Miracle-dz/UniFAR-FTUs huggingface dataset:
The species–accumulation curve and estimation of species richness

Summary 1One of the general characteristics of ecological communities is that the number of species accumulates with increasing area sampled. However, it is important to distinguish between the species–area relationship and species accumulation curves. The species–area relationship is concerned with the number of species in areas of different size irrespective of the identity of the species within the areas, whereas the species accumulation curve is concerned with accumulation rates of new species over the sampled area and depends on species identity. 2We derive an exact analytical expression for the expectance and variance of the species–accumulation curve in all random subsets of samples from a given area. The analytical species accumulation curve may be approximated by a semilog curve. Both the exact accumulation curve and its semilog approximation are independent of the underlying species abundance distributions, but are influenced strongly by the distribution of species among the samples and the spatial relationship of the samples that are randomized. 3To estimate species richness in larger areas than that sampled we take account of the spatial relationship between samples by dividing the sampled area into subareas. First a species–accumulation curve is obtained for randomized samples of all the single subareas. Then the species–accumulation curve for all combinations of two subareas is calculated and the procedure is repeated for all subareas. From these curves a new total species (T–S) curve is obtained from the terminal point of the subarea plots. The T–S curve can then be extrapolated to estimate the probable total number of species in the area studied. 4Data from the Norwegian continental shelf show that extrapolation of the traditional species–accumulation curve gave a large underestimate of total species richness for the whole shelf compared with that predicted by the T–S curve. Application of non-parametric methods also gave large underestimates compared with actual data obtained from more extensive sampling than the data analysed here. Although marine soft sediments sampled in Hong Kong were not as variable as those from the Norwegian shelf, nevertheless here the new method also gave higher estimates of total richness than the traditional species–accumulation approaches. 5Our data show that both the species–accumulation curve and the accompanying T–S curve apply to large heterogeneous areas varying in depth and sediment properties as well as a relatively small homogeneous area with small variation in depth and sediment properties.
Non-LIFO Execution of Cognitive Procedures

Many current theories of human problem solving and skill acquisition assume that people work only on the unsatisfied goal that was created most recently. That is, the architecture obeys a last-in-first-out (LIFO) constraint on the selection of goals. This restriction seems necessary for the proper functioning of automatic learning mechanisms, such as production compilation and chunking. It is argued that this restriction is violated by some subjects on some tasks. In particular, 8 subiects (from a sample of 26) execute subtraction procedures in a way that violates the LIFO constraint. Although there is a great deal of betweenand withinsubject strategy variation in the 8 subjects’ behavior, it can be simply exploined by hypothesizing that (1) the goal selection is not necessarily LIFO, (2) goal selection knowledge is represented by explicit preferences, and (3) the 8 subjects have just a few preferences that are overgeneralized, overspecialized, or missing. The rest of their preferences ore correct. On the other hand, LIFO-bosed models seem unable to explain the strategy variations in any simple way. Thus, it seems that port of the flexibility in humon problem solving comes from having a choice of which goal to work on next. Fortunately, it is simple to ammend automatic learning mechanisms so thot they will function correctly in a non-LIFO architecture.
Real time speaker localization and detection system for camera steering in multiparticipant videoconferencing environments

A real time speaker localization and detection system for videoconferencing environments is presented. In this system, a recently proposed modified Steered Response Power - Phase Transform (SRP-PHAT) algorithm has been used as the core processing scheme. The new SRP-PHAT functional has been shown to provide robust localization performance in indoor environments without the need for having a very fine spatial grid, thus reducing the computational cost required in a practical implementation. Moreover, it has been demonstrated that the statistical distribution of location estimates when a speaker is active can be successfully used to discriminate between speech and non-speech frames by using a criterion of peakedness. As a result, talking participants can be detected and located with significant accuracy following a common processing framework.
Modeling the UWB through-the-wall signal pathloss measurements and Time Domain Ray tracing method

The UWB is a promising short-range communication and localization technology due to its low power transmissions. In this paper, we investigate the UWB through-the-wall propagation with signal frequency range from 1 GHz to 2 GHz based on two path-loss models, i.e., the 802.15. 4a model and the frequency-dependent model. The UWB frequency selective properties for typical wall materials are analyzed. To predict the UWB through-the-wall propagation performance, we further propose an improved Time Domain Ray tracing (TD-Ray) method, which is demonstrated to be superior by experiments.
Agent-based modeling: Methods and techniques for simulating human systems

Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.
Exploiting NonLinearities in GDP Growth for Forecasting and Anticipating Turning Points

We exploit non-linearities in the behavior of GDP growth to produce forecasts of growth itself, and to anticipate regime changes and NBER-dated turning points. The forecasting model we develop features an error-correction mechanism with a drift component that follows regimespeci c trajectories. Success in forecasting depends critically on the incorporation of a exible speci cation that admits wide heterogeneity in trajectories across regimes, including potentially dramatic departures from linearity.
Real-Time Vehicle Classification using a Smart Embedded Device with a `Silicon Retina' Optical Sensor

This paper presents a sensor system for traffic data acquisition capable of discriminating vehicles into two classes (cars and trucks). The system is based on a bio-inspired `silicon retina' optical sensor in a compact embedded system and has been exhaustively evaluated in a real world environment at a highway test site. We further present the classification approach, data from the evaluation of the system under different lighting conditions and analyze sources of classification errors. The System achieves a 94.4% classification rate for cars and 92.2% classification rate for trucks under average daylight conditions.
Night sweats in children: prevalence and associated factors

Objective The authors aimed to examine the prevalence and factors associated with night sweats (NS) in primary school children. Study design Cross-sectional design. Results Among 6381 children (median age 9.2 (7.7–10.7) years) with complete information on NS, 3225 were boys (50.5%). 747 children (11.7%) were reported to have weekly NS in the past 12 months. Boys were more likely than girls to have NS (p<0.0001). Children with NS were more likely to have sleep-related symptoms and respiratory and atopic diseases. In addition, they were more likely to be hyperactive and have frequent temper outbursts. Using an ordinal regression model, NS was found to be significantly associated with male gender, younger age, allergic rhinitis, tonsillitis and symptoms suggestive of obstructive sleep apnoea, insomnia and parasomnia. Conclusion NS is prevalent among school-aged children and is associated with the presence of sleep-related symptoms and respiratory and atopic diseases.
Miniature Pneumatic Curling Rubber Actuator Generating Bidirectional Motion with One Air-Supply Tube

Soft actuators driven by pneumatic pressure are promising actuators for mechanical systems in medical, biological, agriculture, welfare fields and so on, because they can ensure high safety for fragile objects from their low mechanical impedance. In this study, a new rubber pneumatic actuator made from silicone rubber was developed. Composed of one chamber and one air-supply tube, it can generate curling motion in two directions by using positive and negative pneumatic pressure. The rubber actuator, for generating bidirectional motion, was designed to achieve an efficient shape by nonlinear finite element method analysis, and was fabricated by a molding and rubber bonding process using excimer light. The fabricated actuator was able to generate curling motion in two directions successfully. The displacement and force characteristics of the actuator were measured by using a motion capture system and a load cell. As an example application of the actuator, a robotic soft hand with three actuators was constructed and its effectiveness was confirmed by experiments.
Faster optimal single-row placement with fixed ordering

We consider the problem of placing a set of cells in a single row with a given horizontal ordering, minimizing the (weighted) bounding box netlength. We analyze the running time of an algorithm of Kahng, Tucker and Zelikovsky which solves this problem optimally. By using different data structures we are able to improve the worst-case running time in the unweighted case as well as in the presence of netweights.
Mediators in the Relationship Between Minority Stress and Depression Among Young Same-Sex Attracted Women

Minority stress has been associated with poor mental health outcomes among same-sex attracted young adult women, though there is limited research on the underlying factors that could explain how minority stress affects psychological functioning. The current study investigated such factors (trait mindfulness, coping strategies, and social support) that can potentially mediate the relationship between minority stress and depressive symptoms among same-sex attracted college-aged women. The study was completed online, and participants were 175 women between the ages of 18 and 28 who are mainly attracted to other women. Simple mediation analysis results revealed that 3 of the hypothesized protective factors (trait acceptance, avoidance coping, and perceived lesbian, gay, and bisexual social support) partially mediated the relationship between minority stress and depressive symptoms. A multiple mediation analysis indicated that trait acceptance and avoidance coping completely mediated the relationship between minority stress and depressive symptoms when included in a mediation model together. These results demonstrate that more research is needed to understand how minority stress affects mental health through its impact on one’s personal resources and coping strategies, particularly factors such as mindfulness, acceptance, and social support. Furthermore, clinicians working with this population should use treatments that emphasize mindful acceptance and social support.
Control of Directional Errors in Fixed Sequence Multiple Testing

In this paper, we consider the problem of simultaneously testing many two-sided hypotheses when rejections of null hypotheses are accompanied by claims of the direction of the alternative. The fundamental goal is to construct methods that control the mixed directional familywise error rate, which is the probability of making any type 1 or type 3 (directional) error. In particular, attention is focused on cases where the hypotheses are ordered as $H_1 , \ldots, H_n$, so that $H_{i+1}$ is tested only if $H_1 , \ldots, H_i$ have all been previously rejected. In this situation, one can control the usual familywise error rate under arbitrary dependence by the basic procedure which tests each hypothesis at level $\alpha$, and no other multiplicity adjustment is needed. However, we show that this is far too liberal if one also accounts for directional errors. But, by imposing certain dependence assumptions on the test statistics, one can retain the basic procedure. Through a simulation study and a clinical trial example, we numerically illustrate good performance of the proposed procedures compared to the existing mdFWER controlling procedures. The proposed procedures are also implemented in the R-package FixSeqMTP.
Useful attributes identification for Unsupervised Information Extraction result set based on REAdaBoost Naïve Bayes

Unsupervised Information Extraction has attracted great attentions in the literature. However, it is inevitable to include useless noise in the result set. Besides, the proportion of useful attributes and the noise in the result set is greatly imbalanced, and the importance of these two types of data is also different. So how to effectively identify the useful attributes becomes an open question. To address this problem, this paper proposes a revised AdaBoost algorithm-REAdaBoost. The weight coefficient of REAdaBoost is not only decided by the precision of useful attributes, but also correlates with the recall for rare attributes. We use Naïve Bayes as the base classifier, and then apply AdaBoost and REAdaBoost to boost it separately. The experiment results show that on the premise of not increasing the overall error rate, REAdaBoost has better performance than AdaBoost and Naïve Bayes in predicting both the useful attributes and the rare attributes.
KBOC: Keystroke biometrics OnGoing competition

This paper presents the first Keystroke Biometrics Ongoing evaluation platform and a Competition (KBOC) organized to promote reproducible research and establish a baseline in person authentication using keystroke biometrics. The ongoing evaluation tool has been developed using the BEAT platform and includes keystroke sequences (fixed-text) from 300 users acquired in 4 different sessions. In addition, the results of a parallel offline competition based on the same data and evaluation protocol are presented. The results reported have achieved EERs as low as 5.32%, which represent a challenging baseline for keystroke recognition technologies to be evaluated on the new publicly available KBOC benchmark.
Multi-microphone speech dereverberation using expectation-maximization and Kalman smoothing

Speech signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown. In this paper, a multi-microphone algorithm that simultaneously estimates the acoustic system and the clean signal is proposed. An expectation-maximization (EM) scheme is employed to iteratively obtain the maximum likelihood (ML) estimates of the acoustic parameters. In the expectation step, the Kalman smoother is applied to extract the clean signal from the data utilizing the estimated parameters. In the maximization step, the parameters are updated according to the output of the Kalman smoother. Experimental results show a significant dereverberation capabilities of the proposed algorithm with only low speech distortion.
Dual-Regularized One-Class Collaborative Filtering

Collaborative filtering is a fundamental building block in many recommender systems. While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). The main challenges in such one-class setting include the ambiguity of the unobserved examples and the sparseness of existing positive examples. In this paper, we propose a dual-regularized model for one-class collaborative filtering. In particular, we address the ambiguity challenge by integrating two state-of-the-art one-class collaborative filtering methods to enjoy the best of both worlds. We tackle the sparseness challenge by exploiting the side information from both users and items. Moreover, we propose efficient algorithms to solve the proposed model. Extensive experimental evaluations on two real data sets demonstrate that our method achieves significant improvement over the state-of-the-art methods. Overall, the proposed method leads to 7.9% - 21.1% improvement over its best known competitors in terms of prediction accuracy, while enjoying the linear scalability.
Disparity based compression technique for focused plenoptic images

In this paper we present a novel method for compression of focused plenoptic (light field) images. Plenoptic image is an array of lenslet images where the consecutive images act as stereo images. Adjacent lenslet images have overlapping regions leading to higher levels of redundancy than observed in a normal image. We use this overlap to appropriately select a set of reference images from which the remaining lenslet images can be approximately reconstructed. These reference images can be compressed using jpeg. The residue of the reconstructed image from the original image is compressed using either DWT-SPIHT or DCT based compression. Using the proposed method we are able to compress the image at high PSNR at a low bits per pixel (bpp).
Using classroom videos to stimulate professional conversations among pre-service teachers: windows into a mathematics classroom

ABSTRACT This paper reports on the novel use of classroom videos by 71 pre-service teachers enrolled in a one-year graduate diploma pre-service teacher education program across three Australian universities. The classroom videos were of an experienced teacher delivering a Year-6 mathematics lesson. Students observed, discussed and debated practices that were evident. Data were collected via observation protocols and through recorded focussed discussions. Pre-service teachers, even at the early phase of their course, were able to articulate theoretical explanations for events occurring in the lesson. All students, irrespective of the progression in the course, lamented the absence of prior opportunities to engage in analysis and discussion of classroom lessons. The study reinforces the value of dialectical interactions focussed on authentic teaching scenarios.
An Offline Framework for the Diagnosis of Time Reliability by Automatic Vehicle Location Data

Time reliability problems are unavoidable, owing to the stochastic context in which bus services are operated. Therefore, characterizing their reliability and understanding possible sources of unreliability provides an opportunity to keep buses on schedule and/or maintain planned headways. Measuring time reliability is technologically feasible by automatic vehicle location (AVL) systems, which can collect disaggregated data on the delivered service and disclose information on its performance. This paper proposes the first offline framework applicable to any bus route in order to accurately characterize the bus stops and the time periods in which reliability is insufficient, and to disclose the systematic unreliability sources from collected AVL data and select preventive strategies, accordingly. The framework is tested on the real case study of a bus route, using about 40 000 AVL data records provided by the bus operator CTM in Cagliari, Italy. The experimentation shows that this framework can be adopted by transit managers for accurate reliability analysis.
Formal description of human-computer graphical communication

The problems of human-computer communication (HCC) that arise with introduction of user groups with special needs are discussed. We analyze new approaches that increase the quality of HCC in the environment of disabled users or user utilizing mobile devices. In both cases the problem of adaptation of the interactive system (which communicates with the user) to dynamically and very strongly changing context of use (end-device features, environment, user tasks, user capabilities, etc.) should be addressed. We have analyzed existing solutions. The insufficiency of these solutions in the field of adaptation of system data (the data the user is manipulating through the user interface of an interactive system) was identified. We propose new approach for description and manipulation with system data (especially of very complex type e.g. graphical data) that ensure increase of the adaptability of the whole interactive system to the context of use.
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This paper presents an approach to solving classification problems by combining feature selection and neural networks. The main idea is to use techniques from the field of information theory to select a set of important attributes that can be used to classify tuples. A neural network is trained using these attributes; the neural network is then used to classify tuples. In this paper, we discuss data mining , review common approaches and outline our algorithm. We also present preliminary results obtained against a well-known data collection.
EARLY CHILDHOOD SCIENCE EDUCATION TRENDS IN TURKEY: WHERE FROM? WHERE TO?

The current study intends to review both all empirical and theoretical research for understanding and discussing the trend in early childhood science education in Turkey. A summative content analysis was conducted with a total of 136 studies including 69 graduate theses (master thesis and dissertations) and 67 journal articles published from 1999 (the beginning) to the mid-2017. Data were extracted from the full-text of articles and the theses by visiting the websites of the journals and the related databases. The search keywords mainly included the terms: early childhood, preschool, science. The studies were analyzed in terms of publication year, author collaboration, research topics, research methods, and target population. The results revealed that the number of graduate theses and articles in the field of early childhood science is continuously growing. It was identified that collaborative research efforts in early childhood science education was a leading design on the published work. Results determined that a vast majority of the papers used quantitative research methods, mainly conducting surveys and using experimental design. Children, early childhood teachers and prospective teachers were the most common study subjects in the selected papers. The research topics of environmental education, scientific process skills, teaching methods, attitudes and behaviour were received relatively more attention among researchers.