Miracle-dz/UniFAR-FTUs huggingface dataset:
The Dynamics of Non-Financial Value Drivers

This study uses system dynamics to examine the time-series of incremental profits achievable by incorporating a causal chain of non-financial measures (NFMs) into the internal decision-making process. Specifically, I implement a realworld-based system dynamics model that helps researchers reconcile and understand the conflicting results of prior NFM research. The model provides evidence that (1) the magnitude and timing of incremental profits are influenced by the firm’s internal processes, external environment, and quality of information, (2) the incremental profits are affected by the length of time that NFMs have been used in decision making and (3) under certain conditions, NFM usage is not beneficial and even detrimental to financial performance.
Portable peak flow meters: physical characteristics, influence of temperature, altitude, and humidity.

Little is known about the linearity of portable peak flow meters, or about physical gas factors affecting peak expiratory flow (PEF) readings. We therefore tested five portable peak flow meters of three types in an altitude chamber (sea level to 5,500 m) and in a climate chamber at sea level (7-37 degrees C) to determine the influence of the physical conditions of the gas on the reading of the meters. The nonlinear response of the variable orifice meters was confirmed and, when this was corrected for, the readings of these meters were found to be significantly reduced by higher altitude and lower temperature. The readings from a turbine type of peak flow meter were not affected by altitude but were reduced at low temperature. A mathematical model for the variable orifice meters could correct for both their nonlinear behaviour and the effect of gas density (altitude, temperature and humidity). The model showed that correction is not necessary for the differences in gas conditions between calibration and taking of measurements under normal laboratory conditions. All the meters tested had impedances higher than recommended (0.05 kPa.l-1.s) and this may influence PEF at high flows. The mean uncorrected PEF of six healthy subjects when measured with a Mini Wright peak flow meter at sea level and at 3,000 m fell by 5%, but the mean corrected PEF increased by 12%. This increase in PEF was about 60% of that predicted for fully density-dependent flow and agreed with the findings of other similar studies. (ABSTRACT TRUNCATED AT 250 WORDS)
Path Planning of Mobile Robot in Relative Velocity Coordinates

Path planning of mobile robot in dynamic environment is one of the most challenging issues. To be more specific, path planning in multi-obstacle avoidance environment is defined as: given a vehicle A and a target G that are moving, planning a trajectory that will allow the vehicle to catch the target satisfy some specified constrains while avoiding obstacle O, and each of the obstacles can be either mobile or immobile in the environment. The corresponding problem is named target-pursuit and obstacles-avoidance (TPOA) and will be researched extensively in this chapter. The traditional method, such as probability road map, can achieve a successful path in 2D static environments. The planning process using this method generally consists of two phases: a construction and a query phase. In construction stage, the workspace of the robot is sampled randomly for generating candidate waypoints. In the query stage, the waypoints between the start and goal position are connected to be a graph, and the path is obtained by some searching algorithm, such as Dijkstra, A* algorithm and so on. Hraba researched the 3D application of probability road map where A* algorithm is used to find the near-optimal path (Hrabar, 2006). Although probability road map method is provably probabilistically complete (Ladd & Kavraki, 2004), it does not deal with the environment where the information is time-varying. The underlying reason is that this method only focuses on the certain environment. Once some uncertainty appears in the robot workspace, probability road map can not update with the changing environment and plan a valid trajectory for the mobile robot, never an optimal path. Artificial potential field is another traditional method which is generally used in both 2D and 3D environment. The mechanism that the robot is driven by attractive and repulsive force in a cooperative way is simple and often works efficiently even in dynamic environment. Kitamura et al. construct the path planning model based on the artificial potential field in threedimensional space which is described by octree (Kitamura et al, 1995). Traditionally, artificial potential field applies in two dimensions extensively. Also some other field concepts are invented. For example, there are harmonic potential functions (Kim & Khosla, 1992; Fahimi et al, 2009; Cocaud et al, 2008; Zhang & Valavanis, 1997), hydrodynamics (Liu et al, 2007),
Application of Particle Filter to Identification of Tsunami Simulation Model

Data assimilation in geophysics is a method of combining a numerical simulation model and incomplete observation data. A data assimilation problem can be formulated by an estimation using a nonlinear state space model and can be resolved using the particle filter. In this paper, we give a framework for resolving the correction and identification problem of erroneous tsunami simulation models using data assimilation. A numerical experiment is also conducted and its result shows the validity of applying data assimilation and the particle filter to a tsunami simulation model.
Gaussian mixture model based features for stationary human identification in urban radar imagery

In this paper, we propose a Gaussian mixture model (GMM) based approach to discriminate stationary humans from their ghosts and clutter in indoor radar images. More specifically, we use a mixture of Gaussian distributions to model the image intensity histograms corresponding to target and ghost/clutter regions. The mixture parameters, namely, the means, standard deviations, and weights of the component distributions, are used as features and a K-Nearest Neighbor classifier is employed. The performance of the proposed method is evaluated using real-data measurements of multiple humans standing or sitting at different locations in a small room. Experimental results show that the nature of the targets and ghosts/clutter in the image allows successful application of the GMM feature based classifier to distinguish between target and ghost/clutter regions.
Tradeoffs on the Efficient Frontier of Network Disruption Attacks

A communications network is represented as a graph of flow capacities. We study the problem of finding good network disruption attacks or target sets, i.e., a subset of vertices or edges that, once removed, impede communication between particular nodes. Multiple costs are associated with removing vertices or edges. Success in disrupting communications is traded off against the costs of the attack plans: the efficient frontier of attacks is estimated, and the results are studied in cross-linked diagrams. A multicriterial genetic algorithm is used to discover good plans for disrupting the communications network, where the genes correspond to nodes or links to be attacked. The genetic algorithm is seeded with an initial population of single-target genomes, one for each potential target. Multi-target attacks may be generated by breeding. Being on the efficient frontier guarantees a genome's survival to the next generation, so the population size is allowed to vary. The results are studied in interactive diagrams and in an "aggregate view" of the resulting population. Good attacks were found relatively rapidly, and the aggregate view revealed significant targets
Seasonal Heteroskedasticity in Time Series Data: Modeling, Estimation, and Testing

Seasonal heteroskedasticity refers to regular changes in variability over the calendar year. Models for two different forms of seasonal heteroskedasticity were recently proposed by Proietti and by Bell. We examine use of likelihood ratio tests with the models to test for the presence of seasonal heteroskedasticity, and use of model comparison statistics (AIC) to compare the models and to search among alternative patterns of seasonal heteroskedasticity. We apply the models and tests to U.S. Census Bureau monthly time series of housing starts and building permits.
Software architecture for a distributed in-hospital navigation system

Hospital environments are typically large, and even healthy individuals can need a navigation map. When we consider the combination of stress, concerns about health and convalescence, age and other factors that can affect hospital patients, it is clear that navigation can be a problem, and that assistance may be required. This paper focuses on the technical realization of an in-hospital navigation system tailored for users with limited orientation capabilities. A network of navigational terminals installed on appropriate navigation spots throughout a hospital building is used to guide a user to his/her destination. A computer vision method is used for seamless user identification. Software and hardware implementation of navigation terminals, and also network communication, are discussed in this paper.
A survey of researching on micro-blogging

Micro-blogging is an emerging internet communicatio n platform based on the social relationships. It ca n be used to share, disseminate and obtain all kinds of real-time updated information with about 140 words. Currently, micro-blogging becomes more and more pop ular since users exploit it to achieve a wide varie ty of social purposes. According to relevant public da ta, the earliest and most famous micro-blogging -Twitter already has 500 million registered users in the world. Thus we survey the considerable literat ure on Twitter and show masses of research theoretical res ults in the area obtained over the past few years. By reading the paper, we hope authors can gain an in-d epth understanding of the current research focus an d tendency related to the results of the Twitter. Spe cifically, in this paper, we first introduce microblogging and its difference with traditional blogging. Then we list several different research directions about Twitter as well as their research purposes, methods and con clusions. Finally, we show the gaps of previous researches and prospect the future work of micro-bl ogging.
Individual prediction regions for small samples

Follow-up is more and more used in medicine/doping control to identify abnormal results in an individual. Currently, follow-ups are mostly carried out variable by variable using ''reference intervals" that contain the values observable in 95\% of healthy/undoped individuals. Observations of the evolution of the variables over time in a sample of $N$ healthy/undoped individuals, allows these reference intervals to be individualized by taking into account the possible effect of covariables and some previous observations of these variables obtained when the individual was healthy/undoped. For each variable these individualized intervals should contain 95\% of observable values compatible with previous observed values in this individual. General methods to build these intervals are available, but they allow only a variable by variable follow-up whatever the possible correlations over time between them. In this article, we propose a general method to jointly follow-up several correlated variables over time. This methodology relies on a multivariate linear mixed effects model. We first provide a method to estimate the model's parameters. In an asymptotic framework ($N$ large enough), we then derive an individualized prediction region that should contain 95\% of observable values of all variables compatible with previously observed values. Sometimes, the sample size $N$ is not large enough for the asymptotic framework to give a reasonable prediction region. It is for this reason we propose and compare three different prediction regions that should behave better for small $N$. Finally, the whole methodology is illustrated by the follow-up of kidney insufficiency in cats.
Plogs: Materializing Datalog Programs with MapReduce for Scalable Reasoning

With the rapid growth of semantic data, scalable reasoning has attracted more, more attention. However, most existing works about scalable reasoning focus only on RDFS/OWL ter Horst semantics, which are small fragments of OWL 2 RL,, have limitation in expressivity. As OWL 2 RL semantics extended with SWRL rules can be expressed by datalog language, materialization of datalog programs is widely adopted in traditional reasoners. In this paper, we propose a dependency-aware approach on parallel materialization of datalog programs for scalable reasoning. We first present an algorithm to automate the translation from a Datalog rule execution into MapReduce jobs,, make several optimizations for the algorithm to speed up the rule evaluation process. Since the rule execution order has significant impact on reasoning performance due to the dependencies among rules. We then propose a sampling-based method to capture rule dependency,, design a dependency-aware strategy to schedule rule evaluation. Finally, we establish a system to evaluate the proposed approach with a series of semantic rule sets on large synthetic, real knowledge bases. The experimental results show that the proposed optimizations have significant effectiveness, our system achieves approximately linear scalability.
Human Intention Detection and Activity Support System for Ubiquitous Sensor Room

In this paper, we propose the human behavior detection and activity support environment Vivid Room. Human behaviors in Vivid Room are detected by many kinds of sensors embedded in the room (i.e. magnet sensors for doors/drawers, micro-switches for chairs, ID-tags for humans.) and that information is collected by sensor server via RF-tag system and LAN. In order to recognize meaningful human behaviors (i.e. studying, eating, resting, etc.), we’ve employed ID4 based learning system. Also we’ve developed human activity support system by using sound and voice by taking account of human behaviors in the room. The experimental results, which denote the accuracy of human behavior recognition and the quality of human support, are also shown.
QuantiFly: Robust Trainable Software for Automated Drosophila Egg Counting

We report the development and testing of software called QuantiFly: an automated tool to quantify Drosophila egg laying. Many laboratories count Drosophila eggs as a marker of fitness. The existing method requires laboratory researchers to count eggs manually while looking down a microscope. This technique is both time-consuming and tedious, especially when experiments require daily counts of hundreds of vials. The basis of the QuantiFly software is an algorithm which applies and improves upon an existing advanced pattern recognition and machine-learning routine. The accuracy of the baseline algorithm is additionally increased in this study through correction of bias observed in the algorithm output. The QuantiFly software, which includes the refined algorithm, has been designed to be immediately accessible to scientists through an intuitive and responsive user-friendly graphical interface. The software is also open-source, self-contained, has no dependencies and is easily installed (https://github.com/dwaithe/quantifly). Compared to manual egg counts made from digital images, QuantiFly achieved average accuracies of 94% and 85% for eggs laid on transparent (defined) and opaque (yeast-based) fly media. Thus, the software is capable of detecting experimental differences in most experimental situations. Significantly, the advanced feature recognition capabilities of the software proved to be robust to food surface artefacts like bubbles and crevices. The user experience involves image acquisition, algorithm training by labelling a subset of eggs in images of some of the vials, followed by a batch analysis mode in which new images are automatically assessed for egg numbers. Initial training typically requires approximately 10 minutes, while subsequent image evaluation by the software is performed in just a few seconds. Given the average time per vial for manual counting is approximately 40 seconds, our software introduces a timesaving advantage for experiments starting with as few as 20 vials. We also describe an optional acrylic box to be used as a digital camera mount and to provide controlled lighting during image acquisition which will guarantee the conditions used in this study.
Laryngeal Sensitivity in Patients with Amyotrophic Lateral Sclerosis

Recent studies have shown the involvement of the sensory nervous system in patients with amyotrophic lateral sclerosis (ALS). The aim of our study was to investigate the correlation between the laryngeal sensitivity deficit and the type of ALS onset (bulbar or spinal) in a large series of 114 consecutive ALS patients. Participants were subdivided into two groups, bulbar and spinal ALS, according to the clinical onset of disease and submitted to a clinical and instrumental evaluation of swallowing, including a fiber-optic endoscopic evaluation of swallowing with sensory testing. Dysphagia severity was scored using the Penetration–Aspiration Scale (PAS) and the Pooling score (P-score). In addition, three patients with laryngeal sensitivity deficit were submitted to a laryngeal biopsy to assess the status of the sensory innervation. All patients showed a normal glottal closure during phonation and volitional cough. Fifty-six subjects (49%), 14 spinal- and 42 bulbar-onset ALS, showed dysphagia at the first clinical observation (PAS score >1; P-score >5). Dysphagia resulted more frequently in bulbar-onset ALS (P < 0.01). Thirty-eight (33%) patients had a sensory deficit of the larynx. The sensory deficit of the larynx was significantly more frequent in bulbar-onset ALS (P < 0.01). The sensory deficit of the larynx among dysphagic patients was also significantly more frequent in bulbar-onset ALS (P = 0.02). Several abnormalities were found in all three subjects who underwent a laryngeal biopsy: in one patient, no intraepidermal fiber was found; in the other two, the fibers showed morphological changes. Our observations are important to consider for assessment and management of dysphagia in patients with ALS.
Harnessing inner strengths of at-risk university students: relationships between well-being, academic achievement and academic attainment

ABSTRACT Concern for the academic performance and persistence to graduation among university students is well-warranted. Numerous studies have tied performance to overall positive affect in professional settings and to individual internal concepts, indicating an expectation for similar ties in demanding academic settings. For students at increased risk for attrition, social connection and resilience are expected to play a role in buffering against attainment failures. This study examined the relationship between academic achievement, academic attainment, and various dimensions of well-being using a multidimensional well-being scale currently in development. Participants completed the Journey to College Success Scale, and Brief MSLSS-College in an online format, and were asked for permission to examine academic transcript records. We found relationships between the JCSS overall well-being score and academic achievement, a group of dimensions relating to resilience and academic achievement, a relationship between attainment and achievement, and a relationship between overall well-being and attainment, providing preliminary support for these connections.
Correlations in V1 Are Reduced by Stimulation Outside the Receptive Field

The trial-to-trial response variability of nearby cortical neurons is correlated. These correlations may strongly influence population coding performance. Numerous studies have shown that correlations can be dynamically modified by attention, adaptation, learning, and potent stimulus drive. However, the mechanisms that influence correlation strength remain poorly understood. Here we test whether correlations are influenced by presenting stimuli outside the classical receptive field (RF) of visual neurons, where they recruit a normalization signal termed surround suppression. We recorded simultaneously the activity of dozens of cells using microelectrode arrays implanted in the superficial layers of V1 in anesthetized, paralyzed macaque monkeys. We presented annular stimuli that encircled—but did not impinge upon—the RFs of the recorded cells. We found that these “extra-classical” stimuli reduced correlations in the absence of stimulation of the RF, closely resembling the decorrelating effects of stimulating the RFs directly. Our results suggest that normalization signals may be an important mechanism for modulating correlations.
Personality Traits of Illinois Agritourism Entrepreneurs

The number of farms participating in agritourism is on the rise. While many resources are available for individuals that are considering entering the agritourism industry, they tend to focus on the economic, environmental, and/or legal factors of the business model, excluding the role psychological factors, such as, personality traits can have on business creation and success. This article examines the personality traits of Illinois agritourism entrepreneurs as related to the Five-Factor Model (FFM). Illinois agritourism entrepreneurs and nonentrepreneurs only differ statistically for the personality trait of agreeableness and select demographic variables have an influence on the BFI scale score means of select personality traits. This information draws attention to an aspect of business that is often overlooked, specifically in the agriculture industry. University extension offices, farm bureaus, and/or agritourism associations can use the information gleaned from this study when publicizing resource guides for individuals considering entering the agritourism industry. It can also be beneficial to current agritourism businesses for the purposes of succession planning and hiring. [JEL Classification: L260 Entrepreneurship]
Introduction to the special issue on evaluating word sense disambiguation systems

Has system performance on Word Sense Disambiguation (WSD) reached a limit? Automatic systems don't perform nearly as well as humans on the task, and from the results of the SENSEVAL exercises, recent improvements in system performance appear negligible or even negative. Still, systems do perform much better than the baselines, so something is being done right. System evaluation is crucial to explain these results and to show the way forward. Indeed, the success of any project in WSD is tied to the evaluation methodology used, and especially to the formalization of the task that the systems perform. The evaluation of WSD has turned out to be as difficult as designing the systems in the first place.