Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Smoking in Young Adulthood on Smoking Later in Life: Evidence based on the Vietnam Era Draft Lottery

An important, unresolved question for health policymakers and consumers is whether cigarette smoking in young adulthood has lasting effects into later adulthood. The Vietnam era draft lottery offers an opportunity to address this question, because it randomly assigned young men to be more likely to experience conditions favoring cigarette consumption, including highly subsidized prices. Using this natural experiment, we find that military service increased the probability of smoking by 35 percentage points as of 1978-80, when men in the relevant cohorts were aged 25-30, but later in adulthood this effect was substantially attenuated and did not lead to large negative health effects.
1 Visual and acoustic features based emotion detection for advanced driver assistance system

Poor attention of drivers towards driving can cause accidents that can harm the driver or surrounding people. The poor attention is not only caused by the drowsiness of the driver but also due to the various emotions/moods (for example sad, angry, joy, pleasure, despair and irritation) of the driver. The emotions are generally measured by analyzing either head movement patterns or eyelid movements or face expressions or all the lasts together. Concerning emotion recognition visual sensing of face expressions is helpful but generally not always sufficient. Therefore, one needs additional information that can be collected in a non-intrusive manner in order to increase the robustness of the emotion measurement in the frame of a non-intrusive monitoring policy. We find acoustic information to be appropriate, provided the driver generates some vocal signals by speaking, shouting, crying, etc. In this paper, we propose a decision level fusion technique, to fuse the combination of visual sensing of face expressions and pattern recognition from driver’s voice. The result of the proposed approach significantly increase the performance of the automatic driver emotion recognition system.
LEBANESE AMERICAN UNIVERSITY A HYBRID DAMAGE ASSESSMENT AND RECOVERY APPROACH IN MULTILEVEL DATABASES By

In the midst of the technology advancements that we are witnessing nowadays, information security is of an essence. Information warfare is actively introducing new offensive strategies to exploit databases that include sensitive data, or even classified and secret data in the case of the multilevel ones. The attacks vary from stealing sensitive data, to modifying crucial records, or simply corrupting the database. Many approaches to detect and prevent from such attacks are available, from firewalls to intrusion detection systems. But in many cases, prevention can fail, and detection could happen long after the attack. That implies the necessity of damage assessment and recovery to restore the database integrity. We introduce in this work a hybrid lightweight approach based on clustering and graphs to track transactional dependencies in multilevel databases. To the best of our knowledge, targeting multilevel databases has not been tackled before, so we will compare our approach with single level databases approaches, taking into consideration the additional security and integrity constraints imposed by the multilevel nature.
EFFECT OF THE ECOLOGICAL SYSTEM ON LETTUCE PRODUCTION GROWN UNDER DIFFERENT SOILLESS SYSTEMS

Soilless culture is a technique for crop production without soil. Crops are grown in the nutrient solution or on a proper medium, therefore, soilless culture involves no work such tools or machines. But it needs additional costs for growing systems and chemical fertilizers. It offers earlier growth and higher yield. The main aim of this research is to study the effect of aeration on preventing the root problems by comparing the hydroponic system with aeration to the aeroponic system. The obtained results indicated that the nitrogen, phosphorus, potassium, calcium and magnesium consumption rate ranged from 22.10 to 32.63, 26.30 to 34.65, 85.80 to 120.30, 14.88 to 18.37 and 11.34 to 13.64 mg plant-1 day­-1, respectively, in hydroponic system and ranged from 25.65 to 37.35, 25.51 to 38.33, 115.70 to 127.50, 17.15 to 20.83 and10.94 to 14.90 mg plant-1 day­-1, respectively, after 50 days from transplanting in aeroponic system. The root length increased from 17.17 to 19.13 cm with increasing flow rate from 1.0 to 2.0 L h-1 in hydroponic system and increased from 17.45 to 19.56 cm with increasing flow rate from 0.5 to 1.5 L h-1 in aeroponic system. The fresh and dry mass of shoot and root were increased in aeroponic system better than those of hydroponic system. The total nutrients uptake values were higher in aeroponic system than those in hydroponic system. The average nitrogen, phosphorus, potassium, calcium and magnesium uptakes were 3.29, 1.25, 2.46, 0.43 and 0.44 %and 2.13, 0.82, 1.81, 0.32 and 0.40 % for aeroponic and hydroponic system, respectively. The average nitrate content was 155.52 and 113.73 mg plant-1 for aeroponic and hydroponic system, respectively.
Evaluation of Ergonomically Designed CAPTCHAs using Deep Learning Technology

Although most existing text-based CAPTCHAs use distorted images of alphanumerics, they have been criticized because large image distortions make it difficult for human beings to recognize the characters, despite the ease with which computers can eliminate distortions and consequently recognize them. Ergonomically designed CAPTCHAs, which exploit human-specific phenomena, are a solution to this problem. Ergonomic design enables humans to momentarily recognize them, while significantly increasing computational costs for machines to recognize characters. Recently, owing to the development of deep learning, the image recognition capability of machines has improved dramatically. Characters are easily recognized within reasonable time by deep convolutional neural networks (DCNNs), which have similar architectures to visual perception mechanisms of the brain. In this paper, to clarify whether ergonomically designed CAPTCHAs can withstand state-of-the-art methods of deep learning, we use several kinds of DCNNs to measure the classification rates of the characters displayed. With respect to ergonomic designs, we first use Amodal CAPTCHA proposed by Mori et al., which exploits the two human-specific phenomena of amodal completion and aftereffects. We secondly modify Amodal CAPTCHA by adding jagged lines to the edges of characters, aiming to prevent DCNNs from recognizing them correctly, since edges are one of the most fundamental features for DCNNs. Experimental results, however, show that both naive and jagged-lined Amodal CAPTCHAs are almost completely broken. Another approach we conducted is to use only complete characters without shielding as training data, assuming that attackers have no information about how amodal completion and jagged edges were applied. However, even for this assumption, the classification rate of DCNNs is still sufficiently high. On the whole, our results in this paper show that any ergonomic effects such as amodal completion and jagged edges are no longer countermeasures against character recognition by DCNNs.
Creativity Facet Orchestration : the Whys and the Hows

Finally, it is worth mentioning that the organizers acknowledged at the seminar that that some of these issues would be addressed. In fact, it has been recently announced that at the CIG 2015 competition, the organizers will be introducing two new tracks: Learning Track and Procedural Content Generation Track. Although their information has not yet been revealed at the time of writing this report, the former track apparently will allow a training phase.
Construction of a statistical learning tool based on ordinary differential equations to model the digestive behaviour of ross chickens

Being able to monitor and forecast farm animal performances is a strategic problem in the agronomy industry. We use a Data-Model Coupling approach to build a biomimetic Statistical Learning tool taking into account some aspects of the biological dynamics of the animal body. The objective is to build a tool which is able to assimilate data about daily feed consumption and measured performances. The model encompasses several sub-models corresponding to compartments and permitting to mimic a kinetic process divided into several steps. Each sub-model contains parameters which can be learnt by using an optimization algorithm and data. The goal of the ﬁrst application of the model on ﬁeld data was to simulate and predict the growth of chickens. An experiment was performed during 70 days to collect every day the feed consumption and the weight gain of a male and a female chickens. After the learning of the model parameters, the model shows a very good approximation of the chicken’s weight evolution over time.
Recent Progress of Dry Electrostatic Precipitation for PM 2 . 5 Emission Control from Coal-fired Boilers

Electrostatic precipitation (ESP) is a common technology applied in fly ash control in coal-fired power plant over the world, due to its high efficiency, low pressure loss, and low maintenance cost. Since fine particle collection (PM2.5) is much harder than relative large particle, modern ESP requires excellent performance on PM2.5 collection. In this paper, not only the ESP principles and models, but also the modern techniques developed recently for ESP in PM2.5 control will be reviewed, such as electric agglomeration, flow field visualization, modern power source, moving electrode ESP and colder-side ESP. The newly developed ESP index model and its application in ESP sizing and upgrading will be introduced in detail. Besides, several advanced flue gas cleaning systems developed in the past twenty years will be presented and compared, especially for the system with colder-side ESP developed in the recent ESP upgrading activities in China. Keywords—Electrostatic precipitation, PM2.5 control, coal-fired power plant Corresponding author: Keping Yan e-mail address: kyan@zju.edu. cn Huang et al. 69
FAULTS: AN EQUIPMENT MAINTENANCE AND REPAIR TRACKING SYSTEM USING A RELATIONAL DATABASE

FAULTS combines a forms-based user interface with a facility-wide relational database to record equipment maintenance and repair events as they occur. The semantics of preventive maintenance and repair events are formalized to create clear and unambiguous maintenance reports. Storing preventive maintenance and equipment failure information in a structured database has several benefits. Accumulated information is automatically indexed to aid diagnosis of failures as they occur. Equipment failure information isavailable to utility programs for display and sta tistical analysis to produce summaries such as preventive maintenance intervals, mean time between failures, predicted downtimes, performance trends, etc. FAULTS is installed in the U.C. Berkeley Microfabrication Facility where it isused to manage more than a hundred pieces of equipment. The FAULTS system has provided a significant improvement inthe management ofpreventive maintenance and equipment repair information.
Study on Load Sensing Method by Speed of PMSM

The PMSM is widely used in industry. In order to detect faults and to monitor the system, it is important to detect changes in load for systems in which the PMSM is installed.However, the price of sensors is high and it is difficult to install sensors because of space limitations. Therefore, a method is proposed to detect the load using the speed of the motor in a system without sensors. An FOC method for operating the motor and a detection method using speed fluctuation have been studied; however, the speed includes many error components such as motor inertia and measurement error. To improve the accuracy of the speed measurement, the pulse count method has been studied and implemented. Actual experiments were performed to verify the proposed algorithm. The proposed algorithm detects the load variation at approximately the same time as the actual load is applied.
ESARBICA JOURNAL

This article highlights developments that are taking place in the Swedish information management field amidst e-Government development. It also confirms the change in attitude which has led to the involvement of the Swedish National Archives as an important partner in e-Government development. The National Archives was charged with the responsibility to develop two important functions that uphold the open governance structure of Swedish government administrations; namely e-archiving and e-registration. European governments are investing in e-Government development to create more efficient public institutions and to make their information flows available to the citizens. A proactive and holistic information management approach is crucial to the effective management of government information. This requires an information infrastructure that will facilitate the management of the entire information continuum and employees who understand the value of information to promote its effective use. The article draws on the author’s PhD research that identified eight Enterprise Content Management (ECM) factors that are considered by the ECM proponents as crucial to robust information management. These eight factors include; enterprise architecture, business process management, collaboration, system integration, knowledge management, change management, re-purposing of information and the life cycle management of information. Some of these factors are currently being adopted in the Swedish information management environment which further confirms their relevance. Furthermore, Sweden enacted a directive on the repurposing of government information in compliance with the European Public Sector information directive (PSI). The European PSI directive gives different stakeholders in European societies a right to access government information and to develop new electronic services hence boosting innovation. The pluralization of information as per the PSI will require the adoption of the Records Continuum Model (RCM) thinking. This is because the RCM emphasizes the management of information in a way that facilitates its pluralization in different environments. It further combines archives and information management activities which should promote the integration of information resources. This is suitable for the e-Government environment since it would promote the re-purposing of government information. If the eGovernment objectives are to be realized, government institutions must adopt bestpractice, the ECM factors, and progressive models such as the RCM.
An Integrated an Approach for the S e Power System Devklopment

Rocketdyne is under a contract from NASA Lewis Research Center (LeRC) to design, develop, and build the electrical power system for the Space Station. This system provides for electric power generation, conditioning, storage, and distribution. The initial configuration uses photovoltaic power generation. Solar dynamic power generation will be added later. The power system control is based on a hierarchical architecture to support the requirements of automation. In the preliminary design and technology development phase of the program, various modeling techniques and software tools were evaluated for the purpose of meeting the Space Station power system modeling requirements. Rocketdyne and LeRC jointly selected the EASj.5 simulation software, developed by Boeing Computer Services, as a system-level modeling tool. This paper describes the application of the selected analytical modeling approach to represent the entire power system. Typical results of model predictions are also summarized. The equipment modeled includes solar arrays, dc to dc converters, resonant inverters, battery storage system, alternator, transmisNarayan Dravid National Aeronautics and Space Administration Lewis Research Center Cleveland, Ohio sion line, switch gear, and system level microprocessor controls. During the advanced development phase of this program, several models have been developed using this approach.
table isotopes and diet uncover trophic-niche divergence and cological diversification processes of endemic reptiles on Socotra sland

Ecological diversification on islands typically results in divergence of ecological niches. As diet is a major component of species niches, we hypothesize that sister species within island monophyletic groups diversify in their dietary preferences. We have examined this hypothesis in two Haemodracon and four Hemidactylus species endemic reptiles of from Socotra Island (Yemen), corresponding to two independent colonization events. Convergence i.e., similar dietary patterns of phylogenetically unrelated species, was also examined. Trophic niches were studied by the analysis of carbon and nitrogen stable isotopes combined with faecal samples. We collected tail tips (for isotopes) and faecal pellets during two visits in 2013 and 2014 to Socotra. Specific trophic niche widths inferred from stable isotopes were estimated from ellipse-based metrics, whereas interspecific differences were compared by linear mixed models and examined in a phylogenetic framework. From faecal samples, diet variation among species was quantified by the Bray-Curtis index. Isotope and dietary interspecific divergence was compared with Mantel tests. For both isotopes, models detected interspecific differences between sister species i.e., trophic niche divergence and also interspecific similarities of distant lineages that use similar microhabitats i.e., ecolog13 ical convergence. We did not find any phylogenetic signal neither in the interspecific differences in C nor in 15N isotopic values; thus species phylogenetically more closely related did not have more similar isotopic niches. The Mantel test demonstrated similar interspecific divergence using isotopes and faecal samples. In a phylogenetic context, trophic-niche interspecific comparisons highlight some mechanisms that are driving ecological diversification and speciation of Socotra Island. © 2017 Elsevier GmbH. All rights reserved.
Designing Ideal Task Partitioning Scheduling Model In Distributed Computing Scenario

: This paper represents optimal iterative task partitioning scheduling in distributed heterogeneous environment. The main goal of the algorithm is to improve the performance of the schedule in the form of iterations. This algorithm first uses b-level computation to calculate the initial schedule. Main characteristics of our method are optimal scheduling and strong link between partitioning, scheduling and communication. Some important models for task partitioning have also been discussed in this paper. The proposed algorithm improves inter process communication between tasks by using recourses of the system in an efficient manner.
Transfer Learning for Textual Topic Classificaton

The recent developments of Language Modeling led to advances in transfer learning methods in Natural Language Processing. Language Models pretrained on large general datasets achieved state-of-the-art results in a wide range of tasks. The Universal Language Model Fine-tuning represents an effective transfer learning method for text classification. The goal of this thesis is to further test the robustness of this method in scenarios, commonly found in real-world applications.
MUSICWEB : SIMILARITY MODELLING STRATEGIES FOR ARTIST DISCOVERY

MusicWeb is a Web application for music artist discovery that provides a browsing experience using connections that are either extra-musical or tangential to music, such as the artists’ political affiliation or social influence, or intra-musical, such as the artists’ main instrument or most favoured musical key. The connections are further enhanced by thematic analysis of journal articles and blog posts as well as content-based similarity measures focussing on high level musical categories.
Approaching Humans For Help: A Study of Human-Robot Proxemics

In order for a robot to be effective when interacting with a person, it is important for the robot to chose the correct person. Consider an example where a robot is trying to perform a task but it isn’t capable of doing a subtask, like going up a flight of stairs. In this case, the robot would need to ask a person for help with the elevator, in a socially appropriate way. We have conducted an experiment to determine who would be the best candidate to approach in a situation like this. Should the robot choose to approach someone who is very close, with the risk that the person may have already committed to passing? Or someone who is further away, which could result in the person not noticing the robot at all. Our hypothesis is there is some optimal distance that is not too close nor too far away. We tried approaching from different distances to see which distance led to the most successful interactions. The result provide guidance for developers of autonomous robots who need a robot to approach someone for help.
A New Doubly Robust Policy Estimator on Inﬁnite Horizon Reinforcement Learning

Recently inﬁnite horizon off-policy evaluation method based on estimation of density ratio has been proposed (Liu et al., 2018). Before that doubly robust estimator is the strongest baseline in off-policy evaluation in ﬁnite horizon. A natural question is if we can apply doubly robust method in inﬁnite horizon setting. This paper answer that question and reveal an interesting connection be-tween density ratio function and value function. We provide both theoretical and empirical result to show that our method yields signiﬁcantly advantage over previous method.
A novel tempogram generating algorithm based on matching pursuit

Tempogram is one of the most useful representations for tempo, which has many applications, such as music tempo estimation, music structure analysis, music classification, and beat tracking. This paper presents a novel tempogram generating algorithm, which is based on matching pursuit. First, a tempo dictionary is designed in the light of the characteristics of tempo and note onset, then matching pursuit based on the tempo dictionary is executed on the resampled novelty curve, and finally the tempogram is created by assembling the coefficients of matching pursuit. The tempogram created by this algorithm has better resolution, stronger sparsity, and flexibility than those of the traditional algorithms. We demonstrate the properties of the algorithm through experiments and provide an application example for tempo estimation.