Miracle-dz/UniFAR-FTUs huggingface dataset:
An Interpretable Fuzzy DBN-Based Classifier for Indoor User Movement Prediction in Ambient Assisted Living Applications

In this paper, an interpretable fuzzy deep belief network (DBN)-based classifier called deep belief networks-based Takagi-Sugeno-Kang fuzzy classifier (DBN-TSK-FC) is created for indoor user movement prediction in ambient assisted living applications. With its promising classification performance, DBN-TSK-FC features sharing both the powerful neural representation ability of a DBN and the strong uncertainty-handling capability of an interpretable fuzzy representation. On the one hand, DBN-TSK-FC builds its interpretable fuzzy representation in a hierarchical way by applying the classical fuzzy clustering algorithm FCM to obtain fuzzy partitions on the training dataset. Then, it forms interpretable antecedent parts of fuzzy rules as the corresponding fuzzy representation. On the other hand, DBN-TSK-FC builds its DBN-based neural representation in the other hierarchical way. That is, it applies the existing unsupervised DBN pretraining on the training dataset and then takes the neural representation of all the hidden nodes in the top layer of the corresponding DBN as the set of consequent variables of fuzzy rules. In this approach, both the interpretable fuzzy representation and the DBN-based neural representation are further fused to form the corresponding fuzzy rules quickly by using the least learning machine (LLM) on both the fuzzy rules and the labeling information of the original dataset. Therefore, DBN-TSK-FC is essentially a deep TSK fuzzy classifier from the perspective of fuzzy rules, and it indeed avoids the very slow fine-tuning training required after the unsupervised pretraining of the existing DBN learning. The experimental results on the MovementAAL_RSS dataset indicate the effectiveness of the proposed classifier DBN-TSK-FC.
Proteomic Studies of Cholangiocarcinoma and Hepatocellular Carcinoma Cell Secretomes

Cholangiocarcinoma (CCA) and hepatocellular carcinoma (HCC) occur with relatively high incidence in Thailand. The secretome, proteins secreted from cancer cells, are potentially useful as biomarkers of the diseases. Proteomic analysis was performed on the secreted proteins of cholangiocarcinoma (HuCCA-1) and hepatocellular carcinoma (HCC-S102, HepG2, SK-Hep-1, and Alexander) cell lines. The secretomes of the five cancer cell lines were analyzed by SDS-PAGE combined with LC/MS/MS. Sixty-eight proteins were found to be expressed only in HuCCA-1. Examples include neutrophil gelatinase-associated lipocalin (lipocalin 2), laminin 5 beta 3, cathepsin D precursor, desmoplakin, annexin IV variant, and annexin A5. Immunoblotting was used to confirm the presence of lipocalin 2 in conditioned media and cell lysate of 5 cell lines. The results showed that lipocalin 2 was a secreted protein which is expressed only in the conditioned media of the cholangiocarcinoma cell line. Study of lipocalin 2 expression in different types of cancer and normal tissues from cholangiocarcinoma patients showed that lipocalin 2 was expressed only in the cancer tissues. We suggest that lipocalin 2 may be a potential biomarker for cholangiocarcinoma.
Journal of Internet Banking and Commerce

In the retail banking context, convergence of technologies has given birth to different channels of distribution like Automatic Teller machines (ATM), internet banking, and mobile banking. This enables the customer to avail the banking services at any time and any where. These technological interfaces are known as self service technologies (SSTs). Customers availing banking services through these SSTs get more benefits in terms of time, cost and energy. Despite these benefits the customer trial, adoption and repeat usage of SSTs vary among banking customers. Although the kinds of service one can avail from these SST are similar, the patronage among the SSTs differs. The SST channel choice could be attributed to various factors viz., Nature of service to be availed or purpose, Perceived risk, Requirements and Benefits. When it comes to predicting customer priority among alternatives, Analytical hierarchy process (AHP) has been proved as an effective technique. This paper explores the factors influencing customer choice of SSTs by employing AHP technique.
Is There A Relationship Between Temporomandibular Disorders And Tinnitus? A Review Of The Literature

The frequent coexsistence of t innitus and temporomandibular disorders (TMD) has led to the hypothesis that a possible relationship exists between these two conditions. The purpose of this study was to evaluate the association between these two conditions in order to establish an accurate diagnosis and management. Research has shown that the prevalence of tinnitus in TMD patients is a much higher than in the general population. The results of this review of the literature showed a significant correlation between tinnitus and TMD.
Classification of Discourse Coherence Relations: An Exploratory Study using Multiple Knowledge Sources

In this paper we consider the problem of identifying and classifying discourse coherence relations. We report initial results over the recently released Discourse GraphBank (Wolf and Gibson, 2005). Our approach considers, and determines the contributions of, a variety of syntactic and lexico-semantic features. We achieve 81% accuracy on the task of discourse relation type classification and 70% accuracy on relation identification.
How to effectively define and measure maintainability

Maintainability and flexibility at the software level are of predominant importance to drive innovation at the business process level. However, existing definitions of maintainability, such as the Halstead Volume, McCabe's Cyclomatic Complexity or the SEI maintainability index provide a very poor understanding of what maintainability is how it can be assessed and ultimately controlled. This paper explains a new and more effective way to construct software product quality models. The key design principle is the strict separation of activities and properties of the system. This separation facilitates the identification of sound quality criteria and allows to reason about their interdependencies and their effects. The application of this quality modelling approach in large scale commercial software organizations helped to effectively reveal important quality shortcomings and raised the awareness for the importance of long-term quality aspects among developers as well as managers.
Statistical Learning Approach for Robust Melanoma Screening

According to the American Cancer Society, one person dies of melanoma every 57 minutes, although it is the most curable type of cancer if detected early. Thus, computeraided diagnosis for melanoma screening has been a topic of active research. Much of the existing art is based on the Bag-of-Visual-Words (BoVW) model, combined with color and texture descriptors. However, recent advances in the BoVW model, as well as the evaluation of the importance of the many different factors affecting the BoVW model were yet to be explored, thus motivating our work. We show that a new approach for melanoma screening, based upon the state-of-the-art BossaNova descriptors, shows very promising results for screening, reaching an AUC of up to 93.7%. An important contribution of this work is an evaluation of the factors that affect the performance of the two-layered BoVW model. Our results show that the low-level layer has a major impact on the accuracy of the model, but that the codebook size on the mid-level layer is also important. Those results may guide future works on melanoma screening.
k-dimensional Size Functions for Shape Description and Comparison

This paper advises the use of k-dimensional size functions for comparison and retrieval in the context of multidimensional shapes, where by shape we mean something in two or higher dimensions having a visual appearance. The attractive feature of k-dimensional size functions is that they allow to readily establish a similarity measure between shapes of arbitrary dimension, taking into account different properties expressed by a multivalued real function defined on the shape. In particular, we outline the potential of this approach in a series of experiments.
A Modified Characteristic Finite Element Method for a Fully Nonlinear Formulation of the Semigeostrophic Flow Equations

This paper develops a fully discrete modified characteristic finite element method for a coupled system consisting of the fully nonlinear Monge-Ampere equation and a transport equation. The system is the Eulerian formulation in the dual space for B. J. Hoskins' semigeostrophic flow equations, which are widely used in meteorology to model frontogenesis. To overcome the difficulty caused by the strong nonlinearity, we first formulate (at the differential level) a vanishing moment approximation of the semigeostrophic flow equations, a methodology recently proposed by the authors [X. Feng and M. Neilan, J. Sci. Comput. , 38 (2009), pp. 74-98], which involves approximating the fully nonlinear Monge-Ampere equation by a family of fourth-order quasilinear equations. We then construct a fully discrete modified characteristic finite element method for the regularized problem. It is shown that under certain mesh constraint, the proposed numerical method converges with an optimal order rate of convergence. In particular, the obtained error bounds show explicit dependence on the regularization parameter $\varepsilon$. Numerical tests are also presented to validate the theoretical results and to gauge the efficiency of the proposed fully discrete modified characteristic finite element method.
5-HT1A Receptor-Responsive Pedunculopontine Tegmental Neurons Suppress REM Sleep and Respiratory Motor Activity

Serotonin type 1A (5-HT1A) receptor-responsive neurons in the pedunculopontine tegmental nucleus (PPTn) become maximally active immediately before and during rapid eye movement (REM) sleep. A prevailing model of REM sleep generation indicates that activation of such neurons contributes significantly to the generation of REM sleep, and if correct then inactivation of such neurons ought to suppress REM sleep. We test this hypothesis using bilateral microperfusion of the 5-HT1A receptor agonist 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT, 10 μm) into the PPTn; this tool has been shown to selectively silence REM sleep-active PPTn neurons while the activity of wake/REM sleep-active PPTn neurons is unaffected. Contrary to the prevailing model, bilateral microperfusion of 8-OH-DPAT into the PPTn (n = 23 rats) significantly increased REM sleep both as a percentage of the total recording time and sleep time, compared with both within-animal vehicle controls and between-animal time-controls. This increased REM sleep resulted from an increased frequency of REM sleep bouts but not their duration, indicating an effect on mechanisms of REM sleep initiation but not maintenance. Furthermore, an increased proportion of the REM sleep bouts stemmed from periods of low REM sleep drive quantified electrographically. Targeted suppression of 5-HT1A receptor-responsive PPTn neurons also increased respiratory rate and respiratory-related genioglossus activity, and increased the frequency and amplitude of the sporadic genioglossus activations occurring during REM sleep. These data indicate that 5-HT1A receptor-responsive PPTn neurons normally function to restrain REM sleep by elevating the drive threshold for REM sleep induction, and restrain the expression of respiratory rate and motor activities.
ON/OFF Model of Conversational Pattern in Instant Messaging System

Along with the rapid progress of Internet technologies, Instant Messaging (IM) has become increasingly popular due to its social functionalities. Despite its prevalence, little has been done to analyze the characteristics of the conversational pattern among IM users. In this paper, we establish an ON/OFF model to simulate single user conversations in IM system. With data collected from popular Chinese IM software QQ, extensive comparisons have been conducted both on statistical and queuing performances. We therefore confirm that the ON and OFF periods are best-fitted with the log-normal distribution. With the mathematical understanding presented by this paper, we can further design or optimize IM systems toward a more resource-efficient direction, and alleviate its potential impact on wireless networks where resources require meticulous allocation.
The Effects of a Brief Organization, Time Management, and Planning Intervention for At-Risk College Freshmen

ABSTRACT College freshmen who are at greater risk to fail or drop out before attaining a bachelor’s degree are at a distinct disadvantage occupationally, economically, and socially. At-risk students (those who are academically underprepared) are six times more likely to drop out during the first year of college. This study sought to examine the effects of an organization, time management, and planning (OTMP) skills intervention with at-risk freshmen. Freshmen conditionally accepted at a public four-year university and enrolled in a first-year seminar intended to enhance academic success and retention participated in the study. Students completed online surveys regarding impairment, OTMP skill use, inattention symptoms, and executive functioning at the beginning and end of their first semester in college. Intervention students received three lectures (with take-home exercises) designed to teach OTMP skills. Students in the intervention group reported significant improvements compared to the control group on measures of impairment, as well as inattention, with medium to large effects across these significant findings. These results indicate that including OTMP training as one component of an intervention package may improve at-risk students’ adjustment to college.
Evolution of Collective Behaviors by Minimizing Surprise

Similarly to evolving controllers for single robots also controllers for groups of robots can be generated by applying evolutionary algorithms. Usually a fitness function rewards desired behavioral features. Here we investigate an alternative method that generates collective behaviors almost only as a by-product. We roughly follow the idea of Helmholtz that perception is a process based on probabilistic inference and evolve an internal model that is supposed to predict the agent’s future perceptions. Separated from this prediction model the agent also evolves a regular controller. Direct selective pressure, however, is only effective on the prediction model by minimizing prediction error (surprise). Our results show that a number of basic collective behaviors emerge by this approach, such as dispersion, aggregation, and flocking. The probability that a certain behavior emerges and also the difficulty of making correct predictions depends on the swarm density. The reported method has potential to be another simple approach to open-ended evolution analogical to the search for novelty.
Subcultural dynamics in transformation: A multi-perspective study of healthcare professionals

The extensive criticism of the integrative `shared values' view of culture has prompted many researchers to argue that one way of advancing culture research is by examining the ways different subcultures interact in organizational settings. However, existing studies in this area generally explore the nature of subcultures and the differences between them. The findings from a study of two large healthcare organizations suggest that greater insights can be generated into organizational subcultures by studying doctors, nurses and non-clinical managers through multiple analytical lenses. Such analyses help to uncover the wide variation in subcultural values, norms and assumptions, revealing those that are shared, those that differ and those that are characterized by ambiguity both within and across subcultures. It is argued that this approach reveals interesting insights into the dynamics of subcultures and the ways in which different subcultures interpret and respond to management initiated changes designed to transform organizational practices.
Persistent Appreciations and Overshooting: A Normative Analysis

Most economies experience episodes of persistent real exchange rate appreciations, when the question arises whether there is a need for intervention to protect the export sector. This paper presents a model of irreversible destruction where exchange rate intervention may be justified if the export sector is financially constrained. However, the criterion for intervention is not whether there are bankruptcies or not, but whether these can cause a large exchange rate overshooting once the factors behind the appreciation subside. The optimal policy includes ex-ante and ex-post interventions. Ex-ante (that is, during the appreciation phase) interventions have limited effects if the financial resources in the export sector are relatively abundant. In this case the bulk of the intervention takes place ex-post, and is concentrated in the first period of the depreciation phase. In contrast, if the financial constraint in the export sector is tight, the policy is shifted toward ex-ante intervention and it is optimal to lean against the appreciation.
Correlation between Galaxies and Quasi-stellar Objects in the Sloan Digital Sky Survey: A Signal from Gravitational Lensing Magnification?

We report a detection of a galaxy-QSO cross-correlation wGQ in the Sloan Digital Sky Survey early data release over 0. ′2-30' scales. We cross-correlate galaxy samples of different mean depths r′ = 19-22 (G = 0.15-0.35) with the main QSO population (i < 19.1) at Q ≃ 1.6. We find significant positive correlation in all cases except for the faintest QSOs, as expected if the signal were due to weak-lensing magnification. The amplitude of the signal on arcminute scales is about 20% at G = 0.15, decreasing to 10% at G = 0.35. This is a few times larger than currently expected from weak lensing in the ΛCDM models but confirms, at a higher significance, previous measurements by several groups. When compared to the galaxy-galaxy correlation wGG, a weak-lensing interpretation indicates a strong and steep nonlinear amplitude for the underlying matter fluctuations: σ ≃ 400 on scales of 0.2 Mpc h-1, in contradiction with nonlinear modeling of ΛCDM fluctuations. We also detect a normalized skewness (galaxy-galaxy-QSO correlation) of S3 ≃ 21 ± 6 at ≃ 0.15 (S3 ≃ 14 ± 4 at ≃ 0.35), which is several standard deviations low compared to standard ΛCDM expectations. These observational trends can be reconciled with lensing in a flat Λ universe with σ8 ≃ 1, provided that the linear spectrum is steeper (n ≃ 1) than in the ΛCDM model on small (cluster) scales. Under this interpretation, the galaxy distribution traces the matter variance with an amplitude that is 100 times smaller; i.e., galaxies are antibiased with b ≃ 0.1 on small scales, increasing to b ≃ 1 at ≃10 Mpc h-1.
OBSERVATIONS OF DOPPLER BOOSTING IN KEPLER LIGHT CURVES

Among the initial results from Kepler were two striking light curves, for KOI 74 and KOI 81, in which the relative depths of the primary and secondary eclipses showed that the more compact, less luminous object was hotter than its stellar host. That result became particularly intriguing because a substellar mass had been derived for the secondary in KOI 74, which would make the high temperature challenging to explain; in KOI 81, the mass range for the companion was also reported to be consistent with a substellar object. We re-analyze the Kepler data and demonstrate that both companions are likely to be white dwarfs. We also find that the photometric data for KOI 74 show a modulation in brightness as the more luminous star orbits, due to Doppler boosting. The magnitude of the effect is sufficiently large that we can use it to infer a radial velocity amplitude accurate to 1 km s-1. As far as we are aware, this is the first time a radial-velocity curve has been measured photometrically. Combining our velocity amplitude with the inclination and primary mass derived from the eclipses and primary spectral type, we infer a secondary mass of 0.22 ± 0.03 M☉. We use our estimates to consider the likely evolutionary paths and mass-transfer episodes of these binary systems.
Event-Related Brain Potential Correlates of Human Auditory Sensory Memory-Trace Formation

The event-related potential (ERP) component mismatch negativity (MMN) is a neural marker of human echoic memory. MMN is elicited by deviant sounds embedded in a stream of frequent standards, reflecting the deviation from an inferred memory trace of the standard stimulus. The strength of this memory trace is thought to be proportional to the number of repetitions of the standard tone, visible as the progressive enhancement of MMN with number of repetitions (MMN memory-trace effect). However, no direct ERP correlates of the formation of echoic memory traces are currently known. This study set out to investigate changes in ERPs to different numbers of repetitions of standards, delivered in a roving-stimulus paradigm in which the frequency of the standard stimulus changed randomly between stimulus trains. Normal healthy volunteers (n = 40) were engaged in two experimental conditions: during passive listening and while actively discriminating changes in tone frequency. As predicted, MMN increased with increasing number of standards. However, this MMN memory-trace effect was caused mainly by enhancement with stimulus repetition of a slow positive wave from 50 to 250 ms poststimulus in the standard ERP, which is termed here “repetition positivity” (RP). This RP was recorded from frontocentral electrodes when participants were passively listening to or actively discriminating changes in tone frequency. RP may represent a human ERP correlate of rapid and stimulus-specific adaptation, a candidate neuronal mechanism underlying sensory memory formation in the auditory cortex.
INNS Conference on Big Data Generic Object Recognition with Local Receptive Fields Based Extreme Learning Machine

Generic object recognition is the classification of an individual object to a generic category. Intra-class variabilities, such as different objects of the same category, different poses and lighting conditions, cause big troubles for this task. Traditional methods involve plenty of pre-processing steps, such as shape model construction, extraction of hand-crafted features, etc. Moreover, these methods are only effective for some specific dataset. In this paper, we propose to use local receptive fields based extreme learning machine (ELM-LRF) as a general framework for object recognition. It is operated directly on the raw images without any pre-processing, and thus suitable for all different datasets. Additionally, the architecture is simple and only requires few computations, as most connection weights are randomly generated. We have compared the performance of ELM-LRF with state-of-the-art methods on several generic object recognition datasets, NORB, ETH-80 and COIL. It is on par with the best result on ETH-80 and sets the new records for NORB and COIL. c © 2011 Published by Elsevier Ltd.