Miracle-dz/UniFAR-FTUs huggingface dataset:
New tobacco products: do smokers like them?

Background: There is little information about smokers who tried potentially reduced exposure products (PREPs) (Eclipse®, Omni®, Advance Lights®, Accord®, or Ariva®), why they tried them, if they liked these products, and if they will continue to use them. Objectives: The objectives of this qualitative study were to understand: (1) how smokers who tried PREPs learned about them, (2) reasons for first trying PREPs, (3) which PREP(s) they tried, (4) what they thought of the product at first trial, (5) reasons for continuing or discontinuing use, and (6) whether they would recommend PREPs to others. Design: In October 2002, 16 focus group sessions were conducted with current cigarette smokers aged 30–50 years: eight groups in Chattanooga, Tennessee, and eight in Dallas, Texas. Specific focus groups were composed of white men, white women, African American men, African American women, Hispanic men, or Hispanic women. Results: The majority of the participants learned about PREPs through advertising or promotion, family, friends, and co-workers; major reasons given for first trying PREPs were that the products were free or inexpensive, they wanted to stop smoking, they believed the product claims of fewer health risks, or they were curious; most of them tried Eclipse® probably because the focus groups were conducted in the same cities where Eclipse® was introduced; most participants did not like PREPs; most discontinued the use of PREPS, some who continued to use them did so infrequently and also kept smoking their regular brands of cigarettes; and most would not recommend PREPs, although a few might recommend them to specific groups (for example, new smokers, the young, women, curious or health conscious people). Conclusions: Although most established smokers did not like the PREPs they tried and will not recommend them to anyone, a minority of established smokers believe that there may be a market for these products.
Applying Statistical Post-Editing to English-to-Korean Rule-based Machine Translation System

Conventional rule-based machine translation system suffers from its weakness of fluency in the view of target language generation. In particular, when translating English spoken language to Korean, the fluency of translation result is as important as adequacy in the aspect of readability and understanding. This problem is more severe in language pairs such as English-Korean. It’s because English and Korean belong to different language family. So they have distinct characteristics. And this issue is very important factor which effects translation quality. This paper describes a statistical post-editing for improving the fluency of rule-based machine translation system. Through various experiments, we examined the effect of statistical postediting for FromTo-EK 1 system which is a kind of rule-based machine translation system, for spoken language. The experiments showed promising results for translating diverse English spoken language sentences. 1 FromTo-EK is an English-to-Korean rule-based machine translation system for some various domains (patent, paper, email and messenger).
DRAFT On Machine Perception of Sound Ph

One of the desiderata in machine intelligence is that computers must be able to comprehend sounds as humans do. They must know about various sounds, associate them with physical objects, entities or events, be able to recognize and categorize them, know or discover relationships between them, etc. Successful solutions to these tasks is critical to and can have immediate effect on a variety of applications including content based indexing and retrieval of multimedia data on web which has grown exponentially in past few years. Automated machine understanding of sounds in specific forms such as speech, language and music has become fairly advanced, and has successfully been deployed into systems which are now part of daily life. However, the same cannot be said about natural occurring sounds in our environment. The problem is exacerbated by the sheer vastness of number of sound types, the diversity and variability of sound types, the variations in their structure, and even their interpretation. This dissertation aims to expand the scale and scope of machine hearing capabilities by addressing challenges in recognition of sound events, cataloging large number of potential word phrases that identify sounds and using them to categorize and learn relationships for sounds and by finding ways to efficiently evaluate trained models on large scale. On the sound event recognition front we address the major hindrance of lack of labeled data by describing ways to effectively use the vast amount of data on web. We describe methods for audio event detection which uses only weak labels in the learning process, combines weakly supervised learning with fully supervised learning to leverage labeled data in both forms and finally semi-supervised learning approaches which exploits the vast amount of available unlabeled data on web. Further, we describe methods to automatically mine sound related knowledge and relationships from vast amount of information stored in textual data. The third part once again addresses labeling challenges but now during evaluation phase. Evaluation of trained models on large scale once again requires data labeling. We describe ways to precisely estimate the performance of a trained model under restricted labeling budget. In this proposal we describe the completed works in the above directions. Empirical evaluation shows the effectiveness of the proposed methods. For each component framework described, we discuss expected research directions for successful completion of this dissertation. November 20, 2016 DRAFT
Impact on human health of particulate matter emitted from burnings in the Brazilian Amazon region.

OBJECTIVE To analyze the impact on human health of exposure to particulate matter emitted from burnings in the Brazilian Amazon region. METHODS This was an ecological study using an environmental exposure indicator presented as the percentage of annual hours (AH%) of PM2.5 above 80 microg/m3. The outcome variables were the rates of hospitalization due to respiratory disease among children, the elderly and the intermediate age group, and due to childbirth. Data were obtained from the National Space Research Institute and the Ministry of Health for all of the microregions of the Brazilian Amazon region, for the years 2004 and 2005. Multiple regression models for the outcome variables in relation to the predictive variable AH% of PM2.5 above 80 microg/m3 were analyzed. The Human Development Index (HDI) and mean number of complete blood counts per 100 inhabitants in the Brazilian Amazon region were the control variables in the regression analyses. RESULTS The association of the exposure indicator (AH%) was higher for the elderly than for other age groups (beta = 0.10). For each 1% increase in the exposure indicator there was an increase of 8% in child hospitalization, 10% in hospitalization of the elderly, and 5% for the intermediate age group, even after controlling for HDI and mean number of complete blood counts. No association was found between the AH% and hospitalization due to childbirth. CONCLUSIONS The indicator of atmospheric pollution showed an association with occurrences of respiratory diseases in the Brazilian Amazon region, especially in the more vulnerable age groups. This indicator may be used to assess the effects of forest burning on human health.
Information Complexity Is Computable

The information complexity of a function $f$ is the minimum amount of information Alice and Bob need to exchange to compute the function $f$. In this paper we provide an algorithm for approximating the information complexity of an arbitrary function $f$ to within any additive error $\alpha > 0$, thus resolving an open question as to whether information complexity is computable. In the process, we give the first explicit upper bound on the rate of convergence of the information complexity of $f$ when restricted to $b$-bit protocols to the (unrestricted) information complexity of $f$.
MOVING BEYOND FEAR: EXPLORING PERCEPTIONS OF THEORY-BASED GRAPHIC WARNING LABELS AMONG LOW-INCOME, URBAN SMOKERS

ii ACKNOWLEDGEMENTS . v LIST OF TABLES . xi LIST OF FIGURES . xii LIST OF APPENDICES . xiii CHAPTER 1: INTRODUCTION AND SPECIFIC AIMS . 1 Background . 2 Rationale for Research . 13 Theoretical Perspective . 15 Study Aims . 22 Development of Graphic Warning Labels and Interview Guide . 24 Dissertation Overview . 26 CHAPTER 2: LITERATURE REVIEW . 28 Impact of Graphic Warning Labels on Smoking Cessation Behaviors . 29 Graphic Warning Label Effectiveness Among Low SES Groups . 32 Research on Graphic Warning Label Content . 33 Graphic Warning Labels and Risk Communication Theory . 37 Label Efficacy Messages and Impacts on Efficacy . 38
A survey on scheduling and load balancing techniques in cloud computing environment

Now-a-days cloud computing is the most emerging technology due to its elasticity of resource provisioning and the pay-as-you-go pricing model which enables users to pay only according to their need. As cloud can be accessed anytime and anywhere through commodity hardware only its demand is increasing day by day. So it must provide high performance gain to the user and at the same time must be beneficial for the Cloud Service Provider (CSP). To achieve this goal many challenges have to be faced. Load balancing is one of them which helps the CSP to meet the QoS requirements of the users and at the same time maximize his profit by optimum use of the resources. To balance the load in cloud the resources and workloads must be scheduled in an efficient manner. A variety of scheduling algorithms are used by load balancers to determine which backend server to send a request to. The selected server allocates resources and schedules the job dynamically on some virtual machine (VM) located on the same physical machine. It is also the responsibility of the provider to dynamically reallocate or migrate the VM across physical machines for workload consolidation and to avoid over utilization or under utilization of resources. In this paper, we have discussed different algorithms proposed to resolve the issue of load balancing and task scheduling in Cloud Computing. We have mentioned some of their shortcomings for further development. VM migration issues involved in load balancing are also described briefly.
T w o CGTCA Motifs and a GHFlPitl Binding Site Mediate CAMP-dependent Protein Kinase A Regulation of Human Growth Hormone Gene Expression in Rat Anterior Pituitary GC Cells*

We established the cis-acting elements which mediate cAMP responsiveness of the human growth hormone (hGH) gene in transiently transfected rat anterior pituitary tumor GC cells. Analysis of the intact hGH gene or hGH S’-flanking DNA (W-FR) coupled to the hGH cDNA or chloramphenicol acetyltransferase or luciferase genes, indicated that cAMP primarily stimulated hGH promoter activity. Cotransfection of a protein kinase A inhibitory protein cDNA demonstrated that the cAMP response was mediated by protein kinase k Mutational analysis of the hGH promoter identified two core cAMP response element motifs (CGTCA) located at nucleotides -187/-183 (distal cAMP response element; dCRE) and -W/-95 (proximal cAMP response element; pCRE) and a pituitary-specific transcription factor (GHFl/Pitl) binding site at nucleotides -123/-112 (dGHF1) which were required for CAMP responsiveness. GHFl was not a limiting factor, since overexpression of GHFl in cotransfec-tions increased basal but not forskolin induction levels. Gel shift analyses indicated that similar, ubiquitous, thermostable protein(s) specifically bound the pCRE and dCRE motifs. The CGTCA motif-binding factors were C A M P response element binding protein (CREB)/ activating transcription factor-1
Font Similarities with Artificial Neural Networks

In this paper, I attempt to map images of characters of multiple fonts from image space to a latent font space. This is experimented by using three different neural network architectures. The networks attempt at performing both character and font recognition with a subset of hidden nodes responsible for each task. I evaluate this approach by testing the classification performance of the neural network on test data as well as on the classification attempt of the network on unseen fonts. The results on test data are also evaluated in the latent space by k-nearest neighbors (k-NN) and neighbourhood components analysis (NCA) with k-NN. The results show lower misclassification errors in the latent space showing that there is a better space to represent fonts. Interestingly, in the latent space, characters of the same font tend to cluster together suggesting possible similarities in the fonts as well as similarities in unseen fonts.
Asymmetric Planning for Incremental Real-Time Heuristic Search

Real-world applications typically permit only constant time intervals between subsequent moves of an agent interacting with an environment. Hence there is a need to efficiently distribute the limited computation time and also other resources available for planning ahead and for evaluating alternative future action sequences. In this paper we study a particular instance of this general problem, namely, the choice of local search space in real-time heuristic search. For larger local search spaces, the well-known LRTA* algorithm finds shorter solutions, but requires a lot of memory, especially in huge domains such as sliding tile puzzles. We suggest a novel algorithm CLRTA* that uses large, but asymmetric local search space, placed mostly within the previously explored area. In our experiments on sequences of sliding tile puzzles and mazes, CLRTA* produced solutions not much longer than the standard symmetric LRTA* with the same number of states examined per decision, while using much less memory.
Action recognition based on spatio-temporal interest points

Action recognition has already become one of the important research directions in the computer vision domain. Action recognition techniques have made remarkable progress in recent years. This interest is motivated by a wide spectrum of applications in many fields, such as smart surveillance, virtual reality, human computer interaction, and motion analysis. In this paper, we divide action recognition based on spatio-temporal interest points into three fundamental processes: spatio-temporal interest point detection, feature classification and action representation and recognition. We focus on the three areas to take the further study on action recognition based on spatiotemporal interest points. Also we make a comparative studies of the approaches used systematically. We propose the existing problems and possible future development trends of action recognition.
Regularized Multi-view Multi-metric Learning for Action Recognition

Although multi-view datasets have become more accessible in the real-world applications, most state-of-the-art action recognition methods applied to those datasets rely on simple view agreement when combining local information from various views together. This leads to deteriorated performance in situations with view insufficiency and view disagreements. In this paper, we propose a novel framework for boosting action recognition performance by quantifying the connection between the viewpoint and an action. The proposed approach searches for the best combination of multiple views based on a co-learning strategy that simultaneously learns a local distance metric related to each action class and the relationships between each viewpoint and the action category. Consequently, the spatio-temporal representation of each action class in different viewpoints plays a key role in shaping the local distance metric space. We test our method on the IXMAS dataset and shows competitive performance compared to other state-of-the-art methods.
Power-Velocity Characteristics and Jumping Abilities in Male Combat Athletes

Purpose. The aim of the study was to examine differences in power-velocity characteristics, and the maximal power and height of rise of the body’s centre of mass, measured in the counter-movement jump (cMJ) and the spike jump (SPJ), between judoists, boxers and taekwondo athletes. Methods. The study involved 7 judoists, 6 boxers, and 6 taekwondo athletes. The maximal power and height of jump were measured at cMJ and SPJ jumps. Force-velocity and power-velocity relations were determined on the basis of 5 maximal cycle ergometer exercise bouts at increasing external loads of 2.5, 5.0, 7.5, 10.0, and 12.5% of body weight (BW). Results. The absolute and relative power and velocity recorded for an external force-velocity relationship were similar in the groups. A significant difference was only observed between taekwondo athletes and judoists for absolute power at the external load of 2.5% BW ( p < 0.05). The judoists had significantly smaller relative maximal power in SPJ ( p < 0.05) and height of rise of the body mass centre in cMJ ( p < 0.05) than taekwondo athletes. The relative maximal power in cMJ and height of rise of the body mass centre in SPJ was similar in the groups. Conclusions. In martial arts, training and competition should affect physical characteristics, including jumping and power. The power and velocity recorded for an external force-velocity relationship were similar in the groups. Judoists and boxers did not differ in terms of power or height of the jumps. Taekwondo athletes developed the biggest power and height of the jumps. This is consistent with the discipline characteristics.
The use of the operand-recognition paradigm for the study of mental addition in older adults.

OBJECTIVES Determining how individuals solve arithmetic problems is crucial for our understanding of human cognitive architecture. Elderly adults are supposed to use memory retrieval more often than younger ones. However, they might backup their retrieval by reconstructive strategies. In order to investigate this issue, we used the operand-recognition paradigm, which capitalizes on the fact that algorithmic procedures degrade the memory traces of the operands. METHOD Twenty-three older adults (M = 70.4) and 23 younger adults (M = 20.0) solved easy, difficult, and medium-difficulty addition and comparison problems and were then presented with a recognition task of the operands. RESULTS When one-digit numbers with sums larger than 10 were involved (medium-difficulty problem), it was more difficult for younger adults to recognize the operands after addition than comparison. In contrast, in older adults, recognition times of the operands were the same after addition and comparison. DISCUSSION Older adults, in contrast with younger adults, are able to retrieve the results of addition problems of medium difficulty. Contrary to what was suggested, older participants do not seem to resort to backup strategies for such problems. Finally, older adults' reliance on the more efficient retrieval strategy allowed them to catch up to younger adults in terms of solution times.
MVAL: Addressing the Insider Threat by Valuation-based Query Processing

The research presented in this paper is inspired by problems of conventional database security mechanisms to address the insider threat, i.e. authorized users abusing granted privileges for illegal or disadvantageous accesses. The basic idea is to restrict the data one user can access by a valuation of data, e.g. a monetary value of data items, and, based on that, introducing limits for accesses. The specific topic of the present paper is the conceptual background, how the process of querying valuated data leads to valuated query results. For this, by analyzing operations of the relational algebra and SQL, derivation functions are added.
Removing biases in unsupervised learning of sequential patterns

Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize the data. Popular approaches to such learning include (and often combine) frequency-based approaches and statistical analysis. However, the quality of results is often far from satisfactory. Though most previous investigations seek to address method-specific limitations, we instead focus on general (method-neutral) limitations in current approaches. This paper takes two key steps towards addressing such general quality-reducing flaws. First, we carry out an in-depth empirical comparison and analysis of popular sequence learning methods in terms of the quality of information produced, for several synthetic and real-world datasets, under controlled settings of noise. We find that both frequency-based and statistics-based approaches (i) suffer from common statistical biases based on the length of the sequences considered; (ii) are unable to correctly generalize the patterns discovered, thus flooding the results with multiple instances (with slight variations) of the same pattern. We additionally show empirically that the relative quality of different approaches changes based on the noise present in the data: Statistical approaches do better at high levels of noise, while frequency-based approaches do better at low levels of noise. As our second contribution, we develop methods for countering these common deficiencies. We show how to normalize rankings of candidate patterns such that the relative ranking of different-length patterns can be compared. We additionally show the use of clustering, based on sequence similarity, to group together instances of the same general pattern, and choose the most general pattern that covers all of these. The results show significant improvements in the quality of results in all methods, and across all noise settings.
International Journal of Information Management

This paper explores the risk perceptions of key stakeholders in SMEs when making decisions on technology investments. Current literature focuses on the nature of the technology from a technical perspective and its associated beneﬁts to the SME. We seek to make a contribution that builds on the small but growing work, which views investment technology decisions as the outcome of a process of both objective and subjective risk assessment. Evidence presented in this paper suggests that subjective elements play an important part in assessing technology risks. Our empirical ﬁndings are that both e-business experience and the role of the decision-maker within the ﬁrm inﬂuences risk perception, whereas, sector differences are more modest. One implication of our ﬁndings is that policy interventions should be more sensitive and targeted at different types of stakeholders – owners, IT professionals and other individuals rather than on the sector in which the SME operates.