Our automated scores correlate well with established manual scoring techniques (Cumulus and BI-RADS), while being better in separating out the treatment effect.
A NEW BREED OF SUPPLIERS FOR PRODUCT INNOVATIONS: A MULTI-METHOD INVESTIGATION

This study extended the buyer-supplier relationship (BSR) literature by examining how supplier dependence influences buyer firms’ product innovation under the high and low relationalism conditions of BSRs. Using content-analytic case study and survey methodologies in tandem, this study highlighted the innovation-inhibiting effect of supplier dependence under the high relationalism condition.
THE EFFECTS OF ROLE MODELS ON NEW VENTURE ORIENTATION

We explore the effects of exposure to entrepreneurial role models and the preference for either growth ventures or life-style ventures. Hypotheses are drawn based on social learning and social comparison theories to examine which better explains role model effects. Analyses are based on a sample of students in business courses. Findings support hypotheses regarding effects of role models on preference for growth and lifestyle ventures. Overall, findings also show a stronger support for social learning theory predictions than for social comparisons theory.
New perspectives on games and interaction

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd new perspectives on games and interaction to read.
An Energy Effective-Accuracy Routing Algorithm for Wireless Sensor Networks

In this paper, Energy Effective-Accuracy Routing (EEAR) protocol is suggested for wireless sensor networks on the basis of energy saving while communication between sensor nodes on the whole network EEAR can conserve energy until keeping communication and routes leading to sink, by data-center gradient diffusion routing protocol. This is realizing by detection and turning on/off radio frequency and other elements of extra sensor nodes. EEAR, which is inspired from combining Gradient-Based Routing (GBR) route finding and Naps topology management protocol while applying both protocols advantages, keeps nearly constant level of routing accuracy with no need to geographic location information. After establishing communicative layers towards the sink while conserving inter-layer communication, this protocol puts extra nodes in sleeping state. In fact, in each layer, a node can go to sleep state by detecting some other nodes that can do communication duty on behalf of that node. Despite conformity with all data delivery models, EEAR produces considerable results in continuous and event-driven models towards query-driven model. In this paper we have implemented EEAR and compared it with some other methods, including GBR, Naps and GAF. Simulation results show that EEAR without requiring position information, performs at least as well as location based protocols in terms of topology control, routing and energy saving, and increases the packet delivery amount and decreases average packet delay. [Farzad KIANI, Ali AGHAEIRAD, Malik Kemal SIS, Alp KUT, Adil ALPKOCAK. EEAR: An Energy EffectiveAccuracy Routing Algorithm for Wireless Sensor Networks. Life Sci J 2013;10(2):39-45]. (ISSN:10978135). http:www.lifesciencesite. com. 7
Prevalence of diagnosed dementia in subjects with schizophrenia as compared to the general population

1 Background . 3 Cognitive impairment and dementia . 3 Schizophrenia . 4 Cognitive impairment in schizophrenia . 5 Aim/objectives . 7 Participants and Methods . 7 Statistics. 8 Ethics . 8 Results . 8 Discussion. 10 References. 15
THE MINIMUM WEIGHT SPANNING STAR FOREST MODEL OF THE OPTIMAL DIVERSITY MANAGEMENT PROBLEM

In this paper we describe a real application project concerned with the implementation of optimization algorithms to solve a car industry problem known, in the literature, as the Optimal Diversity Management Problem. The minimum weight spanning star forest model for this problem is introduced, its complexity is analyzed, the implemented optimization techniques are presented and the computational results for real data instances are reported. Since the practical problem particularities had strongly conditioned the implemented optimization algorithms, these practical specifications are explored in the paper. Based on the results of this project, the manufacturer currently solves instances that are more than twenty times larger than the largest instances reported for this problem. keywords: p-median; star covering
Vectorization Method on the Color Cloud Image

The vectorization, which is an interdisciplinary combining subjects of the computer vision, computer image processing, computer graphics and artificial intelligence, is an important issue in the digital image processing. This paper makes a study of the vectorization method on the satellite color cloud image, Specific techniques such as cloud image layering, threshold selection, edge detection and tracking, feature point extraction, curve fitting are involved. This paper proposes a color layering algorithm whose parameter is the difference between luminance and saturability by analyzing the color features of the satellite cloud pictures. In this algorithm method, we first generate the reference image and then histogram equalize the reference image, afterwards do de-noises process to the above histogram and smooth it for the threshold choice. After analyzing the uncertainty of satellite cloud image edge, this paper puts forward an edge detection algorithm based on adaptive threshold. In order to modify the shortcomings of edge data of raster and edge data of chain code, this paper puts forward the corner detection method based on edge data chain. Also this paper develops a prototype system of automatic vectorization on satellite cloud images by curve fitting the feature points and gets the final vector graphics. Experiments show that the system has the basic functions of automatic extraction, recognition and vectorization for the information of satellite cloud clouds, and carry out the comprehensive verification and analysis for the theory and algorithm by using the real satellite cloud image data.
The Alzheimer ’ s Disease Centers ’ Uniform Data Set ( UDS ) : The Neuropsychological Test Battery

The neuropsychological test battery from the Uniform Data Set (UDS) of the Alzheimer’s Disease Centers (ADC) program of the National Institute on Aging (NIA) consists of brief measures of attention, processing speed, executive function, episodic memory and language. This paper describes development of the battery and preliminary data from the initial UDS evaluation of 3,268 clinically cognitively normal men and women collected over the first 24 months of utilization. The subjects represent a sample of community-dwelling, individuals who volunteer for studies of cognitive aging. Subjects were considered “clinically cognitively normal” based on clinical assessment, including the Clinical Dementia Rating scale and the Functional Assessment Questionnaire. The results demonstrate performance on tests sensitive to cognitive aging and to the early stages of Alzheimer disease (AD) in a relatively well-educated sample. Regression models investigating the impact of age, education, and gender on test scores indicate that these variables will need to be incorporated in subsequent normative studies. Future plans include: 1) determining the psychometric properties of the battery; 2) establishing normative data, including norms for different ethnic minority groups; Corresponding Author: Sandra Weintraub, PhD, Cognitive Neurology and Alzheimer’s Disease Center, Northwestern University Feinberg School of Medicine, 320 E. Superior, Searle 11–467, Chicago, IL 60611, Phone: 312-908-9013, Fax: 312-908-8789, e-mail: sweintraub@northwestern.edu. NIH Public Access Author Manuscript Alzheimer Dis Assoc Disord. Author manuscript; available in PMC 2010 April 1. Published in final edited form as: Alzheimer Dis Assoc Disord. 2009 ; 23(2): 91–101. doi:10.1097/WAD.0b013e318191c7dd. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript and 3) conducting longitudinal studies on cognitively normal subjects, individuals with mild cognitive impairment, and individuals with AD and other forms of dementia.