Miracle-dz/UniFAR-FTUs huggingface dataset:
Face Detection using Fuzzy Skin Color Segmentation

paper presents a reliable method for detecting human faces in color images. The system is based on the visual information of the face and is commenced with the detection of face regions by connected component analysis of the skin color segmentation of images in HSV,YCBCR,YIQ color model and the Fuzzy membership function. The Fuzzy Membership function is applied to search for possible face regions in an image. The proposed method is robust in that it can deal with illumination changes and moderate rotations.
Regional expression and cellular localization of the Na(+)-dependent inorganic phosphate cotransporter of rat brain

We have recently isolated and identified a brain specific Na(+)- dependent inorganic phosphate (Pi) cotransporter cDNA (rBNPI) from a rat brain cDNA library (Ni, et al., 1994). We now report the regional and developmental expression, as well as the cellular localization, of rBNPI mRNA in the rat brain. In situ hybridization histochemistry reveals that rBNPI mRNA is selectively expressed in neuron-enriched regions of the adult rat brain, such as the cerebral cortex, hippocampus, and cerebellum. Cellular localization of rBNPI transcripts reveals expression in both pyramidal and granule neurons in these regions. By contrast, little to no hybridization signal was observed in white matter-enriched areas such as the corpus callosum. The expression of rBNPI mRNA was determined during pre- and postnatal development of the rat CNS. From embryonic day 17 to early postnatal day 10 (PND 10), there is a rather widespread but diffuse pattern of rBNPI expression in brain. During late postnatal development, however, the expression of rBNPI mRNA becomes confined to discrete populations of neurons in the cerebral cortex, hippocampus, and cerebellum. Thus, rBNPI expression is developmentally regulated and abundant levels of mRNA are found in rather discrete populations of neurons in the adult rat brain. The latter suggests that rBNPI may serve to selectively regulate intracellular Pi transport in certain neurons for either metabolic and (or) signaling events.
Redundancy-Aware Topic Modeling for Patient Record Notes

The clinical notes in a given patient record contain much redundancy, in large part due to clinicians’ documentation habit of copying from previous notes in the record and pasting into a new note. Previous work has shown that this redundancy has a negative impact on the quality of text mining and topic modeling in particular. In this paper we describe a novel variant of Latent Dirichlet Allocation (LDA) topic modeling, Red-LDA, which takes into account the inherent redundancy of patient records when modeling content of clinical notes. To assess the value of Red-LDA, we experiment with three baselines and our novel redundancy-aware topic modeling method: given a large collection of patient records, (i) apply vanilla LDA to all documents in all input records; (ii) identify and remove all redundancy by chosing a single representative document for each record as input to LDA; (iii) identify and remove all redundant paragraphs in each record, leaving partial, non-redundant documents as input to LDA; and (iv) apply Red-LDA to all documents in all input records. Both quantitative evaluation carried out through log-likelihood on held-out data and topic coherence of produced topics and qualitative assessement of topics carried out by physicians show that Red-LDA produces superior models to all three baseline strategies. This research contributes to the emerging field of understanding the characteristics of the electronic health record and how to account for them in the framework of data mining. The code for the two redundancy-elimination baselines and Red-LDA is made publicly available to the community.
Talking about weight in pregnancy: An exploration of practitioners' and women's perceptions

Prevalence of maternal obesity is increasing, with health risks for mother and infant. Effective health promotion depends on sufficient knowledge and appropriate communication skills. We aimed to explore women’s, midwives’ and health visitors’ perceptions of current practice in helping women manage their weight and supporting healthy behaviour change during pregnancy, and their perceived training needs. A modified grounded theory methodology was adopted, based upon critical realist assumptions. Following consultation events with fifty six practitioners to inform data collection tools, twenty (different) practitioners and nine women participated in focus groups. Comparative analysis generated four themes: A core theme, “Discouraging discourses”, described health professionals’ negative beliefs and reactive approach to communicating about weight. “Staff resources” identified limitations in and requirements for practitioner knowledge, skills and tools for effective communication. “Contextual influences” were social factors, which hindered practitioners’ efforts to achieve healthy behaviour change. “Communicating as a Team” identified the importance of and challenges to a team approach. Findings have implications for weight management in pregnancy, practitioner resources, teamwork, and national health promotion campaigns.
LocALE: a location-aware lifecycle environment for ubiquitous computing

The LocALE (location-aware lifecycle environment) framework provides a simple management interface for controlling the lifecycle of CORBA distributed objects. It supports mechanisms for the remote construction, movement, removal and recovery of heterogeneous software objects in a location domain, i.e. a group of hosts on a network within a given physical area. Client applications use LocALE to intelligently control their required services' location and relocation in the network. LocALE offers load-balancing, automatic activation, and fault-tolerance facilities for the services whose lifecycles it controls. It provides the middleware necessary for the efficient implementation of location-aware mobile applications in richly equipped network environments. LocALE's infrastructure has been tested with the development of several follow-me applications that dynamically move with their users as they change location. For illustration, two of these follow-me LocALE-enabled applications are described.
A community driven social recommendation system

Recommendation systems play an important role in suggesting relevant information to users. In this paper, we introduce community-wise social interactions as a new dimension for recommendations and present a social recommendation system using collaborative filtering and community detection approaches. We use (i) community detection algorithm to extract friendship relations among users by analyzing user-user social graph and (ii) user-item based collaborative filtering for rating prediction. We developed our approach using map-reduce framework. Our approach improves scalability, coverage and cold start issue of collaborative filtering based recommendation system. We carried out experiments on MovieLens and Facebook datasets, to predict the rating of the movie and produce top-k recommendations for new (cold start) user. The results are compared with traditional collaborative filtering based recommendation system.
Detecting a Change in School Performance: A Bayesian Analysis for a Multilevel Join Point Problem

A substantial literature on switches in linear regression functions considers situations in which the regression function is discontinuous at an unknown value of the regressor, Xk , where k is the so-called unknown “change point. ” The regression model is thus a two-phase composite of yi ∼ N(β01 + β11xi, σ1 2), i=1, 2,. , k and yi ∼ N(β02 + β12xi, σ2 2), i= k + 1, k + 2,. , n. Solutions to this single series problem are considerably more complex when we consider a wrinkle frequently encountered in evaluation studies of system interventions, in that a system typically comprises multiple members (j = 1, 2, . . . , m) and that members of the system cannot all be expected to change synchronously. For example, schools differ not only in whether a program, implemented system-wide, improves their students’ test scores, but depending on the resources already in place, schools may also differ in when they start to show effects of the program. If ignored, heterogeneity among schools in when the program takes initial effect undermines any program evaluation that assumes that change points are known and that they are the same for all schools. To describe individual behavior within a system better, and using a sample of longitudinal test scores from a large urban school system, we consider hierarchical Bayes estimation of a multilevel linear regression model in which each individual regression slope of test score on time switches at some unknown point in time, kj. We further explore additional results employing models that accommodate case weights and shorter time series.
Nonlinear Trajectory Tracking for Fixed Wing UAVs via Backstepping and Parameter Adaptation

Equipping a fixed wing unmanned air vehicle (UAV) with low-level autopilots, we derive high-level velocity and roll angle control laws for the UAV. Backstepping techniques are applied to design the velocity and roll angle control laws from known velocity and heading angle control laws that explicitly account for velocity and heading rate constraints of the UAV. Regarding unknown autopilot constants, a parameter adaptation technique is used to estimate autopilot parameters. Simulation results on a fixed wing UAV are presented to show the effectiveness of our approach.
An Efficient Medium Access Control Protocol with Parallel Transmission for Wireless Sensor Networks

In this paper, we present a novel low power medium access control protocol for wireless sensor networks (WSNs). The proposed protocol, EP-MAC (Efficient MAC with Parallel Transmission) achieves high energy efficiency and high packet delivery ratio under different traffic load. EP-MAC protocol is basically based on the Time Division Multiple Access (TDMA) approach. The power of Carrier Sense Multiple Access (CSMA) is used in order to offset the fundamental problems that the stand-alone TDMA method suffers from, i.e., problems such as lack of scalability, adaptability to varying situations, etc. The novel idea behind the EP-MAC is that it uses the parallel transmission concept with the TDMA link scheduling. EP-MAC uses the methods for the transmission power adjustment, i.e., uses the minimum level power necessary to reach the intended neighbor within a specified bit error rate [BER] target. This reduces energy consumption, as well as further enhances the scope of parallel transmission of the protocol. The simulation studies support the theoretical results, and validate the efficiency of our proposed EP-MAC protocol.
Soap film smoothing

Summary. Conventional smoothing methods sometimes perform badly when used to smooth data over complex domains, by smoothing inappropriately across boundary features, such as peninsulas. Solutions to this smoothing problem tend to be computationally complex, and not to provide model smooth functions which are appropriate for incorporating as components of other models, such as generalized additive models or mixed additive models. We propose a class of smoothers that are appropriate for smoothing over difficult regions of 2 which can be represented in terms of a low rank basis and one or two quadratic penalties. The key features of these smoothers are that they do not ‘smooth across’ boundary features, that their representation in terms of a basis and penalties allows straightforward incorporation as components of generalized additive models, mixed models and other non‐standard models, that smoothness selection for these model components is straightforward to accomplish in a computationally efficient manner via generalized cross‐validation, Akaike's information criterion or restricted maximum likelihood, for example, and that their low rank means that their use is computationally efficient.
MODELING AND CONTROL OF GAS TURBINE COMBUSTOR WITH DYNAMIC AND ADAPTIVE NEURAL NETWORKS

This paper presents an Artificial Neural Network (ANN)-based modeling technique for prediction of outlet temperature, pressure and mass flow rate of gas turbine combustor. Results obtained by present modeling were compared with those obtained by experiment. The results showed the effectiveness and capability of the proposed modeling technique with reasonable accuracies of about 95 percent. This paper describes a nonlinear SVFAC (State Vector Feedback Adaptive Control) controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the effect of nonlinear factors contained in controller. The controller is adaptively trained to force the plant output and to track an output reference. The proposed Adaptive control system configuration uses two neural networks, a controller network and a model network. The control performance of designed controller is compared with inverse control method and results have shown that, the proposed method has good performance for nonlinear plants such as gas turbine combustor. SVFAC technique is finally generalized for MIMO systems in this paper.
A video-based real-time vehicle detection method by classified background learning

A new two-level real-time vehicle detection method is proposed in order to meet the robustness and efficiency requirements of real world applications. At the high level, pixels of the background image are classified into three categories according to the characteristics of Red, Green, Blue (RGB) curves. The robustness of the classification is further enhanced by using line detection and pattern connectivity. At the lower level, an exponential forgetting algorithm with adaptive parameters for different categories is utilised to calculate the background and reduce the distortion by the small motion of video cameras. Scene tests show that the proposed method is more robust and faster than previous methods, which is very suitable for real-time vehicle detection in outdoor environments, especially concerning locations where the level of illumination changes frequently and speed detection is important.
Gamma Oscillations and Spontaneous Network Activity in the Hippocampus Are Highly Sensitive to Decreases in pO2 and Concomitant Changes in Mitochondrial Redox State

Gamma oscillations have been implicated in higher cognitive processes and might critically depend on proper mitochondrial function. Using electrophysiology, oxygen sensor microelectrode, and imaging techniques, we investigated the interactions of neuronal activity, interstitial pO2, and mitochondrial redox state [NAD(P)H and FAD (flavin adenine dinucleotide) fluorescence] in the CA3 subfield of organotypic hippocampal slice cultures. We find that gamma oscillations and spontaneous network activity decrease significantly at pO2 levels that do not affect neuronal population responses as elicited by moderate electrical stimuli. Moreover, pO2 and mitochondrial redox states are tightly coupled, and electrical stimuli reveal transient alterations of redox responses when pO2 decreases within the normoxic range. Finally, evoked redox responses are distinct in somatic and synaptic neuronal compartments and show different sensitivity to changes in pO2. We conclude that the threshold of interstitial pO2 for robust CA3 network activities and required mitochondrial function is clearly above the “critical” value, which causes spreading depression as a result of generalized energy failure. Our study highlights the importance of a functional understanding of mitochondria and their implications on activities of individual neurons and neuronal networks.
Exploiting Fuzzy-SQL in Case-Based Reasoning

The use of database technologies for implementing CBR techniques i attracting a lot of attention for several reasons. First, the possibility of using standard DBMS for storing and representing cases significantly reduces the effort needed to develop a CBR system; in fact, data of interest are usually already stored into relational databases and used for different purposes as well. Finally, the use of standard query languages, like SQL, may facilitate the introduction of a casebased system into the real-world, by putting retrieval on the same ground of normal database queries. Unfortunately, SQL is not able to deal with queries like those needed in a CBR system, so different approaches have been tried, in order to build retrieval engines able to exploit, at the lower level, standard SQL. In this paper, we present a proposal where case retrieval is implemented byusing a fuzzy version of SQL. In the presented approach a fuzzy notion of similarity is adopted and an extended version of SQL, dealing with fuzzy predicates, is introduced. A case-based system prototype xploiting Fuzzy-SQL as a retrieval engine is then presented.
Feasibility of treating depression in pregnant adolescents using brief interpersonal psychotherapy

ABSTRACT The current study examines a pilot study testing the feasibility of treating antenatal depression in public health clinics serving low-income adolescents using adapted brief interpersonal psychotherapy for depression in pregnant adolescents (IPT-BPA). This research addresses the disproportionate incidence of antenatal depression and its associated negative outcomes, including postpartum depression, among low-income adolescent mothers. Participants (N = 14) were recruited from two county-based public prenatal care clinics. Eligible participants were pregnant adolescents with major depression. Participants received nine sessions of IPT-BPA. Depressive symptoms were measured pre-and post-treatment. Differences were examined using paired t-tests. Most (81%) referred adolescents completed screening, and 73% met eligibility criteria. Of 16 eligible adolescents, 14 (87.5%) consented to participation, and 13 (93%) completed IPT-BPA . Participants who completed IPT-BPA experienced significant decreases in depression. The successful recruitment and retention of pregnant, depressed adolescents combined with significant, positive post-treatment changes in depression support the feasibility of treating adolescent perinatal depression in public health clinics using IPT-BPA.
Extraction of phrases useful for machine translation

To improve the quality of a machine translation system, it is useful to extract and store words and phrases which are salient in a domain and should be translated in a specific way. We tried several existing technologies for salient word extraction and compared their mechanisms and quality. Based on the comparison, we propose a new method which includes pre-processing of sentences in corpora and utilization of syntactic information. We evaluated the usefulness of our new method.
Effects of Mental Health and Trauma Counselling Module towards the Competency of Mental Health and Trauma Counselling Practices

This study aims to examine the effect of Mental Health and Trauma Counselling (MHTC) module toward counsellors’ competency in handling mental health and trauma counselling among school counselling teachers. In addition, this study also examines the effect of MHTC module towards sub constructs’ competencies namely knowledge, skills and awareness based on three demographic aspects that are related to subjects such as sex, age and level of experience of attending MHTC workshop. Quasi-experimental pre-test and post-test treatment group design was used in this study. The sample consists of 48 high school counselling teachers (14 males and 34 females) selected using purposive sampling within Selangor and Perak. Mental Health and Trauma Counselling Skills Instruments (MHTCSI) which contains three sub constructs competencies namely knowledge, skills and awareness are used in this study. Quantitative data of experimental study are analyzed using SPSS version 20. Descriptive statistics of mean, percentage and standard deviation and inferential statistical of t-test were used to analyze the data. The results show that MHTC module is found to be descriptively effective for enhancing the counsellor’s competency and sub constructs’ competencies, namely knowledge, skills and awareness based upon gender, age and level of experience of handling mental health and trauma counselling. The findings by inference using paired sample t-tests have proven that there are significant differences in pre-test and post-test for construct competency [t (47) = -13,865, p
A Method for Stopping Nonconvergent Stochastic Approximation Processes

When a stochastic approximation process satisfies the conditions for convergence there are well-established methods to terminate the iterative process in a manner that allows approximate statistics to be calculated on the final result. Many of these use the asymptotic properties of convergent stochastic approximation. However, such methods converge slowly due to step size restrictions, so in practical application it is common to use a step size that violates the conditions for convergence in order to obtain an answer more quickly. Constant gain stochastic approximation is a special case of this practice. In these cases stopping rules based on asymptotic methods are no longer analytically supportable, and other techniques must be found. This paper presents one such method based on the use of a surrogate process to calculate the stopping condition. A discussion of this approach to stopping stochastic approximation is offered in the context of a simple example, including some empirical results.
Distinguishing the impacts of ozone‐depleting substances and well‐mixed greenhouse gases on Arctic stratospheric ozone and temperature trends

Whether stratospheric cooling due to increases in well‐mixed greenhouse gases (WMGHG) could increase the depletion of Arctic stratospheric ozone has been the subject of scientific and public attention for decades. Here we provide evidence that changes in the concentrations of ozone‐depleting substances (ODS), not WMGHG, have been the primary driver of observed Arctic lower stratospheric trends in both ozone and temperature. We do so by analyzing polar cap ozone and temperature trends in reanalysis data: these clearly suggest that both trends are mainly driven by ODS in the lower stratosphere. This observation‐based finding is supported by results from a stratosphere‐resolving chemistry‐climate model driven with time‐varying ODS and WMGHG, specified in isolation and in combination. Taken together, these results provide strong evidence that ODS are the main driver of changes in the Arctic lower stratospheric temperatures and ozone, whereas WMGHG are the primary driver of changes in the upper stratosphere.
Selection of accurate reference genes in mouse trophoblast stem cells for reverse transcription-quantitative polymerase chain reaction

Mouse trophoblast stem cells (TSCs) form colonies of different sizes and morphologies, which might reflect their degrees of differentiation. Therefore, each colony type can have a characteristic gene expression profile; however, the expression levels of internal reference genes may also change, causing fluctuations in their estimated gene expression levels. In this study, we validated seven housekeeping genes by using a geometric averaging method and identified Gapdh as the most stable gene across different colony types. Indeed, when Gapdh was used as the reference, expression levels of Elf5, a TSC marker gene, stringently classified TSC colonies into two groups: a high expression groups consisting of type 1 and 2 colonies, and a lower expression group consisting of type 3 and 4 colonies. This clustering was consistent with our putative classification of undifferentiated/differentiated colonies based on their time-dependent colony transitions. By contrast, use of an unstable reference gene (Rn18s) allowed no such clear classification. Cdx2, another TSC marker, did not show any significant colony type-specific expression pattern irrespective of the reference gene. Selection of stable reference genes for quantitative gene expression analysis might be critical, especially when cell lines consisting of heterogeneous cell populations are used.